CN111880424B

CN111880424B - Intelligent equipment control method based on Internet of things and big data and cloud control center

Info

Publication number: CN111880424B
Application number: CN202010709897.0A
Authority: CN
Inventors: 周蓉
Original assignee: Zhanjiang Guokehe Road Information Technology Co ltd
Current assignee: Zhanjiang guokehe Road Information Technology Co.,Ltd.
Priority date: 2020-07-22
Filing date: 2020-07-22
Publication date: 2021-05-04
Anticipated expiration: 2040-07-22
Also published as: CN112631141A; CN112612216A; CN111880424A

Abstract

According to the intelligent equipment control method and the cloud control center based on the Internet of things and the big data, the identity of a user is verified and an intelligent air conditioner is controlled to be started, a comprehensive environment description value is determined according to external environment data and a first air conditioner air power value is determined by combining a wind speed matching list, a health state index of the user is determined according to an impact electric signal, the first air conditioner air power value is corrected to obtain a second air conditioner air power value, the intelligent air conditioner is controlled to output according to the second air conditioner air power value, the external environment data collected by a second sensor and the impact electric signal collected by a third sensor are continuously obtained, real-time monitoring of the external environment and the health state of the user is achieved, and therefore flexible adjustment of the output state of the intelligent air conditioner is achieved. So, can realize the automatic regulation to the user's of intelligent air conditioner identification and wind speed to develop a plurality of functions of intelligent air conditioner in the thing networking aspect, improve intelligent air conditioner's intelligent degree.

Description

Intelligent equipment control method based on Internet of things and big data and cloud control center

Technical Field

The disclosure relates to the technical field of control of internet of things and big data, in particular to an intelligent device control method and a cloud control center based on the internet of things and the big data.

Background

With the development of technology, the application of the internet is more and more widespread, and the internet plays an important role in fields such as automatic driving, intelligent manufacturing and intelligent home, and can realize data interaction and processing of devices in the fields. Taking smart homes as an example, smart homes can realize mutual cooperation by means of the internet, thereby providing a safe, convenient and comfortable living environment for users.

Nowadays, the development of smart homes is becoming more and more mature, and the application of the internet to some homes such as televisions, range hoods and air purifiers is more and more perfect. However, for some temperature-regulated homes (e.g., air conditioners), the degree of networking and networking of these homes is not high. For example, as smart homes which are getting hotter and hotter nowadays, the existing functions of the smart air conditioner are still limited to remote control on/off and remote adjustment of wind speed, some functions of the smart air conditioner on the internet of things and big data level are not effectively developed, and the degree of intelligence is low.

Disclosure of Invention

In view of the above, the present disclosure provides an intelligent device control method and a cloud control center based on the internet of things and big data.

In a first aspect, an intelligent device control method based on the internet of things and big data is provided, and the method includes:

acquiring data to be identified collected by a first sensor, performing data extraction on the data to be identified to obtain biological characteristic data used for representing user identity included in the data to be identified, searching whether target data matched with the biological characteristic data exists in a preset database, and controlling an intelligent air conditioner to be started if the target data exists in the preset database;

the method comprises the steps that external environment data collected by a second sensor are parallelly obtained after the intelligent air conditioner is started, a current environment parameter list corresponding to the intelligent air conditioner is generated according to the external environment data, and a comprehensive environment description value of the current environment parameter list is determined;

mapping the comprehensive environment description value to a preset wind speed matching list to obtain an environment mapping value corresponding to the comprehensive environment description value in the wind speed matching list, and determining a first air-conditioning wind power value corresponding to the environment mapping value;

acquiring a cardiac shock electrical signal acquired by a third sensor, determining a health state index of a user based on the cardiac shock electrical signal, and correcting the first air-conditioning air power value according to the health state index to obtain a second air-conditioning air power value;

controlling the intelligent air conditioner to output at the second air conditioner air power value; when the intelligent air conditioner outputs the air power value of the second air conditioner, the external environment data collected by the second sensor and the cardiac shock electric signal collected by the third sensor are continuously obtained, and the output state of the intelligent air conditioner is adjusted according to the continuously obtained external environment data and the cardiac shock electric signal.

In a second aspect, a cloud control center is provided, and is in communication connection with a plurality of sensors, wherein one part of the sensors are integrated in an intelligent air conditioner, and the other part of the sensors are integrated in an intelligent bracelet; the cloud control center is used for:

In a third aspect, a cloud control center is provided, which includes a processor and a memory, which are in communication with each other, and the processor executes a computer program called from the memory to implement the method.

In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when executed, implements the above-described method.

Advantageous effects

The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects.

Firstly, the user identity is verified according to the data to be identified and the intelligent air conditioner is controlled to be started when the verification is passed, secondly, determining a comprehensive environment description value according to the acquired external environment data and determining a corresponding first air conditioner air power value by combining a preset air speed matching list, then, the health state index of the user is determined according to the collected cardiac shock electrical signal of the user, so that the first air-conditioning air power value is corrected according to the health state index to obtain a second air-conditioning air power value, finally, the intelligent air conditioner is controlled to output according to the second air-conditioning air power value and continuously obtain the external environment data collected by the second sensor and the cardiac shock electrical signal collected by the third sensor, therefore, the real-time monitoring of the external environment and the health state of the user can be realized in the running process of the intelligent air conditioner, and the flexible adjustment of the output state of the intelligent air conditioner is realized. So, can realize the user's of intelligent air conditioner identity recognition and realize the automatically regulated to the wind speed of intelligent air conditioner to develop a plurality of functions of intelligent air conditioner in the thing networking aspect, improve the intelligent degree of intelligent air conditioner from this.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.

Fig. 1 is a schematic diagram of a communication architecture of an intelligent device control system based on the internet of things and big data provided by the present disclosure.

Fig. 2 is a flowchart of an intelligent device control method based on the internet of things and big data provided by the present disclosure.

Fig. 3 is a functional module block diagram of an intelligent device apparatus based on the internet of things and big data provided by the present disclosure.

Fig. 4 is a schematic hardware structure diagram of a cloud control center provided by the present disclosure.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

In order to realize the function diversification of the existing intelligent air conditioner on the Internet of things level and improve the Internet intelligentization degree of the intelligent air conditioner, the embodiment of the invention provides an intelligent equipment control method and a cloud control center based on the Internet of things and big data. Referring first to fig. 1, a communication architecture diagram of an internet of things and big data based smart device control system 100 is provided, where the internet of things and big data based smart device control system 100 may include a cloud control center 110 and a plurality of sensors 120.

The cloud control center 110 is in communication connection with the plurality of sensors 120, and at least part of the sensors 120 are integrated inside the intelligent air conditioner. The sensor 120 may be a sensor for acquiring a biometric feature of the user (when the sensor 120 is used for acquiring the biometric feature of the user, the sensor may be integrated on a smart band, and the smart band may be worn on the wrist of the user), or may be a sensor for acquiring external environment data, which is not limited herein. It can be understood that by arranging the cloud control center 110 and the sensor 120, the identity recognition of a user of the intelligent air conditioner and the automatic adjustment of the wind speed and the wind outlet temperature of the intelligent air conditioner can be realized, so that a plurality of functions of the intelligent air conditioner on the internet of things can be developed, and the intelligent degree of the intelligent air conditioner is improved.

On the basis, please refer to fig. 2 in combination, a method for controlling an intelligent device based on the internet of things and big data is provided, and the method may be applied to the cloud control center 110 in fig. 1, where the cloud control center 110 specifically performs the following steps S21 to S25 when implementing the method.

Step S21, acquiring data to be identified collected by a first sensor, performing data extraction on the data to be identified to obtain biological characteristic data used for representing user identity included in the data to be identified, searching whether target data matched with the biological characteristic data exists in a preset database, and controlling the intelligent air conditioner to start if the target data exists in the preset database.

In the present embodiment, the preset database is integrated in the cloud control center 110, and is used for storing preset or entered biometric data. Further, the biometric data corresponding to the data to be recognized can be fingerprint data or voiceprint data collected by the first sensor, the fingerprint data can be collected by the first sensor when the first sensor of the user corresponds to the smart band, and the voiceprint data can be collected by the first sensor when the user inputs voice to the smart band to control the smart air conditioner to be opened.

Step S22, obtaining external environment data collected by a second sensor in parallel after the intelligent air conditioner is started, generating a current environment parameter list corresponding to the intelligent air conditioner according to the external environment data, and determining a comprehensive environment description value of the current environment parameter list.

In a specific implementation, the external environment data may include temperature data, humidity data, illumination data, air mobility data, and the like, which are not limited herein. And the current environment parameter list is used for representing the external environment state corresponding to the intelligent air conditioner.

And step S23, mapping the comprehensive environment description value to a preset wind speed matching list to obtain an environment mapping value corresponding to the comprehensive environment description value in the wind speed matching list, and determining a first air-conditioning wind power value corresponding to the environment mapping value.

In step S23, air-conditioning air power values corresponding to different environment mapping values are recorded in the preset air speed matching list, and the environment mapping values correspond to the air-conditioning air power values one to one.

And step S24, acquiring a cardiac shock electrical signal acquired by a third sensor, determining a health state index of a user based on the cardiac shock electrical signal, and correcting the first air-conditioning air power value according to the health state index to obtain a second air-conditioning air power value.

In this embodiment, the health status coefficient is used to characterize the current health status of the user. It will be appreciated that the third sensor is also integrated in the smart bracelet.

Step S25, controlling the intelligent air conditioner to output at the second air conditioner air power value; when the intelligent air conditioner outputs the air power value of the second air conditioner, the external environment data collected by the second sensor and the cardiac shock electric signal collected by the third sensor are continuously obtained, and the output state of the intelligent air conditioner is adjusted according to the continuously obtained external environment data and the cardiac shock electric signal.

In this embodiment, the adjusting of the output state of the intelligent air conditioner may be a correction or an adjustment of the second air-conditioning air power value, for example, an increase or a decrease of the second air-conditioning air power value. The air-conditioning air power value comprises the temperature of the air-conditioning air and the air speed of the air-conditioning air.

By executing the contents described in the steps S21-S25, firstly, the user identity is verified according to the data to be identified and the intelligent air conditioner is controlled to start when the verification is passed, secondly, the comprehensive environment description value is determined according to the acquired external environment data and the corresponding first air conditioner air power value is determined by combining with the preset air speed matching list, secondly, the health state index of the user is determined according to the collected heart impact electric signal of the user, so that the first air conditioner air power value is corrected according to the health state index to obtain the second air conditioner air power value, and finally, the intelligent air conditioner is controlled to output according to the second air conditioner air power value and continuously acquire the external environment data acquired by the second sensor and the heart impact electric signal acquired by the third sensor, so that the real-time monitoring of the external environment and the health state of the user can be realized in the running process of the, therefore, the output state of the intelligent air conditioner can be flexibly adjusted. So, can realize the user's of intelligent air conditioner identity recognition and realize the automatically regulated to the wind speed of intelligent air conditioner to develop a plurality of functions of intelligent air conditioner in the thing networking aspect, improve the intelligent degree of intelligent air conditioner from this.

In practical applications, the inventor finds that the authentication modes corresponding to different types of biometric data are different, and the biometric data in the same type may be different in different time periods. Therefore, when the target data matched with the biological characteristic data is searched, the two situations need to be considered, otherwise, the search result is inaccurate, and the control accuracy of the intelligent air conditioner is reduced. In order to ensure the accuracy of the search result to improve the control accuracy of the intelligent air conditioner, the data extraction of the data to be identified described in step S21 is performed to obtain the biometric data used for characterizing the identity of the user included in the data to be identified, and whether the target data matching with the biometric data exists is searched in the preset database, which may specifically include the contents described in steps S211 to S213 below.

Step S211, sequentially selecting current data fields from the data to be identified according to the magnitude sequence of the weight of the data fields, and determining a reference data field from a target data field before the weight of the data fields in the data to be identified is greater than the weight of the current data field.

Step S212, acquiring a first feature distribution label of field features in the reference data field; performing biological characteristic category mapping on the current data field according to a mapping path between the first characteristic distribution label and a preset characteristic sequence to obtain a category label of the current data field; extracting category directional data of the category labels to obtain second feature distribution labels of the field features; extracting biological feature data and a biological feature category matched with the biological feature data from the data to be identified based on a similarity coefficient between the first feature distribution label and the second feature distribution label; wherein the biometric categories include a fingerprint category and a voiceprint category.

Step S213, determining a feature data interval corresponding to the biometric data according to the biometric type, and searching whether target data matching the feature data interval exists in the preset database.

In specific implementation, a plurality of interval feature data in the feature data interval may be extracted, then, for each interval feature data in the feature data interval, whether target data matching the interval feature data exists is searched in the preset database, and if one target data exists, it is determined that the biometric data corresponding to the feature data interval passes the user identity authentication.

It can be understood that, when the contents described in steps S211 to S213 are implemented, different types of biometric data corresponding to the data to be identified can be accurately determined, and differences of the different types of biometric data in different time periods are considered, so as to determine the feature data interval. Therefore, the accuracy of the search result aiming at the target data can be ensured according to the characteristic data interval so as to improve the control accuracy of the intelligent air conditioner.

In a specific implementation process, in order to accurately determine the feature data interval to ensure the accuracy of the search result for the target data, the determining, according to the biometric type, the feature data interval corresponding to the biometric data in step S213 may specifically include the following contents described in step S2131 to step S2134.

Step S2131, obtaining category attribute parameters of the biometric category and difference factors corresponding to the biometric category and used for characterizing differences between different time periods of biometric data corresponding to the biometric category, and determining that there are corresponding adjustable factor categories and non-adjustable factor categories for the biometric category based on the category attribute parameters.

Step S2132, calculating conversion coefficients between the difference factors of the biometric category in the non-adjustable factor category and the difference factors of the biometric category in the adjustable factor category based on the difference factors of the biometric category in the adjustable factor category and the correlation weights of the difference factors.

Step S2133, according to the calculated conversion coefficient, transferring a difference factor, in which a conversion coefficient between difference factors of the biometric category in the non-adjustable factor category and in the adjustable factor category is greater than a set coefficient, to the adjustable factor category; wherein, when a plurality of difference factors are included in an unadjustable factor category corresponding to the biometric category, a conversion coefficient between the difference factors of the biometric category in the unadjustable factor category is calculated based on the difference factors of the biometric category in the adjustable factor category and a correlation weight of the difference factors; screening each difference factor under the non-adjustable factor category according to the conversion coefficient among the difference factors; setting a difference coefficient for each target difference factor obtained by screening according to the difference factor of the biological characteristic category under the adjustable factor category and the correlation weight of the difference factor, and transferring at least part of the target difference factors to the adjustable factor category according to the magnitude sequence of the difference coefficients.

Step S2134, listing the difference factors to be processed under the adjustable factor category to construct a difference factor sequence, determining a plurality of difference feature data corresponding to the biological feature data according to the difference factor sequence, and combining the plurality of difference feature data to obtain the feature data interval.

In a specific implementation process, through the contents described in the above steps S2131 to S2134, the characteristic data interval can be accurately determined, so as to ensure the accuracy of the search result for the target data.

In practical applications, the inventor finds that data heterogeneity among different external environment data needs to be considered when determining the comprehensive environment description value, and to achieve this purpose, in step S22, a current environment parameter list corresponding to the smart air conditioner is generated according to the external environment data, and the comprehensive environment description value of the current environment parameter list is determined, which may specifically include the contents described in steps S221 to S225 below.

Step S221, an environment data network between the external environment data is established; the environment data network comprises environment data values extracted from each group of external environment data and data format parameters of the environment data values, the types of the external environment data of each group are different, and each group of external environment data is temperature data, humidity data, illumination data or air fluidity data.

Step S222, node array arrangement is carried out on the environment data network nodes in the environment data network, and a comprehensive environment data value and a data format distribution list of the environment data network nodes are obtained; and determining the node configuration data of the environment data network node according to the comprehensive environment data value and the data format distribution list.

Step S223, sorting the node configuration data according to the size order of the centrality of the environment data network nodes in the environment data network to obtain a configuration data combination, and generating the current environment parameter list according to the configuration data combination.

Step S224, determining data format heterogeneous coefficients and environmental data influence weights between every two list lists in the current environmental parameter list, and constructing a first data set corresponding to the data format heterogeneous coefficients and a second data set corresponding to the environmental data influence weights.

Step S225, determining a heterogeneous evaluation weight of one data format heterogeneous coefficient from the first data set and determining a maximum environmental data influence weight in the second data set as a reference weight in parallel; mapping the heterogeneous evaluation weight to a field bit corresponding to the maximum environmental data influence weight in the second data set to obtain a mapping weight corresponding to the heterogeneous evaluation weight; and determining an incidence relation between the first data set and the second data set based on the mapping weight and the maximum environmental data influence weight, extracting a plurality of list description values in the current environmental parameter list through the incidence relation, and performing weighted summation on the plurality of list description values according to the incidence relation to obtain the comprehensive environmental description value.

In step S225, the multiple list description values are weighted and summed according to the association relationship to obtain the integrated environment description value, which may specifically include the contents described in the following steps: and determining the weighted weight of each list description value according to the incidence relation, and weighting each list description value by adopting the weighted weight of each list description value to obtain the comprehensive environment description value.

In this way, through the above steps S221 to S225, data heterogeneity between different external environment data can be considered when determining the comprehensive environment description value, so that it is ensured that determining the comprehensive environment description value can completely integrate the influence states of different external environment data, thereby ensuring comprehensiveness and reliability of the comprehensive environment description value.

In implementing the above-described embodiment, in order to ensure accuracy and reliability of wind speed adjustment, it is also necessary to take into account the physical state of the user, and for example, if the user is in a fever state, the wind speed may be appropriately increased. In order to achieve the above purpose, it is necessary to accurately determine the health condition of the user, and therefore, in step S24, the health condition index of the user is determined based on the electrical cardiac shock signal, and the second air-conditioning air power value is obtained by correcting the first air-conditioning air power value according to the health condition index, which may specifically include the following descriptions of step S241 to step S243.

And step S241, drawing a ballistocardiogram of the user based on the ballistocardiogram electric signal.

Step S242, determining a global peak-valley value and a local peak-valley value from the ballistocardiogram according to a set time step, and calculating a fluctuation coefficient and map state information of the ballistocardiogram according to the global peak-valley value and the local peak-valley value.

And step S243, determining a value interval where the fluctuation coefficient is located through the graph state information, calculating a health state index of the user according to the value interval, and performing weighted calculation on the first air-conditioning air power value according to a weighting coefficient corresponding to the health state index to obtain a second air-conditioning air power value.

It is understood that when the contents described in the above steps S241 to S243 are applied, the physical state of the user can be taken into consideration, thereby ensuring the accuracy and reliability of the wind speed adjustment.

In a specific embodiment, the determining a value interval where the fluctuation coefficient is located according to the graph state information and calculating the health status index of the user according to the value interval described in step S243 may specifically include the following steps S2431 to S2434.

Step S2431, obtaining a cardioblast state variable from the graph state information, obtaining a pulse frequency of the cardioblast state variable and a first state activity of the cardioblast state variable, and determining a correction coefficient of the fluctuation coefficient according to the first state activity.

Step S2432, in the process of iterating the cardiac shock state variable, acquiring iteration data of the cardiac shock state variable, and acquiring data field distribution information of an iteration trigger identifier of the cardiac shock state variable in the iteration data of the cardiac shock state variable.

Step S2433, acquiring the pulse frequency of the cardioimpact state variable and the second state activity of the cardioimpact state variable by using the data field distribution information of the iterative trigger mark of the cardioimpact state variable in the iterative data of the cardioimpact state variable, and judging whether the second state activity and the correction coefficient of the fluctuation coefficient have a corresponding relation.

Step S2434, if the second state activity level and the correction coefficient of the fluctuation coefficient have the corresponding relationship, correcting the fluctuation coefficient by using the correction coefficient of the fluctuation coefficient to obtain a target fluctuation coefficient, mapping the target fluctuation coefficient to a preset list to obtain a list mapping value corresponding to the target fluctuation coefficient, determining a plurality of dimensional characteristics of the impact map according to a value interval where the list mapping value is located, and calculating the health state index of the user through the plurality of dimensional characteristics.

Therefore, the fluctuation coefficient can be corrected, and the health state index of the user can be accurately calculated.

In an alternative embodiment, in order to improve the real-time performance of the wind speed adjustment, the adjusting of the output state of the smart air conditioner according to the continuously acquired external environment data and the electrical cardiac shock signal described in step S25 may specifically include the following steps S251 to S254.

Step S251, analyzing the running wind speed control thread based on the extracted change data of the external environment data and the signal attenuation rate of the electrical cardiac shock signal, to obtain source code data in the execution parameters included in the wind speed control thread.

And step S252, a second source code set in the wind speed control thread, of which the time slice resource occupancy rate does not increase with the increase of the first source code set with the followability, is extracted as an output state adjustment source code with the modifiable identification by deleting the first source code set with the followability relative to the thread log with the modifiable identification in the source code data.

Step S253, adjusting a first state identifier and a second state identifier of the source code according to the extracted output state, and determining character liveness of a plurality of script characters to be processed for identifying external environment data and a change track of the electrical cardiac shock signal and character consistency weight among different script characters; and screening the script characters based on the determined character liveness of the script characters and the character consistency weight among different script characters, so that the character liveness of the reserved target script characters is greater than a first preset value and the character consistency weight among the reserved target script characters is within a set weight interval.

And step S254, determining a wind speed change curve of a fan control thread of the intelligent air conditioner through the target script characters, and adjusting the wind speed of the intelligent air conditioner based on the wind speed change curve.

In the present embodiment, through the descriptions in the steps S251 to S254, the source code level analysis can be performed on the popular control thread, so that the wind speed can be adjusted in real time and quickly based on the continuously acquired external environment data and the electrical heart shock signal.

It can be understood that the functions corresponding to the contents described in the above methods can be integrated in the cloud control center 110, so that the functions of the existing intelligent air conditioner are expanded, and the intelligent degree of the intelligent air conditioner is improved.

On the basis, please refer to fig. 3 in combination, a functional module block diagram of an intelligent device control apparatus 300 based on the internet of things and big data is provided, where the intelligent device control apparatus 300 specifically includes the following functional modules:

the identity recognition module 310 is configured to acquire to-be-recognized data acquired by a first sensor, perform data extraction on the to-be-recognized data to obtain biometric data used for representing a user identity included in the to-be-recognized data, search whether target data matched with the biometric data exists in a preset database, and control the intelligent air conditioner to start if the target data exists in the preset database;

the environment detection module 320 is configured to concurrently acquire external environment data acquired by a second sensor after the intelligent air conditioner is turned on, generate a current environment parameter list corresponding to the intelligent air conditioner according to the external environment data, and determine a comprehensive environment description value of the current environment parameter list;

a wind speed determining module 330, configured to map the comprehensive environment description value to a preset wind speed matching list, so as to obtain an environment mapping value corresponding to the comprehensive environment description value in the wind speed matching list, and determine a first air-conditioning wind power value corresponding to the environment mapping value;

the wind speed correction module 340 is configured to acquire a cardiac shock electrical signal acquired by a third sensor, determine a health state index of a user based on the cardiac shock electrical signal, and correct the first air-conditioning wind power value according to the health state index to obtain a second air-conditioning wind power value;

the wind speed adjusting module 350 is configured to control the intelligent air conditioner to output the second air conditioner wind power value; when the intelligent air conditioner outputs the air power value of the second air conditioner, the external environment data collected by the second sensor and the cardiac shock electric signal collected by the third sensor are continuously obtained, and the output state of the intelligent air conditioner is adjusted according to the continuously obtained external environment data and the cardiac shock electric signal.

Optionally, the wind speed modification module 340 is specifically configured to:

drawing a ballistocardiogram of the user based on the ballistocardiogram electrical signal;

determining a global peak-valley value and a local peak-valley value from the ballistocardiogram according to a set time step length, and calculating a fluctuation coefficient and image state information of the ballistocardiogram according to the global peak-valley value and the local peak-valley value;

and determining a numerical interval in which the fluctuation coefficient is located according to the graph state information, calculating a health state index of the user according to the numerical interval, and performing weighting calculation on the first air-conditioning air power value according to a weighting coefficient corresponding to the health state index to obtain a second air-conditioning air power value.

Optionally, the wind speed correction module 340 is further configured to:

acquiring a cardioimpact state variable from graph state information, acquiring the pulse frequency of the cardioimpact state variable and the first state activity of the cardioimpact state variable, and determining a correction coefficient of the fluctuation coefficient according to the first state activity;

acquiring iteration data of the cardiac shock state variable in the process of iterating the cardiac shock state variable, and acquiring data field distribution information of an iteration trigger identifier of the cardiac shock state variable in the iteration data of the cardiac shock state variable;

acquiring the pulse frequency of the cardioimpact state variable and the second state activity of the cardioimpact state variable by using the data field distribution information of the iteration trigger identifier of the cardioimpact state variable in the iteration data of the cardioimpact state variable, and judging whether the second state activity and the correction coefficient of the fluctuation coefficient have a corresponding relation;

if the second state activity degree and the correction coefficient of the fluctuation coefficient have the corresponding relation, correcting the fluctuation coefficient by using the correction coefficient of the fluctuation coefficient to obtain a target fluctuation coefficient, mapping the target fluctuation coefficient to a preset list to obtain a list mapping value corresponding to the target fluctuation coefficient, determining a plurality of dimensional characteristics of the ballistocardiogram according to a numerical value interval in which the list mapping value is located, and calculating the health state index of the user through the plurality of dimensional characteristics.

Optionally, the identity module 310 is specifically configured to:

sequentially selecting current data fields from the data to be identified according to the magnitude sequence of the weight of the data fields, and determining a reference data field from a target data field before the weight of the data fields in the data to be identified is greater than that of the current data field;

acquiring a first feature distribution label of field features in the reference data field; performing biological characteristic category mapping on the current data field according to a mapping path between the first characteristic distribution label and a preset characteristic sequence to obtain a category label of the current data field; extracting category directional data of the category labels to obtain second feature distribution labels of the field features; extracting biological feature data and a biological feature category matched with the biological feature data from the data to be identified based on a similarity coefficient between the first feature distribution label and the second feature distribution label; wherein the biometric categories include a fingerprint category and a voiceprint category;

and determining a characteristic data interval corresponding to the biological characteristic data according to the biological characteristic category, and searching whether target data matched with the characteristic data interval exists in the preset database.

Optionally, the identity module 310 is further configured to:

acquiring category attribute parameters of the biological characteristic categories and various difference factors corresponding to the biological characteristic categories and used for representing differences of biological characteristic data corresponding to the biological characteristic categories among different time periods, and determining that the biological characteristic categories have corresponding adjustable factor categories and non-adjustable factor categories based on the category attribute parameters;

calculating conversion coefficients between the difference factors of the biological feature class under the non-adjustable factor class and the difference factors of the biological feature class under the adjustable factor class based on the difference factors of the biological feature class under the adjustable factor class and the correlation weights of the difference factors;

transferring the difference factor of which the conversion coefficient between the difference factors of the biological feature class under the non-adjustable factor class and under the adjustable factor class is larger than a set coefficient to the adjustable factor class according to the calculated conversion coefficient; wherein, when a plurality of difference factors are included in an unadjustable factor category corresponding to the biometric category, a conversion coefficient between the difference factors of the biometric category in the unadjustable factor category is calculated based on the difference factors of the biometric category in the adjustable factor category and a correlation weight of the difference factors; screening each difference factor under the non-adjustable factor category according to the conversion coefficient among the difference factors; setting a difference coefficient for each target difference factor obtained by screening according to the difference factor of the biological characteristic category under the adjustable factor category and the correlation weight of the difference factor, and transferring at least part of target difference factors to the adjustable factor category according to the magnitude sequence of the difference coefficients;

and listing the difference factors to be processed under the adjustable factor category to construct a difference factor sequence, determining a plurality of difference characteristic data corresponding to the biological characteristic data according to the difference factor sequence, and combining the plurality of difference characteristic data to obtain the characteristic data interval.

Optionally, the environment detection module 320 is specifically configured to:

constructing an environment data network between the external environment data; the environment data network comprises environment data values extracted from each group of external environment data and data format parameters of the environment data values, the types of the external environment data of each group are different, and the external environment data of each group are temperature data, humidity data, illumination data or air fluidity data;

carrying out node array arrangement on environment data network nodes in the environment data network to obtain a comprehensive environment data value and a data format distribution list of the environment data network nodes; determining node configuration data of the environment data network node according to the comprehensive environment data value and the data format distribution list;

sequencing the node configuration data according to the centrality sequence of the environment data network nodes in the environment data network to obtain configuration data combinations, and generating the current environment parameter list according to the configuration data combinations;

determining data format heterogeneous coefficients and environmental data influence weights between every two list lists in the current environmental parameter list, and constructing a first data set corresponding to the data format heterogeneous coefficients and a second data set corresponding to the environmental data influence weights;

determining a heterogeneous evaluation weight of one data format heterogeneous coefficient from the first data set and determining a maximum environmental data influence weight in the second data set as a reference weight in parallel; mapping the heterogeneous evaluation weight to a field bit corresponding to the maximum environmental data influence weight in the second data set to obtain a mapping weight corresponding to the heterogeneous evaluation weight; and determining an incidence relation between the first data set and the second data set based on the mapping weight and the maximum environmental data influence weight, extracting a plurality of list description values in the current environmental parameter list through the incidence relation, and performing weighted summation on the plurality of list description values according to the incidence relation to obtain the comprehensive environmental description value.

For the description of the functional modules, please refer to the description of the method shown in fig. 2, which is not described herein again.

Based on the same inventive concept, an intelligent device control system based on the internet of things and big data is further provided, and the detailed description is as follows.

A1. An intelligent equipment control system based on the Internet of things and big data comprises a cloud control center and a plurality of sensors, wherein one part of the sensors are integrated in an intelligent air conditioner, the other part of the sensors are integrated in an intelligent bracelet, and the cloud control center is communicated with each sensor; the cloud control center is used for:

acquiring data to be identified collected by a first sensor, performing data extraction on the data to be identified to obtain biological characteristic data used for representing user identity included in the data to be identified, searching whether target data matched with the biological characteristic data exists in a preset database, and controlling an intelligent air conditioner to be started if the target data exists in the preset database; wherein the first sensor is integrated within the smart bracelet;

the method comprises the steps that external environment data collected by a second sensor are parallelly obtained after the intelligent air conditioner is started, a current environment parameter list corresponding to the intelligent air conditioner is generated according to the external environment data, and a comprehensive environment description value of the current environment parameter list is determined; wherein the second sensor is integrated within the smart air conditioner;

acquiring a cardiac shock electrical signal acquired by a third sensor, determining a health state index of a user based on the cardiac shock electrical signal, and correcting the first air-conditioning air power value according to the health state index to obtain a second air-conditioning air power value; wherein the third sensor is integrated within the smart bracelet;

A2. According to the system described in a1, the determining, by the cloud control center, a health state index of the user based on the electrical cardiac shock signal, and the correcting the first air-conditioning air power value according to the health state index to obtain the second air-conditioning air power value specifically includes:

A3. According to the system described in a2, the determining, by the cloud control center, a value interval in which the fluctuation coefficient is located according to the graph state information and calculating the health state index of the user according to the value interval by the cloud control center includes:

A4. According to the system described in a1, the cloud control center performs data extraction on the data to be recognized to obtain biometric data used for representing user identity included in the data to be recognized, and searching whether target data matched with the biometric data exists in a preset database specifically includes:

A5. According to the system in part a4, the determining, by the cloud control center, the feature data interval corresponding to the biometric data according to the biometric category further includes:

A6. According to the system described in a1, the cloud control center generates a current environmental parameter list corresponding to the smart air conditioner according to the external environmental data, and determining a comprehensive environmental description value of the current environmental parameter list includes:

A7. According to the system described in a6, the obtaining, by the cloud control center, the integrated environment description value by performing weighted summation on the plurality of manifest description values according to the association relationship includes:

determining the weighted weight of each list description value according to the incidence relation;

and weighting each list description value by adopting the weighting weight of each list description value to obtain the comprehensive environment description value.

On the basis of the above, please refer to fig. 4 in combination, a hardware architecture diagram of the cloud control center 110 is further provided, which includes a processor 111 and a memory 112, which are communicated via a bus 113, and the processor 111 reads a computer program from the memory 112 and executes the computer program, so as to implement the method shown in fig. 2. Based on this, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed, implements the method illustrated in fig. 2 above.

The embodiments described above are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the application, but is merely representative of selected embodiments of the application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims. Moreover, all other embodiments that can be made available by a person skilled in the art without making any inventive step based on the embodiments of the present application shall fall within the scope of protection of the present application.

Claims

1. An intelligent device control method based on the Internet of things and big data is characterized by comprising the following steps:

controlling the intelligent air conditioner to output at the second air conditioner air power value; when the intelligent air conditioner outputs the air power value of the second air conditioner, continuously acquiring external environment data acquired by the second sensor and the cardiac shock electric signal acquired by the third sensor, and adjusting the output state of the intelligent air conditioner according to the continuously acquired external environment data and the cardiac shock electric signal;

the method for determining the health state index of the user based on the cardiac shock electrical signal and correcting the first air-conditioning air power value according to the health state index to obtain the second air-conditioning air power value comprises the following steps: drawing a ballistocardiogram of the user based on the ballistocardiogram electrical signal; determining a global peak-valley value and a local peak-valley value from the ballistocardiogram according to a set time step length, and calculating a fluctuation coefficient and image state information of the ballistocardiogram according to the global peak-valley value and the local peak-valley value; determining a value interval where the fluctuation coefficient is located according to the graph state information, calculating a health state index of the user according to the value interval, and performing weighting calculation on the first air-conditioning air power value according to a weighting coefficient corresponding to the health state index to obtain a second air-conditioning air power value;

wherein, determining the value interval where the fluctuation coefficient is located according to the graph state information and calculating the health state index of the user according to the value interval comprises: acquiring a cardioimpact state variable from graph state information, acquiring the pulse frequency of the cardioimpact state variable and the first state activity of the cardioimpact state variable, and determining a correction coefficient of the fluctuation coefficient according to the first state activity; acquiring iteration data of the cardiac shock state variable in the process of iterating the cardiac shock state variable, and acquiring data field distribution information of an iteration trigger identifier of the cardiac shock state variable in the iteration data of the cardiac shock state variable; acquiring the pulse frequency of the cardioimpact state variable and the second state activity of the cardioimpact state variable by using the data field distribution information of the iteration trigger identifier of the cardioimpact state variable in the iteration data of the cardioimpact state variable, and judging whether the second state activity and the correction coefficient of the fluctuation coefficient have a corresponding relation; if the second state activity degree and the correction coefficient of the fluctuation coefficient have the corresponding relation, correcting the fluctuation coefficient by using the correction coefficient of the fluctuation coefficient to obtain a target fluctuation coefficient, mapping the target fluctuation coefficient to a preset list to obtain a list mapping value corresponding to the target fluctuation coefficient, determining a plurality of dimensional characteristics of the ballistocardiogram according to a numerical value interval in which the list mapping value is located, and calculating the health state index of the user through the plurality of dimensional characteristics.

2. The method according to claim 1, wherein the data extraction of the data to be recognized is performed to obtain biometric data used for characterizing the identity of the user, and whether target data matched with the biometric data exists in a preset database is searched, specifically including:

3. The method according to claim 2, wherein a feature data interval corresponding to the biometric data is determined according to the biometric category, and further comprising:

4. The method of claim 1, wherein generating a current environmental parameter list corresponding to the smart air conditioner according to the external environmental data, and determining a comprehensive environmental description value of the current environmental parameter list comprises:

5. The method of claim 4, wherein weighting and summing the manifest description values according to the association to obtain the composite environment description value comprises:

6. The cloud control center is in communication connection with a plurality of sensors, one part of the sensors are integrated in an intelligent air conditioner, and the other part of the sensors are integrated in an intelligent bracelet; the cloud control center is used for:

7. A cloud control center comprising a processor and a memory in communication with each other, the processor implementing the method of any one of claims 1 to 5 by executing a computer program retrieved from the memory.

8. A computer-readable storage medium, on which a computer program is stored which, when executed, implements the method of any of claims 1-5.