CN113130053A - Self-service registration method and device based on user activity data - Google Patents
Self-service registration method and device based on user activity data Download PDFInfo
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- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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Abstract
The invention discloses a self-service registration method and a device based on user activity data, wherein the method comprises the following steps: acquiring activity data of a user within a preset first time interval, and screening abnormal data from the activity data; extracting feature data from the abnormal data; matching registration types according to the characteristic data; and performing background registration by adopting the registration type so as to allow a user to adopt the background registration for treatment. The invention can determine the diagnosis direction of the user according to the actual acquisition condition of the user, and register and diagnose according to the corresponding diagnosis direction, thereby avoiding the condition that the number of registered people is full and the time for diagnosis needs to be reserved after the user arrives at the site, not only saving the registration time of the user, but also facilitating the diagnosis arrangement of a hospital, simultaneously matching the registration type with the actual condition of the user, leading the registration subject to be consistent with the actual sick client of the user, improving the efficiency of the diagnosis of the user and reducing the resource waste of the hospital.
Description
Technical Field
The invention relates to the technical field of medical assistance, in particular to a self-service registration method and device based on user activity data.
Background
With the continuous development of economy, the pace of life of people is faster and faster, however, the fast pace of life causes the body of many people to be in a sub-health state, so that more diseases show signs of youthfulness, and more people are available for treatment in hospitals.
The traditional treatment mode is that a user selects treatment subjects, selects treatment time in a hospital field or a background system, and then applies for registration for treatment.
However, the traditional way of seeing a doctor has the following problems: after the on-site registration usually arrives at the site, the number of people registered on the same day is found to be full, and the time is reserved again for registration and diagnosis, so that a large amount of time is wasted, and the hospital is also congested; the online registration can cause the situation that the patient cannot go to the hospital for a doctor in time after the registration, and medical resources can be wasted; and the user registration generally selects the corresponding registration subject according to the abnormal place of the current body, but the frequently-occurring symptom is not necessarily connected with the affected place, so that the registered subject of the user is not consistent with the actual affected subject, the time of the user is wasted, and the resources of the hospital are wasted.
Disclosure of Invention
The invention provides a self-service registration method and device based on user activity data.
The first aspect of the embodiment of the invention provides a self-service registration method based on user activity data, which comprises the following steps:
acquiring activity data of a user within a preset first time interval, and screening abnormal data from the activity data;
extracting feature data from the abnormal data;
matching registration types according to the characteristic data;
and performing background registration by adopting the registration type so as to allow a user to adopt the background registration for treatment.
In a possible implementation manner of the first aspect, the extracting feature data from the abnormal data includes:
the anomaly data comprises in vivo anomaly data;
calculating a data difference value between the in-vivo abnormal data and preset state data;
judging whether the data difference value is larger than a preset reference value or not;
and if the data difference value is larger than a preset reference value, taking in-vivo abnormal data corresponding to the data difference value as characteristic data.
In a possible implementation manner of the first aspect, the extracting feature data from the abnormal data includes:
the anomaly data comprises a body surface anomaly image;
dividing the body surface abnormal image into a plurality of individual body surface square images, and respectively inputting each body surface square image into a color conversion space to obtain an image color corresponding to each body surface square image;
screening one or more target body surface square images with the same preset color from a plurality of image colors;
splicing the one or more target body surface square images into a target region, and acquiring the region area of the target region;
judging whether the area of the region is larger than a preset first area and smaller than a preset second area, wherein the preset second area is larger than the preset first area;
and if the area of the region is larger than a preset first area and smaller than a preset second area, taking the target region as characteristic data.
In a possible implementation manner of the first aspect, the matching registration types according to the feature data includes:
calculating a matching value of the characteristic data and a preset disease database to obtain a characteristic matching value;
and determining the value range of the feature matching value, and determining the registration type according to the value range.
In a possible implementation manner of the first aspect, the screening abnormal data from the activity data includes:
dividing the activity data into a plurality of interval data according to a preset second time interval, wherein the preset first time interval is greater than the preset second time interval;
respectively acquiring starting point data and end point data of each interval data;
calculating a data change value of the starting point data and the end point data;
and when the data change value is larger than a preset change value, taking the interval data where the data change value is as abnormal data.
A second aspect of an embodiment of the present invention provides a self-service registration apparatus based on user activity data, the apparatus including:
the screening module is used for acquiring activity data of a user within a preset first time interval and screening abnormal data from the activity data;
the extraction module is used for extracting characteristic data from the abnormal data;
the matching module is used for matching registration types according to the characteristic data;
and the registration module is used for performing background registration by adopting the registration type so as to allow a user to adopt the background registration for treatment.
In a possible implementation manner of the second aspect, the extracting module is further configured to:
the anomaly data comprises in vivo anomaly data;
calculating a data difference value between the in-vivo abnormal data and preset state data;
judging whether the data difference value is larger than a preset reference value or not;
and if the data difference value is larger than a preset reference value, taking in-vivo abnormal data corresponding to the data difference value as characteristic data.
In a possible implementation manner of the second aspect, the extracting module is further configured to:
the anomaly data comprises a body surface anomaly image;
dividing the body surface abnormal image into a plurality of individual body surface square images, and respectively inputting each body surface square image into a color conversion space to obtain an image color corresponding to each body surface square image;
screening one or more target body surface square images with the same preset color from a plurality of image colors;
splicing the one or more target body surface square images into a target region, and acquiring the region area of the target region;
judging whether the area of the region is larger than a preset first area and smaller than a preset second area, wherein the preset second area is larger than the preset first area;
and if the area of the region is larger than a preset first area and smaller than a preset second area, taking the target region as characteristic data.
Compared with the prior art, the self-service registration method and device based on the user activity data have the advantages that: the invention can determine the diagnosis direction of the user according to the actual acquisition condition of the user, and register and diagnose according to the corresponding diagnosis direction, thereby avoiding the condition that the number of registered people is full and the time for diagnosis needs to be reserved after the user arrives at the site, not only saving the registration time of the user, but also facilitating the diagnosis arrangement of a hospital, simultaneously matching the registration type with the actual condition of the user, leading the registration subject to be consistent with the actual sick client of the user, improving the efficiency of the diagnosis of the user and reducing the resource waste of the hospital.
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Fig. 1 is a schematic flow chart of a self-service registration method based on user activity data according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a self-service registration apparatus based on user activity data according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious 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.
The current registration method has the following problems: after the on-site registration usually arrives at the site, the number of people registered on the same day is found to be full, and the time is reserved again for registration and diagnosis, so that a large amount of time is wasted, and the hospital is also congested; if the patient goes to a hospital for a doctor before in time after the registration on the network, medical resources are wasted; and the user registration generally selects the corresponding registration subject according to the abnormal place of the current body, but the frequently-occurring symptom is not necessarily connected with the affected place, so that the registered subject of the user is not consistent with the actual affected subject, the time of the user is wasted, and the resources of the hospital are wasted.
In order to solve the above problem, a self-service registration method based on user activity data provided by the embodiments of the present application will be described and explained in detail by the following specific embodiments.
Referring to fig. 1, a flowchart of a self-service registration method based on user activity data according to an embodiment of the present invention is shown. The method may be applied to a user terminal, such as a mobile phone or a smart watch. The user terminal can be connected with a background server of a hospital, and can send a registration request to the background server of the hospital to realize registration operation.
By way of example, the self-service registration method based on the user activity data may include:
and S11, acquiring activity data of the user within a preset first time interval, and screening abnormal data from the activity data.
The activity data may include data of the daily activity of the user, image data of a body surface of the user, data during the activity cycle of the user, physiological data of the user, and the like. In actual operation, when the user uses the user terminal, the user terminal can continuously collect the activity data of the user. Specifically, activity data may be obtained over a first time interval, which may be 1 day, 1 week, 10 days, 1 month, and so forth.
After the activity data of the user is collected, abnormal data can be screened out from the activity data, and the abnormal data can be data which is higher than the average value or lower than the average value in the activity data.
In order to accurately screen the abnormal data from the active data, the step S11 may include the following sub-steps, as an example:
substep S111, dividing the activity data into a plurality of interval data according to a preset second time interval, wherein the preset first time interval is greater than the preset second time interval.
For example, the collected activity data within 10 days, the activity data within 10 days may be divided into 10 interval data, and each interval data is the activity data of the user within one day.
In the substep S112, start point data and end point data of each interval data are acquired, respectively.
And a substep S113 of calculating a data change value of the start point data and the end point data.
And a substep S114, when the data change value is larger than a preset change value, taking the interval data where the data change value is as abnormal data.
The start point data is data of a start time node of the section data, and the end point data is data of an end time node of the section data. The data change value is a data difference value between the starting point data and the end point data.
For example, activity data for 1 day is divided into 12 interval data, each of which is activity data of a user for two hours.
And then respectively acquiring the activity data of 6 points and 0 points and the activity data of 8 points and 0 points, then calculating the data difference value between the activity data of 6 points and 0 points and the activity data of 8 points and 0 points, judging whether the data difference value is larger than a preset change value, and when the data difference value is larger than the preset change value, taking the activity data of 6 points and 0 points to the activity data of 8 points and 0 points corresponding to the data difference value as abnormal data.
And S12, extracting characteristic data from the abnormal data.
Since the abnormal data is also the activity data of the user in a time interval, in order to accurately determine the cause of the abnormality, the feature data may be extracted from the abnormal data.
In an alternative embodiment, the disease or discomfort of the user may be a disease or inflammation of an internal organ, and in order to accurately determine an abnormality in the body of the user, in the present embodiment, the abnormality data includes in-vivo abnormality data, wherein, as an example, the step S12 may include the following sub-steps:
and a substep S121 of calculating a data difference value between the in-vivo abnormal data and preset state data.
In specific implementation, when a user uses a user terminal, the user terminal performs living body detection and collection on the user to obtain physiological data of the user, and in-vivo abnormal data is obtained by screening the physiological data.
The in vivo anomaly data may include: various blood conventions, cell proportions, etc. The preset state data is the normal physiological state data of the human body.
And a substep S122, judging whether the data difference value is larger than a preset reference value.
And S123, if the data difference value is larger than a preset reference value, taking in-vivo abnormal data corresponding to the data difference value as characteristic data.
After the in-vivo abnormal data is obtained, the difference between the in-vivo abnormal data and the preset state data can be calculated to obtain a corresponding difference value, whether the difference value is a preset reference value or not is judged, and if yes, the abnormal condition of the user can be determined.
In another alternative embodiment, the disease of the user may be a traumatic injury, which may be a damage on the surface of the human body, and the abnormality data includes a body surface abnormality image in order to further determine the injured area of the user, wherein, as an example, the step S12 may include the following sub-steps:
and a substep S124 of dividing the body surface abnormal image into a plurality of individual body surface square images, and respectively inputting each body surface square image into a color conversion space to obtain an image color corresponding to each body surface square image.
Specifically, the user terminal can acquire images of all regions of the body of the user in real time, and then screen and extract the images of all the regions to obtain the abnormal body surface images.
In actual operation, the user terminal can divide the body surface abnormal image into a plurality of individual table square images, the specific division number can be adjusted according to actual needs, and the division size can also be adjusted according to actual needs.
After obtaining each body surface square image, each body surface square image may be input into the Lab color space, so as to obtain the corresponding image color of each body surface square image in the Lab color space.
And a substep S125 of screening one or more target body surface square images with the same preset color from the plurality of image colors.
When a user has traumatic injury, the body surface bleeds, so that the color of the injured area is different from the color of the body surface. Specifically, whether the image color corresponding to each body surface square image in the Lab color space is the same as the preset color or not can be judged, and if so, it is determined that the body surface of the user is possibly injured.
Specifically, the preset color is red.
And a substep S126, splicing the one or more target body surface square images into a target region, and acquiring the region area of the target region.
And a substep S127 of judging whether the area of the region is larger than a preset area.
And a substep S128 of taking the target region as characteristic data if the area of the region is larger than a preset area.
Since the red color of the body surface of the user may be clothes or decorations, but not the area where the user is actually injured, in order to further determine the injured area of the user, one or more target body surface block images may be spliced into a target area, and whether the area of the target area is larger than a preset area or not may be determined.
When the area of the region is larger than the preset second area, the area of the region is too large, and the target region is determined to be red and possibly clothes of the user; when the area of the region is smaller than the preset first area and the area of the region is too small, the target region can be determined to be possibly an ornament of the user; when the area of the region is larger than the preset first area and smaller than the preset second area, it can be determined that the user may collide to cause the injury bleeding.
And S13, matching registration types according to the feature data.
After the characteristic data of the user is obtained, the diagnosis direction can be determined according to the characteristic data of the user, and the registration type of the user is determined according to the diagnosis direction.
In order to accurately match the registration type of the user, the registration efficiency is improved, and the resource waste of the hospital is reduced. As an example, step S13 may include the following sub-steps:
and a substep S131 of calculating a matching value of the feature data and a preset disease database to obtain a feature matching value.
The preset disease database may be a disease database stored in a hospital background server, and the database may store features corresponding to each disease. For example, a cold may be characterized by dizziness, runny nose, etc.; pneumonia may be characterized by coughing, fever, etc. For another example, the internal medicine corresponds to headache, platelet-hyper; surgery corresponds to bleeding, fractures, etc.
In this embodiment, the feature data may be subjected to matching calculation with each feature in the disease database to obtain a corresponding feature matching value.
And a substep S132 of determining the value range of the feature matching value and determining the registration type according to the value range.
In this embodiment, each type may correspond to a range of matching values, such as surgical score 0-10, surgical score 10-30, neurological score 30-50, and so forth.
The matching value range corresponding to the feature matching value can be determined, and the registration type is determined according to the matching value range.
And S14, performing background registration by adopting the registration type so that the user can see a doctor by adopting the background registration.
After the registration type is determined, the user terminal can send a registration request to a background server of the hospital according to the registration type, and the registration request can correspond to the registration type, so that the user is helped to perform background registration.
In this embodiment, an embodiment of the present invention provides a self-service registration method based on user activity data, which has the following beneficial effects: the invention can determine the diagnosis direction of the user according to the actual acquisition condition of the user, and register and diagnose according to the corresponding diagnosis direction, thereby avoiding the condition that the number of registered people is full and the time for diagnosis needs to be reserved after the user arrives at the site, not only saving the registration time of the user, but also facilitating the diagnosis arrangement of a hospital, simultaneously matching the registration type with the actual condition of the user, leading the registration subject to be consistent with the actual sick client of the user, improving the efficiency of the diagnosis of the user and reducing the resource waste of the hospital.
An embodiment of the present invention further provides a self-service registration apparatus based on user activity data, and referring to fig. 2, a schematic structural diagram of the self-service registration apparatus based on user activity data according to an embodiment of the present invention is shown.
Wherein, as an example, the self-service registration device based on the user activity data may comprise:
the screening module 201 is configured to acquire activity data of a user within a preset first time interval, and screen abnormal data from the activity data;
an extraction module 202, configured to extract feature data from the abnormal data;
the matching module 203 is used for matching registration types according to the characteristic data;
and the registration module 204 is used for performing background registration by adopting the registration type so as to allow a user to adopt the background registration for treatment.
Further, the extraction module is further configured to:
the anomaly data comprises in vivo anomaly data;
calculating a data difference value between the in-vivo abnormal data and preset state data;
judging whether the data difference value is larger than a preset reference value or not;
and if the data difference value is larger than a preset reference value, taking in-vivo abnormal data corresponding to the data difference value as characteristic data.
Further, the extraction module is further configured to:
the anomaly data comprises a body surface anomaly image;
dividing the body surface abnormal image into a plurality of individual body surface square images, and respectively inputting each body surface square image into a color conversion space to obtain an image color corresponding to each body surface square image;
screening one or more target body surface square images with the same preset color from a plurality of image colors;
splicing the one or more target body surface square images into a target region, and acquiring the region area of the target region;
judging whether the area of the region is larger than a preset first area and smaller than a preset second area, wherein the preset second area is larger than the preset first area;
and if the area of the region is larger than a preset first area and smaller than a preset second area, taking the target region as characteristic data.
Further, the matching module is further configured to:
calculating a matching value of the characteristic data and a preset disease database to obtain a characteristic matching value;
and determining the value range of the feature matching value, and determining the registration type according to the value range.
Further, the screening module is further configured to:
dividing the activity data into a plurality of interval data according to a preset second time interval, wherein the preset first time interval is greater than the preset second time interval;
respectively acquiring starting point data and end point data of each interval data;
calculating a data change value of the starting point data and the end point data;
and when the data change value is larger than a preset change value, taking the interval data where the data change value is as abnormal data.
Further, an embodiment of the present application further provides an electronic device, including: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the self-help registration method based on the user activity data according to the embodiment.
Further, an embodiment of the present application also provides a computer-readable storage medium, where computer-executable instructions are stored, and the computer-executable instructions are configured to enable a computer to execute the self-service registration method based on user activity data according to the foregoing embodiment.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A self-service registration method based on user activity data is characterized by comprising the following steps:
acquiring activity data of a user within a preset first time interval, and screening abnormal data from the activity data;
extracting feature data from the abnormal data;
matching registration types according to the characteristic data;
and performing background registration by adopting the registration type so as to allow a user to adopt the background registration for treatment.
2. The self-service registration method based on user activity data according to claim 1, wherein the extracting feature data from the abnormal data comprises:
the anomaly data comprises in vivo anomaly data;
calculating a data difference value between the in-vivo abnormal data and preset state data;
judging whether the data difference value is larger than a preset reference value or not;
and if the data difference value is larger than a preset reference value, taking in-vivo abnormal data corresponding to the data difference value as characteristic data.
3. The self-service registration method based on user activity data according to claim 1, wherein the extracting feature data from the abnormal data comprises:
the anomaly data comprises a body surface anomaly image;
dividing the body surface abnormal image into a plurality of individual body surface square images, and respectively inputting each body surface square image into a color conversion space to obtain an image color corresponding to each body surface square image;
screening one or more target body surface square images with the same preset color from a plurality of image colors;
splicing the one or more target body surface square images into a target region, and acquiring the region area of the target region;
judging whether the area of the region is larger than a preset first area and smaller than a preset second area, wherein the preset second area is larger than the preset first area;
and if the area of the region is larger than a preset first area and smaller than a preset second area, taking the target region as characteristic data.
4. The self-service registration method based on user activity data according to any one of claims 1-3, wherein the matching registration types according to the feature data comprises:
calculating a matching value of the characteristic data and a preset disease database to obtain a characteristic matching value;
and determining the value range of the feature matching value, and determining the registration type according to the value range.
5. The self-service registration method based on user activity data according to any one of claims 1-3, wherein the screening abnormal data from the activity data comprises:
dividing the activity data into a plurality of interval data according to a preset second time interval, wherein the preset first time interval is greater than the preset second time interval;
respectively acquiring starting point data and end point data of each interval data;
calculating a data change value of the starting point data and the end point data;
and when the data change value is larger than a preset change value, taking the interval data where the data change value is as abnormal data.
6. A self-service registration apparatus based on user activity data, the apparatus comprising:
the screening module is used for acquiring activity data of a user within a preset first time interval and screening abnormal data from the activity data;
the extraction module is used for extracting characteristic data from the abnormal data;
the matching module is used for matching registration types according to the characteristic data;
and the registration module is used for performing background registration by adopting the registration type so as to allow a user to adopt the background registration for treatment.
7. The self-service registration apparatus based on user activity data according to claim 6, wherein the extraction module is further configured to:
the anomaly data comprises in vivo anomaly data;
calculating a data difference value between the in-vivo abnormal data and preset state data;
judging whether the data difference value is larger than a preset reference value or not;
and if the data difference value is larger than a preset reference value, taking in-vivo abnormal data corresponding to the data difference value as characteristic data.
8. The self-service registration apparatus based on user activity data according to claim 6, wherein the extraction module is further configured to:
the anomaly data comprises a body surface anomaly image;
dividing the body surface abnormal image into a plurality of individual body surface square images, and respectively inputting each body surface square image into a color conversion space to obtain an image color corresponding to each body surface square image;
screening one or more target body surface square images with the same preset color from a plurality of image colors;
splicing the one or more target body surface square images into a target region, and acquiring the region area of the target region;
judging whether the area of the region is larger than a preset first area and smaller than a preset second area, wherein the preset second area is larger than the preset first area;
and if the area of the region is larger than a preset first area and smaller than a preset second area, taking the target region as characteristic data.
9. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor when executing the program implements a self-service registration method based on user activity data according to any of claims 1-5.
10. A computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the method of self-help registration based on user activity data according to any one of claims 1-5.
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