CN114205393B - Data reporting method and system of intelligent home system - Google Patents
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Abstract
The invention discloses a data reporting method and a data reporting system of an intelligent home system, which comprise the following steps: step S1, the system conducts incremental sensing on the sleep gesture characterization data of the user collected on the surface of the mattress, and adjusts the sampling frequency of the sleep gesture characterization data based on the sensing result of the incremental sensing; step S2, the system performs incremental compression on the sleep posture characterization data obtained according to the sampling frequency, and obtains the reporting frequency of the sleep posture characterization data based on the compression result of the incremental compression; and step S3, the system reports the sleep posture characterization data based on the reporting frequency. The invention carries out self-adaptive adjustment on the acquisition frequency to ensure that the acquisition frequency with high stability is maintained at a low sampling frequency so as to reduce the acquisition data volume and the occupation of data acquisition and transmission bandwidth in the sleep stage with high stability, and then carries out incremental compression on the sleep gesture representation data so as to realize the simplification of the reported data volume and improve the data reporting efficiency.
Description
Technical Field
The invention relates to the technical field of intelligent home, in particular to a data reporting method and system of an intelligent home system.
Background
The smart home technology is a technical means for automatically adjusting the working states of various smart home devices (such as audio devices, lighting systems, curtain control systems, and the like) by connecting various smart home devices together through the internet of things. The automatic adjustment function of the intelligent home technology greatly facilitates the life of users, so that the technology is widely applied to the life of people.
The prior art cn202010102656.x discloses a data information processing method, a storage medium and an electronic device of an intelligent home system, which are used for acquiring and storing data information of the intelligent home system of a user; counting data information of the intelligent home system of the user in a preset period from the stored data information of the intelligent home system; obtaining a grading result of the preset period of the intelligent home system of the user according to the data information of the intelligent home system of the user in the preset period and the weight corresponding to the data information, wherein the grading result of the preset period represents the activity degree of the intelligent home system of the user in the preset period; and generating a data report of the preset period of the intelligent home system of the user according to the data information and the grading result of the intelligent home system of the user in the preset period, and sending the data report to the user terminal for the user to check so that the user can master the use condition of the intelligent home equipment through the data report.
Although the prior art can realize systematic and intelligent management of home devices, undifferentiated reporting is selected when data is reported, a long-time stable state can occur in a home environment, and at the moment, undifferentiated reporting also can cause high redundancy of home environment data and huge transmission pressure on network bandwidth.
Disclosure of Invention
The invention aims to provide a data reporting method and a data reporting system of an intelligent home system, which aim to solve the technical problems that in the prior art, undifferentiated reporting is selected when data are reported, a long-time stable state can occur in a home environment, at the moment, undifferentiated reporting can cause high redundancy of home environment data, and huge transmission pressure can be caused to network bandwidth.
In order to solve the technical problems, the invention specifically provides the following technical scheme:
a data reporting method of an intelligent home system comprises the following steps:
step S1, the system carries out increment perception on the sleep posture characterization data of the user collected on the surface of the mattress, and adjusts the sampling frequency of the sleep posture characterization data based on the perception result of the increment perception, wherein the increment perception represents that the stability characteristic of the sleep posture characterization data is obtained so that the system can perceive the stability of the sleep stage of the user to plan the sampling frequency;
step S2, the system performs incremental compression on the sleep posture characterization data obtained according to the sampling frequency, and obtains the reporting frequency of the sleep posture characterization data based on the compression result of the incremental compression, wherein the incremental compression represents that the repeatability characteristics of the sleep posture characterization data are obtained so that the system can remove the repeatability data to update the reporting frequency;
and step S3, the system reports the sleep posture characterization data based on the reporting frequency so as to realize the reduction of the reported data quantity and improve the data reporting efficiency.
As a preferred aspect of the present invention, the system performs incremental sensing on the sleep posture characterization data of the user collected on the surface of the mattress, and includes:
setting an increment weight for measuring the stability of the sleep posture characterization data, and carrying out similarity detection on the sleep posture characterization data at the current moment and the sleep posture characterization data at the previous moment, wherein,
if the similarity is larger than or equal to the similarity threshold, performing self-addition processing on the increment weight of the previous moment to obtain the increment weight of the current moment, wherein the calculation formula of the self-addition processing is as follows:
in the formula (I), the compound is shown in the specification,
、
respectively representing the incremental weight of the current moment and the incremental weight of the previous moment;
if the similarity is smaller than the similarity threshold, performing self-subtraction processing on the increment weight of the previous moment to obtain the increment weight of the current moment, wherein a calculation formula of the self-subtraction processing is as follows:
in the formula (I), the compound is shown in the specification,
、
respectively representing the incremental weight of the current moment and the incremental weight of the previous moment;
the calculation formula of the similarity is as follows:
in the formula (I), the compound is shown in the specification,
the characterization is the similarity between the sleeping posture characterization data at the current moment and the sleeping posture characterization data at the previous moment,
、
respectively representing the data of the sleeping posture at the current moment and the data of the sleeping posture at the previous moment, wherein t is a time sequence metering constant.
As a preferred aspect of the present invention, the adjusting the sampling frequency of the sleep posture representing data based on the sensing result of the incremental sensing includes:
setting a base sampling frequency
Incremental weighting based on current time using a logistic function
Adjusting the sampling frequency of the sleep gesture representation data at the current moment, wherein,
if it is
Then adjust the sampling frequency of the current time to
The self-adaptive adjustment of the acquisition frequency of the sleep posture representation data according to the stability is realized, so that the acquisition frequency with high stability is maintained at a low sampling frequency;
if it is
Then adjust the sampling frequency of the current time to
And the self-adaptive adjustment of the acquisition frequency of the sleep posture representation data according to the stability is realized, so that the acquisition frequency with low stability is maintained on the basic sampling frequency.
As a preferred aspect of the present invention, the system performs incremental compression on the sleep posture characterization data obtained according to the sampling frequency, and includes:
setting a compression starting point and a compression end point, and calibrating the compression starting point to a group of sleep posture characterization data to be obtained by the system according to sampling frequency
Start data of
The compression end point is set in the group of sleeping posture representation data
End point data of
At least one of (1) and (b);
sequentially carrying out a group of sleep posture characterization data along the sequence from the compression starting point to the compression end point
Carries out similarity detection on adjacent items, and carries out a group of sleep posture characterization data according to the similarity
The compressed update of (1), wherein,
if the similarity of the adjacent items is larger than or equal to the similarity threshold, removing the latter item in the adjacent items;
if the similarity of the adjacent items is smaller than the similarity threshold, both the front item and the rear item in the adjacent items are reserved;
compressing and updating a set of sleep posture characterization data
As a reported data set;
the similarity calculation formula of the adjacent terms is as follows:
in the formula (I), the compound is shown in the specification,
characterised by adjacent terms
And
the degree of similarity of (a) to (b),
、
respectively characterized by a former sleeping posture characterization data and a latter sleeping posture characterization data in adjacent items,
and is
I and j are metering constants without substantial meaning, and n is represented as the total number of sleep posture characterization data.
As a preferred aspect of the present invention, the obtaining of the reporting frequency of the sleep posture characterization data based on the compression result of the incremental compression includes:
sequentially extracting sampling periods of all sleep posture characterization data in a reported data set, and taking a frequency conversion value of a sampling moment as a reporting frequency according to a conversion function of the periods and the frequency, wherein the conversion function of the reporting frequency and the sampling moment is as follows:
in the formula (I), the compound is shown in the specification,
characterized in that the reporting frequency is a frequency of reporting,
characterized by the sampling period.
As a preferred scheme of the present invention, the system reports the frequency according to the reporting frequency
Reporting all the sleep posture characterization data in the reported data set one by one to keep the orderliness of the sleep posture characterization data.
As a preferable scheme of the present invention, before the similarity detection is performed on the sleep posture characterization data, normalization processing is performed to remove data dimensions.
As a preferred scheme of the present invention, the sampling frequency refers to the number of collected sleep posture characterization data in unit time, and the reporting frequency refers to the number of uploaded sleep posture characterization data in unit time.
As a preferred scheme of the present invention, the present invention provides a reporting system according to the data reporting method of the smart home system, including:
the incremental sensing module is used for incrementally sensing the sleep posture characterization data of the user collected on the surface of the mattress and adjusting the sampling frequency of the sleep posture characterization data based on the sensing result of the incremental sensing, wherein the incremental sensing is represented by acquiring the stability characteristics of the sleep posture characterization data so that a system can sense the stability of the sleep stage of the user to plan the sampling frequency;
the incremental compression module is used for carrying out incremental compression on the sleep posture characterization data obtained according to the sampling frequency, and obtaining the reporting frequency of the sleep posture characterization data based on the compression result of the incremental compression, wherein the incremental compression represents that the repeatability characteristics of the sleep posture characterization data are obtained so that a system can eliminate the repeatability to update the reporting frequency;
and the data reporting module is used for reporting the sleep gesture representation data based on the reporting frequency so as to realize the reduction of the reported data volume and improve the data reporting efficiency.
As a preferred scheme of the present invention, the increment sensing module, the increment compression module and the data reporting module are all integrated with a communication module, and the communication module is configured to implement data transmission of the increment sensing module, the increment compression module and the data reporting module.
Compared with the prior art, the invention has the following beneficial effects:
the sleep posture characterization data is subjected to incremental sensing to adjust the sampling frequency of the sleep posture characterization data, the acquisition frequency is subjected to adaptive adjustment to enable the acquisition frequency with high stability to be maintained at a low sampling frequency so as to reduce the acquisition data volume and the occupation of data acquisition and transmission bandwidth in a sleep stage with high stability, and then the sleep posture characterization data is subjected to incremental compression to determine the reporting frequency of the sleep posture characterization data so as to realize the simplification of the reported data volume and improve the data reporting efficiency.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
Fig. 1 is a flowchart of a data reporting method according to an embodiment of the present invention;
fig. 2 is a structural block diagram of a reporting system according to an embodiment of the present invention.
The reference numerals in the drawings denote the following, respectively:
1-incremental sensing module; 2-an incremental compression module; and 3, a data reporting module.
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.
As shown in fig. 1, the invention provides a data reporting method for an intelligent home system, which comprises the following steps:
the sleep stages of humans can be divided into 5 different stages: during sleep-in period, light sleep period, deep sleep period, and rapid eye movement period, an indispensable data information, namely, user's sleeping posture, exists when mattress user experience data is collected, and the user's sleeping posture can be used to analyze the use parameters of the mattress such as the support and wrapping performance of the mattress to the user, and further used as reference data for further improving the mattress, so that data representing the user's sleeping posture needs to be collected to obtain sleeping posture representation data in the embodiment, such as pressure formed by various sleeping postures of the user felt by the mattress, occupied area of various sleeping postures of the user on the mattress, and the like; when the sleep posture characterization data is collected, due to the regularity of sleep, the user can keep the same sleep posture in a stage, the sleep posture characterization data of the user in the stable stage can also show high similarity, so that the data sampling frequency in the stable stage can be properly reduced according to the stability degree, a series of high-similarity sleep posture characterization data can be prevented from being collected, the collection equipment is prevented from running at high frequency, and the data redundancy is reduced, therefore, the embodiment provides a method for adjusting the sampling frequency according to the sleep stage stability, which specifically comprises the following steps:
step S1, the system carries out increment perception on the sleep posture characterization data of the user collected on the surface of the mattress, and adjusts the sampling frequency of the sleep posture characterization data based on the perception result of the increment perception, and the increment perception indicates that the stability characteristic of the sleep posture characterization data is obtained to enable the system to perceive the stability of the sleep stage of the user to plan the sampling frequency;
the system carries out increment perception on the user sleeping posture characterization data collected on the surface of the mattress, and the increment perception comprises the following steps:
setting an increment weight for measuring the stability of the sleep posture characterization data, and carrying out similarity detection on the sleep posture characterization data at the current moment and the sleep posture characterization data at the previous moment, wherein,
if the similarity is greater than or equal to the similarity threshold, performing self-addition processing on the increment weight of the previous moment to obtain the increment weight of the current moment, wherein the calculation formula of the self-addition processing is as follows:
in the formula (I), the compound is shown in the specification,
、
respectively representing the incremental weight of the current moment and the incremental weight of the previous moment;
the increment weight of the current moment is established on the basis of the increment weight of the previous moment, so that the similarity of the sleep posture representation data of the current moment and the previous moment is high, and the sleep stage stability of the user is improved, so that the increment weight of the current moment is increased, and the high increment weight corresponds to high stability.
If the similarity is smaller than the similarity threshold, self-subtraction processing is carried out on the increment weight of the previous moment to obtain the increment weight of the current moment, and the calculation formula of the self-subtraction processing is as follows:
in the formula (I), the compound is shown in the specification,
、
respectively representing the incremental weight of the current moment and the incremental weight of the previous moment;
the increment weight of the current moment is based on the increment weight of the previous moment, so that the similarity of the sleep posture representation data of the current moment and the previous moment is low, which indicates that the stability of the sleep stage of the user is reduced, and therefore, the increment weight of the current moment is reduced, and the low increment weight corresponds to low stability.
The similarity is calculated by the following formula:
in the formula (I), the compound is shown in the specification,
the characterization is the similarity between the sleeping posture characterization data at the current moment and the sleeping posture characterization data at the previous moment,
、
respectively representing the data of the sleeping posture at the current moment and the data of the sleeping posture at the previous moment, wherein t is a time sequence metering constant.
Adjusting the sampling frequency of the sleep posture characterization data based on the perception result of the incremental perception, comprising the following steps:
setting a base sampling frequency
Incremental weighting based on current time using a logistic function
Adjusting the sampling frequency of the sleep posture representation data at the current moment, wherein,
if the number of the first time interval and the second time interval is less than the preset threshold,
the sampling frequency at the current moment is adjusted to
The self-adaptive adjustment of the acquisition frequency of the sleep posture representation data according to the stability is realized, so that the acquisition frequency with high stability is maintained at a low sampling frequency;
if it is
Then adjust the sampling frequency of the current time to
And the self-adaptive adjustment of the acquisition frequency of the sleep posture representation data according to the stability is realized, so that the acquisition frequency with low stability is maintained on the basic sampling frequency.
When the sleep enters a stable stage, the sleep posture representation data of the user also presents high similarity, namely the similarity between the sleep posture representation data at the current moment and the sleep posture representation data at the previous moment is high, so that the data sampling frequency of the stable stage can be properly reduced according to the stability degree, namely the incremental weight of the current moment is increased, for example, the sleep posture representation data is increased
The data with high similarity indicate that the sleep of the user is possibly in a stable stage at the moment, the sampling frequency after the 2 nd moment is reduced on the basis of the 1 st moment, and the data are collected at the 3 rd moment
Comparing the similarity with the similarity
The higher the same, the higher the degree of the user in the stable stage of sleep, so the sampling frequency after the 3 rd time is reduced on the basis of the 2 nd timeLow, the more similar data quantity, the more the sampling frequency is reduced, thus avoiding the acquisition of a series of highly similar sleep posture characterization data, and the acquisition equipment also avoiding high-frequency operation, reducing data redundancy, while the logistic function is an increasing function, thus being constructed as an adjusting function of the sampling frequency
The method can realize that under the condition of high increment weight, the low sampling frequency is obtained, namely the sampling frequency is reduced in a stable stage, the high-frequency acquisition of a series of data with high similarity is reduced, the data redundancy is avoided, and under the condition of low increment weight, the sampling frequency is uniformly adjusted to be the basic sampling frequency, namely when a stable stage is not maintained, the basic sampling frequency is uniformly adopted for sampling.
Step S2, the system performs incremental compression on the sleep posture characterization data obtained according to the sampling frequency, obtains the reporting frequency of the sleep posture characterization data based on the compression result of the incremental compression, and the incremental compression represents that the repeatability characteristics of the sleep posture characterization data are obtained so that the system can remove the repeatability data to update the reporting frequency;
the method has the advantages that the screening reduction of high-similarity data volume to a certain degree is realized after the adjustment of the sampling frequency is carried out, but a certain degree of data redundancy exists, because the sampling frequency cannot be controlled to just acquire only one sleeping posture representation data in a stable stage, the data is most simplified, and therefore a group of sleeping posture representation data acquired according to the acquisition frequency is compressed and simplified, and the method specifically comprises the following steps:
the system carries out incremental compression on the sleep posture characterization data obtained according to the sampling frequency, and comprises the following steps:
setting a compression starting point and a compression end point, and calibrating the compression starting point to a group of sleep posture characterization data to be obtained by the system according to sampling frequency
Start data of
The compression end point is set in a group of sleeping posture representation data
End point data of
At least one of (1) and (b);
sequentially carrying out a group of sleep posture characterization data along the sequence from the compression starting point to the compression end point
Carries out similarity detection on adjacent items, and carries out a group of sleep posture characterization data according to the similarity
The compressed update of (1), wherein,
if the similarity of the adjacent items is larger than or equal to the similarity threshold, removing the latter item in the adjacent items;
if the similarity of the adjacent items is smaller than the similarity threshold, both the front item and the rear item in the adjacent items are reserved;
compressing and updating a set of sleep posture characterization data
As a reported data set;
the similarity calculation formula of the adjacent terms is as follows:
in the formula (I), the compound is shown in the specification,
characterised by adjacent terms
And
the degree of similarity of (a) to (b),
、
respectively characterized by a former sleeping posture characterization data and a latter sleeping posture characterization data in adjacent items,
and is provided with
I and j are metering constants without substantial meaning, and n is represented as the total number of sleep posture representation data.
The present embodiment provides an example of incremental compression, such as: a group of sleep posture characterization data
，
And
first, as a compression start point and a compression end point, respectively, are calculated
And
the similarity of (1), if
And
is greater than or equal to the similarity threshold, then
Removing and calculating
And
the similarity of (1), if
And
is less than the similarity threshold, then will
Reserving, and obtaining a group of sleep posture characterization data after compression and update by analogy in turn
As a reported data set.
And step S3, the system reports the sleep posture characterization data based on the reporting frequency so as to realize the reduction of the reported data quantity and improve the data reporting efficiency.
Obtaining the reporting frequency of the sleep posture characterization data based on the compression result of the incremental compression, and the method comprises the following steps:
sequentially extracting sampling periods of all sleep posture characterization data in the reported data set, taking a frequency conversion value of a sampling moment as a reporting frequency according to a conversion function of the periods and the frequency, wherein the conversion function of the reporting frequency and the sampling moment is as follows:
in the formula (I), the compound is shown in the specification,
characterized in that the reporting frequency is a frequency of reporting,
characterized by the sampling period.
This embodiment provides an example of reporting frequency settings, from
The sampling period is obtained and the sampling period is,
with a sampling period in between
At the sampling time and
is obtained from the sampling time difference of (2), thereby obtaining the difference according to the conversion function
The reporting frequency of (2) is obtained by analogy in turn
The reporting frequency in between.
The system is based on the reporting frequency
Reporting all the sleep posture characterization data in the reported data set one by one to keep the orderliness of the sleep posture characterization data.
Before similarity detection is carried out on the sleep posture characterization data, normalization processing is carried out to remove data dimensions.
The sampling frequency refers to the number of collected sleep posture characterization data in unit time, and the reporting frequency refers to the number of uploaded sleep posture characterization data in unit time.
As shown in fig. 2, the data reporting method based on the smart home system of the present invention provides a reporting system, including:
the incremental sensing module 1 is used for incrementally sensing the sleep posture characterization data of the user collected on the surface of the mattress, adjusting the sampling frequency of the sleep posture characterization data based on the sensing result of the incremental sensing, and the incremental sensing means that the stability characteristic of the sleep posture characterization data is obtained so that the system can sense the stability of the sleep stage of the user to plan the sampling frequency;
the incremental compression module 2 is used for performing incremental compression on the sleep posture characterization data obtained according to the sampling frequency, obtaining the reporting frequency of the sleep posture characterization data based on the compression result of the incremental compression, and performing the incremental compression to show that the repeatability characteristics of the sleep posture characterization data are obtained so that the system can remove the repeatability data to update the reporting frequency;
and the data reporting module 3 is configured to report the sleep posture characterization data based on the reporting frequency, so as to simplify the reported data amount and improve the data reporting efficiency.
The increment sensing module, the increment compression module and the data reporting module are all integrated with communication modules, and the communication modules are used for realizing data transmission of the increment sensing module, the increment compression module and the data reporting module.
The sleep posture characterization data is subjected to incremental sensing to adjust the sampling frequency of the sleep posture characterization data, the acquisition frequency is subjected to adaptive adjustment to enable the acquisition frequency with high stability to be maintained at a low sampling frequency, so that the acquisition data volume and the occupation of data acquisition and transmission bandwidth in a sleep stage with high stability are reduced, and then the report frequency of the sleep posture characterization data is determined by performing incremental compression on the sleep posture characterization data, so that the report data volume is simplified to improve the data report efficiency.
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present application and such modifications and equivalents should also be considered to be within the scope of the present application.
Claims (8)
1. A data reporting method of an intelligent home system is characterized by comprising the following steps:
step S1, the system carries out increment perception on the sleep posture characterization data of the user collected on the surface of the mattress, and adjusts the sampling frequency of the sleep posture characterization data based on the perception result of the increment perception, wherein the increment perception represents that the stability characteristic of the sleep posture characterization data is obtained so that the system can perceive the stability of the sleep stage of the user to plan the sampling frequency;
step S2, the system performs incremental compression on the sleep posture characterization data obtained according to the sampling frequency, and obtains the reporting frequency of the sleep posture characterization data based on the compression result of the incremental compression, wherein the incremental compression represents that the repeatability characteristics of the sleep posture characterization data are obtained so that the system can remove the repeatability data to update the reporting frequency;
step S3, the system reports the sleep posture characterization data based on the reporting frequency so as to realize the reduction of the reported data quantity and improve the data reporting efficiency;
the system carries out increment perception on user sleeping posture characterization data collected on the surface of the mattress, and comprises the following steps:
setting an increment weight for measuring the stability of the sleep posture characterization data, and carrying out similarity detection on the sleep posture characterization data at the current moment and the sleep posture characterization data at the previous moment, wherein,
if the similarity is larger than or equal to the similarity threshold, performing self-addition processing on the increment weight of the previous moment to obtain the increment weight of the current moment, wherein the calculation formula of the self-addition processing is as follows:
in the formula (I), the compound is shown in the specification,
、
respectively representing the incremental weight of the current moment and the incremental weight of the previous moment;
if the similarity is smaller than the similarity threshold, performing self-subtraction processing on the increment weight of the previous moment to obtain the increment weight of the current moment, wherein a calculation formula of the self-subtraction processing is as follows:
in the formula (I), the compound is shown in the specification,
、
respectively representing the incremental weight of the current moment and the incremental weight of the previous moment;
the calculation formula of the similarity is as follows:
in the formula (I), the compound is shown in the specification,
the characterization is the similarity between the sleeping posture characterization data at the current moment and the sleeping posture characterization data at the previous moment,
、
respectively representing the data as the sleeping posture representation data of the current moment and the sleeping posture representation data of the previous moment, wherein t is a time sequence metering constant;
the adjusting the sampling frequency of the sleep posture representation data based on the perception result of the incremental perception comprises the following steps:
setting a base sampling frequency
Incremental weighting based on current time using a logistic function
Adjusting the sampling frequency of the sleep gesture representation data at the current moment, wherein,
if it is
Then adjust the sampling frequency of the current time to
The self-adaptive adjustment of the acquisition frequency of the sleep posture representation data according to the stability is realized, so that the acquisition frequency with high stability is maintained at a low sampling frequency;
if it is
Then adjust the sampling frequency of the current time to
And the self-adaptive adjustment of the acquisition frequency of the sleep posture representation data according to the stability is realized, so that the acquisition frequency with low stability is maintained on the basic sampling frequency.
2. The data reporting method of the smart home system according to claim 1, wherein: the system carries out incremental compression on the sleep posture characterization data obtained according to the sampling frequency, and comprises the following steps:
setting a compression starting point and a compression end point, and calibrating the compression starting point to a group of sleep posture characterization data to be obtained by the system according to sampling frequency
Start data of (2)
The compression end point is set in the group of sleeping posture representation data
End point data of
At least one of (1) and (b);
sequentially carrying out a group of sleep posture characterization data along the sequence from the compression starting point to the compression end point
Carries out similarity detection on adjacent items, and carries out a group of sleep posture characterization data according to the similarity
The compressed update of (1), wherein,
if the similarity of the adjacent items is larger than or equal to the similarity threshold, removing the latter item in the adjacent items;
if the similarity of the adjacent items is smaller than the similarity threshold, both the front item and the rear item in the adjacent items are reserved;
compressing and updating a set of sleep posture characterization data
As a reported data set;
the similarity calculation formula of the adjacent terms is as follows:
in the formula (I), the compound is shown in the specification,
characterised by adjacent terms
And
the degree of similarity of (a) to (b),
、
respectively characterized by the former sleeping posture characterization data and the latter sleeping posture characterization data in the adjacent items,
and is
I and j are metering constants without substantial meaning, and n is represented as the total number of sleep posture characterization data.
3. The data reporting method of the smart home system according to claim 2, wherein: the obtaining of the reporting frequency of the sleep posture characterization data based on the compression result of the incremental compression comprises:
sequentially extracting sampling periods of all sleep posture characterization data in a reported data set, and taking a frequency conversion value of a sampling moment as a reporting frequency according to a conversion function of the periods and the frequency, wherein the conversion function of the reporting frequency and the sampling moment is as follows:
in the formula (I), the compound is shown in the specification,
characterized in that the reporting frequency is a frequency of reporting,
characterized by the sampling period.
4. The data reporting method of the smart home system according to claim 3, characterized in that: the system reports the frequency according to the report frequency
Reporting all the sleep posture characterization data in the reported data set one by one to keep the orderliness of the sleep posture characterization data.
5. The data reporting method of the smart home system according to claim 4, wherein the sleep posture characterization data is normalized to remove data dimension before similarity detection.
6. The data reporting method of the smart home system according to claim 5, wherein the sampling frequency is the number of collected sleep posture characterization data in unit time, and the reporting frequency is the number of uploaded sleep posture characterization data in unit time.
7. A reporting system for realizing the data reporting method of the smart home system according to any one of claims 1 to 6, comprising:
the incremental sensing module (1) is used for incrementally sensing the sleep posture characterization data of the user collected on the surface of the mattress and adjusting the sampling frequency of the sleep posture characterization data based on the sensing result of the incremental sensing, wherein the incremental sensing is represented by acquiring the stability characteristics of the sleep posture characterization data so that the system can sense the stability of the sleep stage of the user to plan the sampling frequency;
the incremental compression module (2) is used for carrying out incremental compression on the sleep posture characterization data obtained according to the sampling frequency, and obtaining the reporting frequency of the sleep posture characterization data based on the compression result of the incremental compression, wherein the incremental compression represents that the repetitive characteristics of the sleep posture characterization data are obtained so that a system can remove the repetitive data to update the reporting frequency;
and the data reporting module (3) is used for reporting the sleep gesture representation data based on the reporting frequency so as to realize the reduction of the reported data volume and improve the data reporting efficiency.
8. The reporting system of claim 7, wherein the increment sensing module (1), the increment compression module (2) and the data reporting module (3) are all integrated with a communication module, and the communication module is configured to implement data transmission of the increment sensing module, the increment compression module and the data reporting module.
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