CN115175306B - Indoor positioning method of electric power Internet of things based on convolutional neural network - Google Patents

Indoor positioning method of electric power Internet of things based on convolutional neural network Download PDF

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CN115175306B
CN115175306B CN202210729213.2A CN202210729213A CN115175306B CN 115175306 B CN115175306 B CN 115175306B CN 202210729213 A CN202210729213 A CN 202210729213A CN 115175306 B CN115175306 B CN 115175306B
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宋文卓
郭夫然
张亮
宋景博
汪赟
姚晗
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State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Henan Electric Power Co Ltd
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Abstract

The invention discloses an indoor positioning method of an electric power Internet of things based on a convolutional neural network, which comprises the following steps: s1, in an offline fingerprint library construction stage, a probability density function diagram is generated for each acquisition position by using a nuclear density estimation method and is used for constructing an RSSI statistical sample fingerprint library; s2, training the convolutional neural network by using a statistical sample fingerprint library in a training stage; s3, in an online positioning stage, according to the RSSI samples acquired by equipment to be positioned, predicting the position of the equipment based on a convolutional neural network algorithm. The spatial statistical distribution characteristic of the signal intensity and the spatial feature extraction advantage of the convolutional neural network are fully utilized, and an accurate and stable indoor positioning technology can be provided in the application scene of the electric power Internet of things.

Description

Indoor positioning method of electric power Internet of things based on convolutional neural network
Technical Field
The invention relates to the technical field of wireless sensor networks, in particular to an indoor positioning method of an electric power Internet of things based on a convolutional neural network.
Background
With the continuous progress of wireless communication, microelectronics and other technologies, the application of the internet of things in power systems is becoming wider and wider. A variety of wireless communication technologies have been applied to power systems such as LoRa, NB-IoT, OFDM, etc. In an electrical power system, such as a power plant, a substation, etc., many devices are located indoors and location information for these devices is needed when the system is running. However, the conventionally used outdoor positioning technology, such as GPS, is affected by obstacles such as walls, and is difficult to be applied to indoor environments, so indoor positioning in the electric power internet of things is a problem that needs to be solved.
Indoor positioning is being studied more and more at home and abroad. RSSI is the most commonly used indicator in indoor positioning because it is readily available in many wireless communication devices (e.g., loRa, NB-IoT, zigBee, OFDM). Although various indoor positioning methods based on RSSI exist, the methods are convenient to improve in precision, stability and the like, and in a power system, a great deal of problems of interference, noise, shielding and the like exist, so that a high-precision and stable indoor positioning method facing the power Internet of things is needed.
Disclosure of Invention
In order to overcome the defects, the invention aims to provide the indoor positioning method of the electric power Internet of things based on the convolutional neural network, which fully utilizes the spatial statistical distribution characteristic of the signal intensity and the spatial feature extraction advantage of the convolutional neural network and can provide an accurate and stable indoor positioning technology in the application scene of the electric power Internet of things.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an indoor positioning method of an electric power Internet of things based on a convolutional neural network comprises the following steps:
S1, in an offline fingerprint library construction stage, a probability density function diagram is generated for each acquisition position by using a nuclear density estimation method and is used for constructing an RSSI statistical sample fingerprint library;
s2, training the convolutional neural network by using a statistical sample fingerprint library in a training stage;
S3, in an online positioning stage, according to the RSSI samples acquired by equipment to be positioned, predicting the position of the equipment based on a convolutional neural network algorithm.
Optionally, the step of the offline fingerprint library construction stage includes:
S11, let L= (L 1,…,ln) be n RSSI acquisition positions, wherein L i＝(xi,yi,zi is the three-dimensional coordinate of a position L i; let a= (a 1,…,ah) be the set of APs in the positioning region, where a i is the i-th AP; collecting m RSSI samples at any RSSI collecting position l i and recording the RSSI sample set as Wherein/>For the j-th RSSI sample,/>The RSSI value of the kth AP in the jth sample at the acquisition position is obtained; after the RSSI samples are collected for each position to be collected, an original RSSI sample library/>
S12, given an original RSSI sample library R, training a kernel density estimator for a sample R i (i is more than or equal to 1 and less than or equal to n) at any RSSI acquisition position:
s121, give Let/>One kernel density estimator needs to be trained for each AP at RSSI acquisition position l i, where the kernel density estimator f i,k (r) for the kth AP is given by:
Wherein K (·) is a kernel function and u is a smoothing parameter; estimating by using a Gaussian kernel, and determining smoothing parameters by using a maximum likelihood estimation method; thus, there are a total of h kernel density estimators, denoted F i＝(fi,1(r),…,fi,h (r), at RSSI acquisition location l i;
S13, let r min and r max be possible received RSSI minimum and maximum values, and let lambda be step size parameter; then a horizontal axis interval sample point x= (r min,rmin+λ,rmin+2λ,…,rmax) of the RSSI probability density distribution can be created; for any RSSI acquisition position l i, the following operations are performed:
S131, for any AP a k (k is more than or equal to 1 and less than or equal to h), generating corresponding probabilities Yi,k＝(fi,k(rmin),fi,k(rmin+λ),fi,k(rmin+2λ),…,fi,k(rmax)); of all sampling points in X by using a kernel density estimator f i,k (r), and then, taking X= (r min,rmin+λ,rmin+2λ,…,rmax) as a horizontal axis coordinate ,Yi,k＝(fi,k(rmin),fi,k(rmin+λ),fi,k(rmin+2λ),…,fi,k(rmax)) as a vertical axis coordinate to generate a line graph G i,k; obviously, there are h line graphs at any RSSI acquisition position l i, and the line graphs are recorded as G i＝(Gi,1,…,Gi,h in a set;
And S132, after the operation of S131 is performed on all RSSI acquisition positions, a final offline RSSI fingerprint library G= (G 1,…,Gn) can be obtained.
Optionally, the step of training phase includes:
S21, constructing a convolutional neural network training set by using an offline RSSI fingerprint library G, taking i as a label of any RSSI acquisition position l i, and transversely splicing h line graphs to form an image which is marked as P i and is used as characteristic data, so that training data at the acquisition position is marked as D i＝(i,Pi). Finally obtaining training set as D= (D 1,…Dn);
S22, constructing a convolutional neural network n-type classifier by using an existing framework, and marking the classifier as f;
s23, training the classifier f on the training set D.
Optionally, the step of the online positioning stage includes:
S31, for a certain device d to be positioned, continuously collecting m RSSI samples, and recording the RSSI sample set as Wherein/>For the j-th RSSI sample,/>The RSSI value of the kth AP in the jth sample at the acquisition position is obtained;
s32, training a kernel density estimator on the device d to be positioned for each AP, wherein the kernel density estimator f d,k (r) of the kth AP is given by:
Wherein K (·) is a kernel function and u is a smoothing parameter; considering that the signal intensity presents Gaussian distribution, the invention adopts Gaussian kernel to estimate, and the smoothing parameter is determined by adopting a maximum likelihood estimation method; thus, there are a total of h kernel density estimators, denoted as F d＝(fd,1(r),…,fd,h (r)), on the device d to be located;
S33, for any AP a k (k is more than or equal to 1 and less than or equal to h), generating corresponding probabilities Yd,k＝(fd,k(rmin),fd,k(rmin+λ),fd,k(rmin+2λ),…,fd,k(rmax)); of all sampling points in X by using a kernel density estimator f d,k (r), and then, taking X= (r min,rmin+λ,rmin+2λ,…,rmax) as a horizontal axis coordinate ,Yd,k＝(fd,k(rmin),fd,k(rmin+λ),fd,k(rmin+2λ),…,fd,k(rmax)) as a vertical axis coordinate to generate a line graph G d,k; h line diagrams are arranged on the equipment d to be positioned, and the line diagrams are marked as G d＝(Gd,1,…,Gd,h;
S34, positioning by using the trained convolutional neural network f.
Optionally, the step of using the trained convolutional neural network f to perform positioning includes:
S341, transversely splicing the h-piece line graphs in the G d to form an image which is recorded as P d as characteristic data;
S342, transmitting P d into f for classification, taking K positions with maximum f output layer softmax function estimated value, and recording the K positions with maximum probability as respectively Wherein/>
Respectively isIs a three-dimensional coordinate of (2);
s343, the positioning result of the final device d to be positioned is
The invention has the positive beneficial effects that:
Aiming at the characteristics of complex radio frequency environment, serious interference and the like of the electric power Internet of things, the spatial distribution characteristics of the signal intensity are extracted by utilizing the convolutional neural network, and the method is used for accurately and robustly positioning the indoor electric power equipment. The method comprises the following steps: an offline fingerprint library construction stage, a training stage and an online positioning stage. The spatial statistical distribution characteristic of the signal intensity and the spatial feature extraction advantage of the convolutional neural network are fully utilized, and an accurate and stable indoor positioning technology can be provided in the application scene of the electric power Internet of things.
Drawings
Fig. 1 is a schematic block diagram of an indoor positioning method of an electric power internet of things based on a convolutional neural network provided in embodiment 1 of the present invention;
Fig. 2 is a schematic diagram of an original RSSI acquisition provided in step S11 of embodiment 1 of the present invention;
FIG. 3 is a diagram illustrating the creation of a line graph according to step S131 of embodiment 1 of the present invention;
FIG. 4 is a schematic diagram of training set generation provided in step S21 of embodiment 1 of the present invention;
fig. 5 is a schematic diagram of final positioning generation of an output layer provided in step S342 of embodiment 1 of the present invention.
Detailed Description
The invention will be further described with reference to the following embodiments.
Example 1
As shown in fig. 1 to 5, an indoor positioning method of an electric power internet of things based on a convolutional neural network includes the steps of:
S1, in an offline fingerprint library construction stage, a probability density function diagram is generated for each acquisition position by using a nuclear density estimation method and is used for constructing an RSSI statistical sample fingerprint library;
s2, training the convolutional neural network by using a statistical sample fingerprint library in a training stage;
S3, in an online positioning stage, according to the RSSI samples acquired by equipment to be positioned, predicting the position of the equipment based on a convolutional neural network algorithm.
Aiming at the characteristics of complex radio frequency environment, serious interference and the like of the electric power Internet of things, the invention utilizes the convolutional neural network to extract the spatial distribution characteristic of the signal intensity for accurate and robust indoor positioning of the electric power equipment, fully utilizes the spatial statistical distribution characteristic of the signal intensity and the spatial characteristic extraction advantage of the convolutional neural network, and can provide an accurate and stable indoor positioning technology in the application scene of the electric power Internet of things.
The steps of the off-line fingerprint database construction stage comprise:
S11, let L= (L 1,…,ln) be n RSSI acquisition positions, wherein L i＝(xi,yi,zi is the three-dimensional coordinate of a position L i; let a= (a 1,…,ah) be the set of APs in the positioning region, where a i is the i-th AP; collecting m RSSI samples at any RSSI collecting position l i and recording the RSSI sample set as Wherein/>For the j-th RSSI sample,/>The RSSI value of the kth AP in the jth sample at the acquisition position is obtained; after the RSSI samples are collected for each position to be collected, an original RSSI sample library/>
S12, given an original RSSI sample library R, training a kernel density estimator for a sample R i (i is more than or equal to 1 and less than or equal to n) at any RSSI acquisition position:
s121, give Let/>One kernel density estimator needs to be trained for each AP at RSSI acquisition position l i, where the kernel density estimator f i,k (r) for the kth AP is given by:
Wherein K (·) is a kernel function and u is a smoothing parameter; taking the Gaussian distribution of the signal intensity into consideration, estimating by adopting a Gaussian kernel, and determining a smoothing parameter by adopting a maximum likelihood estimation method; thus, there are a total of h kernel density estimators, denoted F i＝(fi,1(r),…,fi,h (r), at RSSI acquisition location l i;
S13, let r min and r max be possible received RSSI minimum and maximum values, and let lambda be step size parameter; then a horizontal axis interval sample point x= (r min,rmin+λ,rmin+2λ,…,rmax) of the RSSI probability density distribution can be created; for any RSSI acquisition position l i, the following operations are performed:
S131, for any AP a k (k is more than or equal to 1 and less than or equal to h), generating corresponding probabilities Yi,k＝(fi,k(rmin),fi,k(rmin+λ),fi,k(rmin+2λ),…,fi,k(rmax)); of all sampling points in X by using a kernel density estimator f i,k (r), and then, taking X= (r min,rmin+λ,rmin+2λ,…,rmax) as a horizontal axis coordinate ,Yi,k＝(fi,k(rmin),fi,k(rmin+λ),fi,k(rmin+2λ),…,fi,k(rmax)) as a vertical axis coordinate to generate a line graph G i,k; obviously, there are h line graphs at any RSSI acquisition position l i, and the line graphs are recorded as G i＝(Gi,1,…,Gi,h in a set;
And S132, after the operation of S131 is performed on all RSSI acquisition positions, a final offline RSSI fingerprint library G= (G 1,…,Gn) can be obtained.
The existing indoor positioning algorithm is mostly positioned based on the mean value, and the statistics and the spatial distribution characteristics of RSSI samples are ignored, so that the invention extracts the statistics characteristics of the original samples aiming at each RSSI acquisition position of the spatial distribution by using a nuclear density estimation method.
The training phase comprises the following steps:
S21, constructing a convolutional neural network training set by using an offline RSSI fingerprint library G, and specifically: for any RSSI acquisition position l i, i is used as its label, and the h line graphs are transversely spliced to form an image, which is denoted as P i as characteristic data, so that training data at the acquisition position is denoted as D i＝(i,Pi). Finally obtaining training set as D= (D 1,…Dn);
s22, constructing a convolutional neural network n-type by using an existing framework (such as AlexNet, VGGNet and the like), and marking the classifier as f;
s23, training the classifier f on the training set D.
The online positioning stage comprises the following steps:
S31, for a certain device d to be positioned, continuously collecting m RSSI samples, and recording the RSSI sample set as Wherein/>For the j-th RSSI sample,/>The RSSI value of the kth AP in the jth sample at the acquisition position is obtained;
s32, training a kernel density estimator on the device d to be positioned for each AP, wherein the kernel density estimator f d,k (r) of the kth AP is given by:
Wherein K (·) is a kernel function and u is a smoothing parameter; considering that the signal intensity presents Gaussian distribution, the invention adopts Gaussian kernel to estimate, and the smoothing parameter is determined by adopting a maximum likelihood estimation method; thus, there are a total of h kernel density estimators, denoted as F d＝(fd,1(r),…,fd,h (r)), on the device d to be located;
S33, for any AP a k (k is more than or equal to 1 and less than or equal to h), generating corresponding probabilities Yd,k＝(fd,k(rmin),fd,k(rmin+λ),fd,k(rmin+2λ),…,fd,k(rmax)); of all sampling points in X by using a kernel density estimator f d,k (r), and then, taking X= (r min,rmin+λ,rmin+2λ,…,rmax) as a horizontal axis coordinate ,Yd,k＝(fd,k(rmin),fd,k(rmin+λ),fd,k(rmin+2λ),…,fd,k(rmax)) as a vertical axis coordinate to generate a line graph G d,k; h line diagrams are arranged on the equipment d to be positioned, and the line diagrams are marked as G d＝(Gd,1,…,Gd,h;
S34, positioning by using the trained convolutional neural network f.
The step of positioning by using the trained convolutional neural network f comprises the following steps:
S341, transversely splicing the h-piece line graphs in the G d to form an image which is recorded as P d as characteristic data;
S342, transmitting P d into f for classification, taking K positions with maximum f output layer softmax function estimated value instead of taking the classification result as the estimated position, and recording the K positions with maximum probability as respectively Wherein the method comprises the steps of Respectively/>Is a three-dimensional coordinate of (2);
s343, the positioning result of the final device d to be positioned is
Aiming at the characteristics of complex radio frequency environment, serious interference and the like of the electric power Internet of things, the spatial distribution characteristics of the signal intensity are extracted by utilizing the convolutional neural network, and the method is used for accurately and robustly positioning the indoor electric power equipment. The spatial statistical distribution characteristic of the signal intensity and the spatial feature extraction advantage of the convolutional neural network are fully utilized, and an accurate and stable indoor positioning technology can be provided in the application scene of the electric power Internet of things.
The concrete steps are as follows:
(1) Unlike available method, which adopts average value to estimate, the present invention utilizes kernel density estimating method to extract the probability statistic features of signal strength in each sampling point in space and the trained probability statistic model retains more features for positioning in image mode.
(2) In addition, the invention does not directly use the result of the convolutional neural network to locate, but uses the mean value of K positions with the maximum estimated value of the softmax function of the output layer to locate, thereby improving the stability of the locating system.
Finally, it is noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention, and that other modifications and equivalents thereof by those skilled in the art should be included in the scope of the claims of the present invention without departing from the spirit and scope of the technical solution of the present invention.

Claims (2)

1. The indoor positioning method of the electric power Internet of things based on the convolutional neural network is characterized by comprising the following steps:
S1, in an offline fingerprint library construction stage, a probability density function diagram is generated for each acquisition position by using a nuclear density estimation method and is used for constructing an RSSI statistical sample fingerprint library;
s2, training the convolutional neural network by using a statistical sample fingerprint library in a training stage;
S3, in an online positioning stage, predicting the position of equipment based on a convolutional neural network algorithm according to an RSSI sample acquired by the equipment to be positioned;
The steps of the off-line fingerprint database construction stage comprise:
s11, let L= (L 1,…,ln) be n RSSI acquisition positions, wherein L i=(xi,yi,zi is the three-dimensional coordinate of a position L i; let a= (a 1,…,ah) be the set of APs in the positioning region, where a i is the i-th AP; collecting m RSSI samples at any RSSI collecting position l i and recording the RSSI sample set as Wherein/>For the j-th RSSI sample,/>The RSSI value of the kth AP in the jth sample at the acquisition position is obtained; after the RSSI samples are collected for each position to be collected, an original RSSI sample library/>；
S12, giving an original RSSI sample library R, and aiming at samples at any RSSI acquisition position(1. Ltoreq.i.ltoreq.n) training the kernel density estimator:
s121, give Let/>; One kernel density estimator needs to be trained for each AP at RSSI acquisition position l i, where the kernel density estimator f i,k (r) for the kth AP is given by:
，
wherein K (·) is a kernel function and u is a smoothing parameter; estimating by using a Gaussian kernel, and determining smoothing parameters by using a maximum likelihood estimation method; thus, there are a total of h kernel density estimators, denoted F i=(fi,1(r),…, fi,h (r), at RSSI acquisition location l i;
S13, let r min and r max be possible received RSSI minimum and maximum values, and let lambda be step size parameter; then a horizontal axis interval sample point x= (r min, rmin+λ, rmin+2λ,…, rmax) of the RSSI probability density distribution can be created; for any RSSI acquisition position l i, the following operations are performed:
S131, for any AP a k (k is more than or equal to 1 and less than or equal to h), generating corresponding probabilities Yi,k=( fi,k(rmin), fi,k(rmin+λ), fi,k(rmin+2λ),…, fi,k(rmax)); of all sampling points in X by using a kernel density estimator f i,k (r), and then, taking X= (r min, rmin+λ, rmin+2λ,…, rmax) as a horizontal axis coordinate ,Yi,k=( fi,k(rmin), fi,k(rmin+λ), fi,k(rmin+2λ),…, fi,k(rmax)) as a vertical axis coordinate to generate a line graph G i,k; obviously, there are h line graphs at any RSSI acquisition position l i, and the line graphs are recorded as G i=(Gi,1,…, Gi,h in a set;
s132, after the operation of S131 is performed on all RSSI acquisition positions, a final offline RSSI fingerprint library G= (G 1,…,Gn) can be obtained;
the online positioning stage comprises the following steps:
S31, for a certain device d to be positioned, continuously collecting m RSSI samples, and recording the RSSI sample set as Wherein/>For the j-th RSSI sample,/>The RSSI value of the kth AP in the jth sample at the acquisition position is obtained;
s32, training a kernel density estimator on the device d to be positioned for each AP, wherein the kernel density estimator f d,k (r) of the kth AP is given by:
Wherein K (·) is a kernel function and u is a smoothing parameter; taking the Gaussian distribution of the signal intensity into consideration, estimating by adopting a Gaussian kernel, and determining a smoothing parameter by adopting a maximum likelihood estimation method; thus, there are a total of h kernel density estimators, denoted as F d=( fd,1(r),…, fd,h (r)), on the device d to be located;
S33, for any AP a k (k is more than or equal to 1 and less than or equal to h), generating corresponding probabilities Yd,k=( fd,k(rmin), fd,k(rmin+λ), fd,k(rmin+2λ),…, fd,k(rmax)); of all sampling points in X by using a kernel density estimator f d,k (r), and then, taking X= (r min, rmin+λ, rmin+2λ,…, rmax) as a horizontal axis coordinate ,Yd,k=( fd,k(rmin), fd,k(rmin+λ), fd,k(rmin+2λ),…, fd,k(rmax)) as a vertical axis coordinate to generate a line graph G d,k; h line diagrams are arranged on the equipment d to be positioned, and the line diagrams are marked as G d=(Gd,1,…, Gd,h;
s34, positioning by using a trained convolutional neural network f;
the step of positioning by using the trained convolutional neural network f comprises the following steps:
S341, transversely splicing the h-piece line graphs in the G d to form an image which is recorded as P d as characteristic data;
S342, transmitting P d into f for classification, taking K positions with maximum f output layer softmax function estimated value, and recording the K positions with maximum probability as respectively Wherein/>，/>Respectively/>Is a three-dimensional coordinate of (2);
s343, the positioning result of the final device d to be positioned is 。
2. The indoor positioning method of the electric internet of things based on the convolutional neural network as set forth in claim 1, wherein the training phase comprises the steps of:
S21, constructing a convolutional neural network training set by using an offline RSSI fingerprint library G, taking i as a label of any RSSI acquisition position l i, transversely splicing h line graphs to form an image which is marked as P i and is used as characteristic data, marking training data on the acquisition position as D i=(i, Pi, and finally obtaining the training set as D= (D 1,…Dn);
s22, constructing a convolutional neural network classifier by using an existing framework, and marking the classifier as f;
s23, training the classifier f on the training set D.
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