CN117692012A - Remote monitoring and transmitting method for temperature data of intelligent sleeping bag - Google Patents
Remote monitoring and transmitting method for temperature data of intelligent sleeping bag Download PDFInfo
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Abstract
The invention relates to the technical field of data compression, in particular to an intelligent sleeping bag temperature data remote monitoring and transmitting method. The method comprises the steps of obtaining original noise degree of target temperature data in a temperature sequence; obtaining the similarity between a local preset window and a neighborhood similarity window of target temperature data; the original noise is adjusted by using the similarity and the environmental temperature data to obtain the corrected noise degree; according to the difference between the target temperature data and the temperature data in the searching preset window and the correction noise degree, the data to be smoothed is screened from the target temperature data, and smoothing is carried out to obtain a smooth temperature sequence; and compressing and storing temperature data in the smooth temperature sequence by using run-length coding. According to the intelligent sleeping bag temperature data compression method, noise data are smoothed before the intelligent sleeping bag temperature data are transmitted, compression transmission is carried out, the runlength of the runlength process of the temperature data is increased, and the compression transmission efficiency of the temperature data of the intelligent sleeping bag is improved.
Description
Technical Field
The invention relates to the technical field of data compression, in particular to an intelligent sleeping bag temperature data remote monitoring and transmitting method.
Background
One of the design goals of smart sleeping bags is to provide a better user experience for the user, and its temperature data is typically used to analyze aspects of the user's sleep pattern, sleeping bag performance, etc., so it is necessary to transmit the temperature data of the smart sleeping bag. Because the temperature in the intelligent sleeping bag has local stability, the run-length code is generally selected to compress and transmit the temperature data of the intelligent sleeping bag; however, the temperature data may contain noise, which affects the length of the run in the process of the run, reduces the compression efficiency of the temperature data of the intelligent sleeping bag, and further affects the transmission efficiency of the temperature data.
Disclosure of Invention
In order to solve the technical problems that the temperature data of the intelligent sleeping bag contain noise to influence the length of a run in the encoding process and reduce the compression transmission efficiency of the temperature data, the invention aims to provide a remote monitoring transmission method for the temperature data of the intelligent sleeping bag, and the adopted technical scheme is as follows:
the invention provides a remote monitoring and transmitting method for temperature data of an intelligent sleeping bag, which comprises the following steps:
acquiring a temperature sequence corresponding to the intelligent sleeping bag in a historical time period;
screening target temperature data from the temperature sequence; for any one target temperature data, acquiring the original noise degree of the target temperature data according to the fluctuation difference between the temperature data in a local preset window of the target temperature data and the environmental temperature data at the corresponding moment;
According to the discrete degree of temperature data in a local preset window of the temperature data in the search preset window of the target temperature data, screening a neighborhood similar window of the target temperature data from the local preset window; obtaining the similarity between the local preset window of the target temperature data and the temperature data in each neighborhood similar window according to the difference between the local preset window of the target temperature data and the variation degree of the temperature data in the neighborhood similar window and the dispersion degree of the temperature data in the neighborhood similar window;
the original noise degree is adjusted by combining the difference of the environmental temperature data at the moment corresponding to the temperature data in the local preset window of the target temperature data and the neighborhood similar window and the difference of the similarity, and the difference of the environmental temperature data at the moment corresponding to the temperature data between the neighborhood similar windows and the difference of the similarity, so that the corrected noise degree of the target temperature data is obtained;
according to the difference between the target temperature data and the temperature data in the searching preset window and the correction noise degree, the data to be smoothed is screened out from the target temperature data; updating each piece of data to be smoothed by utilizing temperature data in a searching preset window of each piece of data to be smoothed to obtain a smoothed temperature sequence;
And compressing and storing temperature data in the smooth temperature sequence by using run-length coding.
Further, the method for screening target temperature data from the temperature sequence comprises the following steps:
selecting any one temperature data except the first temperature data in the temperature sequence as analysis temperature data, and taking the analysis temperature data as target temperature data if the analysis temperature data are not equal to the adjacent temperature data;
and traversing each temperature data in the temperature sequence to obtain target temperature data in the temperature sequence.
Further, the calculation formula of the original noise degree of the target temperature data is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein Z is the original noise degree of the target temperature data; />The method comprises the steps of presetting the r-th temperature data in a window for the part of target temperature data; />The (1) th temperature data in a local preset window of the target temperature data; />Environmental temperature data at the moment corresponding to the r-th temperature data in a local preset window of the target temperature data; />The method comprises the steps that (1) environmental temperature data at the moment corresponding to the (r+1) th temperature data in a local preset window of target temperature data are obtained; r is the total number of temperature data in a local preset window of the target data; />As a function of absolute value.
Further, the method for screening the neighborhood similar window of the target temperature data from the local preset window according to the discrete degree of the temperature data in the local preset window of the temperature data in the search preset window of the target temperature data comprises the following steps:
taking a local preset window of the rest temperature data except the target temperature data in the search preset window of the target temperature data as a neighborhood window of the target temperature data;
taking any one neighborhood window and a local preset window of target temperature data as windows to be tested, selecting any one window to be tested as an analysis window, taking the average value of temperature data in the analysis window as a uniform temperature value, and taking the accumulated sum of the absolute value of the difference value of each temperature data in the analysis window and the uniform temperature value as a temperature discrete value;
taking the inverse of the sum of the temperature discrete value and the second preset positive number as the stability of temperature data in an analysis window;
and if the absolute value of the difference between the local preset window of the target temperature data and the stability of each neighborhood window is smaller than a preset stability threshold, taking the neighborhood window as a neighborhood similar window of the target temperature data.
Further, a calculation formula of the similarity between the local preset window of the target temperature data and the temperature data in each neighborhood similarity window is as follows:
The method comprises the steps of carrying out a first treatment on the surface of the In (1) the->For the target temperature numberSimilarity between the temperature data in the local preset window and the kth neighborhood similar window; />The (1) th temperature data in the kth neighborhood similarity window of the target temperature data; />The (1) th temperature data in the kth neighborhood similarity window of the target temperature data is the (1) th temperature data; />The method comprises the steps of presetting the r-th temperature data in a window for the part of target temperature data; />The (1) th temperature data in a local preset window of the target temperature data; r is the total number of temperature data in a local preset window of the target data; />The total number of temperature data in the kth neighborhood similarity window of the target temperature data; />The stability of the kth neighborhood similarity window for the target temperature data; a is a first preset positive number; />As a function of absolute value.
Further, the calculation formula of the corrected noise degree of the target temperature data is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->The noise degree is corrected for the target temperature data; z is the original noise level of the target temperature data; />The similarity between the kth neighborhood similar window and the (k+1) th neighborhood similar window of the target temperature data; />The similarity between a local preset window of the target temperature data and a kth neighborhood similarity window of the target temperature data is preset; / >The average value of the environmental temperature data at the moment corresponding to the temperature data in the local preset window of the target temperature data is obtained; />The average value of the environmental temperature data at the moment corresponding to the temperature data in the kth neighborhood similar window of the target temperature data; />The average value of the environmental temperature data at the moment corresponding to the temperature data in the k+1th neighborhood similar window of the target temperature data; k is the total number of neighborhood similar windows of the target temperature data; a is a first preset positive number; />As a function of absolute value; norms are normalization functions.
Further, the method for screening the data to be smoothed from the target temperature data comprises the following steps:
in a local preset window of the target temperature data, taking the temperature data before the corresponding moment of the target temperature data as front adjacent data and taking all the temperature data after the corresponding moment of the target temperature data as rear adjacent data;
taking the temperature data before the corresponding moment of the target temperature data as the searching temperature data of the target temperature data in a searching preset window of the target temperature data;
combining the difference between the front adjacent data and the rear adjacent data of the target temperature data, the difference between the searched temperature data of the target temperature data and the correction noise degree to obtain the smoothness possibility of the target temperature data;
And if the corrected noise degree of the target temperature data is larger than a preset abnormal threshold value and the smoothing probability degree is larger than a preset smoothing threshold value, taking the target temperature data as the data to be smoothed.
Further, the calculation formula of the smoothing probability of the target temperature data is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein P is the smoothing probability of the target temperature data; />The noise degree is corrected for the target temperature data; a is the neighborhood temperature difference of target temperature data; />J1 st previous neighbor data that is target temperature data; j1 is the total number of pre-proximity data of the target temperature data;the j2 nd post-proximity data that is the target temperature data; j2 is the total number of post-vicinity data for the target temperature data; t is the total number of search temperature data of the target temperature data; x is target temperature data; />Searching temperature data for the t-th of the target temperature data; a is a first preset positive number; />As a function of absolute value; norms are normalization functions.
Further, the method for acquiring the smoothed temperature sequence comprises the following steps:
for each piece of data to be smoothed, taking the rest temperature data except the data to be smoothed in a searching preset window of the data to be smoothed as data to be tested, and taking the data to be tested with equal values as the data to be tested of the same type;
Taking the time interval between the data to be smoothed and the corresponding time of each piece of data to be smoothed as the time sequence difference of each piece of data to be smoothed; taking the ratio of the total number of the to-be-measured data of the type to which each to-be-measured data belongs to the number of the temperature data in the preset search window as the occurrence frequency of each to-be-measured data;
taking the product of the reciprocal of the time sequence difference and the occurrence frequency of each piece of data to be smoothed as a screening coefficient of each piece of data to be smoothed;
taking the data to be detected corresponding to the largest screening coefficient as updated temperature data of the data to be smoothed;
and updating each piece of data to be smoothed in the temperature sequence into corresponding updated temperature data, and taking the updated temperature sequence as a smoothed temperature sequence.
Further, the second preset positive number is 0.1.
The invention has the following beneficial effects:
in the embodiment of the invention, in order to reduce the calculated amount, target temperature data, namely suspected noise data, is screened out from a temperature sequence; the temperature of the intelligent sleeping bag can change along with the change of the ambient temperature, if the temperature data is inconsistent with the fluctuation of the ambient temperature, the difference between the target temperature data and the ambient temperature data is caused by noise, and the original noise degree of the target temperature data is obtained according to the characteristics; the neighborhood similar window of the target temperature data is similar to the temperature data distribution in the preset local window thereof, the difference of the change degree of the local preset window of the target temperature data and the temperature data in the neighborhood similar window reflects the change consistency degree of the temperature data in the two windows, the dispersion degree of the temperature data in the neighborhood similar window shows the reliability of the change consistency degree, and the two factors of the difference between the change degree of the temperature data in the local preset window of the target temperature data and the temperature data in the neighborhood similar window thereof and the dispersion degree of the temperature data in the neighborhood similar window are combined for analysis, so that the higher the accuracy of the similarity of the local preset window of the target temperature data and the neighborhood similar window is; the similarity of the temperature data in the local preset window and the neighborhood similar window of the target temperature data reflects the possibility that the neighborhood similar window represents noise data to the temperature data in the local preset window, the similarity between different local preset windows of the target temperature data can improve the accuracy of the noise data represented by the target temperature data, meanwhile, the influence of environmental temperature change on the reliability of the temperature data in the window is considered, the original noise degree of the target temperature data is corrected by the factors, and the obtained corrected noise degree represents more accurate noise degree; screening the data to be smoothed from the target temperature data, and updating the data to be smoothed to obtain a smoothed temperature sequence; the smooth temperature sequence is a denoised sequence, the temperature data in the smooth temperature sequence is compressed and stored by using run length coding, the run length is improved, the run coding efficiency is improved at the same time under the condition of no noise influence, and the compression and transmission of the sleeping bag temperature data are facilitated.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for remotely monitoring and transmitting temperature data of an intelligent sleeping bag according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of a specific implementation, structure, characteristics and effects of an intelligent sleeping bag temperature data remote monitoring transmission method according to the invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a specific scheme of an intelligent sleeping bag temperature data remote monitoring and transmitting method, which is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for remotely monitoring and transmitting temperature data of an intelligent sleeping bag according to an embodiment of the invention is shown, where the method includes:
step S1: acquiring a temperature sequence corresponding to the intelligent sleeping bag in a historical time period;
specifically, through the intelligent temperature measurement hardware built in the intelligent sleeping bag, the temperature of the intelligent sleeping bag can be monitored through a mobile phone, the indoor thermometer is used for monitoring the ambient temperature in a room where the intelligent sleeping bag is placed, the temperature data and the ambient temperature data of each moment of the intelligent sleeping bag in a historical time period are obtained, and the temperature data of each moment in the historical time period are arranged according to a time sequence order to obtain a temperature sequence; the elements in the temperature sequence are temperature data. The temperature of the intelligent sleeping bag monitored by the mobile phone is temperature data, and the temperature measured by the indoor thermometer is ambient temperature data.
In the embodiment of the invention, the time length of the historical time period takes the empirical value of 8 hours, the data acquisition frequency is 1 minute once, and the implementer can set the data acquisition frequency according to specific conditions.
Step S2: screening target temperature data from the temperature sequence; and for any one target temperature data, acquiring the original noise degree of the target temperature data according to the fluctuation difference between the temperature data in a local preset window of the target temperature data and the environmental temperature data at the corresponding moment.
Specifically, the temperature in the intelligent sleeping bag has local stability, namely the temperature data of a plurality of parts exist in the temperature sequence are equal, and in order to improve transmission efficiency, the application adopts run length coding to carry out coding compression on the temperature data in the temperature sequence. If noise exists in the temperature data acquisition process, the temperature data influenced by the noise can influence the run-length coding length so as to reduce coding efficiency. Therefore, it is necessary to identify temperature data affected by noise among the temperature data and smooth these temperature data to reduce the influence of noise on the encoding efficiency before encoding the temperature data.
As an example, if the temperature sequence isWhen the temperature data in the temperature sequence is encoded to the fourth continuous 20 by using the run-length encoding, if the next data 21 is affected by noise, the 5 th data in the temperature sequence is 20 without the noise, and the temperature data in the temperature sequence can be encoded to 7 20 by using the run-length encoding; however, because of the noise data 21, the temperature data in the temperature sequence is actually encoded into 4 pieces 20, 1 piece 21, 2 pieces 20, and the noise data affects the run length, reducing the encoding efficiency.
The run-length encoding replaces the continuous same data value with a single value and the repeated times to reduce the data quantity, and accordingly whether the temperature data in the temperature sequence is identical with the adjacent temperature data or not is judged to be the target temperature data which is suspected noise data.
Preferably, the specific acquisition method of the target temperature data is as follows: selecting any one temperature data except the first temperature data in the temperature sequence as analysis temperature data, and taking the analysis temperature data as target temperature data if the analysis temperature data are not equal to the adjacent temperature data; and traversing each temperature data in the temperature sequence to obtain target temperature data in the temperature sequence. And selecting any one target temperature data for subsequent calculation.
It should be noted that, in the embodiment of the present invention, the temperature data at the first time in the set historical time period is not noise data, that is, the first temperature data in the temperature sequence is not target temperature data; if temperature data is analyzedFor the last temperature data in the temperature sequence, < +.>Adjacent temperature data of +.>Is +.>The method comprises the steps of carrying out a first treatment on the surface of the If temperature data is analyzedAnalyzing the temperature data for any one of the temperature data except the first and last temperature data in the temperature sequence Comprises: />Is +.>And the next following temperature data->。
The temperature of the intelligent sleeping bag can change along with the change of the ambient temperature, if the temperature data is inconsistent with the fluctuation of the ambient temperature, the difference between the target temperature data and the ambient temperature data is caused by noise, and the intelligent sleeping bag belongs to abnormal temperature change; according to the fluctuation difference between the temperature data in the local preset window of the target temperature data and the environmental temperature data at the corresponding moment, the original noise degree of the target temperature data is obtained, and the calculation formula of the original noise degree is as follows:
wherein Z is the original noise degree of the target temperature data;the method comprises the steps of presetting the r-th temperature data in a window for the part of target temperature data; />The (1) th temperature data in a local preset window of the target temperature data; />Environmental temperature data at the moment corresponding to the r-th temperature data in a local preset window of the target temperature data; />The method comprises the steps that (1) environmental temperature data at the moment corresponding to the (r+1) th temperature data in a local preset window of target temperature data are obtained; r is the total quantity of temperature data in a local preset window of target data, and a checked value 5 is taken; />As a function of absolute value.
In the embodiment of the invention, the size of the local preset window of each temperature data in the temperature sequence takes an empirical valueAnd the temperature data is positioned at the center of the local preset window, so that the total quantity R of the temperature data in the local preset window of the target temperature data is equal to 5, and an implementer can set the temperature data according to specific conditions.
When (when)The larger the difference between the (r) th and (r+1) th temperature data within the local preset window of the target temperature data ∈>Differences between ambient temperature data at the time corresponding theretoThe larger the difference is, the more the temperature data of the intelligent sleeping bag is inconsistent with the ambient temperature change, the more the possibility that the target temperature data is affected by noise to cause sudden change of the target temperature data is, and the larger the original noise degree Z is; when (when)And when the temperature data of the intelligent sleeping bag is smaller, the temperature data change of the intelligent sleeping bag is consistent with the environmental temperature change, the probability of sudden change of the target temperature data caused by the environmental temperature change is higher, the intelligent sleeping bag belongs to normal temperature change, and the original noise degree Z is smaller.
It should be noted that if the target temperature data is the first two and the last two temperature data in the temperature sequence, the analysis of the original noise level is not performed.
Step S3: screening a neighborhood similar window of the target temperature data from the local preset window according to the discrete degree of the temperature data in the local preset window of the temperature data in the search preset window of the target temperature data; and obtaining the similarity between the local preset window of the target temperature data and the temperature data in each neighborhood similar window according to the difference between the local preset window of the target temperature data and the variation degree of the temperature data in the neighborhood similar window and the discrete degree of the temperature data in the neighborhood similar window.
Because the target temperature data may be affected by noise, the temperature data around the target temperature data is unstable, and in order to improve the accuracy of noise analysis, partial data similar to the temperature data fluctuation in the local preset window of the target temperature data, namely the temperature data in the neighborhood similar window, is searched in the search preset window of the target temperature data, so that the noise influence of the target temperature data is analyzed later.
Preferably, the specific acquisition method of the neighborhood similarity window is as follows: taking a local preset window of the rest temperature data except the target temperature data in the search preset window of the target temperature data as a neighborhood window of the target temperature data; taking any one neighborhood window and a local preset window of target temperature data as windows to be tested, selecting any one window to be tested as an analysis window, taking the average value of temperature data in the analysis window as an average temperature value, and taking the accumulated sum of the absolute value of the difference value of each temperature data and the average temperature value in the analysis window as a temperature discrete value; and taking the inverse of the sum of the temperature discrete value and the second preset positive number as the stability of the temperature data in the analysis window.
The calculation formula of the stability of the temperature data in the analysis window is as follows:
Wherein w is the stability of temperature data in an analysis window;for the->A plurality of temperature data; />The average temperature value of the analysis window is the average value of temperature data in the analysis window; />To analyze the total number of temperature data within the window; b is a second preset positive number, takes an empirical value of 0.01, and acts to prevent the denominator from being 0 to cause meaningless denominator; />Temperature discrete values for the analysis window; />As a function of absolute value.
When (when)The smaller the temperature data distribution of the intelligent sleeping bag in the analysis window is, the more concentrated the temperature data distribution of the intelligent sleeping bag in the analysis window is, the more stable the temperature data in the analysis window is, and the greater the stability w is; when->And when the temperature data distribution of the intelligent sleeping bag in the analysis window is more discrete, the stability of the temperature data in the analysis window is poorer, and the stability w is smaller.
In the embodiment of the invention, the size of the search preset window of the target temperature data takes an empirical valueAnd the target temperature data is positioned at the center of the searching preset window, so that an implementer can set the target temperature data according to specific conditions.
It should be noted that, if the target temperature data is the first 15 and the last 15 temperature data in the temperature sequence, the subsequent step analysis is not performed on the target temperature data; due to the size of the local preset window being a value And starting two neighborhood windows which are not the target temperature data with the last two temperature data in the searching preset window of the target temperature data.
If the absolute value of the difference between the local preset window of the target temperature data and the stability of each neighborhood window is smaller than a preset stability threshold, the neighborhood window is used as a neighborhood similar window of the target temperature data; the neighborhood similar window of the target temperature data is similar to the temperature data distribution in the local preset window. In the embodiment of the invention, the preset stability threshold takes an empirical value of 0.1, and an implementer can set the preset stability threshold according to specific conditions.
The difference between the local preset window of the target temperature data and the change degree of the temperature data in the neighborhood similar window reflects the change consistency degree of the temperature data in the two windows, and the more consistent the change is, the more similar the temperature data in the two windows are; the degree of dispersion of the temperature data in the neighborhood similar window shows the reliability of the degree of similarity, and the two factors of the difference between the local preset window of the target temperature data and the degree of variation of the temperature data in the neighborhood similar window and the degree of dispersion of the temperature data in the neighborhood similar window are combined for analysis, so that the higher the accuracy of the degree of similarity between the local preset window of the obtained target temperature data and the neighborhood similar window is.
The calculation formula of the similarity between the local preset window of the target temperature data and the temperature data in each neighborhood similarity window is as follows:
in the method, in the process of the invention,the similarity between the temperature data in a local preset window of the target temperature data and the temperature data in a kth neighborhood similarity window of the target temperature data is obtained; />The (1) th temperature data in the kth neighborhood similarity window of the target temperature data; />The (1) th temperature data in the kth neighborhood similarity window of the target temperature data is the (1) th temperature data; />The method comprises the steps of presetting the r-th temperature data in a window for the part of target temperature data; />The (1) th temperature data in a local preset window of the target temperature data; r is the total number of temperature data in a local preset window of the target data; />The total number of temperature data in the kth neighborhood similarity window of the target temperature data; />The stability of the k neighborhood similarity window of the target temperature data; a is a first preset positive number, takes an empirical value of 0.1, and acts to prevent the denominator from being zero to cause meaningless denominator; />As a function of absolute value.
The temperature data in the kth neighborhood similar window of the target temperature data is reflected as the increase rate of the temperature data in the kth neighborhood similar window of the target temperature dataThe degree of variation of (2); / >And->Is similar in function.The local preset window presenting the target temperature data and the neighborhood similar window are consistent in the change degree of the temperature data, when +.>The smaller the difference is, the higher the consistency of the temperature data change in the local preset window and the neighborhood similarity window of the target temperature data is, the similarity is +.>The larger. Compared with the temperature data in the local preset window of the target temperature data, the temperature data in the neighborhood similar window of the target temperature data has lower possibility of being influenced by noise, when +.>The smaller the target temperature data is, the smaller the possibility that the temperature data in the local preset window of the target temperature data is noise data is, and the higher the possibility that the target temperature data is represented as normal data is.
At the position ofIn smaller cases, when->When the temperature data is larger, the temperature data in the neighborhood similar window for describing the target temperature data is more stable, and the degree of dispersion of the temperature data in the window is used for enablingThe more reliable the consistency of the changes in temperature data within the two windows is presented, the similarityThe larger.
Since the temperature data of the smart sleeping bag is usually normal room temperature, the temperature data in the embodiment of the invention is not equal to 0.
Step S4: and adjusting the original noise degree by combining the difference and the similarity of the local preset window of the target temperature data and the environmental temperature data at the moment corresponding to the temperature data in the neighborhood similar windows of the target temperature data and the difference and the similarity of the environmental temperature data at the moment corresponding to the temperature data between the neighborhood similar windows to acquire the corrected noise degree of the target temperature data.
The similarity of the temperature data in the local preset window and the neighborhood similar window of the target temperature data reflects the possibility that the neighborhood similar window represents noise data to the temperature data in the local preset window, the similarity between different local preset windows of the target temperature data can improve the accuracy of the noise data represented by the target temperature data, meanwhile, the influence of environmental temperature change on the reliability of the temperature data in the window is considered, the original noise degree of the target temperature data is corrected by the factors, the noise degree is corrected, and the accuracy of the noise data recognition result is improved.
The calculation formula of the correction noise degree of the target temperature data is as follows:
in the method, in the process of the invention,the noise degree is corrected for the target temperature data; z is the original noise level of the target temperature data; />Similarity between the kth neighborhood similar window and the (k+1) th neighborhood similar window of the target temperature data; />The similarity between a local preset window of the target temperature data and a kth neighborhood similar window of the target temperature data is preset;/>the average value of the environmental temperature data at the moment corresponding to the temperature data in the local preset window of the target temperature data is obtained; />The average value of the environmental temperature data at the moment corresponding to the temperature data in the kth neighborhood similar window of the target temperature data; / >The average value of the environmental temperature data at the moment corresponding to the temperature data in the k+1th neighborhood similar window of the target temperature data; k is the total number of neighborhood similar windows of the target temperature data; a is a first preset positive number, takes an empirical value of 0.1, and acts to prevent the denominator from being zero to cause meaningless denominator; />As a function of absolute value; norms are normalization functions.
Because the temperature data in the neighborhood similar window of the target temperature data is less likely to be affected by noise compared with the temperature data in the local preset window of the target temperature data; when (when)When the temperature data in different neighborhood similar windows of the target temperature data are larger, the probability difference that the temperature data in different neighborhood similar windows of the target temperature data are represented as noise data is smaller, and the probability that the temperature data in different neighborhood similar windows of the target temperature data are all normal data is larger; at->The larger the case, when +>When the target temperature data is smaller, the probability that the local preset window of the target temperature data and the temperature data in the neighborhood similar window are expressed as noise data is larger, and the probability that the temperature data in the neighborhood similar window of the target temperature data is normal data is larger, the local of the target temperature data is largerThe greater the possibility that the temperature data in the preset window is represented as noise data, the greater the possibility that the target temperature data is changed by the influence of noise, the smaller the degree of adjustment to Z, i.e. & lt & gt >Approaching 1, correcting noise level +.>The larger.
Considering that the temperature data of the intelligent sleeping bag can be changed due to the temperature change of the external environment, whenWhen the temperature data in the neighborhood similar window is larger, the difference between the local preset window of the target temperature data and the change of the external environment at the corresponding moment of the temperature data in the neighborhood similar window is smaller, which indicates that the influence of the change of the external environment on the change of the temperature data in the two windows is smaller; by->For->Adjusting to ensure that the difference between the probability that the temperature data in the local preset window of the target temperature data and the neighborhood similar window are represented as noise data is more reliable and accurate, and improving the accuracy of correcting the original noise degree Z; />The actions of (A) and->Is similar in function.
It should be noted that, the calculation method of the similarity between the temperature data in any two neighborhood similar windows of the target temperature data is the same as the calculation method of the similarity between the local preset window of the target temperature data and the temperature data in each neighborhood similar window in step S3, and the calculation formula of the similarity between the temperature data in any two neighborhood similar windows of the target temperature data is as follows:
in the method, in the process of the invention,similarity between the kth neighborhood similar window and the (k+1) th neighborhood similar window of the target temperature data; / >The (1) th temperature data in the kth neighborhood similarity window of the target temperature data; />The (1) th temperature data in the kth neighborhood similarity window of the target temperature data is the (1) th temperature data; />The total number of temperature data in the kth neighborhood similarity window of the target temperature data; />The (2) th temperature data in the (k+1) th neighborhood similar window of the target temperature data; />The (2) th temperature data and the (1) th temperature data in the (k+1) th neighborhood similar window of the target temperature data; />The total number of temperature data in the k+1th neighborhood similarity window of the target temperature data; />The stability of the k neighborhood similarity window of the target temperature data; a is a first preset positive number, takes an empirical value of 0.1, and acts to prevent the denominator from being zero to cause meaningless denominator;as a function of absolute value.
Step S5: according to the difference between the target temperature data and the temperature data in the searching preset window and the correction noise degree, the data to be smoothed is screened from the target temperature data; and updating each piece of data to be smoothed by using the temperature data in the searching preset window of each piece of data to be smoothed to obtain a smoothed temperature sequence.
The difference between the target temperature data and the temperature data in the searching preset window reflects data damage when the data is smooth, the correction noise degree reflects the possibility that the target temperature data is represented as noise data, and the analysis is performed by combining the difference between the target temperature data and the temperature data in the searching preset window and the correction noise degree, so that the more the noise of the screened smooth data to be detected is more prominent and the data loss can be reduced after the smooth data is smooth.
Preferably, the specific screening method of the smoothing probability degree is as follows: in a local preset window of the target temperature data, taking the temperature data before the corresponding moment of the target temperature data as front adjacent data and taking all the temperature data after the corresponding moment of the target temperature data as rear adjacent data; taking the temperature data before the corresponding moment of the target temperature data as the searching temperature data of the target temperature data in a searching preset window of the target temperature data; and combining the difference between the front adjacent data and the rear adjacent data of the target temperature data, the difference between the searched temperature data of the target temperature data and the correction noise degree to acquire the smoothness possibility of the target temperature data.
It should be noted that, since the size of the local preset window is a valueThe search preset window has a size ofThe total number J1 of the front neighboring data of the target temperature data is equal to 2, the total number J2 of the rear neighboring data of the target temperature data is equal to 2, and the total number T of the search temperature data of the target temperature data takes the checked value 15.
Because the run length corresponding to the encoding of the target temperature data by using the run encoding depends on the previous temperature data, when the smoothing possibility of the target temperature data is acquired, the temperature data before the corresponding moment of the target temperature data is used as the searching temperature data of the target temperature data to calculate in a searching preset window of the target temperature data, so that the problem that multi-bit data is needed to be obtained backwards in the denoising process to slow down the encoding efficiency is avoided.
The calculation formula of the smoothing probability of the target temperature data is as follows:
/>
wherein P is the smoothing probability of the target temperature data;the noise degree is corrected for the target temperature data; a is the neighborhood temperature difference of target temperature data; />J1 st previous neighbor data that is target temperature data; j1 is the total number of the preceding adjacent data of the target temperature data, taking a checked value of 2; />The j2 nd post-proximity data that is the target temperature data; j2 is the total number of the post-adjacent data of the target temperature data, and the checked value 2 is taken; t is the total quantity of the search temperature data of the target temperature data, and the checked value 15 is taken; x is target temperature data; />Searching temperature data for the t-th of the target temperature data; a is a first preset positive number, takes an empirical value of 0.1, and acts to prevent the denominator from being zero to cause meaningless denominator; />Is absolute valueA function; norms are normalization functions.
When the neighborhood temperature difference A is smaller, the other temperature data except the data in the local preset window of the target temperature data are consistent, the probability that the change of the target temperature data is caused by noise is larger, the probability that the target temperature data need to be smoothed is larger, and the smoothing probability P is larger; when the neighborhood temperature difference a is larger, the more likely that the target temperature data is changed due to the environmental temperature change is shown, the less likely that the target temperature data needs to be smoothed, and the smaller the smoothing probability P is.
When (when)The larger the smoothing of the target temperature data causes information loss, and the smaller the smoothing probability of the target temperature data is, the smaller the smoothing probability P is in order to reduce the data loss. When correcting noise level->The larger the target temperature data is, the larger the possibility that the target temperature data is influenced by noise to generate change is, and the greater the possibility that the target temperature data needs to be smoothed to improve the coding efficiency is, the greater the smoothing possibility P is; when correcting noise level->The smaller the target temperature data is, the greater the possibility that the change of the target temperature data is caused by the change of the environmental temperature is, the more the target temperature data belongs to normal change, and the smaller the possibility that the target temperature data is smoothed in order to keep the temperature change characteristics of the intelligent sleeping bag is, the smaller the smoothing possibility is.
And if the correction noise degree of the target temperature data is larger than the preset abnormal threshold value and the smoothness possibility degree is larger than the preset smoothness threshold value, taking the target temperature data as the data to be smoothed. The target temperature data with the noise correction degree larger than the preset abnormal threshold value is noise data, and on the basis, the noise data with the smoothing probability larger than the preset smoothing threshold value has larger influence on the coding efficiency and is used as the data to be smoothed. According to the method, all data to be smoothed in the temperature sequence are acquired.
In the embodiment of the invention, the preset abnormal threshold takes the empirical value of 0.5, the preset smooth threshold takes the empirical value of 0.5, and the implementer can set the preset abnormal threshold according to specific conditions.
The method and the device utilize temperature data which have highest occurrence frequency in a preset window of searching the data to be smoothed and have the nearest distance to the data to be smoothed, and smooth the data to be smoothed to obtain smoothed temperature data.
Preferably, the method for acquiring the updated temperature data comprises the following steps: for each piece of data to be smoothed, taking the rest temperature data except the data to be smoothed in a searching preset window of the data to be smoothed as data to be tested, and taking the data to be tested with equal values as the data to be tested of the same type; taking the time interval between the data to be smoothed and the corresponding time of each piece of data to be smoothed as the time sequence difference of each piece of data to be smoothed; taking the ratio of the total quantity of the to-be-measured data of the type to which each to-be-measured data belongs to the total quantity of the temperature data in the preset search window as the occurrence frequency of each to-be-measured data; taking the product of the reciprocal of the time sequence difference of each piece of data to be smoothed and the occurrence frequency as a screening coefficient of each piece of data to be smoothed; and taking the data to be measured corresponding to the maximum screening coefficient as updated temperature data of the data to be smoothed.
The calculation formula of the updated temperature data of each data to be smoothed is as follows:
wherein H is updated temperature data of each data to be smoothed;the s-th data to be measured of each data to be smoothed; />For each data to be smoothed; />For the s-th data to be smoothedMeasuring the occurrence frequency of data;the time sequence difference of the s-th data to be measured of each data to be smoothed is obtained; />A set formed by all data to be smoothed of each data to be smoothed; />Screening coefficients of the s-th data to be tested of each data to be smoothed; max is a maximum function.
When (when)When the number of times that the value of the s-th data to be smoothed appears in the searching preset window of the data to be smoothed is larger, the probability that the s-th data to be smoothed is normal temperature data is larger is shown, and the effect of selecting the data to be smoothed is better; time-sequential differentiation->And when the data to be smoothed is smaller, the closer the s-th data to be smoothed is to the data to be smoothed, the larger the influence of the run-length compression on the data to be smoothed is, and the better the effect of selecting the data to be smoothed is.
It should be noted that, if the data to be smoothed has a plurality of filtering coefficients of the data to be smoothed that are all the maximum values, and the data to be smoothed is taken as the extremely selected data, the smoothing rule is set in the embodiment of the invention as follows: rule 1, calculating time intervals between data to be smoothed and corresponding moments of each piece of pole selection data respectively, and taking the pole selection data corresponding to the minimum time interval as update temperature data of the data to be smoothed; rule 2, selecting extremely selected data positioned before the corresponding moment of the data to be smoothed as updated temperature data of the data to be smoothed; wherein the priority of rule 1 is higher than the priority of rule 2.
As an example, the data to be smoothed and the data to be tested are arranged in a time sequence order to obtain a sequenceWherein->For data to be smoothed, ++>Is the data to be smoothed. If->And->The screening coefficients of (a) are all maximum values, then +.>Update temperature data as data to be smoothed; if->The screening coefficients of (a) are all maximum values, then +.>As updated temperature data of the data to be smoothed.
And updating each piece of data to be smoothed in the temperature sequence into corresponding updated temperature data, keeping the rest pieces of temperature data except the data to be smoothed in the temperature sequence unchanged, and taking the updated temperature sequence as a smoothed temperature sequence.
Step S6: and compressing and storing temperature data in the smooth temperature sequence by using run-length coding.
The smoothed temperature sequence is a sequence obtained by smoothing temperature data in the temperature sequence, and the smoothed temperature sequence reduces the influence of noise on the temperature data. And the run length in the run coding process is increased and the compression efficiency is improved by using the run coding to compress and store the temperature data in the smooth temperature sequence. The run length encoding is a technique known to those skilled in the art, and will not be described herein.
The present invention has been completed.
In summary, in the embodiment of the present invention, the method acquires the original noise level of the target temperature data in the temperature sequence; obtaining the similarity between the local preset window of the target temperature data and the neighborhood similar window according to the difference of the change degree of the temperature data in the local preset window of the target temperature data and the neighborhood similar window and the discrete degree of the temperature data in the neighborhood similar window; the original noise is adjusted by using the similarity and the environmental temperature data to obtain the corrected noise degree; according to the difference between the target temperature data and the temperature data in the searching preset window and the correction noise degree, the data to be smoothed is screened from the target temperature data, and smoothing is carried out to obtain a smooth temperature sequence; and compressing and storing temperature data in the smooth temperature sequence by using run-length coding. According to the intelligent sleeping bag temperature data compression method, noise data are smoothed before the intelligent sleeping bag temperature data are transmitted, compression transmission is carried out, the runlength of the runlength process of the temperature data is increased, and the compression transmission efficiency of the temperature data of the intelligent sleeping bag is improved.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (10)
1. The intelligent sleeping bag temperature data remote monitoring and transmitting method is characterized by comprising the following steps of:
acquiring a temperature sequence corresponding to the intelligent sleeping bag in a historical time period;
screening target temperature data from the temperature sequence; for any one target temperature data, acquiring the original noise degree of the target temperature data according to the fluctuation difference between the temperature data in a local preset window of the target temperature data and the environmental temperature data at the corresponding moment;
according to the discrete degree of temperature data in a local preset window of the temperature data in the search preset window of the target temperature data, screening a neighborhood similar window of the target temperature data from the local preset window; obtaining the similarity between the local preset window of the target temperature data and the temperature data in each neighborhood similar window according to the difference between the local preset window of the target temperature data and the variation degree of the temperature data in the neighborhood similar window and the dispersion degree of the temperature data in the neighborhood similar window;
The original noise degree is adjusted by combining the difference of the environmental temperature data at the moment corresponding to the temperature data in the local preset window of the target temperature data and the neighborhood similar window and the difference of the similarity, and the difference of the environmental temperature data at the moment corresponding to the temperature data between the neighborhood similar windows and the difference of the similarity, so that the corrected noise degree of the target temperature data is obtained;
according to the difference between the target temperature data and the temperature data in the searching preset window and the correction noise degree, the data to be smoothed is screened out from the target temperature data; updating each piece of data to be smoothed by utilizing temperature data in a searching preset window of each piece of data to be smoothed to obtain a smoothed temperature sequence;
and compressing and storing temperature data in the smooth temperature sequence by using run-length coding.
2. The method for remotely monitoring and transmitting temperature data of an intelligent sleeping bag according to claim 1, wherein the method for screening target temperature data from a temperature sequence comprises the following steps:
selecting any one temperature data except the first temperature data in the temperature sequence as analysis temperature data, and taking the analysis temperature data as target temperature data if the analysis temperature data are not equal to the adjacent temperature data;
And traversing each temperature data in the temperature sequence to obtain target temperature data in the temperature sequence.
3. The intelligent sleeping bag temperature data remote monitoring and transmitting method according to claim 1, wherein the calculation formula of the original noise degree of the target temperature data is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein Z is the original noise degree of the target temperature data; />The method comprises the steps of presetting the r-th temperature data in a window for the part of target temperature data; />The (1) th temperature data in a local preset window of the target temperature data; />Environmental temperature data at the moment corresponding to the r-th temperature data in a local preset window of the target temperature data; />The method comprises the steps that (1) environmental temperature data at the moment corresponding to the (r+1) th temperature data in a local preset window of target temperature data are obtained; r is the total number of temperature data in a local preset window of the target data; />As a function of absolute value.
4. The method for remotely monitoring and transmitting temperature data of an intelligent sleeping bag according to claim 1, wherein the method for screening neighborhood similar windows of target temperature data from the local preset windows according to the degree of dispersion of temperature data in the local preset windows of the temperature data in the search preset windows of the target temperature data comprises the following steps:
Taking a local preset window of the rest temperature data except the target temperature data in the search preset window of the target temperature data as a neighborhood window of the target temperature data;
taking any one neighborhood window and a local preset window of target temperature data as windows to be tested, selecting any one window to be tested as an analysis window, taking the average value of temperature data in the analysis window as a uniform temperature value, and taking the accumulated sum of the absolute value of the difference value of each temperature data in the analysis window and the uniform temperature value as a temperature discrete value;
taking the inverse of the sum of the temperature discrete value and the second preset positive number as the stability of temperature data in an analysis window;
and if the absolute value of the difference between the local preset window of the target temperature data and the stability of each neighborhood window is smaller than a preset stability threshold, taking the neighborhood window as a neighborhood similar window of the target temperature data.
5. The method for remotely monitoring and transmitting temperature data of an intelligent sleeping bag according to claim 4, wherein a calculation formula of similarity between the local preset window of the target temperature data and the temperature data in each neighborhood similarity window is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the In (1) the- >The similarity between the temperature data in a local preset window of the target temperature data and the temperature data in a kth neighborhood similarity window of the target temperature data is obtained; />The (1) th temperature data in the kth neighborhood similarity window of the target temperature data; />The (1) th temperature data in the kth neighborhood similarity window of the target temperature data is the (1) th temperature data; />The method comprises the steps of presetting the r-th temperature data in a window for the part of target temperature data; />The (1) th temperature data in a local preset window of the target temperature data; r is the total number of temperature data in a local preset window of the target data; />The total number of temperature data in the kth neighborhood similarity window of the target temperature data; />The stability of the kth neighborhood similarity window for the target temperature data; a is a first preset positive number; />As a function of absolute value.
6. The intelligent sleeping bag temperature data remote monitoring and transmitting method according to claim 1, wherein the calculation formula of the corrected noise degree of the target temperature data is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->The noise degree is corrected for the target temperature data; z is the original noise level of the target temperature data; />The similarity between the kth neighborhood similar window and the (k+1) th neighborhood similar window of the target temperature data; / >The similarity between a local preset window of the target temperature data and a kth neighborhood similarity window of the target temperature data is preset; />The average value of the environmental temperature data at the moment corresponding to the temperature data in the local preset window of the target temperature data is obtained; />The average value of the environmental temperature data at the moment corresponding to the temperature data in the kth neighborhood similar window of the target temperature data; />The average value of the environmental temperature data at the moment corresponding to the temperature data in the k+1th neighborhood similar window of the target temperature data; k is the total number of neighborhood similar windows of the target temperature data; a is a first preset positive number; />As a function of absolute value; norms are normalization functions.
7. The method for remotely monitoring and transmitting temperature data of an intelligent sleeping bag according to claim 1, wherein the method for screening data to be smoothed from target temperature data comprises the following steps:
in a local preset window of the target temperature data, taking the temperature data before the corresponding moment of the target temperature data as front adjacent data and taking all the temperature data after the corresponding moment of the target temperature data as rear adjacent data;
taking the temperature data before the corresponding moment of the target temperature data as the searching temperature data of the target temperature data in a searching preset window of the target temperature data;
Combining the difference between the front adjacent data and the rear adjacent data of the target temperature data, the difference between the searched temperature data of the target temperature data and the correction noise degree to obtain the smoothness possibility of the target temperature data;
and if the corrected noise degree of the target temperature data is larger than a preset abnormal threshold value and the smoothing probability degree is larger than a preset smoothing threshold value, taking the target temperature data as the data to be smoothed.
8. The intelligent sleeping bag temperature data remote monitoring and transmitting method of claim 7, wherein the calculation formula of the smoothing probability of the target temperature data is as follows:
，/>the method comprises the steps of carrying out a first treatment on the surface of the Wherein P is the smoothing probability of the target temperature data; />The noise degree is corrected for the target temperature data; a is the neighborhood temperature difference of target temperature data; />J1 st previous neighbor data that is target temperature data; j1 is the total number of pre-proximity data of the target temperature data; />The j2 nd post-proximity data that is the target temperature data; j2 is the total number of post-vicinity data for the target temperature data; t is the total number of search temperature data of the target temperature data; x is target temperature data; />Searching temperature data for the t-th of the target temperature data; a is a first preset positive number; / >As a function of absolute value; norms are normalization functions.
9. The method for remotely monitoring and transmitting temperature data of an intelligent sleeping bag according to claim 1, wherein the method for acquiring the smoothed temperature sequence comprises the following steps:
for each piece of data to be smoothed, taking the rest temperature data except the data to be smoothed in a searching preset window of the data to be smoothed as data to be tested, and taking the data to be tested with equal values as the data to be tested of the same type;
taking the time interval between the data to be smoothed and the corresponding time of each piece of data to be smoothed as the time sequence difference of each piece of data to be smoothed; taking the ratio of the total number of the to-be-measured data of the type to which each to-be-measured data belongs to the number of the temperature data in the preset search window as the occurrence frequency of each to-be-measured data;
taking the product of the reciprocal of the time sequence difference and the occurrence frequency of each piece of data to be smoothed as a screening coefficient of each piece of data to be smoothed;
taking the data to be detected corresponding to the largest screening coefficient as updated temperature data of the data to be smoothed;
and updating each piece of data to be smoothed in the temperature sequence into corresponding updated temperature data, and taking the updated temperature sequence as a smoothed temperature sequence.
10. The method for remotely monitoring and transmitting temperature data of an intelligent sleeping bag of claim 4, wherein the second preset number is 0.1.
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