CN114237072B - Intelligent household door lock integrated control system and method applying cloud control technology - Google Patents
Intelligent household door lock integrated control system and method applying cloud control technology Download PDFInfo
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Abstract
The invention discloses an intelligent home door lock integrated control system and method applying a cloud control technology, wherein an indoor data acquisition module acquires indoor environment data through an indoor sensor and stores the acquired data in a cloud storage in real time; the indoor data acquisition module is used for calling sensor data acquired by an indoor sensor from cloud storage; the data analysis module analyzes the sensor data called by the indoor data acquisition module to obtain a change curve of the data corresponding to each sensor; the indoor condition prediction module predicts the indoor conditions according to the change curve of each sensor data obtained by the data analysis module; and the household door lock control module automatically controls the household door lock to be opened or closed according to the prediction result of the indoor condition prediction module on the indoor condition.
Description
Technical Field
The invention relates to the technical field of intelligent home, in particular to an intelligent home door lock integrated control system and method applying a cloud control technology.
Background
Along with the improvement of people's standard of living, people are also higher and higher to the requirement of safety at home, and then the intelligence lock is walked into people's the field of vision gradually, and it is more intelligent to a certain extent on the basis that possesses the lock basic function, consequently has brought great facility for people's life.
However, the intelligence is only relative, and the intelligence is expressed in that the unlocking modes of the original door lock are diversified, the relevance with the intelligent home system is not strong, and the defect is large.
Meanwhile, the existing home control system only simply controls or adjusts the indoor environment, and in case of emergency or special conditions, such as fire or thick smoke during cooking, the existing home control system cannot create living conditions beneficial to users, and particularly cannot effectively control the door lock according to the indoor environment, so that when the special conditions occur, the home control system can only provide simple alarm, but cannot predict the indoor environment in advance, and control the door lock in advance according to the prediction result, so that the escape time of people is influenced.
In view of the above situation, an intelligent home door lock integrated control system and method applying a cloud control technology are needed.
Disclosure of Invention
The invention aims to provide an intelligent household door lock integrated control system and method applying a cloud control technology to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: use intelligent house lock integration control system of cloud control technique includes:
the indoor data acquisition module acquires indoor environment data through a sensor arranged indoors and stores the acquired data in cloud storage in real time;
the indoor data acquisition module is used for calling sensor data acquired by an indoor sensor from cloud storage;
the data analysis module analyzes the sensor data called by the indoor data acquisition module to obtain a change curve of the data corresponding to each sensor;
the indoor condition prediction module predicts the indoor conditions according to the change curve of each sensor data obtained by the data analysis module;
and the household door lock control module automatically controls the household door lock to be opened or closed according to the prediction result of the indoor condition prediction module on the indoor condition.
According to the invention, through the cooperative cooperation of all modules, the collection, analysis and prediction of indoor data and the control of the furniture door lock according to the prediction result are realized together, the indoor environment condition can be judged in advance, and the door lock is controlled in advance aiming at recognizing some special conditions (the data of a smoke sensor or a gas sensor exceeds a specified range), so that a user can enter and exit the door corresponding to the door lock quickly, the door opening time of the user is saved, and the safety of the user is ensured; the system is installed in cloud storage to process sensor data in real time, and control over the ending door lock is achieved according to a processing result.
Furthermore, the sensors arranged in the indoor data acquisition module comprise smoke sensors and gas sensors, each smoke sensor or each gas sensor corresponds to a unique number,
the smoke sensor and the gas sensor collect the indoor environment once every first unit time, the first unit time is recorded as t5,
before storing the collected data in the cloud storage, the indoor data collection module binds the data with the sensor number corresponding to the data and the time when the data is collected in advance, and stores the binding result into the cloud storage together, wherein the binding result is marked as (a 1, b1, t 1),
wherein a1 represents data collected by sensors, b1 represents a sensor number corresponding to a1, t1 represents the time of collecting a1, the sensor number is an integer greater than or equal to 1, the maximum number is b2, and the total number of the sensor numbers is b 2;
when the indoor data acquisition module retrieves the sensor data acquired by the indoor sensors from the cloud storage, the second unit time is recorded as t2, and the current time is recorded as t0, based on the binding result corresponding to the data in the second unit time before the current time by each sensor.
When the indoor data acquisition module stores the data acquired by the sensors, the sensor numbers and the data acquisition time are bound together, so that the specific data acquired by each sensor at specific time can be quickly called when the data are analyzed, and the data are prevented from being mixed; the current time is acquired so that the current time is taken as a reference point, the current time changes, and the data called when data analysis is performed also changes (the binding result corresponding to the data in the second unit time before the current time is used as the basis for each sensor).
Further, the method for analyzing the sensor data called by the indoor data acquisition module by the data analysis module comprises the following steps:
s1.1, obtaining binding results corresponding to data in a third unit time before the current time and corresponding to the sensors with the same number, recording the third unit time as t3, wherein t3 is smaller than t2, and recording the third unit time before the current time and corresponding to the sensorsThe time corresponding to the ith data in the bit time is recorded as t3i, and the binding result corresponding to the data acquired by the sensor with the number of b1 at t3i is recorded as (a 1)b1-t3iB1, t3 i), recording the total number of data in the third unit time before the current time corresponding to the sensor as k, wherein i is more than or equal to 1 and less than or equal to k;
s1.2, when i is different values, acquiring binding results corresponding to data acquired by the sensor with the number b1 in the time range corresponding to t0-t 2-t 3i, and recording the binding results corresponding to data acquired by the sensor with the number b1 in the time tm in the time range corresponding to t0-t 2-t 3i as (a 1)b1-tmB1, tm), wherein the time tm is equal to or greater than t0-t2 and equal to or less than t3 i;
s1.3, recording the data acquired by the sensor corresponding to the number b1 in the time range corresponding to t0-t 2-t 3i into a blank set one by one according to the time sequence in the corresponding binding result, and recording the blank set as a set Ab1-i；
S1.4, calculating set Ab1-iThe growth rate corresponding to two adjacent elements in the set Ab1-iThe data corresponding to the i1 th element is recorded as
Will set Ab1-iThe growth rate of the correspondence between the i1 th element and the i1+1 th element is recorded as
Set A ofb1-iThe corresponding number of growth rates in (a) is equal to the total number of elements in the set minus 1,
the above-mentioned
；
And S1.5, obtaining a change curve of the data corresponding to each sensor according to the corresponding increase rate in each set.
The data analysis module acquires the binding result corresponding to each data in the previous third unit time based on the current time corresponding to the sensor with the same number, and the binding result corresponds to each data in the previous third unit time based on the current timeThe reason why t3 is smaller than t2 is that in subsequent analysis, each data in the previous third unit time based on the current time needs to be analyzed respectively, and data corresponding to the sensors with the same number in the time from t0 to t2 to t3i form a set, that is, when the value of i is different (i-th data in the previous third unit time), the obtained sets are different, and further, the change curves corresponding to the sets are different; obtain set Ab1-iThe growth rates corresponding to the two middle-adjacent elements are due to the fact that the difference of data base numbers between the collected data corresponding to different types of sensors may be large (for example, the monitoring return value of the conventional fuel gas sensor is 160-220, and the monitoring range of the conventional fuel gas sensor is larger than 0 and smaller than 100% LEL), and the method is not favorable for analyzing the change condition of the sensor data, so that the growth rates corresponding to the data in the set are obtained first, and the corresponding change curves are obtained through the growth rates, so that the analysis results are relatively accurate.
Further, the method for obtaining the variation curve of the data corresponding to each sensor in S1.5 includes the following steps:
s2.1, when the acquired i is different in value, acquiring a set A corresponding to each data acquired by the sensor with the number b1 in the time range corresponding to t0-t 2-t 3ib1-iAnd the corresponding growth rate in the corresponding set;
s2.2, acquiring time nodes corresponding to each growth rate in each set, wherein the set A isb1-iGrowth rate of the corresponding element between the i1 th element and the i1+1 th element
Corresponding to a time of
A corresponding time;
s2.3, obtaining a set Ab1-iThe corresponding growth rate and the corresponding time in the step (2), and in a plane rectangular coordinate system with the time as an x axis and the growth rate as a y axis,
in the set Ab1-iWherein each growth rate is constructed by using the abscissa as each growth rate and the ordinate as each corresponding timeGrowth rate time varying node, and will set Ab1-iThe growth rate time change nodes constructed in the method are respectively marked in a plane rectangular coordinate system, and linear fitting is carried out according to the marked growth rate time change nodes in the plane rectangular coordinate system to obtain a set Ab1-iA corresponding growth rate time variation curve;
s2.4, according to the set A after fittingb1-iCorresponding to the time change curve of the growth rate, the corresponding a1 of the sensor with the number b1 at t3i is obtainedb1-t3iCorresponding growth rate time curve function fb1-t3i(t), wherein t is more than or equal to t3i from t0 to t 2.
The change curve of the corresponding data in the invention specifically refers to a growth rate time change curve, and the change curve reflects the situation that the growth rate changes along with the change of time; the plane rectangular coordinate system is constructed to facilitate subsequent marking of the growth rate time change nodes, linear fitting is carried out according to the marked growth rate time change nodes, then the growth rate change condition in the growth rate time change curve is obtained, and data basis is provided for subsequent indoor condition prediction.
Further, when linear fitting is performed according to the growth rate time change nodes marked in the plane rectangular coordinate system in S2.3, linear fitting template equations stored in the cloud repository are called, linear fitting is performed on the growth rate time change nodes marked in the plane rectangular coordinate system according to the linear fitting template equations respectively to obtain different linear fitting results, one linear fitting template equation corresponds to one linear fitting result,
respectively calculating the distance between each linear fitting result and each growth rate time change node marked in the plane rectangular coordinate system, calculating the sum of the distances respectively corresponding to each growth rate time change node in the same linear fitting result, recording as the fitting deviation value corresponding to the linear fitting result,
comparing the fitting deviation values corresponding to different linear fitting results, and selecting the linear fitting result with the minimum fitting deviation as a set Ab1-iCorresponding growth rate time variation curve.
When the method is used for linear fitting, different linear fitting templates are adopted to obtain different linear fitting results, and then the screening of the linear fitting results is realized according to the fitting deviation values corresponding to the different linear fitting results.
Further, the method for predicting the indoor situation by the indoor situation prediction module comprises the following steps:
s3.1, a1 at t3i corresponding to sensor No. b1b1-t3iCorresponding growth rate time curve function fb1-t3i(t) calculating a growth rate time-varying curve function fb1-t3i(t) average growth rate Db1-t3i；
S3.2, acquiring data corresponding to the sensor with the current time number b1 and recording the data as a1b1-t0；
S3.3, predicting that i is different, the sensor with the number b1 is based on the data corresponding to the current time after the fourth unit time, wherein the fourth unit time is t4,
average growth rate D corresponding to sensor number b1b1-t3iThe product of the fourth unit time t4 and t5 are divided, and the sum of the quotient and 1 is multiplied by a1b1-t0The product of (1) is the prediction result corresponding to the sensor with the number b1, and the sensor with the number b1 corresponds to the a1 at the time of t3ib1-t3iThe corresponding prediction result is marked as Gb1-t3i，
The above-mentioned
。
The indoor condition prediction module of the invention obtains a time change curve function f of the growth rateb1-t3i(t) average growth rate Db1-t3iIs due to Db1-t3iReflect f isb1-t3i(t) the change in the growth rate of the population,
indicating sensing from the current time to a fourth unit time laterThe number of the data collected by the device is D, and the growth rate corresponding to the data collected each time is Db1-t3iD isb1-t3iAnd
the multiplication results in the predicted total growth rate of the corresponding data of the sensor number b1 after the fourth unit time t4 relative to the current time, and therefore, the total growth rate will be
Multiply by a1b1-t0Then the predicted result G can be obtainedb1-t3i。
Further, a time-varying curve function f of the growth rate is calculated in the step S3.1b1-t3i(t) average growth rate Db1-t3iThe method comprises the following steps:
s4.1, obtaining a time change curve function f of the growth rateb1-t3i(t) corresponding time range, and obtaining a time change curve function f of the growth rate according to the time rangeb1-t3i(t) a corresponding maximum time difference of t3i- (t 0-t 2),
s4.2, obtaining a time change curve function f of the growth rateb1-t3i(t) average growth rate Db1-t3i，
The above-mentioned
。
The invention is due to fb1-t3i(t) corresponds to a time range of t0-t 2. ltoreq. t.ltoreq.t 3i, so fb1-t3i(t) the maximum time difference is t3i- (t 0-t 2); in calculating the average growth rate Db1-t3iIn the time, an integral mode is adopted instead of directly calculating the average value of the growth rate in the growth rate time change nodes marked in the planar rectangular coordinate system, the average value of the growth rate in the growth rate time change nodes marked in the planar rectangular coordinate system is obtained only by the growth rate in the growth rate time change nodes, and the average value of the growth rate in the growth rate time change nodes marked in the planar rectangular coordinate system is directly calculated due to the fact that the number of the nodes is small and the growth rate is not uniform along with the time changeThe prediction result has larger deviation, and further the control of the door lock has errors; simultaneous prediction of fb1-t3i(t) average growth rate corresponding to, rather than according to fb1-t3i(t) directly estimating the corresponding increase in the fourth unit time based on the current time according to the time variation because of fb1-t3i(t) corresponds to the growth rate of a certain data segment in the history data, and the data segment has a certain time difference from the current time, so that the data segment passes through fb1-t3i(t) directly estimating the corresponding increase in the fourth unit time based on the current time, which is due to fb1-t3i(t) the time span between the corresponding historical data segment and the time point t0+ t4 is too large, which results in a larger deviation of the prediction result, and fb1-t3i(t) the corresponding average growth rate reflects a more stable growth trend of the data population in the historical data segment, and in contrast, f is adoptedb1-t3i(t) the accuracy corresponding to the prediction result obtained from the corresponding average growth rate is higher;
due to the fact that
Is combined with
Can obtain
。
Furthermore, the household door lock control module comprises a prediction result analysis module and a door lock switch control module,
when the prediction result analysis module acquires that i is different, the sensor with the number b1 predicts the result G based on the data corresponding to the current time after the fourth unit timeb1-t3iThen, each obtained prediction result is analyzed and processed, and an early warning coefficient corresponding to each prediction result is obtained by combining the standard monitoring threshold corresponding to each sensor in the database,
the standard monitoring threshold corresponding to the sensor with the number b1 is recorded as Wb1,
when i is different, the early warning coefficient corresponding to the sensor with the number b1 is recorded as Rb1-i；
The door lock switch control module respectively and sequentially gives out different i values and different numbers b1 of early warning coefficients R corresponding to the sensors after the fourth unit time based on the current timeb1-iRecording the data into a blank set to obtain a first set, wherein the first set is { R }b1-iB1 is more than or equal to |1 and less than or equal to b2 and i is more than or equal to 1 and less than or equal to k },
the door lock switch control module obtains a maximum value of elements in the first set,
is denoted as { Rb1-iB1 is more than or equal to |1 and less than or equal to b2, and i is more than or equal to 1 and less than or equal to k } max,
the door lock switch control module will { R }b1-iI1 ≦ b1 ≦ b2 and 1 ≦ i ≦ k } max with a first threshold,
when { Rb1-iB1 is more than or equal to |1 and less than or equal to b2, and i is more than or equal to 1 and less than or equal to k } max is more than or equal to a first threshold value, the indoor environment is judged to be poor, larger smoke exists, and the door lock is automatically controlled to be opened;
when { Rb1-iAnd if the |1 is not less than b1 and not more than b2 and the 1 is not less than i and not more than k } max is less than the first threshold value, judging that the indoor environment is normal and keeping the original state of the door lock.
The door lock switch control module in the home door lock control module acquires a first set, and comprehensively analyzes the early warning coefficient results corresponding to all sensors at the same time, wherein the number of the early warning coefficients corresponding to the same sensor at the same time is k; the maximum value of each element in the first set is selected to be compared with the first threshold value, so that the most serious condition in the early warning coefficients corresponding to each prediction result is obtained, the maximum value is compared with the first threshold value, the door lock can be effectively controlled, the control state corresponding to the door lock is judged in advance, and the door lock can be effectively controlled.
Further, the prediction analysis module obtains an early warning coefficient Rb1-iThe method comprises the following steps:
s5.1, when the acquired i is different, the sensor with the number b1 corresponds to data after the fourth unit time based on the current timePredicted result Gb1-t3iAnd the standard monitoring threshold corresponding to the sensor with the number b1 is marked as Wb 1;
s5.2, acquiring a limiting coefficient corresponding to the sensor with the number b1, recording the limiting coefficient as Mb1,
different types of sensors have different standard monitoring thresholds,
different types of sensors have different limiting coefficients,
the standard monitoring threshold values corresponding to the sensors with the same kind, the same model and different numbers are the same,
the limiting coefficients corresponding to the sensors with the same type, the same model and different numbers are the same;
s5.3, obtaining an early warning coefficient Rb1-i，
The above-mentioned
。
When the early warning coefficient is calculated, the standard detection threshold corresponding to the sensor with the number b1 is obtained because the sensor has a maximum threshold in a normal state, the standard detection threshold refers to the maximum threshold, and when the prediction data of the sensor exceeds the maximum threshold, the prediction data of the sensor is abnormal; the limiting coefficient corresponding to the sensor with the number b1 is obtained because different sensors monitor different conditions, and the same limiting coefficient is obtained
The corresponding severity of the indoor situation is different, and therefore, the corresponding limiting coefficient needs to be set
Is adjusted so that different sensors are at
Under the same condition, the severity of the corresponding indoor condition is the same (namely, the early warning coefficient is the same).
An intelligent household door lock integrated control method applying a cloud control technology comprises the following steps:
s1, in the indoor data acquisition module, acquiring indoor environment data through an indoor sensor, and storing the acquired data in a cloud storage in real time;
s2, sensor data collected by the indoor sensor are called from cloud storage through the indoor data acquisition module;
s3, analyzing the sensor data called by the indoor data acquisition module through the data analysis module to obtain a variation curve of the data corresponding to each sensor;
s4, in the indoor situation prediction module, predicting the indoor situation according to the change curve of each sensor data obtained by the data analysis module;
and S5, automatically controlling the opening and closing of the home door lock in the home door lock control module according to the prediction result of the indoor situation prediction module on the indoor situation.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, the indoor environment condition is predicted by monitoring and analyzing the indoor environment, the control state of the door lock is judged in advance according to the prediction result, and the corresponding judgment result is executed, so that the door lock can be effectively controlled to be opened and closed according to the predicted indoor condition, and for some emergency or special conditions, such as fire or thick smoke generated during cooking, the survival condition beneficial to a user can be created in advance, the unlocking time of the user is shortened, meanwhile, the circulation of indoor air is enhanced to a certain extent, and the possibility of danger is reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic structural diagram of an intelligent home door lock integrated control system applying a cloud control technology;
fig. 2 is a schematic flow diagram illustrating a method for analyzing sensor data called by an indoor data acquisition module by a data analysis module in the intelligent home door lock integrated control system applying the cloud control technology according to the present invention;
fig. 3 is a schematic flow chart of a method for obtaining a change curve of data corresponding to each sensor in an intelligent home door lock integrated control system applying a cloud control technology according to the present invention;
fig. 4 is a schematic flow diagram of an intelligent home door lock integrated control method applying a cloud control technology.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of 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 invention.
Referring to fig. 1-4, the present invention provides a technical solution: use intelligent house lock integration control system of cloud control technique includes:
the indoor data acquisition module acquires indoor environment data through a sensor arranged indoors and stores the acquired data in cloud storage in real time;
the indoor data acquisition module is used for calling sensor data acquired by an indoor sensor from cloud storage;
the data analysis module is used for analyzing the sensor data called by the indoor data acquisition module to obtain a change curve of the data corresponding to each sensor;
the indoor condition prediction module predicts the indoor conditions according to the change curves of the sensor data obtained by the data analysis module;
and the household door lock control module automatically controls the household door lock to be opened or closed according to the prediction result of the indoor condition prediction module on the indoor condition.
According to the invention, through the cooperative cooperation of all modules, the collection, analysis and prediction of indoor data and the control of the furniture door lock according to the prediction result are realized together, the indoor environment condition can be judged in advance, and the door lock is controlled in advance aiming at recognizing some special conditions (the data of a smoke sensor or a gas sensor exceeds a specified range), so that a user can enter and exit the door corresponding to the door lock quickly, the door opening time of the user is saved, and the safety of the user is ensured; the system is installed in cloud storage to process sensor data in real time, and control over the ending door lock is achieved according to a processing result.
The sensors arranged in the indoor data acquisition module comprise smoke sensors and gas sensors, each smoke sensor or each gas sensor corresponds to a unique number,
the smoke sensor and the gas sensor collect the indoor environment once every first unit time, the first unit time is recorded as t5,
before storing the collected data in the cloud storage, the indoor data collection module binds the data with the sensor number corresponding to the data and the time when the data is collected in advance, and stores the binding result into the cloud storage together, wherein the binding result is marked as (a 1, b1, t 1),
wherein a1 represents data collected by sensors, b1 represents a sensor number corresponding to a1, t1 represents the time of collecting a1, the sensor number is an integer greater than or equal to 1, the maximum number is b2, and the total number of the sensor numbers is b 2;
when the indoor data acquisition module retrieves the sensor data acquired by the indoor sensors from the cloud storage, the second unit time is recorded as t2, and the current time is recorded as t0, based on the binding result corresponding to the data in the second unit time before the current time by each sensor.
When the indoor data acquisition module stores the data acquired by the sensors, the sensor numbers and the data acquisition time are bound together, so that the specific data acquired by each sensor at specific time can be quickly called when the data are analyzed, and the data are prevented from being mixed; the current time is obtained, in order to use the current time as a reference point, the current time changes, and the called data is also returned to be changed when data analysis is performed (the binding result corresponding to the data in the second unit time before the current time is based on each sensor is called).
The method for analyzing the sensor data called by the indoor data acquisition module by the data analysis module comprises the following steps:
s1.1, obtaining binding results corresponding to data in the third unit time before the current time and corresponding to the sensors with the same number, recording the third unit time as t3, wherein t3 is smaller than t2, recording the time corresponding to the ith data in the third unit time before the current time and corresponding to the sensors as t3i, and recording the binding results corresponding to the data acquired at t3i by the sensors with the number of b1 as (a 1)b1-t3iB1, t3 i), recording the total number of data in the third unit time before the current time corresponding to the sensor as k, wherein i is more than or equal to 1 and less than or equal to k;
s1.2, when i is different values, acquiring binding results corresponding to data acquired by the sensor with the number b1 in the time range corresponding to t0-t 2-t 3i, and recording the binding results corresponding to data acquired by the sensor with the number b1 in the time tm in the time range corresponding to t0-t 2-t 3i as (a 1)b1-tmB1, tm), wherein the time tm is equal to or greater than t0-t2 and equal to or less than t3 i;
s1.3, recording the data acquired by the sensor corresponding to the number b1 in the time range corresponding to t0-t 2-t 3i into a blank set one by one according to the time sequence in the corresponding binding result, and recording the blank set as a set Ab1-i；
S1.4, calculating set Ab1-iThe growth rate corresponding to two adjacent elements in the set Ab1-iTo middleThe data corresponding to i1 elements is recorded as
Will set Ab1-iThe growth rate of the correspondence between the i1 th element and the i1+1 th element is recorded as
Set A ofb1-iThe corresponding number of growth rates in (a) is equal to the total number of elements in the set minus 1,
the above-mentioned
；
And S1.5, obtaining a change curve of the data corresponding to each sensor according to the corresponding increase rate in each set.
In this embodiment, the sensor numbered 001 is a smoke sensor, and if i =1, the data collected by the sensor numbered 001 in the time range corresponding to t0-t2 to t3i are respectively 180, 185, 190, 195,
then set A001-1={180,185,190,196}，
Then
，
The data analysis module acquires the binding results corresponding to the data in the previous third unit time based on the current time corresponding to the sensor with the same number, and limits t3 to be smaller than t2, so that the data in the previous third unit time based on the current time need to be analyzed in the subsequent analysis, and the data corresponding to the sensor with the same number in the time from t0 to t2 to t3i form a set, namely when the value of i is equal to the value of iWhen the data are different (the ith data in the previous third unit time), the obtained sets are different, and further, the change curves corresponding to the sets are different; obtain set Ab1-iThe growth rates corresponding to the two middle-adjacent elements are due to the fact that the difference of data base numbers between the collected data corresponding to different types of sensors may be large (for example, the monitoring return value of the conventional fuel gas sensor is 160-220, and the monitoring range of the conventional fuel gas sensor is larger than 0 and smaller than 100% LEL), and the method is not favorable for analyzing the change condition of the sensor data, so that the growth rates corresponding to the data in the set are obtained first, and the corresponding change curves are obtained through the growth rates, so that the analysis results are relatively accurate.
The method for obtaining the change curve of the data corresponding to each sensor in the S1.5 comprises the following steps:
s2.1, when the acquired i is different in value, acquiring a set A corresponding to each data acquired by the sensor with the number b1 in the time range corresponding to t0-t 2-t 3ib1-iAnd the corresponding growth rate in the corresponding set;
s2.2, acquiring time nodes corresponding to each growth rate in each set, wherein the set A isb1-iGrowth rate of the corresponding element between the i1 th element and the i1+1 th element
Corresponding to a time of
A corresponding time;
s2.3, obtaining a set Ab1-iThe corresponding growth rate and the corresponding time in the step (2), and in a plane rectangular coordinate system with the time as an x axis and the growth rate as a y axis,
in the set Ab1-iWherein each growth rate is an abscissa, and the time corresponding to each growth rate is an ordinate to construct a growth rate time change node, and the set A isb1-iThe growth rate time change nodes constructed in the method are respectively marked in a plane rectangular coordinate system, and linear fitting is carried out according to the marked growth rate time change nodes in the plane rectangular coordinate system to obtainSet Ab1-iA corresponding growth rate time variation curve;
s2.4, according to the set A after fittingb1-iCorresponding to the time change curve of the growth rate, the corresponding a1 of the sensor with the number b1 at t3i is obtainedb1-t3iCorresponding growth rate time curve function fb1-t3i(t), wherein t is more than or equal to t3i from t0 to t 2.
The change curve of the corresponding data in the invention specifically refers to a growth rate time change curve, and the change curve reflects the situation that the growth rate changes along with the change of time; the plane rectangular coordinate system is constructed to facilitate subsequent marking of the growth rate time change nodes, linear fitting is carried out according to the marked growth rate time change nodes, then the growth rate change condition in the growth rate time change curve is obtained, and data basis is provided for subsequent indoor condition prediction.
When linear fitting is performed according to the growth rate time change nodes marked in the rectangular plane coordinate system in S2.3, linear fitting template equations stored in the cloud storage are called, linear fitting is performed on the growth rate time change nodes marked in the rectangular plane coordinate system according to the linear fitting template equations respectively to obtain different linear fitting results, one linear fitting template equation corresponds to one linear fitting result,
respectively calculating the distance between each linear fitting result and each growth rate time change node marked in the plane rectangular coordinate system, calculating the sum of the distances respectively corresponding to each growth rate time change node in the same linear fitting result, recording as the fitting deviation value corresponding to the linear fitting result,
comparing the fitting deviation values corresponding to different linear fitting results, and selecting the linear fitting result with the minimum fitting deviation as a set Ab1-iCorresponding growth rate time curve.
When the method is used for linear fitting, different linear fitting templates are adopted to obtain different linear fitting results, and then the screening of the linear fitting results is realized according to the fitting deviation values corresponding to the different linear fitting results.
The method for predicting the indoor condition by the indoor condition prediction module comprises the following steps:
s3.1, a1 at t3i corresponding to sensor No. b1b1-t3iCorresponding growth rate time curve function fb1-t3i(t) calculating a growth rate time-varying curve function fb1-t3i(t) average growth rate Db1-t3i；
S3.2, acquiring data corresponding to the sensor with the current time number b1 and recording the data as a1b1-t0；
S3.3, predicting that i is different, the sensor with the number b1 is based on the data corresponding to the current time after the fourth unit time, wherein the fourth unit time is t4,
average growth rate D corresponding to sensor number b1b1-t3iThe product of the fourth unit time t4 and t5 are divided, and the sum of the quotient and 1 is multiplied by a1b1-t0The product of (1) is the prediction result corresponding to the sensor with the number b1, and the sensor with the number b1 corresponds to the a1 at the time of t3ib1-t3iThe corresponding prediction result is marked as Gb1-t3i，
The above-mentioned
。
The indoor condition prediction module obtains a growth rate time change curve function fb1-t3i(t) average growth rate Db1-t3iIs due to Db1-t3iReflect f isb1-t3i(t) the change in the growth rate of the population,
the number of data collected by the sensor in the process from the current time to the fourth unit time is represented, and the corresponding growth rate of the data collected each time is Db1-t3iD isb1-t3iAnd
multiply to obtainThe predicted sensor number b1 corresponds to the total growth rate of the data after the fourth unit time t4 with respect to the current time, and thus, will be
Multiply by a1b1-t0Then the predicted result G can be obtainedb1-t3i。
Calculating a time variation curve function f of the growth rate in the S3.1b1-t3i(t) average growth rate Db1-t3iThe method comprises the following steps:
s4.1, obtaining a time change curve function f of the growth rateb1-t3i(t) corresponding time range, and obtaining a time change curve function f of the growth rate according to the time rangeb1-t3i(t) a corresponding maximum time difference of t3i- (t 0-t 2),
s4.2, obtaining a time change curve function f of the growth rateb1-t3i(t) average growth rate Db1-t3i，
The above-mentioned
。
In this embodiment, if the time point corresponding to t0 is the 10 th second, t4=5 seconds, t2=10 seconds, t3=6 seconds, the time point corresponding to t3i is the 7 th second, and a1 is a1b1-t0Equal to 200, the first unit time is 1 second,
and sensor number 002 corresponds to a1 at second 7002-7Corresponding growth rate time curve function f002-7(t) is
，
Since 10-10=0 and t3i =7,
f is then002-7(t) the corresponding time range is that t is more than or equal to 0 and less than or equal to 7,
f is then002-7(t) corresponds to an average growth rate of D002-7，
Sensor number 002 corresponds to a1 at second 7002-7Corresponding to a prediction result of G002-7，
The invention is due to fb1-t3i(t) corresponds to a time range of t0-t 2. ltoreq. t.ltoreq.t 3i, so fb1-t3i(t) the maximum time difference is t3i- (t 0-t 2); in calculating the average growth rate Db1-t3iIn the time, an integral mode is adopted instead of directly calculating the average value of the growth rate in the growth rate time change nodes marked in the planar rectangular coordinate system, because the average value is obtained only by the growth rate in the growth rate time change nodes, because the number of the nodes is small and the growth rate is not uniform along with time, the average value of the growth rate in the growth rate time change nodes marked in the planar rectangular coordinate system is directly calculated, so that a prediction result has larger deviation, and further the control of the door lock has errors; simultaneous prediction of fb1-t3i(t) average growth rate corresponding to, rather than according to fb1-t3i(t) directly estimating the corresponding increase in the fourth unit time based on the current time according to the time variation because of fb1-t3i(t) corresponds to the growth rate of a certain data segment in the history data, and the data segment has a certain time difference from the current time, so that the data segment passes through fb1-t3i(t) directly estimating the corresponding increase in the fourth unit time based on the current time, which is due to fb1-t3i(t) the time span between the corresponding historical data segment and the time point t0+ t4 is too large, which results in a larger deviation of the prediction result, and fb1-t3i(t) the corresponding average growth rate reflects a more stable growth trend of the data population in the historical data segment, and in contrast, f is adoptedb1-t3i(t) the accuracy corresponding to the prediction result obtained from the corresponding average growth rate is higher;
due to the fact that
Is combined with
Can obtain
。
The home door lock control module comprises a prediction result analysis module and a door lock switch control module,
when the prediction result analysis module acquires that i is different, the sensor with the number b1 predicts the result G based on the data corresponding to the current time after the fourth unit timeb1-t3iThen, each obtained prediction result is analyzed and processed, and an early warning coefficient corresponding to each prediction result is obtained by combining the standard monitoring threshold corresponding to each sensor in the database,
the standard monitoring threshold corresponding to the sensor with the number b1 is recorded as Wb1,
when i is different, the early warning coefficient corresponding to the sensor with the number b1 is recorded as Rb1-i；
The door lock switch control module respectively and sequentially gives out different i values and different numbers b1 of early warning coefficients R corresponding to the sensors after the fourth unit time based on the current timeb1-iRecording the data into a blank set to obtain a first set, wherein the first set is { R }b1-iB1 is more than or equal to |1 and less than or equal to b2 and i is more than or equal to 1 and less than or equal to k },
the door lock switch control module obtains a maximum value of the elements in the first set,
is denoted as { Rb1-iB1 is not less than |1 but not more than b2 and i is not less than 1 but not more than k } max,
the door lock switch control module will { R }b1-iI1 ≦ b1 ≦ b2 and 1 ≦ i ≦ k } max with a first threshold,
when { Rb1-iB1 is more than or equal to |1 and less than or equal to b2, and i is more than or equal to 1 and less than or equal to k } max is more than or equal to a first threshold value, the indoor environment is judged to be poor, larger smoke exists, and the door lock is automatically controlled to be opened;
when { Rb1-iAnd if the |1 is not less than b1 and not more than b2 and the 1 is not less than i and not more than k } max is less than the first threshold value, judging that the indoor environment is normal and keeping the original state of the door lock.
The door lock switch control module in the home door lock control module acquires a first set, and comprehensively analyzes the early warning coefficient results corresponding to all sensors at the same time, wherein the number of the early warning coefficients corresponding to the same sensor at the same time is k; the maximum value of each element in the first set is selected to be compared with the first threshold value, so that the most serious condition in the early warning coefficients corresponding to each prediction result is obtained, the maximum value is compared with the first threshold value, the door lock can be effectively controlled, the control state corresponding to the door lock is judged in advance, and the door lock can be effectively controlled.
The prediction analysis module obtains an early warning coefficient Rb1-iThe method comprises the following steps:
s5.1, when the obtained i is different, the sensor with the number b1 predicts the result G based on the corresponding data after the fourth unit time of the current timeb1-t3iAnd the standard monitoring threshold corresponding to the sensor with the number b1 is marked as Wb 1;
s5.2, acquiring a limiting coefficient corresponding to the sensor with the number b1, recording the limiting coefficient as Mb1,
different types of sensors have different standard monitoring thresholds,
different types of sensors have different limiting coefficients,
the standard monitoring threshold values corresponding to the sensors with the same kind, the same model and different numbers are the same,
the limiting coefficients corresponding to the sensors with the same type, the same model and different numbers are the same;
s5.3, obtaining an early warning coefficient Rb1-i，
The above-mentioned
。
When the early warning coefficient is calculated, the standard detection threshold corresponding to the sensor with the number b1 is obtained because the sensor is detectingA maximum threshold value exists in a normal state, the standard detection threshold value refers to the maximum threshold value, and when the predicted data of the sensor exceeds the maximum threshold value, the predicted data of the sensor is abnormal; the limiting coefficient corresponding to the sensor with the number b1 is obtained because different sensors monitor different conditions, and the same limiting coefficient is obtained
The corresponding severity of the indoor situation is different, and therefore, the corresponding limiting coefficient needs to be set
Is adjusted so that different sensors are at
Under the same condition, the severity of the corresponding indoor condition is the same (namely, the early warning coefficient is the same).
An intelligent home door lock integrated control method applying a cloud control technology comprises the following steps:
s1, in the indoor data acquisition module, acquiring indoor environment data through an indoor sensor, and storing the acquired data in a cloud storage in real time;
s2, calling sensor data collected by the indoor sensor from cloud storage through the indoor data acquisition module;
s3, analyzing the sensor data called by the indoor data acquisition module through the data analysis module to obtain a variation curve of the data corresponding to each sensor;
s4, in the indoor situation prediction module, predicting the indoor situation according to the change curve of each sensor data obtained by the data analysis module;
and S5, automatically controlling the opening and closing of the home door lock in the home door lock control module according to the prediction result of the indoor situation prediction module on the indoor situation.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. Use intelligent house lock integration control system of cloud control technique, its characterized in that includes:
the indoor data acquisition module acquires indoor environment data through a sensor arranged indoors and stores the acquired data in cloud storage in real time;
the indoor data acquisition module is used for calling sensor data acquired by an indoor sensor from cloud storage;
the data analysis module analyzes the sensor data called by the indoor data acquisition module to obtain a change curve of the data corresponding to each sensor;
the indoor condition prediction module predicts the indoor conditions according to the change curves of the sensor data obtained by the data analysis module;
the home door lock control module automatically controls the home door lock to be opened or closed according to the prediction result of the indoor situation prediction module on the indoor situation;
the sensors arranged in the indoor data acquisition module comprise smoke sensors and gas sensors, each smoke sensor or each gas sensor corresponds to a unique number,
the smoke sensor and the gas sensor collect the indoor environment once every first unit time, the first unit time is recorded as t5,
before storing the collected data in the cloud storage, the indoor data collection module binds the data with the sensor number corresponding to the data and the time when the data is collected in advance, and stores the binding result into the cloud storage together, wherein the binding result is marked as (a 1, b1, t 1),
wherein a1 represents data collected by sensors, b1 represents a sensor number corresponding to a1, t1 represents the time of collecting a1, the sensor number is an integer greater than or equal to 1, the maximum number is b2, and the total number of the sensor numbers is b 2;
when the indoor data acquisition module retrieves sensor data acquired by indoor sensors from cloud storage, the second unit time is recorded as t2, and the current time is recorded as t0, based on the binding result corresponding to the data in the second unit time before the current time of each sensor;
the method for analyzing the sensor data called by the indoor data acquisition module by the data analysis module comprises the following steps:
s1.1, obtaining binding results corresponding to data in the third unit time before the current time and corresponding to the sensors with the same number, recording the third unit time as t3, wherein t3 is smaller than t2, recording the time corresponding to the ith data in the third unit time before the current time and corresponding to the sensors as t3i, and recording the binding results corresponding to the data acquired at t3i by the sensors with the number of b1 as (a 1)b1-t3iB1, t3 i), recording the total number of data in the third unit time before the current time corresponding to the sensor as k, wherein i is more than or equal to 1 and less than or equal to ik；
S1.2, when i is different values, acquiring binding results corresponding to data acquired by the sensor with the number b1 in the time range corresponding to t0-t 2-t 3i, and recording the binding results corresponding to data acquired by the sensor with the number b1 in the time tm in the time range corresponding to t0-t 2-t 3i as (a 1)b1-tmB1, tm), wherein the time tm is equal to or greater than t0-t2 and equal to or less than t3 i;
s1.3, recording the data acquired by the sensor corresponding to the number b1 in the time range corresponding to t0-t 2-t 3i into a blank set one by one according to the time sequence in the corresponding binding result, and recording the blank set as a set Ab1-i；
S1.4, calculating set Ab1-iThe growth rate corresponding to two adjacent elements in the set Ab1-iThe data corresponding to the i1 th element is recorded as
Will set Ab1-iThe growth rate of the correspondence between the i1 th element and the i1+1 th element is recorded as
Set Ab1-iThe corresponding number of growth rates in (a) is equal to the total number of elements in the set minus 1,
the above-mentioned
；
And S1.5, obtaining a change curve of the data corresponding to each sensor according to the corresponding increase rate in each set.
2. The intelligent home door lock integrated control system applying the cloud control technology according to claim 1, characterized in that: the method for obtaining the change curve of the data corresponding to each sensor in the S1.5 comprises the following steps:
s2.1, when the acquired i is different values, acquiring a set corresponding to each data acquired by the sensor with the number b1 in the time range corresponding to t0-t 2-t 3iAnd a isb1-iAnd the corresponding growth rate in the corresponding set;
s2.2, acquiring time nodes corresponding to each growth rate in each set, wherein the set A isb1-iGrowth rate of the corresponding element between the i1 th element and the i1+1 th element
Corresponding to a time of
A corresponding time;
s2.3, obtaining a set Ab1-iThe corresponding growth rate and the corresponding time in the step (2), and in a plane rectangular coordinate system with the time as an x axis and the growth rate as a y axis,
in the set Ab1-iWherein each growth rate is an abscissa, and the time corresponding to each growth rate is an ordinate, constructing a growth rate time change node, and integrating the set Ab1-iThe growth rate time change nodes constructed in the method are respectively marked in a plane rectangular coordinate system, and linear fitting is carried out according to the marked growth rate time change nodes in the plane rectangular coordinate system to obtain a set Ab1-iA corresponding growth rate time variation curve;
s2.4, according to the set A after fittingb1-iCorresponding to the time change curve of the growth rate, the corresponding a1 of the sensor with the number b1 at t3i is obtainedb1-t3iCorresponding growth rate time curve function fb1-t3i(t), wherein t is more than or equal to t3i from t0 to t 2.
3. The intelligent home door lock integrated control system applying the cloud control technology according to claim 2, characterized in that: when linear fitting is performed according to the growth rate time change nodes marked in the rectangular plane coordinate system in S2.3, linear fitting template equations stored in the cloud storage are called, linear fitting is performed on the growth rate time change nodes marked in the rectangular plane coordinate system according to the linear fitting template equations respectively to obtain different linear fitting results, one linear fitting template equation corresponds to one linear fitting result,
respectively calculating the distance between each linear fitting result and each growth rate time change node marked in the plane rectangular coordinate system, calculating the sum of the distances respectively corresponding to each growth rate time change node in the same linear fitting result, recording as the fitting deviation value corresponding to the linear fitting result,
comparing the fitting deviation values corresponding to different linear fitting results, and selecting the linear fitting result with the minimum fitting deviation as a set Ab1-iCorresponding growth rate time variation curve.
4. The intelligent home door lock integrated control system applying the cloud control technology according to claim 2, characterized in that: the method for predicting the indoor condition by the indoor condition prediction module comprises the following steps:
s3.1, a1 at t3i corresponding to sensor No. b1b1-t3iCorresponding growth rate time curve function fb1-t3i(t) calculating a growth rate time-varying curve function fb1-t3i(t) average growth rate Db1-t3i；
S3.2, acquiring data corresponding to the sensor with the current time number b1 and recording the data as a1b1-t0；
S3.3, predicting that i is different, the sensor with the number b1 is based on the data corresponding to the current time after the fourth unit time, wherein the fourth unit time is t4,
average growth rate D for sensor number b1b1-t3iThe product of the fourth unit time t4 and t5 are divided, and the sum of the quotient and 1 is multiplied by a1b1-t0The product of (1) is the prediction result corresponding to the sensor with the number b1, and the sensor with the number b1 corresponds to the a1 at the time of t3ib1-t3iThe corresponding prediction result is marked as Gb1-t3i，
The above-mentioned
。
5. According to claimClaim 4 the intelligent house lock integration control system who uses cloud control technique, its characterized in that: calculating a time variation curve function f of the growth rate in the S3.1b1-t3i(t) average growth rate Db1-t3iThe method comprises the following steps:
s4.1, obtaining a time change curve function f of the growth rateb1-t3i(t) corresponding time range, and obtaining a time change curve function f of the growth rate according to the time rangeb1-t3i(t) a corresponding maximum time difference of t3i- (t 0-t 2),
s4.2, obtaining a time change curve function f of the growth rateb1-t3i(t) average growth rate Db1-t3i，
The above-mentioned
。
6. The intelligent home door lock integrated control system applying the cloud control technology according to claim 5, characterized in that: the home door lock control module comprises a prediction result analysis module and a door lock switch control module,
when the prediction result analysis module acquires that i is different, the sensor with the number b1 predicts the result G based on the data corresponding to the current time after the fourth unit timeb1-t3iThen, each obtained prediction result is analyzed and processed, and an early warning coefficient corresponding to each prediction result is obtained by combining the standard monitoring threshold corresponding to each sensor in the database,
the standard monitoring threshold corresponding to the sensor with the number b1 is recorded as Wb1,
when i is different, the early warning coefficient corresponding to the sensor with the number b1 is recorded as Rb1-i；
The door lock switch control module respectively gives out different i values and different numbers b1 of the early warning coefficients R corresponding to the sensors after the fourth unit time based on the current timeb1-iRecording the data into a blank set to obtain a first set, wherein the first set is { R }b1-iB1 is more than or equal to |1 and less than or equal to b2 and i is more than or equal to 1 and less than or equal to k },
the door lock switch control module obtains a maximum value of the elements in the first set,
is denoted as { Rb1-iB1 is not less than |1 but not more than b2 and i is not less than 1 but not more than k } max,
the door lock switch control module will { R }b1-iI1 ≦ b1 ≦ b2 and 1 ≦ i ≦ k } max with a first threshold,
when { Rb1-iB1 is more than or equal to |1 and less than or equal to b2, and i is more than or equal to 1 and less than or equal to k } max is more than or equal to a first threshold value, the indoor environment is judged to be poor, larger smoke exists, and the door lock is automatically controlled to be opened;
when { Rb1-iAnd if the |1 is not less than b1 and not more than b2 and the 1 is not less than i and not more than k } max is less than the first threshold value, judging that the indoor environment is normal and keeping the original state of the door lock.
7. The intelligent home door lock integrated control system applying the cloud control technology according to claim 6, characterized in that: the prediction analysis module obtains an early warning coefficient Rb1-iThe method comprises the following steps:
s5.1, when the obtained i is different, the sensor with the number b1 predicts the result G based on the corresponding data after the fourth unit time of the current timeb1-t3iAnd the standard monitoring threshold corresponding to the sensor with the number b1 is marked as Wb 1;
s5.2, acquiring a limiting coefficient corresponding to the sensor with the number b1, recording the limiting coefficient as Mb1,
different types of sensors have different standard monitoring thresholds,
different types of sensors have different limiting coefficients,
the standard monitoring threshold values corresponding to the sensors with the same kind, the same model and different numbers are the same,
the limiting coefficients corresponding to the sensors with the same type, the same model and different numbers are the same;
s5.3, obtaining an early warning coefficient Rb1-i，
The above-mentioned
。
8. The intelligent home door lock integrated control method of the intelligent home door lock integrated control system applying the cloud control technology according to any one of claims 1 to 7, is characterized in that: the method comprises the following steps:
s1, in the indoor data acquisition module, acquiring indoor environment data through an indoor sensor, and storing the acquired data in a cloud storage in real time;
s2, sensor data collected by the indoor sensor are called from cloud storage through the indoor data acquisition module;
s3, analyzing the sensor data called by the indoor data acquisition module through the data analysis module to obtain a variation curve of the data corresponding to each sensor;
s4, in the indoor situation prediction module, predicting the indoor situation according to the change curve of each sensor data obtained by the data analysis module;
and S5, automatically controlling the opening and closing of the home door lock in the home door lock control module according to the prediction result of the indoor situation prediction module on the indoor situation.
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