Human body data processing method and device based on healthy cabin and storage medium

Abstract

The invention discloses a human body data processing method and device based on a healthy cabin and a storage medium, and the method comprises the steps: responding to an instruction request of a terminal, recognizing feature data in the instruction request, and carrying out database indexing based on the feature data; if the storage data conforming to the feature data exists in the result of the database index, returning a request result based on the storage data; otherwise, identifying the sensors of the health system based on the feature data, sending a data acquisition instruction to the sensors conforming to the feature data, and returning a request result based on the data acquired by the sensors; according to different instruction requests, different result feedbacks are achieved, the human body data needing to be known by the user can be fed back in time, corresponding suggestions and guidance can be provided according to the requirements of the user in combination with the human body data, the stored human body data are practically applied to the life of the user, and the practical applicability of the human body data is improved.

Description

Technical Field

[0001] The present invention relates to the field of smart health, and in particular to a human body data processing method, device and storage medium based on a health hut. Background Art

[0002] With the development of technology and the improvement of people's living standards, people are becoming increasingly concerned about their health. In addition to seeking medical treatment and physical examinations, they also pay attention to it in their daily lives. With the continuous maturity of big data and medical digital systems, personal basic information, medical records, and physiological data are all fully stored in databases, providing a data foundation for diagnosing human health.

[0003] However, most of the existing medical digital systems are used for query and remote diagnosis, which are disconnected from users' lives and cannot be integrated into their daily lives. In addition, during the data query process, if the database data is missing, it is easy to encounter the problem of being unable to query. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention proposes a human body data processing method, device and storage medium based on a health hut. Based on the request of instructions, it promptly feeds back the information required by the user, applies the human body data information to the user's life, and improves the practical applicability of human body data.

[0005] The following is a technical solution of the present invention. In one aspect, the present invention provides a human body data processing method based on a health hut, comprising: Respond to the terminal's instruction request, identify the characteristic data in the instruction request, and perform database indexing based on the characteristic data; if there is stored data that meets the characteristic data in the result of the database indexing, return the request result based on the stored data; otherwise, identify the sensor of the health system based on the characteristic data, send a data collection instruction to the sensor that meets the characteristic data, and return the request result based on the sensor collected data.

[0006] Preferably, if the instruction request is a query request, before returning the request result, it further includes comparing the stored data with standard feature data; If the difference between the stored data and the standard feature data is higher than a first threshold or lower than a second threshold, the stored data is treated as abnormal data, and an abnormal flag is added as one of the data features of the returned request result.

[0007] Preferably, if the instruction request is a query request, before returning the request result, it further includes comparing the sensor collected data with standard feature data; If the difference between the sensor collected data and the standard feature data is higher than a first threshold or lower than a second threshold, the sensor collected data is regarded as abnormal data, and an abnormal flag is added as one of the data features of the returned request result.

[0008] Preferably, the standard feature data is obtained by training using the correspondence between multiple groups of human body data features and human body data feature index values ​​at different times and in different states.

[0009] Preferably, if the instruction request is an action request, before returning the request result, it also includes performing feature decomposition on the stored data to obtain a number of human feature data.

[0010] Preferably, if the instruction request is an action request, before returning the request result, the method further comprises performing element decomposition on the sensor collected data to obtain a plurality of element information; Mapping the obtained element information with the human body feature data to form multiple arrays; Using the multiple arrays as input to transmit to a trained healthy neural network for training to obtain recommendation indicators; The recommendation indicator is used as one of the data features of the returned request result.

[0011] Preferably, the method for performing database indexing based on the feature data is: Identify features with index marks in feature data; Perform database traversal query based on the characteristics of index tags; or Arrange the feature data in order based on importance rules; Get the features with the highest importance; Perform database traversal queries based on the most important features.

[0012] Preferably, the plurality of human body feature data include one-dimensional human body feature data and multi-dimensional human body feature data.

[0013] In another aspect of the present invention, the present invention provides a human body data processing device based on a health hut, comprising: a response module, responding to an instruction request from a terminal, and identifying feature data in the instruction request; An indexing module, which performs database indexing based on the feature data and returns the indexing results to the processing module; The processing module returns the request result based on the stored data if there is stored data that meets the characteristic data in the index result; otherwise, identifies the sensor of the health system based on the characteristic data, sends a data collection instruction to the sensor that meets the characteristic data, and returns the request result based on the sensor collection data.

[0014] In another aspect of the present invention, the present invention provides a storage medium, which, when instructions of the storage medium are executed by a processor, executes the above-mentioned human body data processing method.

[0015] The beneficial effects of the present invention are: different result feedback is achieved according to different instruction requests, which can not only timely feedback the human body data that the user needs to know, but also provide corresponding suggestions and guidance based on the user's needs and human body data, and actually apply the stored human body data to the user's life, thereby improving the practical applicability of human body data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of the process of the human body data processing method of the present invention; Figure 2 is a schematic diagram of a human body data processing device according to the present invention; In the figure: 1. Response module; 2. Index module; 3. Processing module. DETAILED DESCRIPTION

[0017] To make the technical problems solved by the present invention, the technical solutions adopted, and the technical effects achieved more clearly, the technical solutions of the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] Although existing medical digital systems collect various data and information about the human body and save them in a database, their applicability is not high. They are mostly used for remote medical diagnosis and data display after users' independent queries. However, in users' daily lives, human body data cannot be well applied, resulting in the inability to significantly improve users' physical health.

[0019] Based on this, the present invention provides a human body data processing method based on the health hut, such as Figure 1 As shown, it is applied to the health hut. As the name suggests, the health hut is provided with a health system, including a human sensor for detecting human data, an object sensor for detecting human activities, a database for storing sensor information, a processing unit for corresponding processing of human data, and an intelligent unit for recommending upcoming human activities based on current human activities and human data. The user terminal is provided with an APP and a threshold judgment unit. The user can actively issue an instruction request through the APP. The threshold judgment unit will also trigger an instruction request when it detects that the user's behavioral activities cause a certain item of human data to exceed the threshold.

[0020] Respond to the terminal's instruction request, identify the feature data in the instruction request, and perform database indexing based on the feature data.

[0021] There are many types of command requests, including query requests, such as when a user needs to query their own heart rate information or their recent physical examination information, and action requests, such as when a user requests to provide a suitable meal combination based on the food information in the current health cabin; or when a user requests to provide suitable fitness exercises and data support based on current human body data.

[0022] The characteristic data in the instruction request not only includes the type of instruction request, but also includes the time, request subject (requester's name, identity information, telephone number), and request content.

[0023] The method of performing database indexing based on feature data is as follows: identifying features with index marks in the feature data; performing database traversal query based on the features with index marks; It is also possible to arrange the feature data in order based on importance rules; obtain the feature with the highest degree of importance; and perform a database traversal query based on the feature with the highest degree of importance.

[0024] Specifically, when the currently logged-in user initiates a command request for the first time or initiates a small number of command requests, the health system cannot identify and record the user's query habits and query scope. Therefore, the user's key features such as the request body and request content are indexed to facilitate rapid traversal queries when indexing the database.

[0025] If the currently logged-in user initiates a large number of command requests, the health system of the health hut will record the user's query habits and rank them by importance based on historical query records. When the feature data in the command request identifies the user, the database will be traversed and queried based on the most important features to achieve faster query results.

[0026] If there is stored data that matches the feature data in the database index result, the request result is returned based on the stored data; otherwise, the sensor of the health system is identified based on the feature data, a data collection instruction is sent to the sensor that matches the feature data, and the request result is returned based on the sensor collected data.

[0027] The database periodically updates the stored human body data information. If the information queried in the instruction request is non-real-time information, it can be obtained immediately by querying the database. If the information queried in the instruction request is real-time information, it needs to be obtained in real time through the sensor and the result is returned.

[0028] For example, if a user enters a query for a recent physical examination report on the app, and the database stores the physical examination report information, which meets the user's request requirements, the physical examination report will be returned to the user as the request result. If the user enters a query for the current heart rate information on the app, although the heart rate information is recorded in the database, the time does not match and cannot meet the feature data in the request. Therefore, the health system's body sensors need to collect data and return the collected heart rate information to the user as the request result.

[0029] In one embodiment of the present invention, if the instruction request is a query request, before returning the request result, it also includes comparing the stored data with the standard feature data; if the difference between the stored data and the standard feature data is higher than the first threshold or lower than the second threshold, the stored data is treated as abnormal data, and an abnormal mark is added as one of the data features of the returned request result.

[0030] Specifically, the first threshold referred to in the present invention is the upper threshold of the standard feature data, and the second threshold is the lower threshold of the standard feature data. For different feature data, the first threshold and the second threshold will be changed accordingly.

[0031] For example, a user requests to query the blood pressure value stored in the previous cycle. Before returning the blood pressure value result, the user's blood pressure value stored in the database is subtracted from the standard blood pressure value. If the calculation result is within the threshold range, that is, it is neither higher than the first blood pressure threshold nor lower than the second blood pressure threshold, it means that the user's blood pressure value is within the normal range, and the blood pressure value is returned to the user as the request result.

[0032] If the calculation result is outside the threshold range, that is, above the first blood pressure threshold or below the second blood pressure threshold, the user's blood pressure value is abnormal and an abnormal mark is applied to the blood pressure value. The abnormal mark can be a special symbol or a color that highlights the blood pressure value from the normal display color. The abnormal mark and blood pressure value are returned to the user as the request result.

[0033] In one embodiment of the present invention, if the instruction request is a query request, before returning the request result, it also includes comparing the sensor collected data with the standard feature data; if the difference between the sensor collected data and the standard feature data is higher than the first threshold or lower than the second threshold, the sensor collected data is regarded as abnormal data, and an abnormal mark is added as one of the data features of the returned request result.

[0034] For example, a user requests to query the current heart rate value. Since the database does not store the current heart rate value information, only the historically stored heart rate information, it cannot meet the characteristic requirements of the current real-time heart rate in the instruction request. Therefore, it is necessary to use a sensor for real-time data collection. At this time, if the heart rate collection requires user cooperation, the user will be given corresponding action instructions before issuing instructions to the sensor, and after the user completes the action, an instruction will be issued to the corresponding sensor to collect the heart rate. Before returning the heart rate value result, the user's heart rate value and the standard heart rate value will be differenced. If the calculation result is within the threshold range, that is, it is neither higher than the first heart rate threshold nor lower than the second heart rate threshold, it means that the user's heart rate value is within the normal range, and the heart rate value is returned to the user as the request result.

[0035] If the calculation result is outside the threshold range, that is, above the first heart rate threshold or below the second heart rate threshold, the user's heart rate is abnormal and the heart rate value is marked as abnormal. The abnormal mark can be a special symbol or a color that highlights the heart rate value from the normal display color. The abnormal mark and the heart rate value are returned to the user as the request result.

[0036] In one embodiment of the present invention, the standard feature data is obtained by training using the correspondence between multiple groups of human body data features and human body data feature index values ​​at different times and in different states.

[0037] For example, blood pressure information is collected from human data in different seasons throughout the year, as well as blood pressure information of people of different age groups and human body in different states, such as blood pressure information in exercise, standing, sitting, etc., and the change in blood pressure information after the state changes, and mapped with the index value to obtain standard characteristic blood pressure value data that conforms to human health.

[0038] In one embodiment of the present invention, if the instruction request is an action request, before returning the request result, the method further includes performing feature decomposition on the stored data to obtain a plurality of human feature data. The plurality of human feature data includes one-dimensional human feature data and multi-dimensional human feature data.

[0039] One-dimensional characteristic data of the human body include height, weight, age, gender, heart rate, BMI index, vital capacity, lung ventilation volume, vision, body fat, etc.

[0040] Multi-dimensional characteristic data of the human body include blood sugar, blood pressure information, emotional stress, etc.

[0041] In one embodiment of the present invention, if the instruction request is an action request, before returning the request result, it further includes performing element decomposition on the sensor collected data to obtain a plurality of element information; Mapping the obtained element information with the human body feature data to form multiple arrays; Multiple arrays are used as input and sent to a trained healthy neural network for training to obtain recommendation indicators; the recommendation indicators are used as one of the data features of the returned request result.

[0042] When the instruction request is an action request, the requested feature data includes not only human body data but also object data. Therefore, when processing the action request, it is necessary to perform a comprehensive calculation based on the human body data and object data to obtain the return result, that is, to give a reasonable action suggestion.

[0043] For example, when a user requests a recipe based on the food currently in the refrigerator, the system prioritizes searching the database for the user's body data, such as blood sugar, blood pressure, BMI, and body fat, based on the characteristic data in the request. The system then collects information about the ingredients in the refrigerator from the item sensors, extracts information about the elements that are abundant in each ingredient, and matches this information with the body data, forming multiple arrays with corresponding relationships. This information is then fed into a trained health neural network for training, resulting in a recipe. This recipe information displays the ingredient combinations and the elements added by the user, and is returned to the user as the request result. The user then chooses the ingredients to match according to the recipe.

[0044] Of course, when the threshold judgment unit of the health system detects that the user needs to eat, it can also actively make an instruction request and push the generated result to the user's terminal APP.

[0045] Similarly, when users need to exercise or relax in some way, they can make command requests. Since they require the assistance of object sensors, they are all action requests. When they are action requests, the returned results are combined with the objects in the health hut and human body data to achieve healthier suggestions and guidance feedback.

[0046] In another embodiment of the present invention, the present invention provides a human body data processing device based on a health hut, such as Figure 2 Shown, including: Response module 1, responds to the instruction request of the terminal and identifies the characteristic data in the instruction request; Indexing module 2, which indexes the database based on the feature data and returns the index results to the processing module; Processing module 3, if there is stored data that meets the characteristic data in the index result, returns the request result based on the stored data; otherwise, identifies the sensor of the health system based on the characteristic data, sends a data collection instruction to the sensor that meets the characteristic data, and returns the request result based on the sensor collection data.

[0047] In another embodiment of the present invention, the present invention provides a storage medium. When instructions of the storage medium are executed by a processor, the above-mentioned human body data processing method is executed.

[0048] From the above description, it can be seen that the present invention can achieve different result feedback according to different instruction requests. It can not only timely feedback the human body data that the user needs to know, but also provide corresponding suggestions and guidance based on the user's needs in combination with the human body data. According to the human body data stored in the database or collected in real time by the sensor, the user's current human body state can be accurately understood, and combined with the information of items in the health hut, reasonable suggestions can be given, so that the user can adopt a healthier way of life, and the stored human body data can be actually applied to the user's life, thereby improving the practical applicability of the human body data.

[0049] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0050] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A human body data processing method based on a health hut, applied to the health hut, characterized in that: Methods include: Responding to a command request from a terminal, identifying characteristic data in the command request, and performing a database index based on the characteristic data; If there is stored data that meets the characteristic data in the result of the database index, returning the request result based on the stored data; Otherwise, the sensor of the health system is identified based on the characteristic data, a data collection instruction is sent to the sensor that meets the characteristic data, and a request result is returned based on the sensor collection data.

2. A human body data processing method based on a health hut according to claim 1, characterized in that: If the instruction request is a query request, before returning the request result, it also includes comparing the stored data with standard feature data; If the difference between the stored data and the standard feature data is higher than a first threshold or lower than a second threshold, the stored data is treated as abnormal data, and an abnormal flag is added as one of the data features of the returned request result.

3. The human body data processing method based on the health hut according to claim 1 is characterized in that: If the instruction request is a query request, before returning the request result, it also includes comparing the sensor collected data with standard feature data; If the difference between the sensor collected data and the standard feature data is higher than a first threshold or lower than a second threshold, the sensor collected data is regarded as abnormal data, and an abnormal flag is added as one of the data features of the returned request result.

4. A human body data processing method based on a health hut according to claim 2 or 3, characterized in that: The standard feature data is obtained by training using the correspondence between multiple groups of human body data features and human body data feature index values ​​at different times and in different states.

5. The human body data processing method based on the health hut according to claim 1 is characterized in that: If the instruction request is an action request, before returning the request result, it also includes performing feature decomposition on the stored data to obtain a number of human feature data.

6. The human body data processing method based on the health hut according to claim 5 is characterized in that: If the instruction request is an action request, before returning the request result, it also includes element decomposition of the sensor collected data to obtain a plurality of element information; Mapping the obtained element information with the human body feature data to form multiple arrays; Using the multiple arrays as input to transmit to a trained healthy neural network for training to obtain recommendation indicators; The recommendation indicator is used as one of the data features of the returned request result.

7. The human body data processing method based on the health hut according to claim 1 is characterized in that: The method for performing database indexing based on the feature data is: Identify features with index marks in feature data; Perform database traversal query based on the characteristics of index tags; or Arrange the feature data in order based on importance rules; Get the features with the highest importance; Perform database traversal queries based on the most important features.

8. The human body data processing method based on the health hut according to claim 5 is characterized in that: The plurality of human body feature data include one-dimensional human body feature data and multi-dimensional human body feature data.

9. A human body data processing device based on a health hut, characterized in that: include: A response module, responding to the instruction request of the terminal and identifying feature data in the instruction request; An indexing module, which performs database indexing based on the feature data and returns the indexing results to the processing module; A processing module, if the index result contains stored data that matches the characteristic data, returns the request result based on the stored data; Otherwise, the sensor of the health system is identified based on the characteristic data, a data collection instruction is sent to the sensor that meets the characteristic data, and a request result is returned based on the sensor collection data.

10. A storage medium, characterized in that: When the instructions of the storage medium are executed by the processor, the human body data processing method according to any one of claims 1 to 8 is executed.

Images