CN114974544A - Call grade analysis method and device based on health monitoring data - Google Patents
Call grade analysis method and device based on health monitoring data Download PDFInfo
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Abstract
The embodiment of the disclosure provides a call grade analysis method and device based on health monitoring data, and relates to the technical field of health information monitoring. The call grade analysis method based on the health monitoring data comprises the following steps: acquiring health monitoring historical data of a user; generating portrait data of a user according to the health monitoring historical data; obtaining a call rating rule matched with the health monitoring historical data of the user according to the portrait data; acquiring health monitoring real-time data of a user; according to the call rating rule, the call rating analysis is carried out on the health monitoring real-time data to generate a call rating result.
Description
Technical Field
The invention relates to the technical field of health information monitoring, in particular to a call grade analysis method and device based on health monitoring data.
Background
The household medical equipment comprises a household applicable sleep monitoring belt, a network beeper, a urine wet alarm, a card swiping register and the like, wherein the sleep monitoring belt is used for monitoring vital sign data such as respiration and heartbeat and carrying out data analysis and processing, and the calling equipment comprises a wireless beeper, a wired beeper, a Bluetooth beeper, a 4G beeper and an NB beeper and is used for providing calling, cancelling calling and background statistics functions; the urine wet warning device is used for detecting urine and giving a urine wet warning; the card swiping sign-in equipment is used for counting the nursing completion condition and the nursing time.
The current household medical equipment has single function and poor data intercommunication, so that the health monitoring efficiency of the user is low.
Disclosure of Invention
The embodiment of the disclosure mainly aims to provide a call level analysis method and device based on health monitoring data, which can improve the health monitoring efficiency of a user.
In order to achieve the above object, a first aspect of the embodiments of the present disclosure provides a method for analyzing a call level based on health monitoring data, including:
acquiring health monitoring historical data of a user; the health monitoring data comprises vital sign data, calling data, physiological alarm data and nursing supervision data; the vital sign data comprises respiration data, heartbeat data and body movement data, the calling data comprises calling frequency and calling time, the physiological alarm data comprises physiological alarm frequency and physiological alarm time, and the nursing supervision data comprises nursing frequency and nursing time;
generating portrait data of the user according to the health monitoring historical data;
obtaining a call rating rule matched with the health monitoring historical data of the user according to the portrait data;
acquiring health monitoring real-time data of the user;
and performing call grade analysis on the health monitoring real-time data according to the call grade rule to generate a call grade result.
In some embodiments, the call level result includes a call type and a call level, and the performing the call level analysis on the health monitoring real-time data according to the call rating rule to generate the call level result includes:
generating a vital sign result according to the vital sign data and a preset threshold range; the vital sign result comprises normal vital signs and abnormal vital signs;
generating a calling result according to the calling data; the calling result comprises active calling and inactive calling;
generating a physiological alarm result according to the physiological alarm data; the physiological alarm result comprises a urine wetness alarm and a urine wetness non-alarm;
generating a nursing supervision result according to the nursing supervision data; the nursing supervision result comprises that the check-in time is greater than a preset time interval, and the check-in time is less than or equal to the preset time interval.
In some embodiments, the performing a call rating analysis on the health monitoring real-time data according to the call rating rule to generate a call rating result further includes:
and if the vital sign result is that the vital sign is abnormal and the calling result is that active calling exists, the calling type is a safety alarm and the calling level is high.
In some embodiments, the performing a call rating analysis on the health monitoring real-time data according to the call rating rule to generate a call rating result further includes:
and if the vital sign result is that the vital sign is abnormal and the calling result is that active calling exists or not, the calling type is a safety alarm, and the calling grade is determined according to the nursing supervision result.
In some embodiments, the performing a call rating analysis on the health monitoring real-time data according to the call rating rule to generate a call rating result further includes:
if the vital sign result is that the vital sign is normal and the calling result is that the active call exists or not, or the physiological warning result is that the urine is wet and no warning exists and the calling result is that the active call exists or not, the calling type is a common call, and the calling grade is low.
In some embodiments, the performing a call rating analysis on the health monitoring real-time data according to the call rating rule to generate a call rating result further includes:
and carrying out calling and supervision frequency analysis according to the calling data and the nursing supervision data to obtain a wrong calling detection result.
In some embodiments, the generating of the representation data of the user from the health monitoring history data comprises:
reading the identification information of the user;
labeling the identification information to generate label data of the user;
associating the tag data with the health monitoring history data to generate the representation data.
To achieve the above object, a second aspect of the present disclosure provides a call level analyzing apparatus based on health monitoring data, including:
the historical data acquisition module is used for acquiring the health monitoring historical data of the user; the health monitoring data comprises vital sign data, call data, physiological alarm data and nursing supervision data; the vital sign data comprises respiration data, heartbeat data and body movement data, the calling data comprises calling frequency and calling time, the physiological alarm data comprises physiological alarm frequency and physiological alarm time, and the nursing supervision data comprises nursing frequency and nursing time;
the user portrait generation module is used for generating portrait data of the user according to the health monitoring historical data;
the rating rule matching module is used for obtaining a call rating rule matched with the health monitoring historical data of the user according to the portrait data;
the real-time data acquisition module is used for acquiring the health monitoring real-time data of the user;
and the call grade analysis module is used for carrying out call grade analysis on the health monitoring real-time data according to the call grade rule and generating a call grade result.
To achieve the above object, a third aspect of the present disclosure provides an electronic device, including:
at least one memory;
at least one processor;
at least one program;
the program is stored in a memory and a processor executes the at least one program to implement the method of the present disclosure as described in the above first aspect.
To achieve the above object, a fourth aspect of the present disclosure proposes a storage medium that is a computer-readable storage medium storing computer-executable instructions for causing a computer to perform:
a method as described in the first aspect above.
The call grade analysis method and device based on the health monitoring data, provided by the embodiment of the disclosure, are characterized by firstly acquiring the health monitoring historical data of a user, then generating portrait data of the user according to the health monitoring historical data, further obtaining a call grade rule matched with the health monitoring historical data of the user according to the portrait data, then acquiring the health monitoring real-time data of the user, and finally performing call grade analysis on the health monitoring real-time data according to the call grade rule to generate a call grade result.
Drawings
Fig. 1 is a schematic view of an application scenario of a call level analysis method based on health monitoring data according to an embodiment of the present disclosure.
Fig. 2 is a flowchart of a call level analysis method based on health monitoring data according to an embodiment of the present disclosure.
Fig. 3 is a flowchart of step S250 in fig. 2.
Fig. 4 is a flowchart of step S250 provided in another embodiment in fig. 2.
Fig. 5 is a flowchart of step S220 in fig. 2.
Fig. 6 is a block diagram of a call level analysis apparatus based on health monitoring data according to an embodiment of the present disclosure.
Fig. 7 is a schematic hardware structure diagram of an electronic device according to an embodiment of the present disclosure.
Reference numerals: the system comprises a data processing center 100, a vital signs module 110, a call module 120, a physiological alarm module 130, a care supervision module 140, a historical data acquisition module 610, a user representation generation module 620, a rating rule matching module 630, a real-time data acquisition module 640, a call level analysis module 650, a processor 701, a memory 702, an input/output interface 703, a communication interface 704 and a bus 705.
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 below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It is noted that while functional block divisions are provided in device diagrams and logical sequences are shown in flowcharts, in some cases, steps shown or described may be performed in sequences other than block divisions within devices or flowcharts. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to be limiting of the invention.
The household medical equipment comprises a household applicable sleep monitoring belt, a network beeper, a urine wet alarm, a card swiping register and the like, wherein the sleep monitoring belt is used for monitoring vital sign data such as respiration and heartbeat and carrying out data analysis and processing, and the calling equipment comprises a wireless beeper, a wired beeper, a Bluetooth beeper, a 4G beeper and an NB beeper and is used for providing calling, cancelling calling and background statistics functions; the urine wet warning device is used for detecting urine and giving a urine wet warning; the card swiping sign-in equipment is used for counting the nursing completion condition and the nursing time.
The current household medical equipment, such as a health care system, has the following defects that (1) a plurality of groups of equipment have single function, are installed complicatedly, occupy too much space, are too disordered and have multiple functions; (2) the device data are not associated with each other. The data can not be unified for intelligent analysis; (3) according to the prior art, the intelligent judgment of the health condition of the elderly living alone cannot be realized according to sleep monitoring data, urine wet alarm data, recent call alarm data and service duration of a server; (4) when the elderly who live alone send out a call alarm or the intelligent monitoring equipment triggers an alarm, the service completion condition and the service time of a service provider are difficult to be counted and checked according to the equipment in the prior art under the condition of protecting the privacy of the user; (5) some devices are not networked and cannot submit data to the background.
In addition, in the prior art, a plurality of devices are needed to transmit data to the central server through the internet, and the data are fed back to the terminal after logarithmic analysis processing of the central server.
In summary, the current home medical devices have single function and poor data intercommunication, which results in low efficiency of health monitoring for users.
Based on this, the embodiment of the disclosure provides a call level analysis method and device based on health monitoring data, the method includes the steps of firstly obtaining health monitoring historical data of a user, then generating portrait data of the user according to the health monitoring historical data, further obtaining a call rating rule matched with the health monitoring historical data of the user according to the portrait data, then obtaining health monitoring real-time data of the user, and finally conducting call level analysis on the health monitoring real-time data according to the call rating rule to generate a call level result.
The calling grade analysis device based on the health monitoring data is four-in-one equipment, and has four functions of vital sign monitoring, calling, urine wetness alarming and card swiping sign-in, the four-in-one equipment integrates the card swiping sign-in function, solves the problem of examining and monitoring functions of a server, and can count the service duration of visiting; the four-in-one equipment integrates a 4G networking function, all data and a background are synchronized in real time, and it needs to be explained that service persons include but are not limited to mechanisms and volunteers for providing home servers for the old; integrates the functions of card swiping and sign-in of the protection worker. The completion condition of the nursing task of the nursing worker can be completely recorded. The working condition of the maintainer can be effectively supervised.
In a specific embodiment, the call level analysis device based on the health monitoring data solves the problems of single function, complex installation and excessive space occupation of equipment, reduces the production cost of the equipment, reduces the volume of the equipment and reduces the difficulty of equipment installation, so that the data is processed in one equipment, the accuracy of intelligent alarm is improved, the mutual correlation among the data is ensured, and the problem of unified intelligent data analysis is solved.
The embodiment of the present disclosure provides a method and an apparatus for analyzing a call level based on health monitoring data, which are specifically described in the following embodiments.
The embodiment of the disclosure provides a call level analysis method based on health monitoring data, and relates to the technical field of health information monitoring. The call level analysis method based on the health monitoring data provided by the embodiment of the disclosure can be applied to a terminal, a server side and software running in the terminal or the server side.
Fig. 1 is a schematic view of an application scenario of an embodiment of the present disclosure, in which a call level analysis method based on health monitoring data of the embodiment of the present disclosure is applied to a network system, and the network system includes: the system comprises a data processing center 100, a vital sign module 110, a calling module 120, a physiological alarm module 130 and a nursing supervision module 140, wherein the vital sign module 110, the calling module 120, the physiological alarm module 130 and the nursing supervision module 140 are respectively connected with the data processing center 100.
The embodiment of the disclosure provides a call grade analysis method based on health monitoring data, which comprises the following steps: acquiring health monitoring historical data of a user; the health monitoring data comprises vital sign data, calling data, physiological alarm data and nursing supervision data; the vital sign data comprises respiration data, heartbeat data and body movement data, the calling data comprises calling frequency and calling time, the physiological alarm data comprises physiological alarm frequency and physiological alarm time, and the nursing supervision data comprises nursing frequency and nursing time; generating portrait data of a user according to the health monitoring historical data; obtaining a call rating rule matched with the health monitoring historical data of the user according to the portrait data; acquiring health monitoring real-time data of a user; and performing call grade analysis on the health monitoring real-time data according to the call grade rule to generate a call grade result.
Fig. 2 is an optional flowchart of a call level analysis method based on health monitoring data according to an embodiment of the present disclosure, where the method in fig. 2 may include, but is not limited to, steps S210 to S250, and specifically includes:
s210, acquiring health monitoring historical data of a user;
s220, generating portrait data of the user according to the health monitoring historical data;
s230, obtaining a call rating rule matched with the health monitoring historical data of the user according to the portrait data;
s240, acquiring health monitoring real-time data of a user;
and S250, performing call grade analysis on the health monitoring real-time data according to the call grade rule to generate a call grade result.
In step S210, the user includes, but is not limited to, an elderly person needing care, and the health monitoring historical data is historical data of the user and is used for generating a user portrait, specifically, the health monitoring data includes vital sign data, call data, physiological alarm data, and care supervision data; the vital sign data comprises respiration data, heartbeat data and body movement data, the calling data comprises calling frequency and calling time, the physiological alarm data comprises physiological alarm frequency and physiological alarm time, and the nursing supervision data comprises nursing frequency and nursing time.
In each embodiment of the present application, when data related to the identity or characteristic of a user, such as user information, user behavior data, user history data, and user location information, is processed, permission or consent of the user is obtained, and the collection, use, and processing of the data comply with relevant laws and regulations and standards of relevant countries and regions. In addition, when the embodiment of the present application needs to acquire sensitive personal information of a user, individual permission or individual consent of the user is obtained through a pop-up window or a jump to a confirmation page, and after the individual permission or individual consent of the user is definitely obtained, necessary user-related data for enabling the embodiment of the present application to operate normally is acquired.
In step S220, the user image data is a user image, and the user image is a label that represents the user and is used for other functions, such as personalized recommendation, advertisement system, activity marketing, etc., by data creation.
In step S230, a call rating rule matching the user is assigned to the user according to the user feature represented in the user image. For example, if the heart rate of the user 1 is increased rapidly and has strong correlation with cardiovascular diseases, it is determined that the heart rate of the user 1 is abnormal, it is predicted that the user 1 may have cardiovascular diseases, and then, in the user behavior portrait, it may be determined that the user 1 is a serious disability user, and the physical movement and the calling of the user are both the highest emergency level, and medical care personnel are required to pay attention to the user 1. For another example, if the heart rate and the call of the user 2 are normal, and data expressions such as physical movement, a urine wetness alarm, and a call show correlation, and are concentrated in a certain time period and have a sequence, it may be determined that the physiological call of the user 2 is frequent, so that the weight of the physiological alarm in the call rating rule of the user 2 is increased.
In step S240, the health monitoring real-time data of the user is health monitoring data collected in real time, and is used for predicting the health condition of the user in real time and determining whether to send an alarm of a corresponding type and level.
In step S250, various items of data of the user are analyzed by using a rating rule obtained based on the user figure, and an analysis result is obtained.
The embodiment of the disclosure provides a call grade analysis method based on health monitoring data, which includes the steps of firstly obtaining health monitoring historical data of a user, then generating portrait data of the user according to the health monitoring historical data, further obtaining a call grade rule matched with the health monitoring historical data of the user according to the portrait data, then obtaining health monitoring real-time data of the user, finally carrying out call grade analysis on the health monitoring real-time data according to the call grade rule, and generating a call grade result.
In some embodiments, the call level result includes a call type and a call level, and the call level analysis is performed on the health monitoring real-time data according to the call rating rule to generate the call level result, including: generating a vital sign result according to the vital sign data and a preset threshold range; the vital sign result comprises normal vital signs and abnormal vital signs; generating a calling result according to the calling data; the calling result comprises active calling and inactive calling; generating a physiological alarm result according to the physiological alarm data; the physiological alarm result comprises a urine wet alarm and a urine wet non-alarm; generating a nursing supervision result according to the nursing supervision data; the nursing supervision result comprises that the check-in time is greater than the preset time interval, and the check-in time is less than or equal to the preset time interval.
Fig. 3 is a flow chart of step S250 in some embodiments, and step S250 illustrated in fig. 3 includes, but is not limited to, steps S310 to S340:
s310, generating a vital sign result according to the vital sign data and a preset threshold range;
s320, generating a calling result according to the calling data;
s330, generating a physiological alarm result according to the physiological alarm data;
and S340, generating a nursing supervision result according to the nursing supervision data.
In step S310, the vital sign result includes that the vital sign is normal and the vital sign is abnormal, specifically, if each item of the vital sign value of the user falls within a normal vital sign range, it indicates that the vital sign is normal, otherwise, the vital sign is abnormal.
In step S320, the calling result includes active calling and inactive calling, and if the user finds that the body is different and presses the case of active calling, the calling result is active calling, otherwise, the calling result is inactive calling.
In step S330, the physiological warning result includes a urine wet warning and a urine wet non-warning, and if the urine wet warning occurs, the physiological warning result indicates that the urine wet warning is the physiological warning, otherwise, the physiological warning result indicates that the urine wet non-warning is the physiological warning.
In step S340, the result of the care supervision includes that the check-in time is greater than the preset time interval and the check-in time is less than or equal to the preset time interval. The preset time interval is due time for nursing by a nurse, if the check-in time is longer than the due time, the nurse is not required to punch the card on time, and in this case, the risk of the user is relatively high.
In some embodiments, performing a call rating analysis on the health monitoring real-time data according to the call rating rule to generate a call rating result, further comprising: if the vital sign result is abnormal, and the calling result is active calling, the calling type is safe alarm, and the calling level is high. If the vital sign result is abnormal, and the calling result is whether active calling exists or not, the calling type is a safety alarm, and the calling grade is determined according to the nursing supervision result. If the vital sign result is that the vital sign is normal and the calling result is that the active call exists or not, or the physiological warning result is that the urine is wet and no warning exists and the calling result is that the active call exists or not, the calling type is a common call and the calling grade is low. And carrying out calling and supervision frequency analysis according to the calling data and the nursing supervision data to obtain a wrong calling detection result.
Fig. 4 is a flowchart of step S250 in other embodiments, and step S250 illustrated in fig. 4 includes, but is not limited to, steps S410 to S440:
s410, if the vital sign result is abnormal, and the calling result is active calling, the calling type is safe alarm, and the calling level is high;
and S420, if the vital sign result is abnormal and the calling result is active calling or not, the calling type is a safety alarm, and the calling grade is determined according to the nursing supervision result.
S430, if the vital sign result is that the vital sign is normal and the call result is active call or not, or the physiological alarm result is that there is no alarm for urine wetness and the call result is active call or not, the call type is normal call and the call level is low.
And S440, performing calling and monitoring frequency analysis according to the calling data and the nursing monitoring data to obtain a wrong calling detection result.
In step S410, if the vital sign data is obviously abnormal and the user has a trigger call signal, it may be determined that the call is a heavy emergency call, specifically, the call type is a safety alarm and the call level is high.
In step S420, if the vital sign data is obviously abnormal, but the system does not receive a call signal, and combines with the user behavior (breathing, heartbeat, body movement, and urine test) for a period of time, it can be initially determined that the user is in a healthy state, and an emergency alarm is to be triggered, and at this time, the call level is determined according to the result of the care supervision.
In step S430, if the sign data is normal, there is no urine alarm, and only the call is made. It can be determined that the call is a general behavior call and the urgency is not strong, and specifically, the call type is a general call and the call level is low.
In step S440, it can be determined whether the call is a wrong call by combining the frequency and time of the call data with the frequency and time of the card swiping sign-in, for example, in the history data, the time period of the call made by the user 3 using the monitoring device is 12 o ' clock to 18 o ' clock, but the call information of the user 3 is received suddenly at 10 o ' clock, it is determined that the call is likely to be a wrong call.
In a specific embodiment, the correspondence between the health monitoring data and the call level results is shown in table 1, where table 1 shows the call level results corresponding to different health monitoring data.
TABLE 1
It should be noted that table 1 shows the call rating rule of a certain user, for example, when the vital signs are normal and there is an active call, and the check-in time is less than the preset time interval, the call type is a help alarm, and the call level is low.
In some embodiments, generating portrait data for a user from health monitoring history data includes: reading identification information of a user; labeling the identification information to generate label data of the user; the tag data is correlated with the health monitoring history data to generate the representation data.
Fig. 5 is a flowchart of step S220 in some embodiments, and step S220 illustrated in fig. 5 includes, but is not limited to, steps S510 to S540:
s510, reading identification information of a user;
s520, performing labeling processing on the identification information to generate label data of the user;
s530, the label data is correlated with the health monitoring historical data to generate portrait data.
In steps S510 to S530, the identification information of the user includes, but is not limited to, a unique user identification, such as a user id, a mobile phone number, and the like, and the labeling process specifically includes labeling the user with a label from four dimensions, i.e., a vital sign, a call condition, a physiological alarm condition, and a care supervision condition.
The embodiment of the present disclosure provides a call level analysis device based on health monitoring data, including: the historical data acquisition module is used for acquiring the health monitoring historical data of the user; the health monitoring data comprises vital sign data, calling data, physiological alarm data and nursing supervision data; the vital sign data comprises respiration data, heartbeat data and body movement data, the calling data comprises calling frequency and calling time, the physiological alarm data comprises physiological alarm frequency and physiological alarm time, and the nursing supervision data comprises nursing frequency and nursing time; the user portrait generation module is used for generating portrait data of the user according to the health monitoring historical data; the rating rule matching module is used for obtaining a call rating rule matched with the health monitoring historical data of the user according to the portrait data; the real-time data acquisition module is used for acquiring health monitoring real-time data of a user; and the call grade analysis module is used for carrying out call grade analysis on the health monitoring real-time data according to the call grade rule and generating a call grade result.
Referring to fig. 6, fig. 6 illustrates an embodiment of a call level analyzing apparatus based on health monitoring data, wherein the call level analyzing apparatus based on health monitoring data includes: the system comprises a historical data acquisition module 610, a user portrait generation module 620, a rating rule matching module 630, a real-time data acquisition module 640 and a call level analysis module 650, wherein the historical data acquisition module 610 is connected with the user portrait generation module 620, the user portrait generation module 620 is connected with the rating rule matching module 630, the rating rule matching module 630 is connected with the real-time data acquisition module 640, and the real-time data acquisition module 640 is connected with the call level analysis module 650.
The specific implementation of the call level analyzing apparatus based on health monitoring data of this embodiment is basically the same as the specific implementation of the call level analyzing method based on health monitoring data, and belongs to the same inventive concept, and is not described herein again.
An embodiment of the present disclosure further provides an electronic device, including:
at least one memory;
at least one processor;
at least one program;
the programs are stored in the memory and the processor executes the at least one program to implement the present disclosure to implement the above-described call level analysis method based on health monitoring data.
Referring to fig. 7, fig. 7 illustrates a hardware structure of an electronic device according to another embodiment, where the electronic device includes:
the processor 701 may be implemented by a general-purpose CPU (central processing unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits, and is configured to execute a relevant program to implement the technical solution provided by the embodiment of the present disclosure;
the memory 702 may be implemented in a ROM (read only memory), a static memory device, a dynamic memory device, or a RAM (random access memory). The memory 702 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present disclosure is implemented by software or firmware, the relevant program codes are stored in the memory 702 and called by the processor 701 to execute the call level analysis method based on health monitoring data according to the embodiments of the present disclosure;
an input/output interface 703 for realizing information input and output;
the communication interface 704 is used for realizing communication interaction between the device and other devices, and can realize communication in a wired manner (for example, USB, network cable, etc.) or in a wireless manner (for example, mobile network, WIFI, bluetooth, etc.); and
a bus 705 that transfers information between the various components of the device (e.g., the processor 701, the memory 702, the input/output interface 703, and the communication interface 704);
wherein the processor 701, the memory 702, the input/output interface 703 and the communication interface 704 are communicatively connected to each other within the device via a bus 705.
The embodiment of the present disclosure also provides a storage medium, which is a computer-readable storage medium, and the computer-readable storage medium stores computer-executable instructions, which are used to make a computer execute the above-mentioned call level analysis method based on health monitoring data.
The call grade analysis method and device based on the health monitoring data, provided by the embodiment of the disclosure, firstly acquire the health monitoring historical data of a user, then generate portrait data of the user according to the health monitoring historical data, further obtain a call grade rule matched with the health monitoring historical data of the user according to the portrait data, then acquire the health monitoring real-time data of the user, and finally perform call grade analysis on the health monitoring real-time data according to the call grade rule to generate a call grade result.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The embodiments described in the embodiments of the present disclosure are for more clearly illustrating the technical solutions of the embodiments of the present disclosure, and do not constitute a limitation to the technical solutions provided in the embodiments of the present disclosure, and it is obvious to those skilled in the art that the technical solutions provided in the embodiments of the present disclosure are also applicable to similar technical problems with the evolution of technology and the emergence of new application scenarios.
It will be appreciated by those skilled in the art that the solutions shown in fig. 1-5 are not intended to limit the embodiments of the present disclosure, and may include more or fewer steps than those shown, or some of the steps may be combined, or different steps.
The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
One of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing programs, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, and therefore do not limit the scope of the claims of the embodiments of the present disclosure. Any modifications, equivalents and improvements within the scope and spirit of the embodiments of the present disclosure should be considered within the scope of the claims of the embodiments of the present disclosure by those skilled in the art.
Claims (10)
1. A call grade analysis method based on health monitoring data is characterized by comprising the following steps:
acquiring health monitoring historical data of a user; the health monitoring data comprises vital sign data, calling data, physiological alarm data and nursing supervision data; the vital sign data comprises respiration data, heartbeat data and body movement data, the calling data comprises calling frequency and calling time, the physiological alarm data comprises physiological alarm frequency and physiological alarm time, and the nursing supervision data comprises nursing frequency and nursing time;
generating portrait data of the user according to the health monitoring historical data;
obtaining a call rating rule matched with the health monitoring historical data of the user according to the portrait data;
acquiring health monitoring real-time data of the user;
and performing call grade analysis on the health monitoring real-time data according to the call grade rule to generate a call grade result.
2. The method of claim 1, wherein the call level results comprise call type and call level, and wherein the performing the call level analysis on the health monitoring real-time data according to the call rating rule to generate the call level results comprises:
generating a vital sign result according to the vital sign data and a preset threshold range; the vital sign result comprises normal vital signs and abnormal vital signs;
generating a calling result according to the calling data; the calling result comprises active calling and inactive calling;
generating a physiological alarm result according to the physiological alarm data; the physiological alarm result comprises a urine wetness alarm and a urine wetness non-alarm;
generating a nursing supervision result according to the nursing supervision data; the nursing supervision result comprises that the check-in time is greater than a preset time interval, and the check-in time is less than or equal to the preset time interval.
3. The method of claim 2, wherein the performing a call rating analysis on the health monitoring real-time data according to the call rating rule to generate a call rating result further comprises:
and if the vital sign result is that the vital sign is abnormal and the calling result is that active calling exists, the calling type is a safety alarm and the calling level is high.
4. The method of claim 2, wherein the performing a call rating analysis on the health monitoring real-time data according to the call rating rule to generate a call rating result further comprises:
and if the vital sign result is that the vital sign is abnormal and the calling result is that active calling exists or not, the calling type is a safety alarm, and the calling grade is determined according to the nursing supervision result.
5. The method of claim 2, wherein the performing a call rating analysis on the health monitoring real-time data according to the call rating rule to generate a call rating result further comprises:
if the vital sign result is that the vital sign is normal and the calling result is that the active call exists or not, or the physiological warning result is that the urine is wet and no warning exists and the calling result is that the active call exists or not, the calling type is a common call, and the calling grade is low.
6. The method of claim 2, wherein the performing a call rating analysis on the health monitoring real-time data according to the call rating rule to generate a call rating result further comprises:
and carrying out calling and supervision frequency analysis according to the calling data and the nursing supervision data to obtain a wrong calling detection result.
7. The method of any of claims 1 to 6, wherein generating the representation data of the user from the health monitoring history data comprises:
reading the identification information of the user;
labeling the identification information to generate label data of the user;
associating the tag data with the health monitoring history data to generate the representation data.
8. A call level analysis apparatus based on health monitoring data, comprising:
the historical data acquisition module is used for acquiring the health monitoring historical data of the user; the health monitoring data comprises vital sign data, calling data, physiological alarm data and nursing supervision data; the vital sign data comprises respiration data, heartbeat data and body movement data, the calling data comprises calling frequency and calling time, the physiological alarm data comprises physiological alarm frequency and physiological alarm time, and the nursing supervision data comprises nursing frequency and nursing time;
the user portrait generation module is used for generating portrait data of the user according to the health monitoring historical data;
the rating rule matching module is used for obtaining a call rating rule matched with the health monitoring historical data of the user according to the portrait data;
the real-time data acquisition module is used for acquiring the health monitoring real-time data of the user;
and the call grade analysis module is used for carrying out call grade analysis on the health monitoring real-time data according to the call grade rule and generating a call grade result.
9. An electronic device, comprising:
at least one memory;
at least one processor;
at least one program;
the programs are stored in a memory, and a processor executes the at least one program to implement:
the method of any one of claims 1 to 7.
10. A storage medium that is a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform:
the method of any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117672520A (en) * | 2023-12-08 | 2024-03-08 | 启康保(北京)健康科技有限公司 | Intelligent medical early warning system and method based on user treatment data |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117672520A (en) * | 2023-12-08 | 2024-03-08 | 启康保(北京)健康科技有限公司 | Intelligent medical early warning system and method based on user treatment data |
| CN117672520B (en) * | 2023-12-08 | 2024-09-27 | 启康保(北京)健康科技有限公司 | Intelligent medical early warning system and method based on user treatment data |
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