CN114785824A - Intelligent Internet of things big data transmission method and system - Google Patents
Intelligent Internet of things big data transmission method and system Download PDFInfo
- Publication number
- CN114785824A CN114785824A CN202210357624.3A CN202210357624A CN114785824A CN 114785824 A CN114785824 A CN 114785824A CN 202210357624 A CN202210357624 A CN 202210357624A CN 114785824 A CN114785824 A CN 114785824A
- Authority
- CN
- China
- Prior art keywords
- data
- time sequence
- degree
- time series
- calculating
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 37
- 230000005540 biological transmission Effects 0.000 title claims abstract description 32
- 239000013598 vector Substances 0.000 claims abstract description 59
- 239000011159 matrix material Substances 0.000 claims abstract description 36
- 238000012549 training Methods 0.000 claims abstract description 17
- 238000012545 processing Methods 0.000 claims description 21
- 238000013144 data compression Methods 0.000 claims description 10
- 238000009826 distribution Methods 0.000 claims description 7
- 238000007689 inspection Methods 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 238000007906 compression Methods 0.000 description 3
- 230000006835 compression Effects 0.000 description 3
- 238000000638 solvent extraction Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
- H04L69/04—Protocols for data compression, e.g. ROHC
Abstract
The invention relates to an intelligent Internet of things big data transmission method and system, wherein the method comprises the steps of segmenting Internet of things data to obtain data vectors at all times, and obtaining a plurality of sets of data vectors of the same type according to the data vectors at all times, wherein each set is time sequence data; acquiring the stationarity degree, white noise coincidence rate and trend coincidence degree of time sequence data; calculating the complexity of each time sequence data according to the stationarity degree, the white noise coincidence rate and the trend coincidence degree, and calculating the loss influence weight of the time sequence data according to the complexity; calculating the correlation of each two types of time sequence data, and calculating the loss influence degree of the time sequence data according to the correlation and the loss influence weight; the method comprises the steps of obtaining an initialization weight matrix according to the loss influence degree, carrying out network training based on the initialization weight matrix, and outputting compressed data by using a trained network.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a big data transmission method and system of an intelligent Internet of things.
Background
With the development of information technology, the application of the internet + becomes wider and wider, so that the high-efficiency and low-loss transmission of the data of the intelligent internet of things becomes an important research aspect. In order to improve the transmission efficiency of data, data needs to be compressed before transmission, but data loss is inevitably caused after data compression, so a data compression method is proposed, which can improve the compression transmission efficiency and simultaneously ensure that the data information loss is as low as possible.
The traditional data compression method is a self-supervision method, namely, people do not need to label data, only need to transmit the data to the network, can finish training by themselves and transmit processing results, and meanwhile, the method has a wide application scene.
However, in the conventional self-coding compression network, the self-coding network generally performs self-training convergence through a random initial weight matrix to obtain final compression data, but this method has the disadvantages that the network data training is slow, and the data compression efficiency is affected.
Therefore, an intelligent internet of things big data transmission method and system are needed.
Disclosure of Invention
The invention provides an intelligent Internet of things big data transmission method and system, and aims to solve the existing problems.
The invention discloses an intelligent Internet of things big data transmission method and system, which adopt the following technical scheme: the method comprises the following steps:
the method comprises the steps that data of the Internet of things are segmented to obtain data vectors of all moments, the data vectors of all the moments are classified to obtain a plurality of sets of data vectors of the same type, and each type of data vector is formed into time sequence data;
obtaining the confidence coefficient of each unit root of the time sequence data, and calculating the stability degree of the time sequence data according to the confidence coefficient;
acquiring white noise coincidence rate of each time sequence data, and acquiring trend coincidence degree of each time sequence data;
calculating the complexity of each time sequence data according to the stationarity degree, the white noise coincidence rate and the trend coincidence degree, and calculating the loss influence weight of each time sequence data according to the complexity;
calculating the correlation of each two types of time sequence data, and calculating the loss influence degree of each time sequence data according to the correlation and the loss influence weight;
and acquiring an initialization weight matrix according to the loss influence degree, carrying out network training based on the initialization weight matrix, carrying out data compression on the Internet of things by using the trained network, and outputting compressed data.
Preferably, the step of segmenting the data of the internet of things to obtain data vectors at all times, and the step of classifying the data vectors at all times to obtain a plurality of sets of data vectors of the same type comprises the following steps:
obtaining the dimensionality of single moment data of the Internet of things;
dividing the data of the internet of things into corresponding data vectors at the 1 st moment, data vectors at the 2 nd moment and data vectors at the … nth moment according to the dimensionality of the data at each single moment;
extracting data at the same position in the data vector at each moment to obtain the data vector of the same type;
and obtaining a plurality of homogeneous data vector sets according to the homogeneous data vectors.
Preferably, the step of obtaining the confidence level of the unit root of each time series data, and calculating the stationarity degree of the time series data according to the confidence level includes:
obtaining trend information of each time series data through a least square method; eliminating trend information in the time series data;
acquiring the confidence coefficient of the unit root of the time sequence data with the trend information eliminated by a unit root inspection method;
and calculating the stationarity degree of the time series data through the confidence coefficient.
Preferably, the step of acquiring the trend conformity degree of each time series data includes:
obtaining processed first time sequence data by carrying out differential processing on the time sequence data;
analyzing the first time series data by using a polynomial regression model to obtain a trend equation, and obtaining a data prediction value corresponding to each moment of the first time series data according to the trend equation;
calculating a difference value between data corresponding to each moment in the first time sequence data and a data prediction value;
and acquiring the square mean of all the difference values, wherein the square mean is the trend coincidence degree.
Preferably, the step of calculating the complexity of each time series data according to the stationarity degree, the white noise coincidence rate and the trend coincidence degree comprises:
the complexity of each time series data is calculated according to the following formula (1):
wherein, FiComplexity of time series data representing the ith category, BiWhite noise coincidence rate, H, representing time series data of ith categoryiShows the degree of conformity of the trend of the time series data of the ith category, PiThe degree of stationarity of the time series data is indicated,
for the over-parameter, 0.2 was taken.
Preferably, the step of calculating the loss influence weight of the time series data according to the complexity comprises:
the loss influence weight of each time series data is calculated according to the following formula (2):
wherein Q isiLoss impact weight, F, representing the ith category of time series dataiThe complexity of the ith category of time series data is represented, and N represents the dimension of the ith category of time series data, namely the number of moments.
Preferably, the step of calculating the correlation between each two types of time series data comprises:
the correlation is calculated according to the following formula (3):
wherein, I represents the ith type of time series data, J represents the jth type of time series data, cov (I, J) represents the covariance of the data sequence, var (I) represents the variance of the ith type of time series data, var (J) represents the variance of the jth type of time series data, and Xi,jAnd representing the correlation coefficient of the i-th class and the j-th class time series data.
Preferably, the step of calculating the loss influence degree of each time series data according to the correlation and the loss influence weight includes:
the degree of influence of the loss was calculated according to the following formula (4):
wherein Q isiWeight, Q, representing the loss impact of class i time series datajRepresenting the loss impact weight of the j-th class of timing data. Xi,jShowing the correlation between the ith time sequence data and the jth time sequence data,
shows the influence of the loss of the i-th time series data on other data, YiIndicating the degree of influence of loss of the i-th time series data.
Preferably, the step of obtaining the initialization weight matrix according to the degree of influence of loss includes:
assuming that a weight vector multiplied by certain time sequence data and ith time sequence data of the internet of things is an M-dimensional vector, the initialization rule of the weight matrix of the input layer at positions 1-M is as follows:
using the loss influence degree as a mean value and the variance sigma1,σ2…,σMConstructing M normal distributions;
generating a corresponding random number by utilizing each normal distribution;
initializing a weight matrix of the input layer according to the random number as an initialized weight value of the corresponding position;
the initialized weight value of the weight matrix of the hidden layer is alpha, the alpha is an empirical value of 10, and the weight matrix of the hidden layer is initialized, so that the initialized weight matrix is obtained.
The invention also comprises an intelligent Internet of things big data transmission system, which comprises:
the data partitioning module is used for partitioning the data of the Internet of things to obtain data vectors at all times, classifying the data vectors at all times to obtain a set of a plurality of data vectors of the same type, and forming each type of data vector into time sequence data;
the first data processing module is used for acquiring the confidence coefficient of each unit root of the time sequence data and calculating the stationarity degree of the time sequence data according to the confidence coefficient;
the second data processing module is used for acquiring the white noise coincidence rate of each time sequence data and acquiring the trend coincidence degree of each time sequence data;
the third data processing module is used for calculating the complexity of each time sequence data according to the stationarity degree, the white noise coincidence rate and the trend coincidence degree, and calculating the loss influence weight of each time sequence data according to the complexity;
the fourth data processing module is used for calculating the correlation of each two types of time sequence data and calculating the loss influence degree of each time sequence data according to the correlation and the loss influence weight;
and the data transmission module is used for acquiring the initialized weight matrix according to the loss influence degree, carrying out network training based on the initialized weight matrix, carrying out data compression on the Internet of things by using the trained network, and outputting compressed data.
The invention has the beneficial effects that: according to the method and the system for transmitting the big data of the intelligent Internet of things, the loss influence weight corresponding to the time sequence data is obtained through the complexity of data change of the time sequence data, the loss influence degree of the data is obtained according to the loss influence weight and the correlation of various types of time sequence data, the initialized weight matrix is obtained through the loss influence degree, and the initialized weight matrix is used for carrying out grid training, so that the convergence of the grid training is high, the time of the grid training is shortened, and the transmission speed of the output compressed data is improved.
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 embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of general steps of an embodiment of a big data transmission method and system of an intelligent internet of things of the invention;
FIG. 2 is a flow chart of the acquisition of time series data of FIG. 1;
FIG. 3 is a flow chart of calculating the degree of stationarity of the time series data of FIG. 1;
fig. 4 is a flowchart of the trend conformity degree of the time series data acquired in fig. 1.
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 obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The embodiment of the invention discloses an intelligent Internet of things big data transmission method and system, wherein the method comprises the following steps:
s1, because the data of the Internet of things are a series of time sequence data generally, the time sequence data of the Internet of things has the following characteristics under the normal condition: the data dimensionality of each moment is the same; the homogeneous data is generally in the same position in the data vector at each moment, so that the data of the internet of things needs to be divided into the data vectors at each moment, then the data of the internet of things is divided to obtain the data vectors at each moment, the data vectors at each moment are classified to obtain a plurality of sets of homogeneous data vectors, and each type of data vector forms a time sequence data.
Specifically, S11, obtaining the dimensionality of the single moment data of the Internet of things data; s12, dividing the data of the Internet of things into corresponding data vectors at the 1 st moment, the 2 nd moment and the … nth moment according to the dimensionality of each single moment; s13, extracting the data at the same position in the data vectors at each moment to obtain the data vectors of the same type, wherein the data at the same position in the general time sequence data are the data of the same type; and S14, obtaining a plurality of homogeneous data vector sets according to the homogeneous data vectors.
And S2, acquiring the confidence coefficient of each unit root of the time sequence data, and calculating the stationarity degree of the time sequence data according to the confidence coefficient.
Specifically, S21, obtaining trend information of each time series data through a least square method; eliminating trend information in the time series data; s22, obtaining the confidence coefficient of the unit root of the time sequence data of the elimination trend information through a unit root inspection method; s23, calculating the stationarity degree of the time series data according to the confidence coefficient, and calculating the stationarity degree according to the following formula (5):
wherein, muiConfidence, P, representing the root of the unit of existence of the ith category of time series dataiRepresenting the degree of stationarity, P, of the time-series data of the ith categoryiA larger value indicates a higher probability that the data converges to a certain trend, and indicates a stronger regularity of the data.
And S3, acquiring the white noise coincidence rate of each time sequence data, and acquiring the trend coincidence degree of each time sequence data.
Specifically, S31, obtaining a white noise confidence B through white noise detectioniConfidence of white noise BiThat is, the white noise coincidence rate of the time series data is reflected, and the larger the white noise coincidence rate is, the larger the randomness of the time series data is, the rule thereof isThe regularity is poor.
S32, the step of obtaining the trend conformity degree of each time series data comprises the following steps: s321, carrying out difference processing on the time sequence data to obtain processed first time sequence data; s322, analyzing the first time series data by using a polynomial regression model to obtain a trend equation, and obtaining a data prediction value corresponding to each moment of the first time series data according to the trend equation; s323, calculating a difference value between data corresponding to each moment in the first time series data and a data predicted value; s324, obtaining the square mean value H of all the difference valuesiMean square value HiI.e. the mean square value H which is the degree of conformity of the trendiThe larger the value is, the smaller the degree of coincidence of the trend of the time-series data is reflected.
And S4, calculating the complexity of each time sequence data according to the stationarity degree, the white noise coincidence rate and the trend coincidence degree, and calculating the loss influence weight of each time sequence data according to the complexity.
Specifically, the complexity of each time series data is calculated according to the following formula (1):
wherein, FiComplexity of time series data representing the ith category, BiWhite noise coincidence rate, H, representing time series data of ith categoryiShows the degree of trend conformity of the time series data of the ith category, PiThe degree of stationarity of the time series data is indicated,
for the over-parameter, 0.2 was taken.
The loss influence weight of each time series data is calculated according to the following formula (2):
wherein QiLoss impact weight, F, representing the ith category of time series dataiTo representThe complexity of the ith category of time series data, N, indicates the dimension of the ith category of time series data, i.e., the number of times.
And S5, calculating the correlation of each two types of time sequence data, and calculating the loss influence degree of each time sequence data according to the correlation and the loss influence weight.
Specifically, the correlation is calculated according to the following formula (3):
wherein I represents the I-th class time series data, J represents the J-th class time series data, cov (I, J) represents the covariance of the data sequence, Var (I) represents the variance of the I-th class time series data, Var (J) represents the variance of the J-th class time series data, and Xi,jAnd representing the correlation coefficient of the i-th class and the j-th class time series data.
The degree of influence of loss was calculated according to the following formula (4):
wherein Q isiWeight, Q, representing the loss impact of class i time series datajRepresenting the loss impact weight of the j-th class of timing data. Xi,jShowing the correlation between the ith type time sequence data and the jth type time sequence data,
showing the influence of the loss of class i time series data on other data, YiIndicating the degree of influence of loss of the i-th type time series data.
S6, acquiring an initialization weight matrix according to the loss influence degree, carrying out network training based on the initialization weight matrix, carrying out data compression of the Internet of things by using the trained network, and outputting compressed data, wherein the loss function adopted by the network is a cross entropy loss function.
Specifically, S61, assume that a weight vector obtained by multiplying certain type of time series data of the internet of things data by i-th type of time series data is an M-dimensional vectorThen, the initialization rule of the weight matrix of the input layer at positions 1 to M is: with the loss influence degree as the mean and the variance σ1,σ2…,σMConstructing M normal distributions; according to the experience of sigma1、σ2…σMRespectively take out
Where δ represents the influence horizon variance of all time series data, S represents the dimensionality of data at a single moment, M represents the several normal distributions, and S62 generates corresponding random numbers using each normal distribution; the random numbers are approximately distributed around the weight values, so that S63, the weight matrix of the input layer is initialized according to the random numbers as the initialized weight values of the corresponding positions; s64 initializes the weight matrix of the hidden layer with the initialized weight value α of the weight matrix of the hidden layer being a value of 10, thereby obtaining the initialized weight matrix. And S62, specifically, performing Internet of things data compression by using the trained network, and outputting data with the minimum dimension as compressed data.
The invention also discloses an intelligent Internet of things big data transmission system, which comprises: the device comprises a data segmentation module, a first data processing module, a second data processing module, a third data processing module, a fourth data processing module and a data transmission module; the data segmentation module is used for segmenting the data of the Internet of things to obtain data vectors at all times, classifying the data vectors at all times to obtain a set of a plurality of data vectors of the same type, and forming each type of data vector into time sequence data; the first data processing module is used for acquiring the confidence coefficient of each unit root of the time sequence data and calculating the stationarity degree of the time sequence data according to the confidence coefficient; the second data processing module is used for acquiring the white noise coincidence rate of each time sequence data and acquiring the trend coincidence degree of each time sequence data; the third data processing module is used for calculating the complexity of each time sequence data according to the stationarity degree, the white noise coincidence rate and the trend coincidence degree, and calculating the loss influence weight of each time sequence data according to the complexity; the fourth data processing module is used for calculating the correlation of each two types of time sequence data and calculating the loss influence degree of each time sequence data according to the correlation and the loss influence weight; and the data transmission module is used for acquiring an initialized weight matrix according to the loss influence degree, finishing self-coding network training on the basis of the initialized weight matrix, extracting data with the minimum dimension after the network training is finished, and transmitting the data with the minimum dimension to the target port.
In summary, the invention provides an intelligent internet of things big data transmission method and system, wherein loss influence weights corresponding to time sequence data are obtained through complexity of data variation of the time sequence data, loss influence degrees of the data are obtained according to the loss influence weights and correlation of various types of time sequence data, an initialization weight matrix is obtained through the loss influence degrees, and grid training is performed by using the initialized initialization weight matrix, so that convergence of the grid training is fast, time of the grid training is shortened, and transmission speed of output compressed data is improved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.
Claims (10)
1. An intelligent Internet of things big data transmission method is characterized by comprising the following steps:
the method comprises the steps of segmenting data of the Internet of things to obtain data vectors of all moments, classifying the data vectors of all moments to obtain a plurality of sets of data vectors of the same type, wherein each type of data vector is formed into time sequence data;
obtaining the confidence coefficient of each unit root of the time sequence data, and calculating the stability degree of the time sequence data according to the confidence coefficient;
acquiring white noise coincidence rate of each time sequence data, and acquiring trend coincidence degree of each time sequence data;
calculating the complexity of each time sequence data according to the stationarity degree, the white noise coincidence rate and the trend coincidence degree, and calculating the loss influence weight of each time sequence data according to the complexity;
calculating the correlation of each two types of time sequence data, and calculating the loss influence degree of each time sequence data according to the correlation and the loss influence weight;
and acquiring an initialization weight matrix according to the loss influence degree, carrying out network training based on the initialization weight matrix, carrying out data compression on the Internet of things by using the trained network, and outputting compressed data.
2. The intelligent Internet of things big data transmission method according to claim 1, wherein the step of segmenting Internet of things data to obtain data vectors at all times, and the step of classifying the data vectors at all times to obtain a plurality of sets of homogeneous data vectors comprises the steps of:
obtaining the dimensionality of single moment data of the Internet of things;
dividing the data of the internet of things into corresponding data vectors at the 1 st moment, data vectors at the 2 nd moment and data vectors at the … nth moment according to the dimensionality of the data at each single moment;
extracting data at the same position in the data vector at each moment to obtain the data vector of the same type;
and obtaining a plurality of homogeneous data vector sets according to the homogeneous data vectors.
3. The big data transmission method of the intelligent Internet of things according to claim 1, wherein the confidence level of a unit root of each time series data is obtained, and the step of calculating the stationarity degree of the time series data according to the confidence level comprises the following steps:
obtaining trend information of each time series data through a least square method; eliminating trend information in the time series data;
acquiring the confidence coefficient of the unit root of the time sequence data with the trend information eliminated by a unit root inspection method;
and calculating the stationarity degree of the time series data through the confidence coefficient.
4. The big data transmission method of the intelligent Internet of things according to claim 1, wherein the step of obtaining the trend conformity degree of the time series data comprises the following steps:
obtaining processed first time sequence data by carrying out differential processing on the time sequence data;
analyzing the first time series data by using a polynomial regression model to obtain a trend equation, and obtaining a data prediction value corresponding to each moment of the first time series data according to the trend equation;
calculating a difference value between data corresponding to each moment in the first time sequence data and a data prediction value;
and acquiring the square mean of all the difference values, wherein the square mean is the trend coincidence degree.
5. The big data transmission method of the intelligent Internet of things as claimed in claim 1, wherein the step of calculating the complexity of the time series data according to the stationarity degree, the white noise coincidence rate and the trend coincidence degree comprises:
the complexity of each time series data is calculated according to the following formula (1):
wherein, FiComplexity of time series data representing the ith category, BiWhite noise coincidence rate, H, representing time series data of ith categoryiShows the degree of conformity of the trend of the time series data of the ith category, PiIndicates the degree of stationarity of the time series data,
for the over-parameter, 0.2 was taken.
6. The intelligent Internet of things big data transmission method according to claim 1, wherein the step of calculating the loss influence weight of the time series data according to the complexity comprises the following steps:
the loss influence weight of each time series data is calculated according to the following formula (2):
wherein Q isiLoss impact weight, F, representing the ith category of time series dataiThe complexity of the ith category of time series data is shown, and N shows the dimension of the ith category of time series data, namely the number of moments.
7. The intelligent Internet of things big data transmission method and system as claimed in claim 1, wherein the step of calculating the correlation between every two types of time series data comprises:
the correlation is calculated according to the following formula (3):
wherein I represents the I-th class time series data, J represents the J-th class time series data, cov (I, J) represents the covariance of the data sequence, Var (I) represents the variance of the I-th class time series data, Var (J) represents the variance of the J-th class time series data, and Xi,jAnd the correlation coefficient of the i-th class and the j-th class time sequence data is shown.
8. The intelligent Internet of things big data transmission method according to claim 1, wherein the step of calculating the loss influence degree of the time series data according to the correlation and the loss influence weight comprises the following steps:
the degree of influence of the loss was calculated according to the following formula (4):
wherein Q isiWeight, Q, representing the loss impact of class i time series datajRepresenting the loss impact weight of the j-th class of timing data. Xi,jIndicating the phase of the ith type time sequence data and the jth type time sequence dataThe relevance of the product is as follows,
shows the influence of the loss of the i-th time series data on other data, YiIndicating the degree of influence of loss of the i-th type time series data.
9. The big data transmission method of the intelligent Internet of things according to claim 1, wherein the step of obtaining the initialization weight matrix according to the loss influence degree comprises the following steps:
assuming that a weight vector multiplied by certain time sequence data and ith time sequence data of the internet of things is an M-dimensional vector, the initialization rule of the weight matrix of the input layer at positions 1-M is as follows:
with the loss influence degree as the mean and the variance σ1,σ2…,σMConstructing M normal distributions;
generating a corresponding random number by utilizing each normal distribution;
initializing a weight matrix of the input layer according to the random number as an initialized weight value of the corresponding position;
the initialized weight value of the weight matrix of the hidden layer is alpha, the alpha is an empirical value of 10, and the weight matrix of the hidden layer is initialized, so that the initialized weight matrix is obtained.
10. The big data transmission system of the intelligent internet of things as claimed in any one of claims 1 to 9, comprising:
the data segmentation module is used for segmenting the data of the Internet of things to obtain data vectors at all times, classifying the data vectors at all times to obtain a set of a plurality of data vectors of the same type, and forming each type of data vector into time sequence data;
the first data processing module is used for acquiring the confidence coefficient of each unit root of the time sequence data and calculating the stability degree of the time sequence data according to the confidence coefficient;
the second data processing module is used for acquiring the white noise coincidence rate of each time sequence data and acquiring the trend coincidence degree of each time sequence data;
the third data processing module is used for calculating the complexity of each time sequence data according to the stationarity degree, the white noise coincidence rate and the trend coincidence degree, and calculating the loss influence weight of each time sequence data according to the complexity;
the fourth data processing module is used for calculating the correlation of each two types of time sequence data and calculating the loss influence degree of each time sequence data according to the correlation and the loss influence weight;
and the data transmission module is used for acquiring the initialized weight matrix according to the loss influence degree, performing network training based on the initialized weight matrix, performing data compression of the Internet of things by using the trained network, and outputting compressed data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210357624.3A CN114785824B (en) | 2022-04-06 | Intelligent Internet of things big data transmission method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210357624.3A CN114785824B (en) | 2022-04-06 | Intelligent Internet of things big data transmission method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114785824A true CN114785824A (en) | 2022-07-22 |
| CN114785824B CN114785824B (en) | 2024-05-14 |
Family
ID=
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115659070A (en) * | 2022-12-28 | 2023-01-31 | 鸿基骏业环保科技有限公司 | Water flow data transmission method based on NB-IOT intelligent water meter |
| CN116934431A (en) * | 2023-09-19 | 2023-10-24 | 贵昌集团有限公司 | Electronic commerce data intelligent management system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180139227A1 (en) * | 2016-10-31 | 2018-05-17 | Jask Labs Inc. | Method for predicting security risks of assets on a computer network |
| CN112085254A (en) * | 2020-08-05 | 2020-12-15 | 中国地质大学（武汉） | Prediction method and model based on multi-fractal cooperative measurement gating cycle unit |
| CN112149717A (en) * | 2020-09-03 | 2020-12-29 | 清华大学 | Confidence weighting-based graph neural network training method and device |
| CN113489674A (en) * | 2021-05-25 | 2021-10-08 | 南京邮电大学 | Malicious traffic intelligent detection method and application for Internet of things system |
| CN113642754A (en) * | 2021-04-23 | 2021-11-12 | 北京工业大学 | Complex industrial process fault prediction method based on RF noise reduction self-coding information reconstruction and time convolution network |
| CN114067118A (en) * | 2022-01-12 | 2022-02-18 | 湖北晓雲科技有限公司 | Processing method of aerial photogrammetry data |
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180139227A1 (en) * | 2016-10-31 | 2018-05-17 | Jask Labs Inc. | Method for predicting security risks of assets on a computer network |
| CN112085254A (en) * | 2020-08-05 | 2020-12-15 | 中国地质大学（武汉） | Prediction method and model based on multi-fractal cooperative measurement gating cycle unit |
| CN112149717A (en) * | 2020-09-03 | 2020-12-29 | 清华大学 | Confidence weighting-based graph neural network training method and device |
| CN113642754A (en) * | 2021-04-23 | 2021-11-12 | 北京工业大学 | Complex industrial process fault prediction method based on RF noise reduction self-coding information reconstruction and time convolution network |
| CN113489674A (en) * | 2021-05-25 | 2021-10-08 | 南京邮电大学 | Malicious traffic intelligent detection method and application for Internet of things system |
| CN114067118A (en) * | 2022-01-12 | 2022-02-18 | 湖北晓雲科技有限公司 | Processing method of aerial photogrammetry data |
Non-Patent Citations (2)
| Title |
|---|
| 杨静;辛宇;谢志强;: "面向物联网传感器事件监测的双向反馈系统", 计算机学报, no. 03 * |
| 桑夏夏;李旭伟;: "一种金融时间序列区域分割方法的研究", 四川大学学报(自然科学版), no. 06 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115659070A (en) * | 2022-12-28 | 2023-01-31 | 鸿基骏业环保科技有限公司 | Water flow data transmission method based on NB-IOT intelligent water meter |
| CN116934431A (en) * | 2023-09-19 | 2023-10-24 | 贵昌集团有限公司 | Electronic commerce data intelligent management system |
| CN116934431B (en) * | 2023-09-19 | 2023-12-05 | 贵昌集团有限公司 | Electronic commerce data intelligent management system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Jeon et al. | Prediction of ship fuel consumption by using an artificial neural network | |
| CN112001306A (en) | Electroencephalogram signal decoding method for generating neural network based on deep convolution countermeasure | |
| CN109063719B (en) | Image classification method combining structure similarity and class information | |
| CN111814626B (en) | Dynamic gesture recognition method and system based on self-attention mechanism | |
| CN114004333A (en) | Oversampling method for generating countermeasure network based on multiple false classes | |
| CN115393933A (en) | Video face emotion recognition method based on frame attention mechanism | |
| CN111371611B (en) | Weighted network community discovery method and device based on deep learning | |
| CN112215199A (en) | SAR image ship detection method based on multi-receptive-field and dense feature aggregation network | |
| CN113326748B (en) | Neural network behavior recognition method adopting multidimensional correlation attention model | |
| CN111144500A (en) | Differential privacy deep learning classification method based on analytic Gaussian mechanism | |
| CN112800882A (en) | Mask face posture classification method based on weighted double-flow residual error network | |
| CN106909944B (en) | Face picture clustering method | |
| CN112926645A (en) | Electricity stealing detection method based on edge calculation | |
| Chen et al. | SA-SinGAN: self-attention for single-image generation adversarial networks | |
| CN114785824B (en) | Intelligent Internet of things big data transmission method and system | |
| CN114785824A (en) | Intelligent Internet of things big data transmission method and system | |
| Yang et al. | CNN-GRUA-FC stock price forecast model based on multi-factor analysis | |
| CN114049675B (en) | Facial expression recognition method based on light-weight two-channel neural network | |
| CN115661627A (en) | Single-beam underwater target identification method based on GAF-D3Net | |
| CN116311349A (en) | Human body key point detection method based on lightweight neural network | |
| CN114724245A (en) | CSI-based incremental learning human body action identification method | |
| CN104166837A (en) | Method of visual voice recognition with selection of groups of most relevant points of interest | |
| CN110826726B (en) | Target processing method, target processing device, target processing apparatus, and medium | |
| CN105975996A (en) | Image segmentation method based on K-means and Nystrom approximation | |
| CN112597890A (en) | Face recognition method based on multi-dimensional Taylor network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20240418 Address after: 518000 office building 5C, Hony Plaza, No. 1, Financial Street, Nanshan street, Qianhai Shenzhen Hong Kong cooperation zone, Shenzhen, Guangdong Applicant after: Shenzhen Qianhai UFIDA Lihe Technology Service Co.,Ltd. Country or region after: China Address before: 450044 Room 301, unit 1, building 16, phase III, Min'an Beijun, Qinghuayuan Road, Xincheng Road, Huiji District, Zhengzhou City, Henan Province Applicant before: Zhengzhou Runsheng Electronic Technology Co.,Ltd. Country or region before: China |
|
| GR01 | Patent grant |