CN117954130A - Intelligent health consultation system based on Internet of things - Google Patents
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Abstract
The invention relates to the technical field of medical information, and provides an intelligent health consultation system based on the Internet of things, wherein the method comprises the following steps: acquiring current first body basic index information of a user, first history medical record information of the user and family medical record information corresponding to the user, wherein the generation time of the first body basic index information is later than that of the first history medical record information; extracting features of the first historical medical record information to obtain feature results, and obtaining a first score based on the feature results, the first body basic index information and a preset health score model; and adjusting the first score according to the family medical record information and the first history medical record information to obtain a second score, and sending different health prompt information to the user according to the second score. The method can detect the health state of the patient, namely the user in real time, and carry out different prompts according to the health state, thereby avoiding the condition delay.
Description
Technical Field
The invention relates to the technical field of medical information, in particular to an intelligent health consultation system based on the Internet of things.
Background
When a patient suffers from a certain disease, the patient is uncomfortable at home or in other places except hospitals, and the patient can be related to the disease, if the patient is related to the disease, but the health degree of the patient cannot be monitored in time, psychological tension and fear can be caused by the patient, and the illness state can be delayed in serious cases.
Disclosure of Invention
The invention aims to provide an intelligent health consultation system based on the Internet of things, so as to solve the problems.
In order to achieve the above object, the embodiment of the present application provides the following technical solutions:
the embodiment of the application provides an intelligent health consultation system based on the Internet of things, which comprises the following steps:
The acquisition module is used for acquiring current first body basic index information of a user, first history medical record information of the user and family medical record information corresponding to the user, wherein the generation time of the first body basic index information is later than that of the first history medical record information;
The extraction module is used for carrying out feature extraction on the first historical medical record information to obtain a feature result, and obtaining a first score based on the feature result, the first body basic index information and a preset health score model;
And the sending module is used for adjusting the first score according to the family medical record information and the first history medical record information to obtain a second score, and sending different health prompt information to the user according to the second score.
In a second aspect, the embodiment of the application provides an intelligent health consultation method based on the Internet of things.
Acquiring current first body basic index information of a user, first history medical record information of the user and family medical record information corresponding to the user, wherein the generation time of the first body basic index information is later than that of the first history medical record information;
extracting features of the first historical medical record information to obtain a feature result, and obtaining a first score based on the feature result, the first body basic index information and a preset health score model;
And adjusting the first score according to the family medical record information and the first history medical record information to obtain a second score, and sending different health prompt information to the user according to the second score.
In a third aspect, an embodiment of the present application provides an intelligent health consultation device based on the internet of things, where the device includes a memory and a processor. The memory is used for storing a computer program; the processor is used for realizing the intelligent health consultation method based on the Internet of things when executing the computer program.
In a fourth aspect, an embodiment of the present application provides a readable storage medium, where a computer program is stored, where the computer program, when executed by a processor, implements the steps of the intelligent health consultation method based on the internet of things.
The beneficial effects of the invention are as follows:
1. In the invention, the medical history information is acquired and the physical index information is also acquired when the original data is acquired, and then the case information and the physical index information are simultaneously input into a preset health scoring model, so that the health score corresponding to the current moment can be obtained by real-time calculation; meanwhile, family genetic history is considered in some families, so that the medical record information of the patient is compared with the medical record information of the families in similarity, the patient is prompted to strengthen health importance in the mode, a health score is finally obtained, and finally, health prompt is carried out on the patient in different prompt modes according to different final health scores.
2. The method can detect the health state of the patient, namely the user in real time, and carry out different prompts according to the health state, thereby avoiding the condition delay.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments 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.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an intelligent health consultation method based on the Internet of things according to the embodiment of the invention;
FIG. 2 is a schematic diagram of an intelligent health consultation system based on the Internet of things according to the embodiment of the present invention;
Fig. 3 is a schematic structural diagram of an intelligent health consultation device based on the internet of things according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals or letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Example 1
As shown in fig. 1, the embodiment provides an intelligent health consultation method based on the internet of things, which includes step S1, step S2 and step S3.
Step S1, acquiring current first body basic index information of a user, first history medical record information of the user and family medical record information corresponding to the user, wherein the generation time of the first body basic index information is later than that of the first history medical record information;
in this step, in the current first body basic index information of the user, the first body basic index information may include basic indexes such as blood pressure, blood sugar, blood fat, heart rate, and the like;
In the step, after a user suffers from a disease, the disease may be related to some indexes of the body, and when the user wants to acquire the current health evaluation in real time, the current index information and the history medical record can be input into a preset model, so that the health evaluation can be obtained;
Meanwhile, although the current first body basic index information of the user is obtained in the step, the current first body basic index information is calculated according to the body basic index information in a period of time before the current moment, because the situation that the first body basic index information at the current moment is steeply increased possibly caused by factors such as tension and the like when the user detects the current first body basic index information is considered, the current first body basic index information is calculated according to the body basic index information in the period of time before the current moment in the step, and the specific steps are the step S11 and the step S12;
Step S11, acquiring basic body index information of the user at different moments in a preset time period, wherein the preset time period comprises a current moment and the cut-off moment of the preset time period is the current moment; arranging all body basic index information of the user in a preset time period in order from small to large, dividing the arranged body basic index information into four equal parts by using three interval points, wherein the three interval points are a first quartile, a second quartile and a third quartile in sequence;
in this step, the preset time period may be set in a user-defined manner; the basic body index information also comprises basic indexes such as blood pressure, blood sugar, blood fat, heart rate and the like, and is processed according to the methods of the step S11 and the step S12 to obtain first basic body index information corresponding to each basic body index;
Meanwhile, the step can be understood that, for example, the basic body index information is a blood glucose index, the preset time period is the first 12 hours of the current time, that is, if the current time is 13 points, the blood glucose index of the first 12 hours and the blood glucose index of the current time are obtained; after all blood sugar indexes are obtained, the fact that abnormal values possibly exist in the blood sugar indexes is considered, for example, the blood sugar indexes which are very large or very small are eliminated, so that the abnormal values are eliminated in the step, average value calculation is carried out on all the blood sugar values after the elimination, the average value of the blood sugar indexes in the current moment and the previous period is used as the current first body basic index information of the user, and compared with the blood sugar indexes which are collected in real time at the current moment and are used as the current first body basic index information of the user, the average value calculation in the step can be more representative of the current first body basic index information of the user;
Step S12, subtracting the preset multiple quartile distance from the first quartile to obtain a first numerical value, and adding the preset multiple quartile distance to the third quartile to obtain a second numerical value; the first numerical value and the second numerical value form a first numerical value range, and the body basic index information which is not in the first numerical value range is marked as first abnormal data; and obtaining the first body basic index information according to the first abnormal data and the body basic index information at different moments.
In the step, the value of the preset multiple is 1.5; after the first abnormal data is obtained by calculation, the step adopts another abnormal value removing method, and finally intersection calculation is carried out on the abnormal data obtained by the two methods, so that final abnormal data is obtained, and the accuracy of abnormal data identification can be improved by the method;
Meanwhile, in the step, the specific implementation step of obtaining the first body basic index information according to the first abnormal data and the body basic index information at different moments includes step S121;
Step S121, collecting all body basic index information to obtain a body basic index information set, arbitrarily selecting two data in the body basic index information set to be combined to obtain data pairs, and clustering all the data pairs by using a clustering algorithm to obtain a plurality of clustering results; obtaining a threshold range corresponding to each clustering result according to the clustering result and a Laida criterion, marking the maximum numerical range formed by all threshold ranges as a second numerical range, and marking the body basic index information which is not in the second numerical range as second abnormal data; and solving an intersection of the first abnormal data and the second abnormal data to obtain final abnormal data, removing the final abnormal data from the body basic index information set to obtain a first set, and carrying out mean value calculation on all data in the first set to obtain the current first body basic index information of the user.
In this step, the maximum range formed by the entire threshold range is noted as the second value range, which can be understood as: for example, two threshold ranges, the first threshold range is 1-8, the second threshold range is 2-10, and then the maximum value range formed by the two threshold ranges is 1-10;
s2, carrying out feature extraction on the first historical medical record information to obtain a feature result, and obtaining a first score based on the feature result, the first body basic index information and a preset health score model;
the specific implementation steps of the step comprise a step S21 and a step S22;
S21, analyzing the data types contained in the history medical record information, respectively mapping each type of data to obtain vectors corresponding to each type of data, and vector splicing the vectors corresponding to each type of data to obtain first vectors corresponding to each type of data;
In this step, in consideration of the fact that different types of data, such as discrete data, text data, etc., may be included in the history information, this step performs a mapping process for each type of data;
Step S22, obtaining the characteristic result based on the first vector, combining the characteristic result with the current body basic index information to obtain combined data, and inputting the combined data into a preset health scoring model to obtain a first score.
The specific implementation steps of the step comprise a step S221 and a step S222;
Step S221, performing standardization processing on each first vector to obtain second vectors, and performing feature extraction on each second vector to obtain feature data corresponding to each second vector; inputting the second vectors into a softmax function to obtain weight information corresponding to the second vectors, carrying out weighted calculation on feature data and weight information corresponding to each second vector to obtain first features, and combining all the first features to obtain feature results; acquiring second body basic index information of a historical user and second historical medical record information of the historical user, wherein the generation time of the second body basic index information is later than that of the second historical medical record information;
Step S222, obtaining a corresponding historical feature result according to the second historical medical record information, combining the historical feature result with the corresponding second body basic index information to obtain a historical data combination, marking the marking information as health score, marking each marked historical data combination as sample data, and training a convolutional neural network model by using all the historical data to obtain the health score model.
In this step, the corresponding historical characteristic result is obtained according to the second historical medical record information, and the specific calculation method can refer to the method of "extracting the characteristics of the first historical medical record information to obtain the characteristic result";
In the step, training a convolutional neural network model by using all historical data to obtain the health scoring model, wherein the specific implementation steps comprise a step S2221 and a step S2222;
Step S2221, classifying the first history medical record information according to the integrity of the first history medical record information, to obtain complete first history medical record information, general complete first history medical record information and incomplete first history medical record information;
In this step, some first history medical record information may have some information missing, which results in reduced integrity, so in this step, a manual method may be used to classify the first history medical record information;
step S2222, taking the training data corresponding to the complete first history medical record information as a first gradient training sample, taking the training data corresponding to the general complete first history medical record information as a second gradient training sample, taking the training data corresponding to the incomplete first history medical record information as a third gradient training sample, and sequentially inputting the first gradient training sample, the second gradient training sample and the third gradient training sample into the convolutional neural network model for training, thereby obtaining the health scoring model.
In the step, the more complete the data is considered, the lower the training difficulty is, so that training samples are sequentially input into the step for training according to the degree of the integrity, the convergence of the health scoring model obtained after training can be realized to be globally optimal by the method, and the training quality of the health scoring model is further improved;
and step S3, adjusting the first score according to the family medical record information and the first history medical record information to obtain a second score, and sending different health prompt information to the user according to the second score.
In the step, considering that if the disease suffered by the user is similar to the family medical record, the user needs to be reminded of paying more attention to the current physical health, so that the step carries out similarity calculation on the family medical record information and the first historical medical record information, and the first score is adjusted according to a similarity calculation result; meanwhile, the specific implementation steps of the step comprise a step S31 and a step S32;
step S31, obtaining first keywords corresponding to the family medical record information, forming a first keyword vector according to all the first keywords, obtaining second keywords corresponding to the first history medical record information, and forming a second keyword vector according to all the second keywords;
In the step, the first keyword and the second keyword can be obtained by a manual method;
And S32, calculating the similarity between the first keyword vector and the second keyword vector, carrying out difference calculation on the similarity and a preset similarity threshold value, obtaining an adjustment score according to a difference calculation result, and summing the adjustment score and the first score to obtain the second score.
In this step, a correspondence table of the difference calculation result and the adjustment score may be constructed in advance, and the adjustment score is obtained according to this correspondence table; meanwhile, the second scores are different, and the sent health prompt information is correspondingly different.
Example 2
As shown in fig. 2, the present embodiment provides an intelligent health consultation system based on the internet of things, which includes an acquisition module 701, an extraction module 702 and a sending module 703.
An obtaining module 70, configured to obtain current first body basic index information of a user, first history medical record information of the user, and family medical record information corresponding to the user, where generation time of the first body basic index information is later than generation time of the first history medical record information;
The extracting module 702 is configured to perform feature extraction on the first historical medical record information to obtain a feature result, and obtain a first score based on the feature result, the first body basic index information and a preset health score model;
and the sending module 703 is configured to adjust the first score according to the family medical record information and the first history medical record information to obtain a second score, and send different health prompt information to the user according to the second score.
In a specific embodiment of the disclosure, the extraction module 702 further includes an analysis unit 7021 and a combination unit 7022.
The analysis unit 7021 is configured to analyze data types included in the history medical record information, respectively map each type of data to obtain a vector corresponding to each type of data, and vector splice the vectors corresponding to each type of data to obtain a first vector corresponding to each type of data;
the combining unit 7022 is configured to obtain the feature result based on the first vector, combine the feature result with the current body basic index information to obtain combined data, and input the combined data into a preset health scoring model to obtain a first score.
In a specific embodiment of the disclosure, the combining unit 7022 further includes an extracting unit 70221 and a first training unit 70222.
An extracting unit 70221, configured to perform normalization processing on each first vector to obtain second vectors, and perform feature extraction on each second vector to obtain feature data corresponding to each second vector; inputting the second vectors into a softmax function to obtain weight information corresponding to the second vectors, carrying out weighted calculation on feature data and weight information corresponding to each second vector to obtain first features, and combining all the first features to obtain feature results; acquiring second body basic index information of a historical user and second historical medical record information of the historical user, wherein the generation time of the second body basic index information is later than that of the second historical medical record information;
The first training unit 70222 is configured to obtain a corresponding historical feature result according to the second historical medical record information, combine the historical feature result with the corresponding second body basic index information to obtain a historical data combination, label the historical data combination with health score information, record each labeled historical data combination as one sample data, and train the convolutional neural network model by using all the historical data to obtain the health score model.
In one embodiment of the present disclosure, the first training unit 70222 further includes a classification unit 702221 and a second training unit 702222.
The classifying unit 702221 is configured to classify the first history medical record information according to the integrity of the first history medical record information, so as to obtain complete first history medical record information, general complete first history medical record information and incomplete first history medical record information;
The second training unit 702222 is configured to take training data corresponding to the complete first historical medical record information as a first gradient training sample, take training data corresponding to the general complete first historical medical record information as a second gradient training sample, take training data corresponding to the incomplete first historical medical record information as a third gradient training sample, and sequentially input the first gradient training sample, the second gradient training sample and the third gradient training sample into the convolutional neural network model for training, so as to obtain the health scoring model.
In a specific embodiment of the disclosure, the sending module 703 further includes a first obtaining unit 7031 and a first calculating unit 7032.
A first obtaining unit 7031, configured to obtain a first keyword corresponding to the family medical record information, form a first keyword vector according to all the first keywords, obtain a second keyword corresponding to the first history medical record information, and form a second keyword vector according to all the second keywords;
The first calculating unit 7032 is configured to calculate a similarity between the first keyword vector and the second keyword vector, calculate a difference between the similarity and a preset similarity threshold, obtain an adjustment score according to a result of the difference calculation, and sum the adjustment score and the first score to obtain the second score.
In a specific embodiment of the disclosure, the obtaining module 701 further includes a second obtaining unit 7011 and a second calculating unit 7012.
A second obtaining unit 7011, configured to obtain body basic index information of the user at different moments in a preset time period, where the preset time period includes a current time and a deadline of the preset time period is the current time; arranging all body basic index information of the user in a preset time period in order from small to large, dividing the arranged body basic index information into four equal parts by using three interval points, wherein the three interval points are a first quartile, a second quartile and a third quartile in sequence;
A second calculating unit 7012, configured to subtract the preset multiple of quartile distance from the first quartile to obtain a first value, and add the preset multiple of quartile distance to the third quartile to obtain a second value; the first numerical value and the second numerical value form a first numerical value range, and the body basic index information which is not in the first numerical value range is marked as first abnormal data; and obtaining the first body basic index information according to the first abnormal data and the body basic index information at different moments.
In a specific embodiment of the disclosure, the second computing unit 7012 further includes a third computing unit 70121.
The third computing unit 70121 is configured to aggregate all body basic index information to obtain a body basic index information set, arbitrarily select two data in the body basic index information set, combine the two data to obtain data pairs, and cluster all the data pairs by using a clustering algorithm to obtain a plurality of clustering results; obtaining a threshold range corresponding to each clustering result according to the clustering result and a Laida criterion, marking the maximum numerical range formed by all threshold ranges as a second numerical range, and marking the body basic index information which is not in the second numerical range as second abnormal data; and solving an intersection of the first abnormal data and the second abnormal data to obtain final abnormal data, removing the final abnormal data from the body basic index information set to obtain a first set, and carrying out mean value calculation on all data in the first set to obtain the current first body basic index information of the user.
It should be noted that, regarding the system in the above embodiment, the specific manner in which the respective modules perform the operations has been described in detail in the embodiment regarding the method, and will not be described in detail herein.
Example 3
Corresponding to the above method embodiment, the embodiment of the disclosure further provides an intelligent health consultation device based on the internet of things, where the intelligent health consultation device based on the internet of things described below and the intelligent health consultation method based on the internet of things described above can be referred to correspondingly.
Fig. 3 is a block diagram illustrating an intelligent health advisory facility 800 based on the internet of things, in accordance with an exemplary embodiment. As shown in fig. 3, the intelligent health consultation apparatus 800 based on the internet of things may include: a processor 801, a memory 802. The internet of things-based intelligent health advisory facility 800 may also include one or more of a multimedia component 803, an i/O interface 804, and a communication component 805.
The processor 801 is configured to control overall operation of the intelligent health consultation device 800 based on the internet of things, so as to complete all or part of the steps in the intelligent health consultation method based on the internet of things. The memory 802 is used to store various types of data to support operation at the internet of things-based intelligent health advisory facility 800, which may include, for example, instructions for any application or method operating on the internet of things-based intelligent health advisory facility 800, as well as application related data, such as contact data, messages sent and received, pictures, audio, video, and the like. The Memory 802 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted through the communication component 805. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is configured to perform wired or wireless communication between the intelligent health advisory facility 800 and other devices based on the internet of things. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near FieldCommunication, NFC for short), 2G, 3G, or 4G, or a combination of one or more thereof, the corresponding communication component 805 may therefore include: wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the intelligent health consultation apparatus 800 based on the internet of things may be implemented by one or more Application Specific Integrated Circuits (ASIC), digital signal processor (DIGITALSIGNAL PROCESSOR DSP), digital signal processing device (DIGITAL SIGNAL Processing Device DSPD), programmable logic device (Programmable Logic Device PLD), field programmable gate array (Field Programmable GATE ARRAY FPGA), controller, microcontroller, microprocessor or other electronic components for executing the intelligent health consultation method based on the internet of things.
In another exemplary embodiment, there is also provided a computer readable storage medium including program instructions which, when executed by a processor, implement the steps of the intelligent health consultation method based on the internet of things described above. For example, the computer readable storage medium may be the memory 802 including the program instructions described above, which are executable by the processor 801 of the intelligent health consultation device 800 based on the internet of things to complete the intelligent health consultation method based on the internet of things described above.
Example 4
Corresponding to the above method embodiments, the embodiments of the present disclosure further provide a readable storage medium, where a readable storage medium described below and an intelligent health consultation method based on the internet of things described above may be referred to correspondingly.
A readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the intelligent health consultation method based on the internet of things of the above method embodiment.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, which may store various program codes.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. An intelligent health consultation system based on the internet of things, which is characterized by comprising:
The acquisition module is used for acquiring current first body basic index information of a user, first history medical record information of the user and family medical record information corresponding to the user, wherein the generation time of the first body basic index information is later than that of the first history medical record information;
The extraction module is used for carrying out feature extraction on the first historical medical record information to obtain a feature result, and obtaining a first score based on the feature result, the first body basic index information and a preset health score model;
And the sending module is used for adjusting the first score according to the family medical record information and the first history medical record information to obtain a second score, and sending different health prompt information to the user according to the second score.
2. The intelligent health consultation system based on the internet of things according to claim 1, wherein the extraction module includes:
The analysis unit is used for analyzing the data types contained in the history medical record information, mapping each type of data to obtain vectors corresponding to each type of data, and vector splicing the vectors corresponding to each type of data to obtain first vectors corresponding to each type of data;
The combination unit is used for obtaining the characteristic result based on the first vector, combining the characteristic result with the current body basic index information to obtain combination data, and inputting the combination data into a preset health scoring model to obtain a first score.
3. The intelligent health consultation system based on the internet of things according to claim 2, characterized by a combination unit including:
The extraction unit is used for carrying out standardization processing on each first vector to obtain second vectors, and carrying out feature extraction on each second vector to obtain feature data corresponding to each second vector; inputting the second vectors into a softmax function to obtain weight information corresponding to the second vectors, carrying out weighted calculation on feature data and weight information corresponding to each second vector to obtain first features, and combining all the first features to obtain feature results; acquiring second body basic index information of a historical user and second historical medical record information of the historical user, wherein the generation time of the second body basic index information is later than that of the second historical medical record information;
The first training unit is used for obtaining a corresponding historical characteristic result according to the second historical medical record information, combining the historical characteristic result with the corresponding second body basic index information to obtain a historical data combination, marking the marking information as health scores, marking each marked historical data combination as one sample data, and training the convolutional neural network model by utilizing all the historical data to obtain the health score model.
4. The intelligent health consultation system based on the internet of things according to claim 3, wherein the first training unit includes:
the classification unit is used for classifying the first historical medical record information according to the integrity of the first historical medical record information to obtain complete first historical medical record information, general complete first historical medical record information and incomplete first historical medical record information;
The second training unit is configured to take training data corresponding to the complete first historical medical record information as a first gradient training sample, take training data corresponding to the general complete first historical medical record information as a second gradient training sample, take training data corresponding to the incomplete first historical medical record information as a third gradient training sample, and sequentially input the first gradient training sample, the second gradient training sample and the third gradient training sample into the convolutional neural network model for training, so as to obtain the health scoring model.
5. The intelligent health consultation system based on the internet of things according to claim 1, wherein the transmitting module includes:
The first acquisition unit is used for acquiring first keywords corresponding to the family medical record information, forming a first keyword vector according to all the first keywords, acquiring second keywords corresponding to the first history medical record information, and forming a second keyword vector according to all the second keywords;
And the first calculation unit is used for calculating the similarity between the first keyword vector and the second keyword vector, carrying out difference calculation on the similarity and a preset similarity threshold value, obtaining an adjustment score according to a difference calculation result, and summing the adjustment score and the first score to obtain the second score.
6. The intelligent health consultation system based on the internet of things according to claim 1, wherein the acquisition module includes:
The second acquisition unit is used for acquiring basic body index information of the user at different moments in a preset time period, wherein the preset time period comprises the current moment and the cut-off moment of the preset time period is the current moment; arranging all body basic index information of the user in a preset time period in order from small to large, dividing the arranged body basic index information into four equal parts by using three interval points, wherein the three interval points are a first quartile, a second quartile and a third quartile in sequence;
The second calculation unit is used for subtracting the quartile distance of the preset multiple from the first quartile to obtain a first numerical value, and adding the quartile distance of the preset multiple to the third quartile to obtain a second numerical value; the first numerical value and the second numerical value form a first numerical value range, and the body basic index information which is not in the first numerical value range is marked as first abnormal data; and obtaining the first body basic index information according to the first abnormal data and the body basic index information at different moments.
7. The internet of things-based intelligent health counseling system according to claim 6, wherein the second computing unit comprises:
The third calculation unit is used for collecting all body basic index information to obtain a body basic index information set, two data are arbitrarily selected from the body basic index information set to be combined to obtain data pairs, and a clustering algorithm is used for clustering all the data pairs to obtain a plurality of clustering results; obtaining a threshold range corresponding to each clustering result according to the clustering result and a Laida criterion, marking the maximum numerical range formed by all threshold ranges as a second numerical range, and marking the body basic index information which is not in the second numerical range as second abnormal data; and solving an intersection of the first abnormal data and the second abnormal data to obtain final abnormal data, removing the final abnormal data from the body basic index information set to obtain a first set, and carrying out mean value calculation on all data in the first set to obtain the current first body basic index information of the user.
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