CN113539483A - Chronic disease screening service system based on cloud computing - Google Patents
Chronic disease screening service system based on cloud computing Download PDFInfo
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- CN113539483A CN113539483A CN202110879713.XA CN202110879713A CN113539483A CN 113539483 A CN113539483 A CN 113539483A CN 202110879713 A CN202110879713 A CN 202110879713A CN 113539483 A CN113539483 A CN 113539483A
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Abstract
The invention discloses a cloud computing-based chronic disease screening service system, relates to the technical field of chronic disease screening service, solves the technical problem that influence factors of sick personnel in corresponding areas cannot be collected in the prior art, so that the body adjustment direction of the sick personnel is not clear, reasonably pushes hospitals, completes medical resource sharing through analysis and pushing, and improves the treatment efficiency of patients; collecting influence factors of chronic diseases, accurately judging and collecting the influence factors, enabling the adjustment direction of sick personnel to be more definite, and enabling the sick personnel to be prevented accurately; carry out the early warning suggestion to sick personnel, strengthen sick personnel's maintenance consciousness to early warning object peripheral region is the collection area, can improve the accuracy of coordinate system, and let the importance of early warning object more clear maintenance with two kinds of sick personnel's different information, thereby effectively improve early warning object's chronic disease maintenance consciousness, be favorable to reducing chronic disease patient's mortality.
Description
Technical Field
The invention relates to the technical field of chronic disease screening service, in particular to a cloud computing-based chronic disease screening service system.
Background
The chronic diseases are all called chronic non-infectious diseases, specifically refer to diseases which do not form infection and have long-term accumulation to form disease form damage, are not specific to certain diseases, but are generalized general names of diseases which have hidden onset, long course of disease, prolonged illness state, lack of exact infectious biological etiological evidence, complex etiology and some diseases which are not completely confirmed; chronic diseases mainly refer to a group of diseases represented by cardiovascular and cerebrovascular diseases (hypertension, coronary heart disease, apoplexy and the like), diabetes, malignant tumors, chronic obstructive pulmonary diseases (chronic tracheitis, emphysema and the like), abnormal spirit, psychosis and the like;
the patent with the application number of CN2019114120092 discloses a chronic disease management system and method based on informatization integration, a chronic disease patient signs a contract with a chronic disease management platform through a plurality of medical institutions, the management platform provides a service pack matched with the state of the patient, information sharing of the chronic disease patient is realized through a service bus platform, the chronic disease patient is integrally managed according to a chronic disease management path set by the management platform, professional diagnosis and treatment can be achieved even at home or in a community hospital, and medical resource sharing of informatization integration is realized.
In this patent, although sharing of medical resources is possible to improve the treatment efficiency of patients, patients in each area cannot be screened, and subsequent analysis of influence factors cannot be performed on the basis of the screened patients in each area, resulting in unclear adjustment directions of patients; in addition, the patient cannot be warned in real time, so that the maintenance consciousness of the patient is reduced, and the morbidity of chronic diseases in the area is improved.
Disclosure of Invention
The invention aims to provide a chronic disease screening service system based on cloud computing, which reasonably pushes a hospital, completes medical resource sharing by analyzing pushing and improves the treatment efficiency of patients; collecting influence factors of chronic diseases, accurately judging and collecting the influence factors, enabling the adjustment direction of sick personnel to be more definite, and enabling the sick personnel to be prevented accurately; the early warning prompt is carried out on the sick personnel, the maintenance consciousness of the sick personnel is enhanced, the speed of recovering the sick personnel to be normal is accelerated, and the chronic disease rate of the corresponding screening area is reduced; the surrounding area of the early warning object is used as the acquisition area, the accuracy of a coordinate system can be improved, and the importance of more clear maintenance of the early warning object is realized by using different information of two sick personnel, so that the maintenance consciousness of the chronic disease of the early warning object is effectively improved, and the death rate of patients with the chronic disease is favorably reduced.
The purpose of the invention can be realized by the following technical scheme:
a chronic disease screening service system based on cloud computing comprises a screening platform and a service platform; the screening platform comprises a data acquisition terminal, a processor and a data sending terminal; the service platform comprises a server, an early warning management unit, a prediction unit and a pushing unit;
the screening platform is used for collecting chronic patients in the screening area, receiving the screening area and the screening signals sent by an administrator through the data collection terminal, and sending the corresponding screening area and the screening signals to the processor; the region dividing unit divides the screening region in the server and sends the divided sub-screening regions and corresponding screening personnel to the crowd analysis unit; analyzing corresponding screening personnel in each sub-screening area through a crowd analysis unit, and judging whether the screening personnel have chronic diseases or not; analyzing the sick personnel by a factor acquisition unit, and acquiring influence factors of chronic diseases; the affected personnel, the unaffected personnel, the selected influence factors and the indirect influence factors are sent to a service platform through a data sending terminal, and the service platform stores the affected personnel, the unaffected personnel, the selected influence factors and the indirect influence factors into a server after receiving the influence factors;
the service platform receives the sick personnel, the selected influence factors and the indirect influence factors, the early warning management unit carries out early warning prompt on the sick personnel, and the sick personnel in the screening area are predicted through the prediction unit.
As a further solution of the present invention, the area dividing unit specifically divides the process as follows:
dividing the screening area into a plurality of sub-screening areas, and marking screening personnel in the sub-screening areas as i, wherein i is a positive integer greater than 1; the age groups of the screening personnel corresponding to the divided sub-screening areas are the same, the screening age groups are divided into 20-35 years, 36-45 years and more than 45 years, the proportion of the number of the screening personnel corresponding to the sub-screening areas to the total number of the screening personnel corresponding to the sub-screening areas is the same, otherwise, the screening areas are judged to be unqualified in division, and the divided sub-screening areas and the screening personnel corresponding to the sub-screening areas are sent to a crowd analysis unit.
As a further solution of the present invention, the crowd analysis unit specifically analyzes the process as follows:
setting a blood sugar value measurement time threshold t, and carrying out fasting and full blood sugar value measurement on screening personnel, wherein fasting measurement time is twenty minutes before breakfast, and full measurement time is twenty minutes after supper; setting a normal range of fasting blood glucose and a normal range of satiety blood glucose, and if any value of the measured value of fasting blood glucose or the measured value of satiety blood glucose is not in the corresponding normal range of blood glucose, judging that the blood glucose of the corresponding screening person is unqualified;
collecting the times and frequency of unqualified blood sugar value monitoring by screening personnel within a blood sugar value measuring time threshold, and respectively marking the times and frequency of unqualified blood sugar value monitoring as CSi and PLi; acquiring a chronic disease judgment coefficient Xi of a screening worker through a formula Xi = CSi × a1+ PLi × a 2; comparing the chronic disease judgment coefficient Xi of the screening person with a chronic disease judgment coefficient threshold value: if the chronic disease judgment coefficient Xi of the screening personnel is larger than or equal to the chronic disease judgment coefficient threshold value, judging that the corresponding screening personnel suffers from chronic diseases, marking the corresponding screening personnel as sick personnel and sending the sick personnel to the data sending terminal and the factor acquisition unit; if the chronic disease determination coefficient Xi of the screening personnel is smaller than the chronic disease determination coefficient threshold value, the corresponding screening personnel is determined not to suffer from the chronic disease, the corresponding screening personnel is marked as the personnel who do not suffer from the chronic disease, and the personnel who do not suffer from the chronic disease are sent to the data sending terminal.
As a further solution of the present invention, the specific analysis process of the factor collecting unit is as follows:
selecting three sick persons from a sub-screening area in which the sick persons exist in the screening area, wherein the three sick persons correspond to three age groups respectively; collecting life information of the sick personnel selected in each sub-screening area, and if the ratio of the number of the selected sick personnel of any data in the life information is more than or equal to 80% of the total number of the selected sick personnel, marking the corresponding data as preselected influence factors; if the ratio of the number of the selected sick persons to the number of the selected sick persons in any data in the life information is less than 80% of the total number of the selected sick persons, marking the corresponding data as irrelevant influence factors; selecting three persons without diseases in the screening area, wherein the three persons without diseases correspond to three age groups respectively, collecting life information of the selected persons without diseases, and if the number of persons without preselected influence factors in the life information corresponding to the selected persons without diseases is more than or equal to 90% of the total number of the selected persons without diseases, marking the corresponding preselected influence factors as selected influence factors; if the number of people without the preselected influence factors in the life information corresponding to the selected persons without the disease is less than 90% of the total number of the selected persons without the disease, marking the corresponding preselected influence factors as indirect influence factors; and sending the selected influence factors and the indirect influence factors to a data sending terminal.
As a further solution of the present invention, the specific warning prompting process of the warning management unit is as follows:
collecting the geographical position of the sick person, and setting the geographical position of the sick person as a center and two kilometers as a radius as a collection area corresponding to the sick person; marking the corresponding sick personnel as early warning objects; acquiring unworthy sick personnel and unworthy sick personnel in a collection area corresponding to the early warning object; the patient is maintained and takes medicine at the right time and takes exercise regularly; the patients without maintenance are indicated as patients who take medicines on time and do not exercise regularly; the hospital stay frequency and the average whole-day movement time corresponding to the patient without maintenance and the patient with maintenance are collected, the time is taken as an X axis, the patient without maintenance is taken as a left Y axis, the patient with maintenance is taken as a right Y axis to establish a coordinate system, a hospital stay frequency curve and a movement time curve are established in the coordinate system, the curve is updated in real time according to the time, and the coordinate system updated in real time is sent to a mobile phone terminal of an early warning object in a picture mode.
As a further solution of the present invention, the prediction unit specifically predicts as follows:
collecting life information of the persons not affected by the disease in the screening area, collecting selected influence factors and indirect influence factors existing in the life information of the persons not affected by the disease, and marking the selected influence factors and the indirect influence factors as risk factors; acquiring the existence time of the risk factors corresponding to the persons not suffering from the disease, and acquiring the fluctuation trend of the chronic disease judgment coefficient corresponding to the persons not suffering from the disease within the existence time of the risk factors, wherein the fluctuation trend is divided into a growth trend and a reduction trend; if the number of the risk factors is increased or the fluctuation trend of the chronic disease judgment coefficient is an increasing trend, generating a predicted risk signal, marking the corresponding non-diseased person as a dangerous non-diseased person, and sending the dangerous non-diseased person and the predicted risk signal to a server and a mobile phone terminal corresponding to the non-diseased person.
As a further solution of the present invention, the pushing unit specifically pushes the following:
the method comprises the steps of collecting hospitals in a screening area and marking the hospitals as o, wherein o is a positive integer larger than 1, collecting the average hospitalization time for stabilizing blood sugar value recovery of sick personnel of each hospital, the maximum trimming difference value of blood sugar value of the sick personnel and the average amount of money spent by the sick personnel in the hospitalization time, obtaining the pushing coefficient Po of the hospitals in the screening area through analysis, sequencing the hospitals in the screening area according to the sequence of the corresponding pushing coefficient values from large to small, and marking the hospital with the first sequencing as a priority pushing hospital.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the screening area in the server is divided by the area dividing unit, so that the screening accuracy is improved, and meanwhile, the influence condition of chronic diseases can be clearly reflected according to the area difference; adjusting each sub-screening area to ensure that the corresponding proportion of each sub-screening area is the same, and preventing the accuracy of screening data from being reduced due to too many people;
2. in the invention, the factor acquisition unit analyzes the sick personnel, acquires the influence factors of the chronic diseases, accurately judges and acquires the influence factors, ensures that the adjustment direction of the sick personnel is more definite, simultaneously ensures that the sick personnel can be accurately prevented, and reduces the probability of the chronic diseases;
3. in the invention, the early warning management unit carries out early warning prompt on the sick personnel, enhances the maintenance consciousness of the sick personnel, accelerates the speed of the sick personnel to recover to normal, and reduces the chronic disease rate of the corresponding screening area; the surrounding area of the early warning object is used as the acquisition area, the accuracy of a coordinate system can be improved, and the importance of more clear maintenance of the early warning object is realized by using different information of two sick personnel, so that the maintenance consciousness of the chronic disease of the early warning object is effectively improved, and the death rate of patients with the chronic disease is favorably reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is an overall schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a cloud computing-based chronic disease screening service system includes a screening platform and a service platform; the screening platform comprises a data acquisition terminal, a processor and a data sending terminal, wherein the processor is in bidirectional communication connection with the data acquisition terminal and the data sending terminal; the service platform comprises a server, an early warning management unit, a prediction unit and a pushing unit; the server is in bidirectional communication connection with the early warning management unit, the prediction unit and the pushing unit;
the data acquisition terminal is used for receiving the screening area and the screening signal sent by the administrator and sending the corresponding screening area and the screening signal to the processor;
the region dividing unit is used for dividing the screening region in the server and dividing and screening the screening region, so that the screening accuracy is improved, and meanwhile, the influence conditions of chronic diseases can be clearly reflected according to region differences;
dividing the screening area into a plurality of sub-screening areas, and marking screening personnel in the sub-screening areas as i, wherein i is a positive integer greater than 1; the age groups of the screening personnel corresponding to the divided sub-screening areas are the same, the screening age groups are divided into 20-35 years, 36-45 years and more than 45 years, the ratio of the number of people in the screening age groups corresponding to the sub-screening areas to the total number of people in the corresponding sub-screening areas is the same, if the number value of people is more than a value threshold value, the difference of the number of people in the single digit is ignored, namely if the number of people in the sub-screening area A is 20-35 people, the total number of people in the sub-screening area A is 500; the number of 20-35 people in the B screening area is 20, and the total number of people in the B screening area is 200; judging that the division of the A sub-screening area and the B sub-screening area is qualified; otherwise, judging that the screening area is unqualified in division, adjusting each sub-screening area, ensuring that the corresponding proportion of each sub-screening area is the same, and preventing the accuracy of screening data from being reduced due to too many people; sending the divided sub-screening areas and corresponding screening personnel to a crowd analysis unit;
the crowd analysis unit is used for analyzing corresponding screening personnel in each sub-screening area and judging whether the screening personnel have chronic diseases or not, diabetes is used as a screening standard in the screening of the chronic diseases, and other types of chronic diseases can be used as the screening standard; the specific analysis process is as follows:
step S1: setting a blood sugar value measurement time threshold t, and carrying out fasting and full blood sugar value measurement on screening personnel, wherein fasting measurement time is twenty minutes before breakfast, and full measurement time is twenty minutes after supper;
step S2: setting a normal range of fasting blood glucose and a normal range of satiety blood glucose, and if any value of the measured value of fasting blood glucose or the measured value of satiety blood glucose is not in the corresponding normal range of blood glucose, judging that the blood glucose of the corresponding screening person is unqualified;
collecting the times and frequency of unqualified blood sugar value monitoring by screening personnel within a blood sugar value measuring time threshold, and respectively marking the times and frequency of unqualified blood sugar value monitoring as CSi and PLi; acquiring a chronic disease judgment coefficient Xi of a screening worker through a formula Xi = CSi × a1+ PLi × a2, wherein a1 and a2 are both preset weight coefficients and take values of 1.1 and 0.8 respectively; the chronic disease judgment coefficient is a numerical value used for evaluating the probability of chronic diseases of the screening personnel by carrying out normalization processing on the measurement parameters of the screening personnel; the frequency of unqualified blood sugar value monitoring and the frequency of unqualified blood sugar value monitoring can be obtained through a formula, and the higher the chronic disease judgment coefficient is, the higher the probability of chronic diseases of screening personnel is;
step S3: comparing the chronic disease judgment coefficient Xi of the screening person with a chronic disease judgment coefficient threshold value: if the chronic disease judgment coefficient Xi of the screening personnel is larger than or equal to the chronic disease judgment coefficient threshold value, judging that the corresponding screening personnel suffers from chronic diseases, marking the corresponding screening personnel as sick personnel and sending the sick personnel to the data sending terminal and the factor acquisition unit; if the chronic disease determination coefficient Xi of the screening personnel is smaller than the chronic disease determination coefficient threshold value, determining that the corresponding screening personnel is not suffering from chronic diseases, marking the corresponding screening personnel as persons not suffering from the diseases and sending the persons not suffering from the diseases to the data sending terminal;
factor collection unit is used for carrying out the analysis to sick personnel, gathers chronic disease's influence factor, accurately judges and gathers influence factor, makes sick personnel adjustment direction more clear and definite, makes sick personnel prevention accurate not simultaneously, reduces chronic disease probability, and concrete analytic process is as follows:
step SS 1: selecting three sick persons from a sub-screening area in which the sick persons exist in the screening area, wherein the three sick persons correspond to three age groups respectively; collecting life information of the sick personnel selected in each sub-screening area, wherein the life information of the sick personnel comprises four data, namely the tobacco age, drinking frequency, average daily exercise duration and body mass index of the sick personnel;
step SS 2: if the ratio of the number of the selected sick persons to the number of the selected sick persons in any data in the life information is more than or equal to 80% of the total number of the selected sick persons, marking the corresponding data as a preselected influence factor; if the ratio of the number of the selected sick persons to the number of the selected sick persons in any data in the life information is less than 80% of the total number of the selected sick persons, marking the corresponding data as irrelevant influence factors;
step SS 3: selecting three persons without diseases in the screening area, wherein the three persons without diseases correspond to three age groups respectively, collecting life information of the selected persons without diseases, and if the number of persons without preselected influence factors in the life information corresponding to the selected persons without diseases is more than or equal to 90% of the total number of the selected persons without diseases, marking the corresponding preselected influence factors as selected influence factors; if the number of people without the preselected influence factors in the life information corresponding to the selected persons without the disease is less than 90% of the total number of the selected persons without the disease, marking the corresponding preselected influence factors as indirect influence factors;
step SS 4: sending the selected influence factors and the indirect influence factors to a data sending terminal;
the data sending terminal sends the affected person, the unaffected person, the selected influence factor and the indirect influence factor to the service platform, and the service platform stores the affected person, the unaffected person, the selected influence factor and the indirect influence factor into the server after receiving the influence factors;
early warning administrative unit is used for carrying out the early warning suggestion to sick personnel, strengthens sick personnel's maintenance consciousness, accelerates sick personnel speed of recovering normal, reduces the chronic disease rate that corresponds the screening region, and concrete early warning suggestion process is as follows:
collecting the geographical position of the sick person, and setting the geographical position of the sick person as a center and two kilometers as a radius as a collection area corresponding to the sick person; marking the corresponding sick personnel as early warning objects; acquiring unworthy sick personnel and unworthy sick personnel in a collection area corresponding to the early warning object; the patient is maintained and takes medicine at the right time and takes exercise regularly; the patients without maintenance are indicated as patients who take medicines on time and do not exercise regularly;
acquiring hospitalization frequency and average whole-day movement time corresponding to the patients without maintenance and the patients with maintenance, establishing a coordinate system by taking the time as an X axis, the patients without maintenance as a left Y axis and the patients with maintenance as a right Y axis, establishing a hospitalization frequency curve and a movement time curve in the coordinate system, updating the curves in real time according to the time, and sending the coordinate system updated in real time to a mobile phone terminal of an early warning object in the form of pictures; the surrounding area of the early warning object is taken as an acquisition area, the accuracy of a coordinate system can be improved, and the importance of the early warning object in maintenance is clearer by using different information of two sick persons, so that the maintenance consciousness of the chronic disease of the early warning object is effectively improved, and the death rate of a patient with the chronic disease is favorably reduced;
the prediction unit is used for predicting the persons without suffering from the diseases in the screening area, analyzing and predicting the persons without suffering from the diseases, and preventing the persons without suffering from the diseases from changing the chronic diseases to cause the increase of the prevalence rate of the chronic diseases in the corresponding area, wherein the specific prediction process is as follows:
collecting life information of the persons not affected by the disease in the screening area, collecting selected influence factors and indirect influence factors existing in the life information of the persons not affected by the disease, and marking the selected influence factors and the indirect influence factors as risk factors; acquiring the existence time of the risk factors corresponding to the persons not suffering from the disease, and acquiring the fluctuation trend of the chronic disease judgment coefficient corresponding to the persons not suffering from the disease within the existence time of the risk factors, wherein the fluctuation trend is divided into a growth trend and a reduction trend;
if the number of the risk factors is increased or the fluctuation trend of the chronic disease judgment coefficient is an increasing trend, generating a predicted risk signal, marking the corresponding non-diseased person as a dangerous non-diseased person, and sending the dangerous non-diseased person and the predicted risk signal to a server and a mobile phone terminal corresponding to the non-diseased person;
the propelling movement unit is used for the reasonable propelling movement hospital of the personnel of falling ill who stores in the server, according to the reasonable propelling movement hospital of the treatment information of the personnel of falling ill to other hospitals, improve the treatment effeciency of the personnel of falling ill, prevent that the personnel of falling ill from appearing spending the time treatment but treatment effeciency is low, lead to the state of an illness of the personnel of falling ill to in time obtain stably, thereby cause the mortality of the personnel of falling ill, treatment information is including long data, quantity data and cost data, long data presentation is for the long average length of being in hospital of the blood sugar value recovery of the personnel of falling ill from a hospital, quantity data presentation is for the maximum trimming difference of the blood sugar value of the personnel of falling ill, cost data presentation is for the average amount of money of the personnel of falling ill from a hospital long cost, specific propelling movement process is as follows:
collecting hospitals in the screening area and marking the hospitals as o, wherein o is a positive integer larger than 1, collecting the average hospitalization time for stabilizing blood sugar value of sick personnel in each hospital, the maximum trimming difference value of blood sugar value of the sick personnel and the average amount spent by the sick personnel in the hospitalization time, and respectively marking the average amount as SCo, CZo and JEo;
by the formula
Acquiring a pushing coefficient Po of a hospital in a screening area, wherein v1, v2 and v3 are all preset weight coefficients, beta is an error correction factor, and the value is 1.5; the pushing coefficient is a numerical value used for evaluating the pushing probability of the hospital obtained by carrying out normalization processing on the parameters of the hospital in the screening area; the larger the average hospitalization duration and the average amount can be obtained through a formula, the smaller the pushing coefficient is, and the smaller the probability of hospital pushing in the screening area is represented;
and sorting the hospitals in the screening area according to the descending order of the corresponding pushing coefficient values, and marking the hospital which is sorted first as a priority pushing hospital.
A chronic disease screening service system based on cloud computing is characterized in that when the system works, chronic patients in a screening area are collected through a screening platform, screening areas and screening signals sent by an administrator are received through a data collection terminal, and the corresponding screening areas and the screening signals are sent to a processor; the region dividing unit divides the screening region in the server and sends the divided sub-screening regions and corresponding screening personnel to the crowd analysis unit; analyzing corresponding screening personnel in each sub-screening area through a crowd analysis unit, and judging whether the screening personnel have chronic diseases or not; analyzing the sick personnel by a factor acquisition unit, and acquiring influence factors of chronic diseases; the affected personnel, the unaffected personnel, the selected influence factors and the indirect influence factors are sent to a service platform through a data sending terminal, and the service platform stores the affected personnel, the unaffected personnel, the selected influence factors and the indirect influence factors into a server after receiving the influence factors; the service platform receives the sick personnel, the selected influence factors and the indirect influence factors, the early warning management unit carries out early warning prompt on the sick personnel, and the sick personnel in the screening area are predicted through the prediction unit.
The above formulas are all calculated by taking the numerical value of the dimension, the formula is a formula which obtains the latest real situation by acquiring a large amount of data and performing software simulation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (7)
1. A cloud computing-based chronic disease screening service system is characterized by comprising a screening platform and a service platform; the screening platform comprises a data acquisition terminal, a processor and a data sending terminal; the service platform comprises a server, an early warning management unit, a prediction unit and a pushing unit;
the screening platform is used for collecting chronic patients in the screening area, receiving the screening area and the screening signals sent by an administrator through the data collection terminal, and sending the corresponding screening area and the screening signals to the processor; the region dividing unit divides the screening region in the server and sends the divided sub-screening regions and corresponding screening personnel to the crowd analysis unit; analyzing corresponding screening personnel in each sub-screening area through a crowd analysis unit, and judging whether the screening personnel have chronic diseases or not; analyzing the sick personnel by a factor acquisition unit, and acquiring influence factors of chronic diseases; the affected personnel, the unaffected personnel, the selected influence factors and the indirect influence factors are sent to a service platform through a data sending terminal, and the service platform stores the affected personnel, the unaffected personnel, the selected influence factors and the indirect influence factors into a server after receiving the influence factors;
the service platform receives the sick personnel, the selected influence factors and the indirect influence factors, the early warning management unit carries out early warning prompt on the sick personnel, and the sick personnel in the screening area are predicted through the prediction unit.
2. The cloud-computing-based chronic disease screening service system as claimed in claim 1, wherein the regional division unit is specifically divided as follows:
dividing the screening area into a plurality of sub-screening areas, and marking screening personnel in the sub-screening areas as i, wherein i is a positive integer greater than 1; the age groups of the screening personnel corresponding to the divided sub-screening areas are the same, the screening age groups are divided into 20-35 years, 36-45 years and more than 45 years, the proportion of the number of the screening personnel corresponding to the sub-screening areas to the total number of the screening personnel corresponding to the sub-screening areas is the same, otherwise, the screening areas are judged to be unqualified in division, and the divided sub-screening areas and the screening personnel corresponding to the sub-screening areas are sent to a crowd analysis unit.
3. The cloud-computing-based chronic disease screening service system as claimed in claim 1, wherein the population analysis unit has the following specific analysis process:
setting a blood sugar value measurement time threshold t, and carrying out fasting and full blood sugar value measurement on screening personnel, wherein fasting measurement time is twenty minutes before breakfast, and full measurement time is twenty minutes after supper; setting a normal range of fasting blood glucose and a normal range of satiety blood glucose, and if any value of the measured value of fasting blood glucose or the measured value of satiety blood glucose is not in the corresponding normal range of blood glucose, judging that the blood glucose of the corresponding screening person is unqualified;
collecting the times and frequency of unqualified blood sugar value monitoring by screening personnel within a blood sugar value measuring time threshold, and respectively marking the times and frequency of unqualified blood sugar value monitoring as CSi and PLi; acquiring a chronic disease judgment coefficient Xi of a screening worker through a formula Xi = CSi × a1+ PLi × a 2; comparing the chronic disease judgment coefficient Xi of the screening person with a chronic disease judgment coefficient threshold value: if the chronic disease judgment coefficient Xi of the screening personnel is larger than or equal to the chronic disease judgment coefficient threshold value, judging that the corresponding screening personnel suffers from chronic diseases, marking the corresponding screening personnel as sick personnel and sending the sick personnel to the data sending terminal and the factor acquisition unit; if the chronic disease determination coefficient Xi of the screening personnel is smaller than the chronic disease determination coefficient threshold value, the corresponding screening personnel is determined not to suffer from the chronic disease, the corresponding screening personnel is marked as the personnel who do not suffer from the chronic disease, and the personnel who do not suffer from the chronic disease are sent to the data sending terminal.
4. The cloud-computing-based chronic disease screening service system as claimed in claim 1, wherein the factor collecting unit has the following specific analysis process:
selecting three sick persons from a sub-screening area in which the sick persons exist in the screening area, wherein the three sick persons correspond to three age groups respectively; collecting life information of the sick personnel selected in each sub-screening area, and if the ratio of the number of the selected sick personnel of any data in the life information is more than or equal to 80% of the total number of the selected sick personnel, marking the corresponding data as preselected influence factors; if the ratio of the number of the selected sick persons to the number of the selected sick persons in any data in the life information is less than 80% of the total number of the selected sick persons, marking the corresponding data as irrelevant influence factors; selecting three persons without diseases in the screening area, wherein the three persons without diseases correspond to three age groups respectively, collecting life information of the selected persons without diseases, and if the number of persons without preselected influence factors in the life information corresponding to the selected persons without diseases is more than or equal to 90% of the total number of the selected persons without diseases, marking the corresponding preselected influence factors as selected influence factors; if the number of people without the preselected influence factors in the life information corresponding to the selected persons without the disease is less than 90% of the total number of the selected persons without the disease, marking the corresponding preselected influence factors as indirect influence factors; and sending the selected influence factors and the indirect influence factors to a data sending terminal.
5. The cloud-computing-based chronic disease screening service system as claimed in claim 1, wherein the specific early warning prompting process of the early warning management unit is as follows:
collecting the geographical position of the sick person, and setting the geographical position of the sick person as a center and two kilometers as a radius as a collection area corresponding to the sick person; marking the corresponding sick personnel as early warning objects; acquiring unworthy sick personnel and unworthy sick personnel in a collection area corresponding to the early warning object; the patient is maintained and takes medicine at the right time and takes exercise regularly; the patients without maintenance are indicated as patients who take medicines on time and do not exercise regularly; the hospital stay frequency and the average whole-day movement time corresponding to the patient without maintenance and the patient with maintenance are collected, the time is taken as an X axis, the patient without maintenance is taken as a left Y axis, the patient with maintenance is taken as a right Y axis to establish a coordinate system, a hospital stay frequency curve and a movement time curve are established in the coordinate system, the curve is updated in real time according to the time, and the coordinate system updated in real time is sent to a mobile phone terminal of an early warning object in a picture mode.
6. The cloud-computing-based chronic disease screening service system as claimed in claim 1, wherein the prediction unit is specifically configured to perform the following prediction processes:
collecting life information of the persons not affected by the disease in the screening area, collecting selected influence factors and indirect influence factors existing in the life information of the persons not affected by the disease, and marking the selected influence factors and the indirect influence factors as risk factors; acquiring the existence time of the risk factors corresponding to the persons not suffering from the disease, and acquiring the fluctuation trend of the chronic disease judgment coefficient corresponding to the persons not suffering from the disease within the existence time of the risk factors, wherein the fluctuation trend is divided into a growth trend and a reduction trend; if the number of the risk factors is increased or the fluctuation trend of the chronic disease judgment coefficient is an increasing trend, generating a predicted risk signal, marking the corresponding non-diseased person as a dangerous non-diseased person, and sending the dangerous non-diseased person and the predicted risk signal to a server and a mobile phone terminal corresponding to the non-diseased person.
7. The cloud-computing-based chronic disease screening service system as claimed in claim 1, wherein the pushing unit specifically pushes as follows:
the method comprises the steps of collecting hospitals in a screening area and marking the hospitals as o, wherein o is a positive integer larger than 1, collecting the average hospitalization time for stabilizing blood sugar value recovery of sick personnel of each hospital, the maximum trimming difference value of blood sugar value of the sick personnel and the average amount of money spent by the sick personnel in the hospitalization time, obtaining the pushing coefficient Po of the hospitals in the screening area through analysis, sequencing the hospitals in the screening area according to the sequence of the corresponding pushing coefficient values from large to small, and marking the hospital with the first sequencing as a priority pushing hospital.
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