CN116602622A - Human health detection data acquisition system and method based on Internet of things - Google Patents
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Abstract
The invention provides a human health detection data acquisition system and method based on the Internet of things, wherein the system comprises: the verification subsystem is used for carrying out identity verification on a target person wearing the wearable device for pre-verification; the acquisition subsystem is used for acquiring human health detection data of corresponding target personnel through wearable equipment based on the internet of things technology if the target personnel pass identity verification; the uploading subsystem is used for transmitting the human health detection data to a preset data center node; and the reminding subsystem is used for attempting to acquire the abnormal data item in the human health detection data, and if the acquisition attempt is successful, determining the abnormal information to remind. The human health detection data acquisition system and method based on the Internet of things are more suitable for carrying out identity verification on target personnel; the medical staff is not required to manually detect and analyze the user health detection data, so that the labor cost is greatly reduced, and the system is more convenient and intelligent.
Description
Technical Field
The invention relates to the technical field of electric digital data processing, in particular to a human health detection data acquisition system and method based on the Internet of things.
Background
At present, when traditional intelligent wearing equipment (for example, an intelligent watch) detects health data of a human body, the detection data are usually recorded directly, a situation that a user (for example, a child in home) who is not the wearing equipment wears at will can occur, under the situation, the data recorded by the intelligent watch are not self-owned, an acquisition object is unsuitable, meanwhile, after the health detection data of the user are acquired, the user cannot intuitively know the difference between the health detection data (for example, blood pressure, heart rate and the like) and standard values, and if the user goes to a medical institution such as a hospital or a physical examination center to carry out manual detection and analysis, the user is inconvenient and intelligent.
In view of this, a solution is needed.
Disclosure of Invention
The invention aims to provide a human health detection data acquisition system based on the Internet of things, which is used for carrying out identity verification on target personnel wearing equipment, so that health detection data of personnel wearing the wearing equipment at will are prevented from being acquired, and the system is more suitable; the Internet of things technology is introduced, the user health detection data are collected and uploaded to the data center node, medical staff are not required to manually detect and analyze the user health detection data, the labor cost is greatly reduced, and the system is more convenient and intelligent.
The embodiment of the invention provides a human health detection data acquisition system based on the Internet of things, which comprises the following steps:
the verification subsystem is used for carrying out identity verification on a target person wearing the wearable device for pre-verification;
the acquisition subsystem is used for acquiring human health detection data of corresponding target personnel through wearable equipment based on the internet of things technology if the target personnel pass identity verification;
the uploading subsystem is used for transmitting the human health detection data to a preset data center node;
and the reminding subsystem is used for attempting to acquire the abnormal data item in the human health detection data, and if the acquisition attempt is successful, determining the abnormal information to remind.
Preferably, the verification subsystem comprises:
the first identity verification module is used for acquiring the identity ID and the first verification ID input by the pre-verified target personnel, and if the first verification ID is consistent with the second verification ID correspondingly set by the identity ID, the identity verification of the corresponding target personnel is passed;
and/or the number of the groups of groups,
the second identity verification module is used for continuously acquiring the biological characteristic information input by the target personnel, analyzing the biological characteristic information to obtain at least one input biological characteristic, and if the input biological characteristic is consistent with the preset login verification biological characteristic, passing the identity verification of the corresponding target personnel.
Preferably, the acquisition subsystem comprises:
the trigger type acquisition module is used for acquiring the trigger type of a trigger mechanism for triggering the detection of the wearable equipment;
the first acquisition module of the human health detection data is used for acquiring the human health detection data of the corresponding target personnel directly through the wearable equipment if the trigger type is direct trigger;
the data attribute acquisition module is used for acquiring the data attribute of the target detection data if the trigger type is indirect trigger;
the health detection guide mode generation module is used for generating a template based on a preset health detection guide mode and generating a health detection guide mode according to the data attribute of the target detection data;
the human body health detection data second acquisition module is used for guiding the corresponding target personnel to finish the guiding action based on the health detection guiding mode, and acquiring the human body health detection data of the corresponding target personnel through the wearable equipment after the guiding is finished.
Preferably, the human health detection data second acquisition module includes:
the first data type acquisition sub-module is used for acquiring a first data type of the human health detection data;
the query sub-module is used for querying a preset data type-sensor number library, and the data type-sensor number library comprises: a plurality of one-to-one second data types and first sensor numbers;
The second sensor number determining submodule is used for determining a first sensor number corresponding to a second data type consistent with the first data type in the data type-sensor number library and used as a second sensor number;
the sensing signal acquisition sub-module is used for acquiring sensing signals of the sensors corresponding to the second sensor numbers;
the restoration rule acquisition submodule is used for acquiring restoration rules of the induction signals;
the human health detection data determining sub-module is used for determining human health detection data according to the induction signals based on the restoration rule.
Preferably, the sensing signal acquisition sub-module includes:
the first device type acquisition unit is used for acquiring the pre-sensing signal of the sensor corresponding to the second sensor number and the first device type;
the pre-sensing signal processing scheme inquiring unit is used for inquiring a preset pre-sensing signal processing scheme library, and the pre-sensing signal processing scheme library comprises: a plurality of one-to-one second device types and a pre-sense signal processing scheme;
a target signal processing scheme determining unit, configured to determine a pre-sensing signal processing scheme corresponding to a second device type consistent with the first device type in the pre-sensing signal processing scheme library, and use the pre-sensing signal processing scheme as a target signal processing scheme;
And the induction signal acquisition unit is used for carrying out signal processing on the pre-induction signal based on the target signal processing scheme to acquire a processed induction signal.
Preferably, the restoration rule obtaining submodule includes:
a channel characteristic information acquisition unit for acquiring channel characteristic information of a transmission channel of the sensing signal;
a first signal influence relation determining unit for determining a first signal influence relation of channel characteristics of the channel to the sensing signal transmitted in the channel based on the channel characteristic information;
the restoration rule generation template acquisition unit is used for acquiring a preset restoration rule generation template;
and the restoration rule determining unit is used for generating a template based on the restoration rule and determining the restoration rule of the induction signal according to the first signal influence relation.
Preferably, the first signal influence relation determination unit includes:
the channel characteristic feature extraction template acquisition subunit is used for acquiring a preset channel characteristic feature extraction template;
the channel characteristic value acquisition subunit is used for carrying out characteristic extraction on the channel characteristic information based on the channel characteristic extraction template to obtain a plurality of channel characteristic values;
a channel characteristic type obtaining subunit, configured to obtain a channel characteristic type corresponding to the channel characteristic value;
An influence relation determination model determination subunit configured to determine an influence relation determination model corresponding to the channel feature type based on the channel feature type;
the second signal influence relation acquisition subunit is used for inputting each channel characteristic value into a corresponding influence relation determination model to acquire at least one second signal influence relation;
and the integration subunit is used for integrating each second signal influence relation to obtain a first signal influence relation.
Preferably, the reminding subsystem comprises:
the first data item acquisition module is used for carrying out data splitting on the human health detection data to obtain a plurality of first data items;
the traversal module is used for traversing the first data items in sequence and taking the traversed first data items as second data items;
the standard data acquisition module is used for acquiring standard data corresponding to the second data item;
a standard deviation determining module for determining a standard deviation of the second data item based on the second data item and the standard data item;
the abnormal data item judging module is used for judging that the corresponding second data item is an abnormal data item if the standard deviation degree is greater than or equal to a preset standard deviation degree threshold value;
wherein, standard data acquisition module includes:
The control record acquisition sub-module is used for acquiring human health detection data control records with healthy detection results;
the first personnel information determination submodule is used for analyzing the human body health detection data comparison record and determining the first personnel information of the detected personnel in the human body health detection data comparison record;
the second personnel information acquisition sub-module is used for acquiring second personnel information of the target personnel corresponding to the second data item;
the personnel information feature extraction template acquisition sub-module is used for acquiring a preset personnel information feature extraction template;
the first personnel information characteristic value acquisition sub-module is used for carrying out characteristic extraction on the first personnel information to obtain a plurality of first personnel information characteristic values;
the second personnel information characteristic value acquisition sub-module is used for carrying out characteristic extraction on the second personnel information to obtain a plurality of second personnel information characteristic values;
the first association sub-module is used for determining a first reference value based on the first personnel information characteristic value and the second personnel information characteristic value and associating the first reference value with the corresponding human health detection data comparison record;
the third data type acquisition sub-module is used for acquiring a third data type of a third data item recorded in the human health detection data comparison record;
A fourth data type obtaining sub-module for obtaining a fourth data type of the second data item;
the second data characteristic value extraction submodule is used for respectively extracting a first data characteristic value of the third data item and a second data characteristic value of the second data item based on a preset data characteristic extraction template;
the first association sub-module is used for determining a second reference value based on the first data characteristic value and the second data characteristic value and associating the second reference value with the corresponding human health detection data comparison record;
the reference degree determining submodule is used for accumulating and calculating each first reference value and each second reference value associated with the human health detection data comparison record to obtain the reference degree corresponding to the human health detection data comparison record;
the target comparison record determining submodule is used for determining a corresponding human body health detection data comparison record with the reference degree being greater than or equal to a preset reference degree threshold value and taking the corresponding human body health detection data comparison record as a target human body health detection data comparison record;
the average value calculation sub-module is used for obtaining a fifth data item corresponding to the second data item in the target human health detection data comparison record and calculating the average value of the fifth data item;
and the standard data determining submodule is used for taking the average numerical value as standard data corresponding to the second data item.
Preferably, the reminding subsystem comprises:
the abnormal information generation template acquisition module is used for acquiring a preset abnormal information generation template;
the abnormal information generation module is used for generating a template based on the abnormal information and generating abnormal information according to the abnormal data item;
the abnormal information transmission module is used for transmitting the abnormal information to the equipment node corresponding to the abnormal data item;
the abnormal information reminding module is used for reminding corresponding target personnel to check the abnormal information based on a preset reminding rule after the abnormal information reaches the equipment node.
The human health detection data acquisition method based on the Internet of things provided by the embodiment of the invention comprises the following steps:
step 1: carrying out identity verification on a target person wearing the wearable device for pre-verification;
step 2: if the target personnel pass the identity verification, acquiring human health detection data of the corresponding target personnel through wearable equipment based on the internet of things technology;
step 3: transmitting the human health detection data to a preset data center node;
step 4: and attempting to acquire abnormal data items in the human health detection data, and if the acquisition attempt is successful, determining abnormal information to remind.
The beneficial effects of the invention are as follows:
According to the invention, the identity of the target person wearing the wearable equipment is verified, so that the health detection data of the person wearing the wearable equipment at will is prevented from being acquired, and the method is more suitable; the Internet of things technology is introduced, the user health detection data are collected and uploaded to the data center node, medical staff are not required to manually detect and analyze the user health detection data, the labor cost is greatly reduced, and the system is more convenient and intelligent.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a schematic diagram of a human health detection data acquisition system based on the internet of things in an embodiment of the invention;
Fig. 2 is a schematic diagram of a human health detection data acquisition method based on the internet of things in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The embodiment of the invention provides a human health detection data acquisition system based on the Internet of things, which is shown in fig. 1 and comprises the following steps:
the verification subsystem 1 is used for carrying out identity verification on a target person wearing the wearable device for pre-verification;
the acquisition subsystem 2 is used for acquiring human health detection data of corresponding target personnel through wearable equipment based on the internet of things technology if the target personnel pass identity verification;
the uploading subsystem 3 is used for transmitting the human health detection data to a preset data center node;
and the reminding subsystem 4 is used for attempting to acquire the abnormal data item in the human health detection data, and if the acquisition attempt is successful, determining the abnormal information to remind.
The working principle and the beneficial effects of the technical scheme are as follows:
the wearing device is, for example: a smart watch. The target personnel for pre-verification is a user wearing the wearable device, and the wearable device generally used for human health detection can only bind one user at a time, so that the identity of the target personnel needs to be verified. If the identity verification of the target person passes, the wearable device worn by the target person is described as belonging to the target person, based on the technology of the internet of things, the human health detection data (such as heart rate, blood oxygen, blood fat and the like) of the target person passing the identity verification are collected through the wearable device, and the technology of the internet of things belongs to the category of the prior art and is not repeated. And delivering the collected human health detection data to a preset data center node (such as a user health detection data processing cloud platform) so as to be convenient for more comprehensively detecting the collected human health detection data. The abnormal data items in the human health detection data are, for example: the heart rate is too fast, and abnormal information is determined to remind (for example, "you are too fast in current heart rate, please lighten exercise intensity").
According to the application, the identity of the target person wearing the wearable equipment is verified, so that the health detection data of the person wearing the wearable equipment at will is prevented from being acquired, and the method is more suitable; the Internet of things technology is introduced, the user health detection data are collected and uploaded to the data center node, medical staff are not required to manually detect and analyze the user health detection data, the labor cost is greatly reduced, and the system is more convenient and intelligent.
In one embodiment, the verification subsystem includes:
the first identity verification module is used for acquiring the identity ID and the first verification ID input by the pre-verified target personnel, and if the first verification ID is consistent with the second verification ID correspondingly set by the identity ID, the identity verification of the corresponding target personnel is passed;
and/or the number of the groups of groups,
the second identity verification module is used for continuously acquiring the biological characteristic information input by the target personnel, analyzing the biological characteristic information to obtain at least one input biological characteristic, and if the input biological characteristic is consistent with the preset login verification biological characteristic, passing the identity verification of the corresponding target personnel.
The working principle and the beneficial effects of the technical scheme are as follows:
there are two ways to authenticate the pre-authenticated target person. First kind: acquiring an identity ID and a first verification ID input by a target person of pre-verification; the identity ID input by the target person for pre-verification is, for example: the login account number of the intelligent wearable device of the target person, the first verification ID is, for example: the login account corresponds to a preset password. The second verification ID may be determined by querying a preset identity verification information base, and if the first verification ID and the second verification ID are identical (i.e., the password is correctly entered), the identity of the corresponding target person is verified. Second kind: continuously acquiring biometric information input by a target person, wherein the biometric information comprises but is not limited to: the fingerprint information, the face information and the iris information are analyzed to obtain at least one input biological characteristic (such as the fingerprint of a right index finger), and if the input biological characteristic is consistent with a preset login verification biological characteristic (such as the login fingerprint stored in the intelligent wearable device and used for login), the corresponding target person passes the identity verification.
According to the application, two ways are introduced to carry out identity verification on the target personnel subjected to the pre-verification, so that the comprehensiveness of the verification mode of the identity verification is improved, and the verification rationality is improved.
In one embodiment, an acquisition subsystem includes:
the trigger type acquisition module is used for acquiring the trigger type of a trigger mechanism for triggering the detection of the wearable equipment;
the first acquisition module of the human health detection data is used for acquiring the human health detection data of the corresponding target personnel directly through the wearable equipment if the trigger type is direct trigger;
the data attribute acquisition module is used for acquiring the data attribute of the target detection data if the trigger type is indirect trigger;
the health detection guide mode generation module is used for generating a template based on a preset health detection guide mode and generating a health detection guide mode according to the data attribute of the target detection data;
the human body health detection data second acquisition module is used for guiding the corresponding target personnel to finish the guiding action based on the health detection guiding mode, and acquiring the human body health detection data of the corresponding target personnel through the wearable equipment after the guiding is finished.
The working principle and the beneficial effects of the technical scheme are as follows:
The trigger types include: direct triggering and indirect triggering, the direct triggering is as follows: the user can wear the wearing equipment at a preset position directly (for example, wear the intelligent watch on the wrist), and the indirect triggering is as follows: when the user uses the device, the user needs to perform a preset operation to trigger the wearable device to perform measurement of the health detection data, for example: the user is required to put a finger at a designated position of the wearing device to measure the blood fat of the user. If the wearable device is directly triggered, the human health detection data (such as step number, mileage and the like) are acquired directly through the wearable device, and if the trigger type is indirectly triggered, the data attribute (such as blood fat data) of the target detection data is acquired. Generating a template based on a preset health detection guiding mode, and generating a health detection guiding mode according to the data attribute of the target detection data (guiding a user to perform what operation can enable the intelligent wearable device to detect the target data); the preset health detection guiding mode generation template constraint only generates the health detection guiding mode, and does not generate other contents, specifically may be, for example: please double-click the side sensing capacitor area of the smart watch triggers the blood lipid detection mode. After the guiding is finished, human health detection data of corresponding target personnel are collected through the wearable equipment.
According to the application, the trigger type is introduced, and when the trigger type is indirect trigger, a template is generated according to the data attribute of the target detection data and the introduced health detection guiding mode to determine the health detection guiding mode, so that the accuracy of acquiring the health detection guiding mode is improved; according to the health detection guiding mode, the target personnel is guided to acquire the human health detection data after finishing the guiding action, the acquisition efficiency of the human health detection data is improved, the problem of poor use experience caused by unclear operation methods of wearing equipment by a user is avoided, and the human body detection method is more humanized.
In one embodiment, the human health detection data second acquisition module includes:
the first data type acquisition sub-module is used for acquiring a first data type of the human health detection data;
the query sub-module is used for querying a preset data type-sensor number library, and the data type-sensor number library comprises: a plurality of one-to-one second data types and first sensor numbers;
the second sensor number determining submodule is used for determining a first sensor number corresponding to a second data type consistent with the first data type in the data type-sensor number library and used as a second sensor number;
The sensing signal acquisition sub-module is used for acquiring sensing signals of the sensors corresponding to the second sensor numbers;
the restoration rule acquisition submodule is used for acquiring restoration rules of the induction signals;
the human health detection data determining sub-module is used for determining human health detection data according to the induction signals based on the restoration rule.
The working principle and the beneficial effects of the technical scheme are as follows:
the first data type is, for example: heart rate data. The second data type in the preset data type-sensor number base is: all data types that this intelligent wearing equipment can detect, first sensor serial number is: and the sensor ID corresponding to the second data type. And determining a first sensor number corresponding to the first data type and taking the first sensor number as a second sensor number. The sensing signals of the sensors corresponding to the second sensor numbers are as follows: the user wants to detect the sensing signal of the acquisition sensor of the data. After the sensor obtains the sensing signal, a restoration rule (for example, how to cancel channel interference) of the sensing signal is obtained, wherein the restoration rule is determined according to the channel characteristics of a transmission channel of the sensing signal, and the channel characteristics refer to: characteristics of the signal as it propagates in the transmission channel, such as: bandwidth, latency, etc. Based on the above-described restoration rule, human health detection data (human health detection data that the user wants to detect, for example, heart rate 70 times/minute) is determined from the sensing signal.
According to the application, the data type-sensor number library is introduced, the sensor for detecting the human health detection data is determined, and meanwhile, the reduction rule is introduced, so that the sensing signal of the sensor is reduced, the acquisition suitability of the human health detection data is improved, and the detection accuracy of the human health detection data is further improved.
In one embodiment, the inductive signal acquisition sub-module includes:
the first device type acquisition unit is used for acquiring the pre-sensing signal of the sensor corresponding to the second sensor number and the first device type;
the pre-sensing signal processing scheme inquiring unit is used for inquiring a preset pre-sensing signal processing scheme library, and the pre-sensing signal processing scheme library comprises: a plurality of one-to-one second device types and a pre-sense signal processing scheme;
a target signal processing scheme determining unit, configured to determine a pre-sensing signal processing scheme corresponding to a second device type consistent with the first device type in the pre-sensing signal processing scheme library, and use the pre-sensing signal processing scheme as a target signal processing scheme;
and the induction signal acquisition unit is used for carrying out signal processing on the pre-induction signal based on the target signal processing scheme to acquire a processed induction signal.
The working principle and the beneficial effects of the technical scheme are as follows:
The pre-sense signal is: the electric signal sensed by the sensor end, the first device type is: classification type of sensor device, for example: a heart rate sensor module. The second device type in the pre-sense signal processing scheme library is: the classification types of all sensor devices are as follows: signal processing schemes corresponding to pre-sensing signals received by the sensor of the second device type, for example: and (5) high-frequency filtering. And determining a preprocessing scheme corresponding to the second device type consistent with the first device type as a target signal processing scheme, and performing signal processing (such as noise reduction, filtering and the like) on the pre-sensing signal received by the sensor based on the target signal processing scheme to obtain a sensing signal.
According to the application, a pre-sensing signal processing scheme library is introduced, a target signal processing scheme suitable for the pre-sensing signal received by the sensor is determined, and the pre-sensing signal is subjected to signal processing, so that the accuracy of sensing signal acquisition is further improved.
In one embodiment, the restoration rule acquisition submodule includes:
a channel characteristic information acquisition unit for acquiring channel characteristic information of a transmission channel of the sensing signal;
a first signal influence relation determining unit for determining a first signal influence relation of channel characteristics of the channel to the sensing signal transmitted in the channel based on the channel characteristic information;
The restoration rule generation template acquisition unit is used for acquiring a preset restoration rule generation template;
and the restoration rule determining unit is used for generating a template based on the restoration rule and determining the restoration rule of the induction signal according to the first signal influence relation.
The working principle and the beneficial effects of the technical scheme are as follows:
after the sensor generates the induction signal, the signal is transmitted to the sending terminal, and the induction signal is inaccurate due to the influence of the characteristics of a transmission channel in the transmission process, so that the acquired human health detection data is also inaccurate.
Channel characteristic information of the transmission channel of the sensing signal is, for example: channel bandwidth, additive noise, etc. Based on the channel characteristic information, a first signal influence relationship is determined (what influence the channel characteristic has on the signal transmitted in the channel, e.g., amplitude-frequency distortion, phase-frequency distortion, etc.). The preset restoration rule generation template constraint only generates a restoration rule, and does not generate other information, for example: and adding a linear compensation network at the induction signal receiving end to restore the induction signal. A template is generated based on the restoration rule, and a restoration rule of the sensing signal is determined according to the first signal influence relation (a method for reducing the influence of the first signal influence relation is used for restoring the sensing signal).
According to the method and the device, the first signal influence relation is determined according to the acquired channel characteristic information, the restoration rule generation template is introduced, and the restoration rule of the induction signal is determined according to the first signal influence relation, so that the restoration rationality of the induction signal is improved.
In one embodiment, the first signal influence relation determination unit comprises:
the channel characteristic feature extraction template acquisition subunit is used for acquiring a preset channel characteristic feature extraction template;
the channel characteristic value acquisition subunit is used for carrying out characteristic extraction on the channel characteristic information based on the channel characteristic extraction template to obtain a plurality of channel characteristic values;
a channel characteristic type obtaining subunit, configured to obtain a channel characteristic type corresponding to the channel characteristic value;
an influence relation determination model determination subunit configured to determine an influence relation determination model corresponding to the channel feature type based on the channel feature type;
the second signal influence relation acquisition subunit is used for inputting each channel characteristic value into a corresponding influence relation determination model to acquire at least one second signal influence relation;
and the integration subunit is used for integrating each second signal influence relation to obtain a first signal influence relation.
The working principle and the beneficial effects of the technical scheme are as follows:
the preset channel characteristic feature extraction template is as follows: the preset template for extracting the characteristic value of the channel is used for extracting the characteristic feature of the channel only by constraint of the characteristic feature extraction template of the channel, other information is not extracted, and the characteristic feature of the channel is as follows: quantized representations of channel characteristics, such as: what bandwidth is, again for example: what is the time delay. And carrying out feature extraction on the channel characteristic information based on the channel characteristic feature extraction template to obtain a plurality of channel characteristic values. Determining an influence relation determination model corresponding to the channel feature type based on the channel feature type; the influence relation determination model is as follows: mathematical formulas characterizing the effect of channel eigenvalues on the signal. Inputting each channel characteristic value into a corresponding influence relation determining model, wherein the influence relation determining model is correspondingly set by manpower according to the channel characteristic type, and at least one second signal influence relation is obtained, and the second signal influence relation is as follows: among the outputs of the influence relation determination model, the output that is judged to have an influence on the signal, for example: the channel additive noise is input into an influence relation determination model corresponding to the additive noise, and a second signal influence relation of the additive noise on the channel transmission signal is determined (for example, the larger the additive noise is, the more serious the signal distortion is). And integrating all the second signal influence relations to obtain a first signal influence relation.
According to the application, a channel characteristic feature extraction template is introduced, a channel characteristic value is extracted, an influence relation determination model is determined according to different channel characteristic types of the channel characteristic value, a second signal influence relation output by the influence relation determination model is integrated, a first signal influence relation is obtained, and the acquisition suitability and comprehensiveness of the first signal influence relation are improved.
In one embodiment, the reminder subsystem includes:
the first data item acquisition module is used for carrying out data splitting on the human health detection data to obtain a plurality of first data items;
the traversal module is used for traversing the first data items in sequence and taking the traversed first data items as second data items;
the standard data acquisition module is used for acquiring standard data corresponding to the second data item;
a standard deviation determining module for determining a standard deviation of the second data item based on the second data item and the standard data item;
the abnormal data item judging module is used for judging that the corresponding second data item is an abnormal data item if the standard deviation degree is greater than or equal to a preset standard deviation degree threshold value;
wherein, standard data acquisition module includes:
the control record acquisition sub-module is used for acquiring human health detection data control records with healthy detection results;
The first personnel information determination submodule is used for analyzing the human body health detection data comparison record and determining the first personnel information of the detected personnel in the human body health detection data comparison record;
the second personnel information acquisition sub-module is used for acquiring second personnel information of the target personnel corresponding to the second data item;
the personnel information feature extraction template acquisition sub-module is used for acquiring a preset personnel information feature extraction template;
the first personnel information characteristic value acquisition sub-module is used for carrying out characteristic extraction on the first personnel information to obtain a plurality of first personnel information characteristic values;
the second personnel information characteristic value acquisition sub-module is used for carrying out characteristic extraction on the second personnel information to obtain a plurality of second personnel information characteristic values;
the first association sub-module is used for determining a first reference value based on the first personnel information characteristic value and the second personnel information characteristic value and associating the first reference value with the corresponding human health detection data comparison record;
the third data type acquisition sub-module is used for acquiring a third data type of a third data item recorded in the human health detection data comparison record;
a fourth data type obtaining sub-module for obtaining a fourth data type of the second data item;
The second data characteristic value extraction submodule is used for respectively extracting a first data characteristic value of the third data item and a second data characteristic value of the second data item based on a preset data characteristic extraction template;
the first association sub-module is used for determining a second reference value based on the first data characteristic value and the second data characteristic value and associating the second reference value with the corresponding human health detection data comparison record;
the reference degree determining submodule is used for accumulating and calculating each first reference value and each second reference value associated with the human health detection data comparison record to obtain the reference degree corresponding to the human health detection data comparison record;
the target comparison record determining submodule is used for determining a corresponding human body health detection data comparison record with the reference degree being greater than or equal to a preset reference degree threshold value and taking the corresponding human body health detection data comparison record as a target human body health detection data comparison record;
the average value calculation sub-module is used for obtaining a fifth data item corresponding to the second data item in the target human health detection data comparison record and calculating the average value of the fifth data item;
and the standard data determining submodule is used for taking the average numerical value as standard data corresponding to the second data item.
The working principle and the beneficial effects of the technical scheme are as follows:
The human health detection data is subjected to data splitting to obtain a plurality of first data items (for example, arterial blood oxygen saturation is 97%). And traversing the first data items in turn, and taking the first data items being traversed as second data items.
The human health detection data control record comprises: first person information of the person to be detected (for example, sex, age, weight, etc. of the person to be detected) and human health detection data of the person to be detected. The second person information is, for example: the sex, age and weight of the target person of the second data item are detected. Based on a preset personnel information feature extraction template, respectively extracting a first personnel information feature value (for example, sex woman and age 23) and a second personnel information feature value (for example, sex woman and age 30), wherein the preset personnel information feature extraction template is as follows: and acquiring a preset template for extracting the personnel information characteristic value, acquiring the personnel information type (such as sex information) of the second personnel information characteristic value, and simultaneously acquiring the weight value of the personnel information type. And calculating a matching value of the first personnel information characteristic value, wherein the personnel information type of the first personnel information characteristic value is consistent with that of the second personnel information characteristic value. And correspondingly multiplying the weight value and the matching value, determining a first reference value, and associating with the corresponding human health detection data comparison record.
The third data type is, for example: gesture data. The fourth data type is a data type corresponding to the second data item, and the fourth data type is, for example: motion data. Based on a preset data feature extraction template, respectively extracting a first data feature value (such as walking speed, swing arm frequency, balance or not) of human health detection data of a third data type and a second data feature value (such as running speed, swing arm frequency, balance or not) of a second data item of a fourth data type, wherein the preset data feature extraction template is constrained to only extract the data feature value and does not extract other contents. Based on the first data eigenvalue, a first data eigenvector is constructed, based on the second data eigenvalue, a second data eigenvector is constructed, and the construction vector based on the eigenvalue belongs to the category of the prior art, and is not described in detail. And calculating a vector included angle between the first data feature vector and the second data feature vector, determining a second reference value (the second reference value is a cosine value of the vector included angle) based on the vector included angle, and associating the second reference value with the corresponding human health detection data comparison record.
And accumulating and calculating each first reference value and each second reference value associated with the human health detection data comparison record to obtain the reference degree of the human health detection data comparison record. If the reference degree is greater than or equal to a preset reference degree threshold (the reference degree threshold is preset by a person), the corresponding human health detection data comparison record is used as a target human health detection data comparison record, a fifth data item corresponding to the second data item in the target human health detection data comparison record is obtained, an average value of the fifth data item (for example, an average value of the values of the fifth data item) is calculated, the average value is used as standard data of the second data item (for example, the standard data of the heart rate is 85 times/min, and for example, the standard data of the QT interval is 440ms for men and 440ms for women, and the QT interval is the total time interval of the ventricular depolarization and repolarization processes).
Determining a standard deviation of the second data item based on the second data item and the standard data item; the standard deviation is: the greater the standard deviation, the more likely the corresponding second data item is abnormal data, as a result of dividing the absolute value of the data difference between the value corresponding to the second data item and the value corresponding to the standard data item by the value corresponding to the standard data item. If the standard deviation degree is greater than or equal to a preset standard deviation degree threshold (for example, 0.15), the corresponding second data item is an abnormal data item.
According to the method, the standard data item is introduced, the standard deviation degree of the second data item is calculated based on the standard data item and the second data item, the rationality and the accuracy of the standard deviation degree determination are improved, the standard deviation threshold of the standard deviation degree is introduced, the second data item is subjected to abnormality judgment, and the reliability of the abnormality judgment is improved.
In one embodiment, the reminder subsystem includes:
the abnormal information generation template acquisition module is used for acquiring a preset abnormal information generation template;
the abnormal information generation module is used for generating a template based on the abnormal information and generating abnormal information according to the abnormal data item;
the abnormal information transmission module is used for transmitting the abnormal information to the equipment node corresponding to the abnormal data item;
The abnormal information reminding module is used for reminding corresponding target personnel to check the abnormal information based on a preset reminding rule after the abnormal information reaches the equipment node.
The working principle and the beneficial effects of the technical scheme are as follows:
the preset abnormal information generation template constraint only generates abnormal information, for example: based on which anomaly data item, which anomaly information is generated. The abnormality information is, for example: "your blood oxygen saturation is low". The abnormality information is transferred to the device node corresponding to the abnormality data item (communication node of the wearable device corresponding to the abnormality data item, the communication node being in communication connection with the data center node). When the abnormal information reaches the equipment node, based on a preset reminding rule (based on what abnormal information is generated), reminding a corresponding target person to view the abnormal information (for example, a linear motor arranged in the intelligent wearing equipment continuously vibrates, and a display area of the intelligent wearing equipment displays the abnormal information).
According to the method, the abnormal information generation template is introduced, corresponding abnormal information is generated according to the abnormal data item, and the standardization of abnormal information generation is improved; the reminding rule is introduced to remind the target personnel to check the abnormal information in time, so that the method is more intelligent and humanized.
The embodiment of the invention provides a human health detection data acquisition method based on the Internet of things, which is shown in fig. 2 and comprises the following steps:
step 1: carrying out identity verification on a target person wearing the wearable device for pre-verification;
step 2: if the target personnel pass the identity verification, acquiring human health detection data of the corresponding target personnel through wearable equipment based on the internet of things technology;
step 3: transmitting the human health detection data to a preset data center node;
step 4: and attempting to acquire abnormal data items in the human health detection data, and if the acquisition attempt is successful, determining abnormal information to remind.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. Human health detection data acquisition system based on thing networking, its characterized in that includes:
the verification subsystem is used for carrying out identity verification on a target person wearing the wearable device for pre-verification;
the acquisition subsystem is used for acquiring human health detection data of corresponding target personnel through wearable equipment based on the internet of things technology if the target personnel pass identity verification;
The uploading subsystem is used for transmitting the human health detection data to a preset data center node;
and the reminding subsystem is used for attempting to acquire the abnormal data item in the human health detection data, and if the acquisition attempt is successful, determining the abnormal information to remind.
2. The human health detection data acquisition system based on the internet of things of claim 1, wherein the verification subsystem comprises:
the first identity verification module is used for acquiring the identity ID and the first verification ID input by the pre-verified target personnel, and if the first verification ID is consistent with the second verification ID correspondingly set by the identity ID, the identity verification of the corresponding target personnel is passed;
and/or the number of the groups of groups,
the second identity verification module is used for continuously acquiring the biological characteristic information input by the target personnel, analyzing the biological characteristic information to obtain at least one input biological characteristic, and if the input biological characteristic is consistent with the preset login verification biological characteristic, passing the identity verification of the corresponding target personnel.
3. The human health detection data acquisition system based on the internet of things as set forth in claim 1, wherein the acquisition subsystem comprises:
the trigger type acquisition module is used for acquiring the trigger type of a trigger mechanism for triggering the detection of the wearable equipment;
The first acquisition module of the human health detection data is used for acquiring the human health detection data of the corresponding target personnel directly through the wearable equipment if the trigger type is direct trigger;
the data attribute acquisition module is used for acquiring the data attribute of the target detection data if the trigger type is indirect trigger;
the health detection guide mode generation module is used for generating a template based on a preset health detection guide mode and generating a health detection guide mode according to the data attribute of the target detection data;
the human body health detection data second acquisition module is used for guiding the corresponding target personnel to finish the guiding action based on the health detection guiding mode, and acquiring the human body health detection data of the corresponding target personnel through the wearable equipment after the guiding is finished.
4. The human health detection data acquisition system based on the internet of things of claim 3, wherein the human health detection data second acquisition module comprises:
the first data type acquisition sub-module is used for acquiring a first data type of the human health detection data;
the query sub-module is used for querying a preset data type-sensor number library, and the data type-sensor number library comprises: a plurality of one-to-one second data types and first sensor numbers;
The second sensor number determining submodule is used for determining a first sensor number corresponding to a second data type consistent with the first data type in the data type-sensor number library and used as a second sensor number;
the sensing signal acquisition sub-module is used for acquiring sensing signals of the sensors corresponding to the second sensor numbers;
the restoration rule acquisition submodule is used for acquiring restoration rules of the induction signals;
the human health detection data determining sub-module is used for determining human health detection data according to the induction signals based on the restoration rule.
5. The human health detection data acquisition system based on the internet of things of claim 4, wherein the induction signal acquisition sub-module comprises:
the first device type acquisition unit is used for acquiring the pre-sensing signal of the sensor corresponding to the second sensor number and the first device type;
the pre-sensing signal processing scheme inquiring unit is used for inquiring a preset pre-sensing signal processing scheme library, and the pre-sensing signal processing scheme library comprises: a plurality of one-to-one second device types and a pre-sense signal processing scheme;
a target signal processing scheme determining unit, configured to determine a pre-sensing signal processing scheme corresponding to a second device type consistent with the first device type in the pre-sensing signal processing scheme library, and use the pre-sensing signal processing scheme as a target signal processing scheme;
And the induction signal acquisition unit is used for carrying out signal processing on the pre-induction signal based on the target signal processing scheme to acquire a processed induction signal.
6. The human health detection data acquisition system based on the internet of things of claim 4, wherein the restoration rule acquisition sub-module comprises:
a channel characteristic information acquisition unit for acquiring channel characteristic information of a transmission channel of the sensing signal;
a first signal influence relation determining unit for determining a first signal influence relation of channel characteristics of the channel to the sensing signal transmitted in the channel based on the channel characteristic information;
the restoration rule generation template acquisition unit is used for acquiring a preset restoration rule generation template;
and the restoration rule determining unit is used for generating a template based on the restoration rule and determining the restoration rule of the induction signal according to the first signal influence relation.
7. The internet of things-based human health detection data acquisition system of claim 6, wherein the first signal influence relationship determination unit comprises:
the channel characteristic feature extraction template acquisition subunit is used for acquiring a preset channel characteristic feature extraction template;
The channel characteristic value acquisition subunit is used for carrying out characteristic extraction on the channel characteristic information based on the channel characteristic extraction template to obtain a plurality of channel characteristic values;
a channel characteristic type obtaining subunit, configured to obtain a channel characteristic type corresponding to the channel characteristic value;
an influence relation determination model determination subunit configured to determine an influence relation determination model corresponding to the channel feature type based on the channel feature type;
the second signal influence relation acquisition subunit is used for inputting each channel characteristic value into a corresponding influence relation determination model to acquire at least one second signal influence relation;
and the integration subunit is used for integrating each second signal influence relation to obtain a first signal influence relation.
8. The human health detection data acquisition system based on the internet of things of claim 1, wherein the reminding subsystem comprises:
the first data item acquisition module is used for carrying out data splitting on the human health detection data to obtain a plurality of first data items;
the traversal module is used for traversing the first data items in sequence and taking the traversed first data items as second data items;
the standard data acquisition module is used for acquiring standard data corresponding to the second data item;
A standard deviation determining module for determining a standard deviation of the second data item based on the second data item and the standard data item;
the abnormal data item judging module is used for judging that the corresponding second data item is an abnormal data item if the standard deviation degree is larger than or equal to a preset standard deviation degree threshold value.
9. The human health detection data acquisition system based on the internet of things of claim 1, wherein the reminding subsystem comprises:
the abnormal information generation template acquisition module is used for acquiring a preset abnormal information generation template;
the abnormal information generation module is used for generating a template based on the abnormal information and generating abnormal information according to the abnormal data item;
the abnormal information transmission module is used for transmitting the abnormal information to the equipment node corresponding to the abnormal data item;
the abnormal information reminding module is used for reminding corresponding target personnel to check the abnormal information based on a preset reminding rule after the abnormal information reaches the equipment node.
10. The human health detection data acquisition method based on the Internet of things is characterized by comprising the following steps of:
step 1: carrying out identity verification on a target person wearing the wearable device for pre-verification;
step 2: if the target personnel pass the identity verification, acquiring human health detection data of the corresponding target personnel through wearable equipment based on the internet of things technology;
Step 3: transmitting the human health detection data to a preset data center node;
step 4: and attempting to acquire abnormal data items in the human health detection data, and if the acquisition attempt is successful, determining abnormal information to remind.
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