CN115334471A

CN115334471A - Human body induction control method and device based on wireless Internet of things

Info

Publication number: CN115334471A
Application number: CN202210955156.XA
Authority: CN
Inventors: 曾政军; 张新年; 王潮飞
Original assignee: Xuxiang Intelligent Shenzhen Co ltd
Current assignee: Xuxiang Intelligent Shenzhen Co ltd
Priority date: 2022-08-10
Filing date: 2022-08-10
Publication date: 2022-11-11

Abstract

The embodiment of the application discloses a human body induction control method and a human body induction control device based on a wireless Internet of things, which are applied to wireless Internet of things equipment, wherein the wireless Internet of things equipment is connected with P Internet of things chips, P is an integer greater than 1, and the method comprises the following steps: q physiological state parameters of the user are obtained through the P Internet of things chips, wherein Q is an integer larger than 1; classifying the Q physiological state parameters to obtain A physiological state parameter sets, wherein each physiological state parameter set corresponds to a disease label, and A is a positive integer; determining a probability value corresponding to each disease label according to the A-type physiological state parameter set to obtain A probability values; selecting the maximum value of the A probability values; and carrying out early warning operation according to the maximum value. By adopting the embodiment of the application, the intelligence of the wireless Internet of things can be improved.

Description

Human body induction control method and device based on wireless Internet of things

Technical Field

The application relates to the technical field of communication or the technical field of Internet of things, in particular to a human body induction control method and device based on wireless Internet of things.

Background

With the rapid development of communication technology and electronic technology, the life of modern people is becoming more and more, and various electronic products for people are more and more. The development of thing networking has also brought various changes for people's life style, and wireless thing networking not only can realize the interconnection between thing and the thing, can also realize the interdynamic between wireless thing networking and the people, but, the thing networking of target is not intelligent enough, consequently, the problem of how to promote the intelligence of wireless thing networking is urgent to be solved.

Disclosure of Invention

The embodiment of the application provides a human body induction control method and device based on a wireless Internet of things, and the method and device are beneficial to improving the intelligence of the wireless Internet of things.

In a first aspect, an embodiment of the present application provides a human body sensing control method based on a wireless internet of things, which is applied to a wireless internet of things device, where the wireless internet of things device is connected to P internet of things chips, P is an integer greater than 1, and the method includes:

obtaining Q physiological state parameters of the user through the P Internet of things chips, wherein Q is an integer larger than 1;

classifying the Q physiological state parameters to obtain A physiological state parameter sets, wherein each physiological state parameter set corresponds to a disease label, and A is a positive integer;

determining a probability value corresponding to each disease label according to the A-type physiological state parameter set to obtain A probability values;

selecting the maximum value of the A probability values;

and carrying out early warning operation according to the maximum value.

In a second aspect, an embodiment of the present application provides a human body induction control device based on wireless internet of things, is applied to wireless internet of things equipment, P internet of things chips are connected to wireless internet of things equipment, and P is an integer greater than 1, the device includes: an acquisition unit, a classification unit, a determination unit, a selection unit and an early warning unit, wherein,

the acquisition unit is used for acquiring Q physiological state parameters of the user through the P Internet of things chips, wherein Q is an integer larger than 1;

the classification unit is used for classifying the Q physiological state parameters to obtain A-type physiological state parameter sets, each physiological state parameter set corresponds to a disease label, and A is a positive integer;

the determining unit is used for determining a probability value corresponding to each disease label according to the A-type physiological state parameter set to obtain A probability values;

the selecting unit is used for selecting the maximum value in the A probability values;

and the early warning unit is used for carrying out early warning operation according to the maximum value.

In a third aspect, an embodiment of the present application provides a wireless internet of things device, including a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing the steps in the first aspect of the embodiment of the present application.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program makes a computer perform some or all of the steps described in the first aspect of the embodiment of the present application.

In a fifth aspect, embodiments of the present application provide a computer program product, where the computer program product comprises a non-transitory computer-readable storage medium storing a computer program, where the computer program is operable to cause a computer to perform some or all of the steps as described in the first aspect of embodiments of the present application. The computer program product may be a software installation package.

The embodiment of the application has the following beneficial effects:

the human body sensing control method and the human body sensing control device based on the wireless internet of things are applied to wireless internet of things equipment, the wireless internet of things equipment is connected with P internet of things chips, P is an integer larger than 1, Q physiological state parameters of a user are obtained through the P internet of things chips, Q is an integer larger than 1, the Q physiological state parameters are classified to obtain A-type physiological state parameter sets, each physiological state parameter set corresponds to a disease label, A is a positive integer, the probability value corresponding to each disease label is determined according to the A-type physiological state parameter sets, A probability values are obtained, the maximum value in the A probability values is selected, and early warning operation is carried out according to the maximum value.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic flowchart of a human body sensing control method based on a wireless internet of things according to an embodiment of the present application;

fig. 2 is a schematic flowchart of another human body sensing control method based on a wireless internet of things according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of a wireless internet of things device provided in an embodiment of the present application;

fig. 4 is a block diagram of functional units of a human body sensing control device based on a wireless internet of things according to an embodiment of the present application.

Detailed Description

In order to make the technical solutions of the present application better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

The wireless internet of things equipment described in the embodiment of the invention can comprise at least one of the following components: the intelligent lighting equipment, the smart mobile phone, intelligent distribution box, intelligent switch controller, intelligent control panel, intelligent power socket, intelligent gateway, intelligent coordinator, intelligent node, intelligent router, intelligent set-top box, intelligent ammeter, intelligent television, tablet computer, intelligent refrigerator, intelligent washing machine, intelligent massage chair, intelligent desk, intelligent air conditioner, intelligent humidifier, edge server, intelligent lampblack absorber, intelligent microwave oven, intelligent purifier, intelligent rice cooker, intelligent room heater, intelligent door, intelligent fan, intelligent water dispenser, intelligent curtain, intelligent closestool, smart mobile phone, intelligent security system, intelligent furniture, intelligent robot etc. of sweeping the floor, do not limit here.

The following describes embodiments of the present application in detail.

Referring to fig. 1, fig. 1 is a schematic flowchart of a human body induction control method based on a wireless internet of things provided in an embodiment of the present application, and the method is applied to a wireless internet of things device, where the wireless internet of things device is connected to P internet of things chips, P is an integer greater than 1, and as shown in the figure, the human body induction control method based on the wireless internet of things includes:

101. q physiological state parameters of the user are obtained through the P Internet of things chips, and Q is an integer larger than 1.

In this application embodiment, P thing networking chips are connected to wireless thing networking device, and P is for being greater than 1 integer, and inside the human body can be implanted to thing networking chip, perhaps, also can be the chip of wearable equipment. Each chip of the internet of things is equivalent to a sensor for detecting one or more physiological state parameters, and the physiological state parameters can be detected through the chip of the internet of things. A wireless Internet of things system or a wireless Internet of things network is formed between the wireless Internet of things equipment and the P Internet of things chips.

Wherein the physiological state parameter may comprise at least one of: body temperature, heart rate, respiratory rate, epinephrine content, blood lipid content, blood glucose content, and the like, without limitation.

In specific implementation, Q physiological state parameters of a user can be obtained through P internet of things chips, Q is an integer greater than 1, and each physiological state parameter can be a physiological state parameter at a time point or a physiological state parameter within a period of time.

102. And classifying the Q physiological state parameters to obtain A physiological state parameter sets, wherein each physiological state parameter set corresponds to a disease label, and A is a positive integer.

In a specific implementation, the disease label may be preset or default to the system, different disease labels may correspond to different physiological state parameters, and the disease label may include at least one of the following: asthma, allergy, fever, myocardial infarction, anemia, hypoglycemia, cerebral thrombosis, etc., without limitation.

In specific implementation, Q physiological state parameters can be classified based on the physiological state parameters required by each disease label to obtain a class a physiological state parameter set, each physiological state parameter set corresponds to one disease label, and a is a positive integer. A may be an integer greater than 1, or A may be equal to 1.

103. And determining a probability value corresponding to each disease label according to the A-type physiological state parameter set to obtain A probability values.

In specific implementation, different disease labels may correspond to different neural network models, or different disease labels may correspond to one neural network model, and feature extraction may be performed on each physiological state parameter set in the class a physiological state parameter set to obtain a corresponding a feature set, and then the a feature sets are input to the corresponding neural network model to perform operation to obtain a corresponding probability value, which may be used to indicate a probability of occurrence of a corresponding disease.

Wherein the neural network model may include at least one of: a recurrent neural network model, a convolutional neural network model, a fully-connected neural network model, etc., without limitation.

104. And selecting the maximum value of the A probability values.

In specific implementation, the maximum value of the A probability values can be selected, and the maximum value is often expressed as the real situation of a user, so that early warning can be guaranteed.

105. And carrying out early warning operation according to the maximum value.

In concrete realization, the maximum value is corresponding to the corresponding disease label, and different disease labels can correspond different early warning operations, help promoting the early warning precision. For example, the user may be prompted to take medication or may also seek help from others around.

Optionally, in the step 105, performing an early warning operation according to the maximum value may include the following steps:

51. acquiring a target disease label corresponding to the maximum value and a target physiological state parameter set corresponding to the maximum value;

52. determining a target early warning level according to the target physiological state parameter set;

53. determining a target control parameter corresponding to the target early warning level according to the target disease label;

54. and performing corresponding early warning operation according to the target control parameters.

In the embodiment of the application, the target disease label corresponding to the maximum value and the target physiological state parameter set corresponding to the maximum value can be obtained, different early warning levels reflected by different target physiological state parameter sets are different, further, the target early warning level can be determined according to the target physiological state parameter set, then, the target control parameter corresponding to the target early warning level is determined according to the target disease label, the control parameters corresponding to different early warning levels are different, and then, corresponding early warning operation can be performed according to the target control parameter, so that the early warning accuracy and effectiveness can be improved.

Wherein the control parameter may comprise at least one of: voice prompt, ask for help to doctors, ask for help to surrounding people, alarm, positioning, and the like, without limitation. The voice prompt may include at least one of: prompting for medication, prompting for physical action, and the like, and is not limited herein.

Optionally, the target physiological state parameter set includes a plurality of physiological state parameters, and the step 52 of determining the target early warning level according to the target physiological state parameter set may include the following steps:

521. determining a body condition evaluation value corresponding to each physiological state parameter in the plurality of physiological state parameters to obtain a plurality of body condition evaluation values;

522. determining a target weight set corresponding to the target disease label according to a mapping relation between a preset disease label and the weight set; the target weight set comprises a plurality of weights, and the weights correspond to the physiological state parameters one to one;

523. determining a target body condition assessment value according to the plurality of body condition assessment values and the target weight set;

524. and determining the target early warning grade corresponding to the target physical condition evaluation value according to a mapping relation between a preset physical condition evaluation value and the early warning grade.

In the embodiment of the application, different disease labels correspond to different weight sets, each weight set can correspond to a plurality of weights, each weight can correspond to a class of physiological state parameters, and the mapping relationship between the preset disease labels and the weight sets can be stored in advance. The mapping relationship between the preset body condition evaluation value and the early warning level can be stored in advance.

Specifically, a physical condition evaluation value corresponding to each physiological condition parameter in a plurality of physiological condition parameters can be determined to obtain a plurality of physical condition evaluation values, a target weight set corresponding to a target disease label is determined according to a mapping relation between a preset disease label and a weight set, the target weight set comprises a plurality of weights, the weights correspond to the physiological condition parameters one by one, weighting operation is performed according to the plurality of physical condition evaluation values and the target weights to obtain a target physical condition evaluation value, further, a target early warning grade corresponding to the target physical condition evaluation value can be determined according to the mapping relation between the preset physical condition evaluation value and the early warning grade, further, corresponding early warning can be performed based on the physical condition of a user, and the accuracy and the effectiveness of the early warning are improved.

Optionally, the step 524, determining a target body condition evaluation value according to the plurality of body condition evaluation values and the target weight set, may include the following steps:

a1, determining weights of the target weight set, which are larger than a preset threshold value, to obtain at least one weight;

a2, acquiring the sensitivity of the sensor corresponding to the at least one weight to obtain at least one sensitivity;

a3, carrying out optimization processing according to the at least one sensitivity and the body condition evaluation value corresponding to each sensitivity to obtain at least one reference body condition evaluation value;

and A4, carrying out weighting operation according to the at least one reference body condition evaluation value, the at least one weight, other weights except the at least one weight in the target weight set, and other body condition evaluation values except the body condition evaluation value corresponding to the at least one sensitivity of the plurality of body condition evaluation values to obtain the target body condition evaluation value.

Wherein, the preset threshold value can be preset or default to the system. In specific implementation, in the embodiment of the present application, weights in the target weight set that are greater than the preset threshold may be determined, and at least one weight is obtained, where if the weight is greater than the preset threshold, the weight is large, and in order to ensure accuracy of the body condition evaluation, the accuracy of the weight needs to be high.

Furthermore, the sensitivity of the sensor corresponding to at least one weight can be obtained to obtain at least one sensitivity, and then optimization processing is performed according to the at least one sensitivity and the body condition evaluation value corresponding to each sensitivity to obtain at least one reference body condition evaluation value.

Then, a weighting operation can be performed according to the at least one reference body condition evaluation value and the at least one weight, the other weights except the at least one weight in the target weight set, and the other body condition evaluation values except the body condition evaluation value corresponding to the at least one sensitivity of the plurality of body condition evaluation values to obtain a target body condition evaluation value.

Optionally, in the step A3, performing optimization processing according to the at least one sensitivity and the body condition evaluation value corresponding to each sensitivity to obtain at least one reference body condition evaluation value, which may include the following steps:

a31, determining an optimization coefficient corresponding to each sensitivity in the at least one sensitivity according to a mapping relation between preset sensitivities and optimization coefficients to obtain at least one optimization coefficient;

and A32, optimizing the corresponding body condition evaluation value by the at least one optimization coefficient to obtain the at least one reference body condition evaluation value.

In the embodiment of the application, the mapping relation between the preset sensitivity and the optimization coefficient can be stored in advance, different sensitivities correspond to different optimization coefficients, and then at least one optimization coefficient can be used for optimizing the corresponding physical condition evaluation value to obtain at least one reference physical condition evaluation value.

Optionally, in step 53, determining the target control parameter corresponding to the target early warning level according to the target disease label may include the following steps:

531. selecting a target mapping relation corresponding to the target disease label from a mapping relation set, wherein the mapping relation set comprises a plurality of mapping relations, each mapping relation corresponds to one disease label, and each mapping relation is a mapping relation between an early warning grade and a control parameter;

532. and determining a target control parameter corresponding to the target early warning level according to the target mapping relation.

In specific implementation, a mapping relationship set may be stored in advance, where the mapping relationship set may include a plurality of mapping relationships, each mapping relationship corresponds to one disease label, and each mapping relationship is a mapping relationship between an early warning level and a control parameter.

Specifically, a target mapping relation corresponding to a target disease label can be selected from the mapping relation set, and then a target control parameter corresponding to a target early warning grade is determined according to the target mapping relation, so that early warning accuracy is guaranteed, time is strived for rescue, diseases can be found at the first time, and rescue efficiency is improved.

The human body induction control method based on the wireless internet of things is applied to wireless internet of things equipment, the wireless internet of things equipment is connected with P internet of things chips, P is an integer larger than 1, Q physiological state parameters of a user are obtained through the P internet of things chips, Q is an integer larger than 1, the Q physiological state parameters are classified to obtain A-type physiological state parameter sets, each physiological state parameter set corresponds to a disease label, A is a positive integer, the probability value corresponding to each disease label is determined according to the A-type physiological state parameter sets, A probability values are obtained, the maximum value of the A probability values is selected, early warning operation is carried out according to the maximum value, therefore, various physiological state parameters of the user can be obtained based on the internet of things chip, then the physiological state parameters are classified according to diseases, corresponding probabilities are analyzed, corresponding early warning operation is carried out, intelligence of the wireless internet of things is improved, corresponding rescue can be prompted, and safety of the user is guaranteed.

Consistent with the embodiment shown in fig. 1, please refer to fig. 2, where fig. 2 is a schematic flowchart of a human body induction control method based on a wireless internet of things provided in the embodiment of the present application, and is applied to a wireless internet of things device, where the wireless internet of things device is connected to P internet of things chips, P is an integer greater than 1, and as shown in the figure, the human body induction control method based on the wireless internet of things includes:

201. q physiological state parameters of the user are obtained through the P Internet of things chips, and Q is an integer larger than 1.

202. And classifying the Q physiological state parameters to obtain A physiological state parameter sets, wherein each physiological state parameter set corresponds to a disease label, and A is a positive integer.

203. And determining a probability value corresponding to each disease label according to the A-type physiological state parameter set to obtain A probability values.

204. And selecting the maximum value of the A probability values.

205. And judging whether the maximum value is larger than a specified threshold value.

206. And when the maximum value is larger than the specified threshold value, carrying out early warning operation according to the maximum value.

Wherein the specified threshold may be preset or system default. If the maximum value is greater than a predetermined threshold value, it is highly probable that the corresponding disorder is present.

For the detailed description of the steps 201 to 206, reference may be made to corresponding steps of the human body sensing control method based on the wireless internet of things described in fig. 1, and details are not repeated herein.

The human body induction control method based on the wireless internet of things is applied to wireless internet of things equipment, the wireless internet of things equipment is connected with P internet of things chips, P is an integer larger than 1, Q physiological state parameters of a user are obtained through the P internet of things chips, Q is an integer larger than 1, the Q physiological state parameters are classified to obtain A-type physiological state parameter sets, each physiological state parameter set corresponds to a disease label, A is a positive integer, the probability value corresponding to each disease label is determined according to the A-type physiological state parameter sets, A probability values are obtained, the maximum value of the A probability values is selected, whether the maximum value is larger than a specified threshold value is judged, and when the maximum value is larger than the specified threshold value, early warning operation is performed according to the maximum value.

Referring to fig. 3, fig. 3 is a schematic structural diagram of a wireless internet of things device provided in an embodiment of the present application, and as shown in the drawing, the wireless internet of things device includes a processor, a memory, a communication interface, and one or more programs, and is applied to an internet of things device, the wireless internet of things device is connected to P internet of things chips, P is an integer greater than 1, the one or more programs are stored in the memory and configured to be executed by the processor, and in an embodiment of the present application, the programs include instructions for performing the following steps:

q physiological state parameters of the user are obtained through the P Internet of things chips, wherein Q is an integer larger than 1;

selecting the maximum value of the A probability values;

and carrying out early warning operation according to the maximum value.

Optionally, in the aspect of performing the early warning operation according to the maximum value, the program includes instructions for executing the following steps:

acquiring a target disease label corresponding to the maximum value and a target physiological state parameter set corresponding to the maximum value;

determining a target early warning level according to the target physiological state parameter set;

determining a target control parameter corresponding to the target early warning level according to the target disease label;

and carrying out corresponding early warning operation according to the target control parameter.

Optionally, the target physiological state parameter set includes a plurality of physiological state parameters, and in the aspect of determining the target early warning level according to the target physiological state parameter set, the program includes instructions for performing the following steps:

determining a body condition evaluation value corresponding to each physiological state parameter in the plurality of physiological state parameters to obtain a plurality of body condition evaluation values;

determining a target weight value set corresponding to the target disease label according to a mapping relation between a preset disease label and the weight value set; the target weight set comprises a plurality of weights, and the weights correspond to the physiological state parameters one to one;

determining a target body condition assessment value according to the plurality of body condition assessment values and the target weight value set;

and determining the target early warning grade corresponding to the target physical condition evaluation value according to a mapping relation between a preset physical condition evaluation value and the early warning grade.

Optionally, in the determining a target body condition assessment value according to the plurality of body condition assessment values and the target weight set, the program includes instructions for:

determining the weight values of the target weight value set which are larger than a preset threshold value to obtain at least one weight value;

acquiring the sensitivity of the sensor corresponding to the at least one weight to obtain at least one sensitivity;

performing optimization processing according to the at least one sensitivity and the body condition evaluation value corresponding to each sensitivity to obtain at least one reference body condition evaluation value;

and performing weighting operation according to the at least one reference body condition evaluated value, the at least one weight, other weights except the at least one weight in the target weight set, and other body condition evaluated values except the body condition evaluated value corresponding to the at least one sensitivity of the plurality of body condition evaluated values to obtain the target body condition evaluated value.

Optionally, in the aspect of obtaining at least one reference body condition evaluation value by performing optimization processing according to the at least one sensitivity and the body condition evaluation value corresponding to each sensitivity, the program includes instructions for performing the following steps:

determining an optimization coefficient corresponding to each sensitivity in the at least one sensitivity according to a mapping relation between preset sensitivity and the optimization coefficient to obtain at least one optimization coefficient;

and optimizing the corresponding body condition evaluation value by using the at least one optimization coefficient to obtain the at least one reference body condition evaluation value.

Optionally, in the aspect of determining the target control parameter corresponding to the target early warning level according to the target condition tag, the program includes instructions for performing the following steps:

selecting a target mapping relation corresponding to the target disease label from a mapping relation set, wherein the mapping relation set comprises a plurality of mapping relations, each mapping relation corresponds to one disease label, and each mapping relation is a mapping relation between an early warning grade and a control parameter;

and determining a target control parameter corresponding to the target early warning level according to the target mapping relation.

It can be seen that, in the wireless internet of things device described in the embodiment of the present application, the wireless internet of things device is connected to P internet of things chips, P is an integer greater than 1, Q physiological state parameters of a user are obtained through the P internet of things chips, Q is an integer greater than 1, the Q physiological state parameters are classified to obtain a class a physiological state parameter set, each physiological state parameter set corresponds to a disease label, a is a positive integer, a probability value corresponding to each disease label is determined according to the class a physiological state parameter set, a probability value is obtained, a maximum value of the a probability values is selected, and an early warning operation is performed according to the maximum value.

Fig. 4 is a block diagram of functional units of a human body sensing control device 400 based on a wireless internet of things according to an embodiment of the present application. This human body induction control device 400 based on wireless thing networking is applied to wireless thing networking equipment, P thing networking chips are connected to wireless thing networking equipment, and P is for being greater than 1 integer, the device includes: an obtaining unit 401, a classifying unit 402, a determining unit 403, a selecting unit 404 and an early warning unit 405, wherein,

the obtaining unit 401 is configured to obtain Q physiological state parameters of the user through the P internet of things chips, where Q is an integer greater than 1;

the classification unit 402 is configured to classify the Q physiological state parameters to obtain a class a physiological state parameter set, where each physiological state parameter set corresponds to a disease label, and a is a positive integer;

the determining unit 403 is configured to determine, according to the class a physiological state parameter set, a probability value corresponding to each disease label to obtain a probability values a;

the selecting unit 404 is configured to select a maximum value of the a probability values;

the early warning unit 405 is configured to perform an early warning operation according to the maximum value.

Optionally, in the aspect of performing the early warning operation according to the maximum value, the early warning unit 405 is specifically configured to:

Optionally, the target physiological state parameter set includes a plurality of physiological state parameters, and in the aspect of determining a target early warning level according to the target physiological state parameter set, the early warning unit 405 is specifically configured to:

Optionally, in the aspect of determining the target body condition evaluation value according to the plurality of body condition evaluation values and the target weight set, the early warning unit 405 is specifically configured to:

and performing weighting operation according to the at least one reference body condition evaluation value, the at least one weight value, other weight values except the at least one weight value in the target weight value set, and other body condition evaluation values except the body condition evaluation value corresponding to the at least one sensitivity of the plurality of body condition evaluation values to obtain the target body condition evaluation value.

Optionally, in the aspect of performing optimization processing according to the at least one sensitivity and the body condition evaluation value corresponding to each sensitivity to obtain at least one reference body condition evaluation value, the early warning unit 405 is specifically configured to:

Optionally, in the aspect of determining the target control parameter corresponding to the target early warning level according to the target condition label, the early warning unit 405 is specifically configured to:

It can be seen that the human body sensing control device based on the wireless internet of things described in the embodiment of the application is applied to wireless internet of things equipment, the wireless internet of things equipment is connected with P internet of things chips, P is an integer greater than 1, Q physiological state parameters of a user are obtained through the P internet of things chips, Q is an integer greater than 1, the Q physiological state parameters are classified to obtain a class a physiological state parameter set, each physiological state parameter set corresponds to a disease label, a is a positive integer, a probability value corresponding to each disease label is determined according to the class a physiological state parameter set to obtain a probability value, a maximum value of the probability values is selected, and an early warning operation is performed according to the maximum value.

It can be understood that the functions of the program modules of the human body sensing control device based on the wireless internet of things according to the method in the method embodiment may be specifically implemented, and the specific implementation process may refer to the relevant description of the method embodiment, which is not described herein again.

Embodiments of the present application further provide a computer storage medium, where the computer storage medium stores a computer program for electronic data exchange, and the computer program makes a computer execute part or all of the steps of any one of the methods as described in the above method embodiments.

Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods as described in the above method embodiments. The computer program product may be a software installation package.

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art will recognize that the embodiments described in this specification are preferred embodiments and that acts or modules referred to are not necessarily required for this application.

In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units is only one type of division of logical functions, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some interfaces, indirect coupling or communication connection between devices or units, and may be in an electrical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the above-mentioned method of the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and various media capable of storing program codes.

Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, read-Only memories (ROMs), random Access Memories (RAMs), magnetic or optical disks, and the like.

The foregoing embodiments have been described in detail, and specific examples are used herein to explain the principles and implementations of the present application, where the above description of the embodiments is only intended to help understand the method and its core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, the specific implementation manner and the application scope may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims

1. A human body induction control method based on a wireless Internet of things is characterized by being applied to wireless Internet of things equipment, wherein the wireless Internet of things equipment is connected with P Internet of things chips, P is an integer larger than 1, and the method comprises the following steps:

classifying the Q physiological state parameters to obtain A-type physiological state parameter sets, wherein each physiological state parameter set corresponds to a disease label, and A is a positive integer;

selecting the maximum value of the A probability values;

and carrying out early warning operation according to the maximum value.

2. The method of claim 1, wherein performing the pre-warning operation according to the maximum value comprises:

3. The method of claim 2, wherein the target set of physiological state parameters comprises a plurality of physiological state parameters, and wherein determining a target early warning level from the target set of physiological state parameters comprises:

determining a target body condition assessment value according to the plurality of body condition assessment values and the target weight set;

4. The method of claim 3, wherein determining a target physical condition assessment value from the plurality of physical condition assessment values and the set of target weights comprises:

5. The method according to claim 4, wherein the performing optimization processing according to the at least one sensitivity and the body condition evaluation value corresponding to each sensitivity to obtain at least one reference body condition evaluation value comprises:

6. The method of claim 2, wherein determining target control parameters corresponding to the target early warning level from the target condition label comprises:

7. The utility model provides a human body induction control device based on wireless thing networking, its characterized in that is applied to wireless thing networking equipment, P thing networking chips are connected to wireless thing networking equipment, and P is for being greater than 1 integer, the device includes: an acquisition unit, a classification unit, a determination unit, a selection unit and an early warning unit, wherein,

8. The apparatus according to claim 7, wherein in the aspect of performing the warning operation according to the maximum value, the warning unit is specifically configured to:

and performing corresponding early warning operation according to the target control parameters.

9. A wireless internet of things device comprising a processor, a memory for storing one or more programs and configured for execution by the processor, the programs comprising instructions for performing the steps of the method of any of claims 1-6.

10. A computer-readable storage medium, characterized in that a computer program for electronic data exchange is stored, wherein the computer program causes a computer to perform the method according to any of the claims 1-6.