CN114974544A - Call grade analysis method and device based on health monitoring data - Google Patents

Abstract

The embodiments of the present disclosure provide a call level analysis method and device based on health monitoring data, and relate to the technical field of health information monitoring. The call rating analysis method based on health monitoring data includes: acquiring the user's health monitoring history data; generating user portrait data according to the health monitoring history data; obtaining a call rating rule matching the user's health monitoring history data according to the portrait data; Obtain the real-time health monitoring data of the user; perform call level analysis on the real-time health monitoring data according to the call rating rules, and generate a call level result. The technical solutions provided by the embodiments of the present disclosure can improve the efficiency of the user's health monitoring.

Description

Call level analysis method and device based on health monitoring data

technical field

The present invention relates to the technical field of health information monitoring, in particular to a method and device for analyzing call levels based on health monitoring data.

Background technique

Home medical equipment includes sleep monitoring belts for home use, network pagers, urine wetness alarms, card check-in devices, etc. Among them, sleep monitoring belt equipment is used to monitor vital sign data such as breathing and heartbeat, and perform data analysis and processing, calling The equipment includes wireless pager, wired pager, Bluetooth pager, 4G pager, and NB pager, which are used to provide call, cancel call, and background statistics functions; the urine wetness alarm device is used to detect urine and give urine wetness alarm. ; The swipe card sign-in device is used to count the completion of nursing and nursing time.

The current home medical equipment has a single function and poor data communication, resulting in low efficiency of user health monitoring.

SUMMARY OF THE INVENTION

The main purpose of the embodiments of the present disclosure is to propose a call level analysis method and device based on health monitoring data, which can improve the efficiency of user health monitoring.

To achieve the above object, a first aspect of the embodiments of the present disclosure provides a call level analysis method based on health monitoring data, including:

Obtain the user's health monitoring history data; the health monitoring data includes vital sign data, call data, physiological alarm data, and nursing supervision data; the vital sign data includes respiratory data, heartbeat data, and body motion data, and the call data includes Call frequency and call time, the physiological alarm data includes physiological alarm frequency and physiological alarm time, and the nursing supervision data includes nursing frequency and nursing time;

generating the profile data of the user according to the health monitoring historical data;

Obtain a call rating rule matching the health monitoring history data of the user according to the portrait data;

obtain the real-time health monitoring data of the user;

The call level analysis is performed on the health monitoring real-time data according to the call rating rule, and a call level result is generated.

In some embodiments, the call level result includes a call type and a call level, and the call level analysis is performed on the real-time health monitoring data according to the call rating rule to generate a call level result, including:

Generate vital sign results according to the vital sign data and a preset threshold range; the vital sign results include normal vital signs and abnormal vital signs;

Generate a call result according to the call data; the call result includes an active call and no active call;

Generate a physiological alarm result according to the physiological alarm data; the physiological alarm result includes a urine wetness alarm and a urine wetness no alarm;

A nursing supervision result is generated according to the nursing supervision data; the nursing supervision result 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.

In some embodiments, the performing call level analysis on the real-time health monitoring data according to the call rating rules to generate a call level result, further comprising:

If the vital sign result is that the vital sign is abnormal, and the call result is that there is an active call, the call type is a security alarm, and the call level is high.

In some embodiments, the performing call level analysis on the real-time health monitoring data according to the call rating rules to generate a call level result, further comprising:

If the vital sign result is that the vital sign is abnormal, and the call result is whether there is an active call, the call type is a safety alarm, and the call level is determined according to the nursing supervision result.

In some embodiments, the performing call level analysis on the real-time health monitoring data according to the call rating rules to generate a call level result, further comprising:

If the vital sign result is that the vital sign is normal and the call result is whether there is an active call, or the physiological alarm result is that there is no wetness alarm and the call result is whether there is an active call, then the call type is a general call, so The call level is low.

In some embodiments, the performing call level analysis on the real-time health monitoring data according to the call rating rules to generate a call level result, further comprising:

According to the call data and the nursing supervision data, call and supervision frequency analysis is performed to obtain a false call detection result.

In some embodiments, generating the profile data of the user according to the health monitoring historical data includes:

read the identification information of the user;

tagging the identification information to generate tag data of the user;

The label data is associated with the health monitoring historical data to generate the portrait data.

To achieve the above object, a second aspect of the present disclosure provides a call level analysis device based on health monitoring data, including:

The historical data acquisition module is used to acquire the historical data of the user's health monitoring; the health monitoring data includes vital sign data, call data, physiological alarm data, and nursing supervision data; the vital sign data includes respiratory data, heartbeat data, body motion data data, the call data includes call frequency and call time, the physiological alarm data includes physiological alarm frequency and physiological alarm time, and the nursing supervision data includes nursing frequency and nursing time;

a user portrait generation module, configured to generate portrait data of the user according to the health monitoring historical data;

A rating rule matching module, configured to obtain a call rating rule matching the health monitoring historical data of the user according to the portrait data;

a real-time data acquisition module for acquiring real-time health monitoring data of the user;

A call grade analysis module, configured to perform call grade analysis on the real-time health monitoring data according to the call grade rule, and generate 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 the memory, and the processor executes the at least one program to implement the method of the present disclosure as described in the first aspect above.

In order to achieve the above object, a fourth aspect of the present disclosure provides a storage medium, which is a computer-readable storage medium, and the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used for Make the computer execute:

The method as described in the first aspect above.

The method and device for call level analysis based on health monitoring data proposed in the embodiments of the present disclosure first acquire the user's health monitoring history data, then generate user portrait data according to the health monitoring history data, and then obtain the user's health monitoring history according to the portrait data. Call rating rules that match the data, then obtain the real-time health monitoring data of the user, and finally perform call rating analysis on the real-time health monitoring data according to the call rating rules to generate call rating results. Efficiency of health monitoring.

Description of drawings

FIG. 1 is a schematic diagram of an application scenario of a call level analysis method based on health monitoring data provided by an embodiment of the present disclosure.

FIG. 2 is a flowchart of a call level analysis method based on health monitoring data provided by 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 by another embodiment in FIG. 2 .

FIG. 5 is a flowchart of step S220 in FIG. 2 .

FIG. 6 is a block diagram of a module of an apparatus for analyzing a call level based on health monitoring data provided by an embodiment of the present disclosure.

FIG. 7 is a schematic diagram of a hardware structure of an electronic device provided by an embodiment of the present disclosure.

Reference numerals: data processing center 100, vital signs module 110, call module 120, physiological alarm module 130, nursing supervision module 140, historical data acquisition module 610, user portrait generation module 620, rating rule matching module 630, 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 ways

In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application.

It should be noted that although the functional modules are divided in the schematic diagram of the device, and the logical sequence is shown in the flowchart, in some cases, the modules may be divided differently from the device, or executed in the order in the flowchart. steps shown or described. The terms "first", "second" and the like in the description and claims and the above drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

Unless otherwise defined, 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 terms used herein are for the purpose of describing the embodiments of the present invention only, and are not intended to limit the present invention.

Home medical equipment includes sleep monitoring belts for home use, network pagers, urine wetness alarms, card check-in devices, etc. Among them, sleep monitoring belt equipment is used to monitor vital sign data such as breathing and heartbeat, and perform data analysis and processing, calling The equipment includes wireless pager, wired pager, Bluetooth pager, 4G pager, and NB pager, which are used to provide call, cancel call, and background statistics functions; the urine wetness alarm device is used to detect urine and give urine wetness alarm. ; The swipe card sign-in device is used to count the completion of nursing and nursing time.

The current home medical equipment, such as health care system, has the following shortcomings: (1) multiple sets of equipment have single functions, complicated installation, take up too much space, the equipment is too chaotic, and none of the equipment has multiple functions; (2) equipment data unrelated to each other. Unable to unify data for intelligent analysis; (3) According to the existing technology, it is impossible to intelligently judge the health status of the elderly living alone based on sleep monitoring data, urine wet alarm data, recent call alarm data, and service time of the service provider; (4) When the elderly living alone live alone When a person issues a call alarm or an intelligent monitoring device triggers an alarm, it is difficult to count and evaluate the service completion status and service time of the service provider according to the existing technical equipment under the condition of protecting user privacy; (5) Some devices are not connected to the Internet and cannot be Submit data to the backend.

In addition, in the current technology, multiple devices need to be used to transmit data to a central server through the Internet. After the central server performs logarithmic analysis and processing, it is fed back to the terminal. This method relies heavily on the server and on the network.

To sum up, the current home medical equipment has a single function and poor data communication, resulting in low efficiency of user health monitoring.

Based on this, the embodiments of the present disclosure provide a call level analysis method and device based on health monitoring data, which first acquires the user's health monitoring history data, then generates user portrait data according to the health monitoring history data, and then obtains the user's profile data according to the portrait data. The call rating rules that match the historical health monitoring data of the user, then obtain the real-time health monitoring data of the user, and finally perform a call rating analysis on the real-time health monitoring data according to the call rating rules, and generate a call rating result. The technical solutions provided by the embodiments of the present disclosure The efficiency of the user's health monitoring can be improved.

The embodiment of the present disclosure provides a call level analysis device based on health monitoring data, which is a four-in-one device with four functions of vital sign monitoring, calling, urine wetness alarm, and card-swiping sign-in. The four-in-one device integrates the card-swiping sign-in function to solve It has the functions of assessing and supervising service providers, and can make statistics on the duration of door-to-door service; the four-in-one device integrates 4G networking functions, and all data is synchronized with the background in real time. It should be noted that service providers include but are not limited to the elderly at home. Organizations and volunteers that provide door-to-door servers; integrated caregiver swiping card check-in function. A complete record of the completion of nursing tasks. Can effectively supervise the work of nurses.

In a specific embodiment, the call level analysis device based on health monitoring data solves the problems of single function, complicated installation and excessive space occupation of the equipment, reduces the production cost of the equipment, reduces the volume of the equipment, and reduces the installation of the equipment The difficulty of data processing makes the data processed in one device, which improves the accuracy of intelligent alarms, ensures the correlation between data, and solves the problem of intelligent analysis of unified data. This data can be used in the background for machine learning, and can also be used for The front end makes intelligent judgments on the user's health status, and the data is collected in the terminal, which solves the problem of "intelligent behavior analysis in nursing equipment relies too much on network and background services" in the current technology.

Embodiments of the present disclosure provide a method and apparatus for analyzing call levels based on health monitoring data, which are specifically described by the following embodiments. First, the method and apparatus for analyzing call levels based on health monitoring data in the embodiments of the present disclosure are described.

The call level analysis method based on health monitoring data provided by the embodiments of the present disclosure relates to the technical field of health information monitoring. The call level analysis method based on the health monitoring data provided by the embodiments of the present disclosure can be applied to the terminal, also can be applied to the server, and can also be software running in the terminal or the server.

FIG. 1 is a schematic diagram of an application scenario of an embodiment of the present disclosure. The call level analysis method based on health monitoring data in the embodiment of the present disclosure is applied to a network system. The network system includes: a data processing center 100, a vital sign module 110, a call module 120, The physiological alarm module 130 and the nursing supervision module 140 , wherein the vital signs 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 .

An embodiment of the present disclosure proposes a call level analysis method based on health monitoring data, which includes: obtaining historical data of health monitoring of users; health monitoring data including vital sign data, call data, physiological alarm data, and nursing supervision data; vital sign data Including breathing data, heartbeat data, body motion data, calling data including calling frequency and calling time, physiological alarm data including physiological alarm frequency and physiological alarm time, nursing supervision data including nursing frequency and nursing time; Profile data; obtain call rating rules that match the user's health monitoring historical data according to the profile data; obtain the user's real-time health monitoring data; perform call level analysis on the real-time health monitoring data according to the call rating rules, and generate call level results.

FIG. 2 is an optional flowchart of a method for analyzing call levels based on health monitoring data provided by an embodiment of the present disclosure. The method in FIG. 2 may include, but is not limited to, steps S210 to S250, specifically including:

S210, obtain the user's health monitoring history data;

S220, generating user portrait data according to the health monitoring historical data;

S230, obtaining a call rating rule matching the user's health monitoring history data according to the portrait data;

S240, obtain the real-time data of the user's health monitoring;

S250: Perform call level analysis on the real-time health monitoring data according to the call rating rule, and generate a call level result.

In step S210, the user includes but is not limited to the elderly who need nursing care, and the health monitoring historical data is the user's historical data, which is used to generate a user portrait. Specifically, the health monitoring data includes vital sign data, call data, physiological alarm data, nursing care Supervision data; vital sign data includes respiratory data, heartbeat data, and body motion data, call data includes call frequency and call time, physiological alarm data includes physiological alarm frequency and physiological alarm time, and nursing supervision data includes nursing frequency and nursing time.

It should be noted that, in each specific embodiment of the present application, when it is necessary to perform relevant processing based on user information, user behavior data, user historical data, and user location information and other data related to user identity or characteristics, the data will be obtained first. The user's permission or consent, and the collection, use and processing of these data will abide by the relevant laws, regulations and standards of the relevant countries and regions. In addition, when the embodiment of this application needs to obtain the user's sensitive personal information, the user's individual permission or individual consent will be obtained through a pop-up window or jumping to a confirmation page. Obtain necessary user-related data for enabling the embodiments of the present application to operate normally.

In step S220, the user's portrait data is the user's portrait, and the user's portrait is to create a label to describe the user through the data, and use it for other functions, such as personalized recommendation, advertising system, event marketing, and so on.

In step S230, according to the characteristics of the user reflected in the user portrait, a matching call rating rule is assigned to the user. For example, if the increased heart rate of user 1 is strongly correlated with cardiovascular disease, it is determined that user 1 has an abnormal heart rate, and it is predicted that user 1 may have cardiovascular disease. Then, in the user behavior portrait, it can be determined that user 1 is severely ill. If the user is able to use it, the user's physical activity and calls are at the highest emergency level, and medical staff need to focus on user 1 care. For another example, if user 2's heart rate and calls are normal, and data such as physical activity, wetness alarm, and calls appear to be correlated, concentrated in a certain period of time and in a sequence, it can be judged that user 2's physiological calls are more frequent. Therefore, the weight of physiological alerts in User 2's call rating rules is increased.

In step S240, the real-time health monitoring data of the user is the health monitoring data collected in real time, which is used to predict the user's health condition in real time, and determine whether to issue an alarm of a corresponding type and level.

In step S250, various data of the user are analyzed by using the rating rules obtained based on the user portrait to obtain an analysis result.

The embodiments of the present disclosure provide a call level analysis method based on health monitoring data, first acquiring the user's health monitoring history data, then generating user portrait data according to the health monitoring history data, and then obtaining the user's health monitoring history data according to the portrait data. Match the call rating rules, then obtain the real-time health monitoring data of the user, and finally perform a call rating analysis on the health monitoring real-time data according to the call rating rules, and generate a call rating result. The technical solution provided by the embodiments of the present disclosure can improve the health of the user. Monitoring efficiency.

In some embodiments, the call level result includes call type and call level, and the call level analysis is performed on the real-time health monitoring data according to the call rating rule to generate the call level result, including: generating the vital sign result according to the vital sign data and a preset threshold range The vital signs results include normal vital signs and abnormal vital signs; call results are generated according to call data; call results include active calls and no active calls; physiological alarm results are generated according to physiological alarm data; physiological alarm results include urine wetness alarm, urine wetness There is no alarm; the nursing supervision result is generated according to the nursing supervision data; the nursing supervision result 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.

FIG. 3 is a flowchart of step S250 in some embodiments. 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 call result according to the call data;

S330, generating a physiological alarm result according to the physiological alarm data;

S340, generating a nursing supervision result according to the nursing supervision data.

In step S310, the vital signs results include normal vital signs and abnormal vital signs. Specifically, if the values of various vital signs of the user fall within the normal vital sign range, it means that the vital signs are normal; otherwise, the vital signs are abnormal.

In step S320, the call result includes active call and no active call. If the user perceives that his body is abnormal and presses the case of active call, the call result is active call, otherwise, there is no active call.

In step S330, the physiological alarm result includes a wetness alarm and no wetness alarm. If a wetness alarm occurs, it means that the result of the physiological alarm is a wetness alarm. Otherwise, the result of the physiological alarm is a wetness no alarm.

In step S340, the nursing supervision result 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. Among them, the preset time interval is the required time for the nurse to perform nursing care. If the check-in time is longer than the required time, it means that the nurse did not punch in on time. In this case, the risk to the user is relatively high.

In some embodiments, the call level analysis is performed on the real-time health monitoring data according to the call rating rule, and the call level result is generated, further comprising: if the vital sign result is an abnormal vital sign and the call result is an active call, the call type is a security alarm , the call level is high. If the vital sign result is abnormal vital sign, and the call result is whether there is an active call, the call type is a safety alarm, and the call level is determined according to the nursing supervision result. If the vital signs result is normal vital signs and the call result is whether there is an active call, or the physiological alarm result is urination without alarm and the call result is whether there is an active call, the call type is general call and the call level is low. According to the call data and nursing supervision data, the frequency of calls and supervision is analyzed, and the detection results of false calls are obtained.

FIG. 4 is a flowchart of step S250 in other embodiments. Step S250 illustrated in FIG. 4 includes but is not limited to steps S410 to S440:

S410, if the vital sign result is that the vital sign is abnormal, and the call result is that there is an active call, the call type is a security alarm, and the call level is high;

S420, if the vital sign result is abnormal vital sign, and the call result is whether there is an active call, the call type is a safety alarm, and the call level is determined according to the nursing supervision result.

S430 , if the vital signs result is normal vital signs and the call result is whether there is an active call, or the physiological alarm result is urination without alarm and the call result is whether there is an active call, the call type is general call and the call level is low.

S440, analyze the frequency of calls and supervision according to the call data and nursing supervision data, and obtain a false call detection result.

In step S410, if the vital sign data is obviously abnormal and the user has a triggering call signal, it can be determined as a heavy emergency call, specifically, the call type is security alarm and the call level is high.

In step S420, if the vital sign data is obviously abnormal, but the system does not receive the call signal, and combined with the user's behavior (respiration, heartbeat, body movement, urine test) in the previous period, it can be preliminarily judged that the user has a health An emergency alarm is triggered, and the call level is determined according to the nursing supervision result.

In step S430, if the vital sign data is normal, there is no urine alarm, and there is only a call. Then, it can be judged that the call is a general behavior call, and the urgency is not strong. Specifically, the call type is a general call, and the call level is low.

In step S440, by combining the frequency and time of the call data with the frequency and time of swiping the card to sign in, it can be determined whether the call is a false call. For example, in the historical data, the time period when user 3 uses the monitoring device to make a call is 12:00 At 18:00, but suddenly the call information of user 3 is received at 10:00, it is judged that the call is likely to be a wrong call.

In a specific embodiment, the corresponding relationship between the health monitoring data and the call level results is shown in Table 1, which shows the call level results corresponding to different health monitoring data.

Table 1

It should be noted that Table 1 reflects the call rating rules of a user. For example, when the vital signs are normal, there is an active call, and the check-in time is less than the preset time interval, the call type is help alarm and the call level is low.

In some embodiments, generating the user's portrait data according to the health monitoring history data includes: reading the user's identification information; tagging the identification information to generate the user's label data; associating the label data with the health monitoring history data, Generate image data.

FIG. 5 is a flowchart of step S220 in some embodiments. Step S220 illustrated in FIG. 5 includes but is not limited to steps S510 to S540:

S510, read the identification information of the user;

S520, tagging the identification information to generate user tag data;

S530, associating the label data with the health monitoring historical data to generate portrait data.

In steps S510 to S530, the user's identification information includes but is not limited to the user's unique identification, such as user id, mobile phone number, etc., and the labeling process specifically includes four dimensions: vital signs, calling situation, physiological alarm situation, and nursing supervision situation. Tag users.

An embodiment of the present disclosure proposes a call level analysis device based on health monitoring data, including: a historical data acquisition module for acquiring historical data of a user's health monitoring; the health monitoring data includes vital sign data, call data, physiological alarm data, Nursing supervision data; vital sign data includes respiratory data, heartbeat data, and body motion data; call data includes call frequency and call time; physiological alarm data includes physiological alarm frequency and physiological alarm time; nursing supervision data includes nursing frequency and nursing time; user The profile generation module is used to generate the profile data of the user according to the health monitoring historical data; the rating rule matching module is used to obtain the call rating rules matching the user's health monitoring historical data according to the profile data; the real-time data acquisition module is used to obtain The real-time health monitoring data of the user; the call level analysis module is used to analyze the call level of the real-time health monitoring data according to the call rating rules, and generate the call level result.

Please refer to FIG. 6. FIG. 6 illustrates a call level analysis device based on health monitoring data according to an embodiment. The call level analysis device based on health monitoring data includes: a historical data acquisition module 610, a user portrait generation module 620, and 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, and the rating rule matching module 630 is connected with the real-time data The acquisition module 640 is connected, and the real-time data acquisition module 640 is connected with the call level analysis module 650 .

The specific implementation of the health monitoring data-based call level analysis apparatus in this embodiment is basically the same as the specific implementation of the health monitoring data-based call level analysis method, which belongs to the same inventive concept, and will not be repeated here.

Embodiments of the present disclosure also provide an electronic device, including:

at least one memory;

at least one processor;

at least one program;

The program is stored in the memory, and the processor executes the at least one program to implement the above-mentioned call level analysis method based on health monitoring data in the present disclosure.

Please refer to FIG. 7. FIG. 7 illustrates a hardware structure of an electronic device according to another embodiment. The electronic device includes:

The processor 701 can be implemented by a general-purpose CPU (Central Processing Unit, central processing unit), a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc., for executing relevant programs to achieve The technical solutions provided by the embodiments of the present disclosure;

The memory 702 may be implemented in the form of a ROM (ReadOnly Memory, read-only memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory, random access memory). The memory 702 may store an operating system and other application programs. When implementing the technical solutions provided by the embodiments of the present specification through software or firmware, the relevant program codes are stored in the memory 702 and invoked by the processor 701 to execute the implementation of the present disclosure. Example call level analysis method based on health monitoring data;

Input/output interface 703, used to realize information input and output;

The communication interface 704 is used to realize the communication interaction between the device and other devices, and the communication can be realized by wired means (such as USB, network cable, etc.), or by wireless means (such as mobile network, WIFI, Bluetooth, etc.); and

a bus 705 that transfers information between the various components of the device (eg, processor 701, memory 702, input/output interface 703, and communication interface 704);

The processor 701 , the memory 702 , the input/output interface 703 and the communication interface 704 realize the communication connection among each other within the device through the bus 705 .

Embodiments of the present disclosure further provide a storage medium, where the storage medium is a computer-readable storage medium, where the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to cause a computer to execute the above-mentioned health monitoring data based on call level analysis method.

The method and device for call level analysis based on health monitoring data proposed in the embodiments of the present disclosure first acquire the user's health monitoring history data, then generate user portrait data according to the health monitoring history data, and then obtain the user's health monitoring history according to the portrait data. Call rating rules that match the data, then obtain the real-time health monitoring data of the user, and finally perform call rating analysis on the real-time health monitoring data according to the call rating rules, generate call rating results, and improve the health of users through the technical solutions provided by the embodiments of the present disclosure Monitoring efficiency.

As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. Additionally, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory may optionally include memory located remotely from the processor, which may be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

The embodiments described in the embodiments of the present disclosure are for the purpose of illustrating the technical solutions of the embodiments of the present disclosure more clearly, and do not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure. With the emergence of application scenarios, the technical solutions provided by the embodiments of the present disclosure are also applicable to similar technical problems.

Those skilled in the art can understand that the technical solutions shown in FIGS. 1-5 do not constitute a limitation on the embodiments of the present disclosure, and may include more or less steps than those shown in the drawings, or combine certain steps, or different A step of.

The apparatus embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, functional modules/units in the systems, and devices can be implemented as software, firmware, hardware, and appropriate combinations thereof.

The terms "first", "second", "third", "fourth", etc. (if any) in the description of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It is to be understood that data so used may be interchanged under appropriate circumstances such that the embodiments of the application described herein can be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having", and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those expressly listed Rather, those steps or units may include other steps or units not expressly listed or inherent to these processes, methods, products or devices.

It should be understood that, in this application, "at least one (item)" refers to one or more, and "a plurality" refers to two or more. "And/or" is used to describe the relationship between related objects, indicating that there can be three kinds of relationships, for example, "A and/or B" can mean: only A, only B, and both A and B exist , where A and B can be singular or plural. The character "/" generally indicates that the associated objects are an "or" relationship. "At least one item(s) below" or similar expressions thereof refer to any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (a) of a, b or c, can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c" ", where a, b, c can be single or multiple.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

The integrated unit, if implemented in the form of a software functional unit and sold or used as an independent product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of software products in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, and the computer software products are stored in a storage medium , including multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, Read-Only Memory (ROM for short), Random Access Memory (RAM for short), magnetic disk or CD, etc. that can store programs medium.

The preferred embodiments of the embodiments of the present disclosure have been described above with reference to the accompanying drawings, which are not intended to limit the scope of the rights of the embodiments of the present disclosure. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and spirit of the embodiments of the present disclosure shall fall within the scope of the rights of the embodiments of the present disclosure.

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.

Images