Method and device for controlling power internet of things flow, and computer program product
A method and a device for controlling power Internet of Things flow, and a computer program product are provided. The method includes: determining a probability formula for reduction of network user flow, according to probability formulas of signal power over limit, poor quality of service, channel congestion and signal transmission delay occurring in each channel; determining a probability formula for cascading flow reduction of N network users; establishing a network user flow minimum control quantity function according to the probability formula for cascading flow reduction of N network users; determining a plurality of population particles according to a value range corresponding to the parameter of the network user flow minimum control quantity function; and determining the network user flow minimum control function as a fitness function, and optimizing population particles to obtain an optimal population particle. Power Internet of Things flow control is performed according to the optimal population particle.
1 . A method for controlling power Internet of Things flow, comprising:
determining a probability formula for reduction of network user flow according to a probability formula of signal power over-limit, a probability formula of poor quality of service, a probability formula of channel congestion and a probability formula of signal transmission delay occurring in each channel, the probability formula for reduction of network user flow is a formula for calculating probability of reducing network user flow;
determining a probability formula for cascading flow reduction of N network users according to the probability formula for reduction of network user flow, wherein 1≤N≤N v , and N v is a number of network users, the probability formula for cascading flow reduction of N network users is a formula for calculating probability of reducing cascading flow of N network users;
establishing a network user flow minimum control quantity function according to the probability formula for cascading flow reduction of N network users, wherein the network user flow minimum control quantity function is a function for calculating a minimum value of network user flow control quantity when cascading flow of N network users is reduced;
determining a plurality of population particles according to value ranges corresponding to parameters of the network user flow minimum control quantity function, wherein variables of the population particles comprise all the parameters of the network user flow minimum control quantity function and the values of the parameters in the population particles are different;
determining the network user flow minimum control quantity function as a fitness function, and optimizing a plurality of population particles by using an immune particle swarm optimization algorithm, so as to obtain an optimal population particle; and
performing power Internet of Things flow control according to the parameters of the optimal population particle.
2 . The method according to claim 1 , wherein before determining the probability formula for reduction of network user flow according to the probability formula of signal power over-limit, the probability formula of poor quality of service, the probability formula of the channel congestion and the probability formula of signal transmission delay occurring in each channel comprises:
establishing the probability formula of signal power over-limit occurring in each channel
p
Li
,
t
P
=
exp
[
K
Li
,
t
P
(
P
Li
,
t
-
P
Li
,
t
re
)
P
Li
,
t
re
]
-
k
Li
,
t
P
,
wherein
k
Li
,
t
P
is an influence coefficient of a signal power of an i-th channel at a moment t on flow control, P Li,t is a signal power of the i-th channel at the moment t,
P
Li
,
t
re
is a maximum signal power allowed for the i-th channel at the moment t, and
K
Li
,
t
P
is a signal power control coefficient of the i-th channel at the moment t;
establishing the probability formula of poor quality of service occurring in each channel
p
Li
,
t
S
=
exp
[
K
Li
,
t
S
(
S
Li
,
t
-
S
Li
,
t
re
)
S
Li
,
t
re
]
-
k
Li
,
t
S
,
wherein
k
Li
,
t
S
is an influence coefficient of quality of service of the i-th channel on flow control at a moment t, S Li,t is quality of service of the i-th channel at the moment t,
S
Li
,
t
re
is normal quality of service of the i-th channel at the moment t, and
K
Li
,
t
S
is a quality of service control coefficient of the i-th channel at the moment t; establishing the probability formula of channel congestion occurring in each channel
p
Li
,
t
ZS
=
exp
(
V
Li
,
t
K
Li
,
t
ZS
-
1
)
-
k
Li
,
t
ZS
,
wherein
k
Li
,
t
ZS
is an influence coefficient of the i-th channel blocking state on flow control at a moment t, V Li,t a blocking degree of the i-th channel at the moment t, and
K
Li
,
t
ZS
is a bandwidth coefficient related to a channel design parameter and flow control;
establishing the probability formula of signal transmission delay occurring in each channel
p
Li
,
t
SY
=
exp
(
t
Li
,
t
K
Li
,
t
SY
-
1
)
-
k
Li
,
t
SY
,
wherein
k
Li
,
t
SY
is an influence coefficient of the i-th channel transmission time at a moment t on flow control, t Li,t is a signal transmission time of the i-th channel at the moment t, and
k
Li
,
t
SY
is a coefficient related to a channel design parameter and flow control.
3 . The method according to claim 2 , wherein determining the probability formula for reduction of network user flow according to the probability formula of signal power over-limit, the probability formula of poor quality of service, the probability formula of channel congestion and the probability formula of signal transmission delay occurring in each channel, the method comprising:
calculating the probability of one or more of signal power over-limit, poor quality of service, channel blockage and signal transmission delay occurring in any channel according to the probability formula of the signal power over-limit, the quality of service under-limit, the channel blockage and the signal transmission delay occurring in each channel, so as to obtain the probability formula for reduction of network user flow
p
L
=
1
-
∏
i
=
1
N
L
(
1
-
p
Li
,
t
P
)
-
∏
i
=
1
N
L
(
1
-
p
Li
,
t
S
)
-
∏
i
=
1
N
L
(
1
-
p
Li
,
t
ZS
)
-
∏
i
=
1
N
L
(
1
-
p
Li
,
t
SY
)
,
N L is a number of channel.
4 . The method according to claim 3 , wherein determining the probability formula for cascading flow reduction of N network users according to the probability formula for reduction of network user flow comprises:
according to the probability formula for reduction of network user flow p L , determining the probability formula for cascading flow reduction of N network users,
p
v
MU
=
{
p
L
1
network
user
traffic
is
decreased
k
v
2
U
p
L
2
network
user
traffic
is
decreased
k
v
2
U
p
L
3
network
user
traffic
is
decreased
⋮
k
v
N
v
U
p
L
N
v
network
user
traffic
is
decreased
,
p
v
MU
is a probability of network users suffering cascading flow is reduced, and
k
v
2
u
,
k
v
3
U
,
…
,
k
v
N
v
U
are respectively coefficients of 2, 3, . . . , N v network users suffering cascading flow is reduced.
5 . The method according to claim 4 , wherein establishing the network user flow minimum control quantity function according to the probability formula for cascading flow reduction of N network users comprises:
determining a network user flow control quantity function
V
v
MU
=
V
1
v
p
L
+
(
V
1
v
+
V
2
v
)
k
v
2
FL
k
v
2
U
p
L
+
…
+
k
v
N
v
FL
k
v
N
v
U
p
L
∑
n
=
1
N
v
V
nv
according to the probability formula for cascading flow reduction of N network users, wherein
V
v
MU
is the flow control quantity when cascading flow of a plurality of network users is reduced, V 1v , V 2v . . . V nv are the flow control quantity of the first user, the second user, . . . , the nth user when the cascading flow of the network user is reduced;
k
v
1
FL
,
k
v
2
FI
,
k
v
3
FL
,
…
,
k
v
N
v
FL
are the percentage of the flow control quantity when the cascading flow of one two, three, . . . , N v network users is reduced, respectively;
determining the network user flow minimum control quantity function
min
V
v
MU
according to the network user flow control quantity function
V
v
MU
.
6 . The method according to claim 1 , wherein determining the network user flow minimum control function as the fitness function, and optimizing a plurality of population particles by using the immune particle swarm optimization algorithm, so as to obtain the optimal population particle comprises:
calculating fitness of each of the population particles;
setting a number of iterations t to be 1, and starting iteration calculation;
a first calculation step: calculating concentration of the population particles;
a division step: dividing the population particles of which the fitness is higher than an average value of fitness of all the population particles and the concentration is lower than an average value of concentration of all the population particles into sub-group S A , and dividing remaining population particles into sub-group S B ;
a first update step: performing position update on the sub-group S A to obtain updated sub-group SA, calculating multiple population particle fitness of the updated sub-group S A , and updating individual optimality
p
best
_
A
t
of the sub-group S A according to a fitness competition mechanism;
a vaccination step, extracting vaccine information according to a vaccine selection method, and vaccinating the subgroup S B to obtain vaccinated sub-group S B ;
a second calculation step, calculating fitness of the vaccinated sub-group S B , and updating individual optimality
p
best
_
B
t
of the vaccinated sub-group S B according to the fitness competition mechanism;
a second update step: synthesizing the updated sub-group S A and the vaccinated sub-group S B into a population S C , performing position updating on the population S C to obtain updated population S C , calculating multiple population fitness of the updated population S C , and updating individual optimality
p
best
t
and global optimality
p
gbest
t
of the updated population S C according to the fitness competition mechanism;
repeating the first calculation step, the division step, the first update step, the vaccination step, the second calculation step and the second update step at least once in sequence until the individual optimal
p
best
t
and the global optimal
p
gbest
t
meet a target requirement or the number of iterations t is greater than a maximum number of iterations, and exiting cycle and outputting the optimal population particle.
7 . The method according to claim 1 , wherein performing power Internet of Things flow control according to the variables of the optimal population particle comprises:
determining the flow control quantity of each network user according to the parameter of the optimal population particle, so that a sum of all the flow control quantity of the network users is the smallest.
8 . The method according to claim 2 , wherein determining the network user flow minimum control function as the fitness function, and optimizing a plurality of population particles by using the immune particle swarm optimization algorithm, so as to obtain the optimal population particle comprises:
calculating fitness of each of the population particles;
setting a number of iterations t to be 1, and starting iteration calculation;
a first calculation step: calculating concentration of the population particles;
a division step: dividing the population particles of which the fitness is higher than an average value of fitness of all the population particles and the concentration is lower than an average value of concentration of all the population particles into sub-group S A , and dividing remaining population particles into sub-group S B ;
a first update step: performing position update on the sub-group S A to obtain updated sub-group S A , calculating multiple population particle fitness of the updated sub-group S A , and updating individual optimality
p
best_A
t
of the sub-group S A according to a fitness competition mechanism;
a vaccination step, extracting vaccine information according to a vaccine selection method, and vaccinating the subgroup S B to obtain vaccinated sub-group S B ;
a second calculation step, calculating fitness of the vaccinated sub-group S B , and updating individual optimality
p
best_B
t
of the vaccinated sub-group S B according to the fitness competition mechanism;
a second update step: synthesizing the updated sub-group S A and the vaccinated sub-group S B into a population S C , performing position updating on the population S C to obtain updated population S C , calculating multiple population fitness of the updated population S C , and updating individual optimality
p
best
t
and global optimality
p
gbest
t
of the updated population S C according to the fitness competition mechanism;
repeating the first calculation step, the division step, the first update step, the vaccination step, the second calculation step and the second update step at least once in sequence until the individual optimal
p
best
t
and the global optimal
p
gbest
t
meet a target requirement or the number of iterations t is greater than a maximum number of iterations, and exiting cycle and outputting the optimal population particle.
9 . The method according to claim 3 , wherein determining the network user flow minimum control function as the fitness function, and optimizing a plurality of population particles by using the immune particle swarm optimization algorithm, so as to obtain the optimal population particle comprises:
calculating fitness of each of the population particles;
setting a number of iterations t to be 1, and starting iteration calculation;
a first calculation step: calculating concentration of the population particles;
a division step: dividing the population particles of which the fitness is higher than an average value of fitness of all the population particles and the concentration is lower than an average value of concentration of all the population particles into sub-group S A , and dividing remaining population particles into sub-group S B ;
a first update step: performing position update on the sub-group S A to obtain updated sub-group S A , calculating multiple population particle fitness of the updated sub-group SA, and updating individual optimality
p
best_A
t
of the sub-group S A according to a fitness competition mechanism;
a vaccination step, extracting vaccine information according to a vaccine selection method, and vaccinating the subgroup S B to obtain vaccinated sub-group S B ;
a second calculation step, calculating fitness of the vaccinated sub-group S B , and updating individual optimality
p
best_B
t
of the vaccinated sub-group S B according to the fitness competition mechanism;
a second update step: synthesizing the updated sub-group S A and the vaccinated sub-group S B into a population S C , performing position updating on the population S C to obtain updated population S C , calculating multiple population fitness of the updated population S C , and updating individual optimality
p
best
t
and global optimality
p
gbest
t
of the updated population S C according to the fitness competition mechanism;
repeating the first calculation step, the division step, the first update step, the vaccination step, the second calculation step and the second update step at least once in sequence until the individual optimal
p
best
t
and the global optimal
p
gbest
t
meet a target requirement or the number of iterations t is greater than a maximum number of iterations, and exiting cycle and outputting the optimal population particle.
10 . The method according to claim 4 , wherein determining the network user flow minimum control function as the fitness function, and optimizing a plurality of population particles by using the immune particle swarm optimization algorithm, so as to obtain the optimal population particle comprises:
calculating fitness of each of the population particles;
setting a number of iterations t to be 1, and starting iteration calculation;
a first calculation step: calculating concentration of the population particles;
a division step: dividing the population particles of which the fitness is higher than an average value of fitness of all the population particles and the concentration is lower than an average value of concentration of all the population particles into sub-group S A , and dividing remaining population particles into sub-group S B ;
a first update step: performing position update on the sub-group S A to obtain updated sub-group S A , calculating multiple population particle fitness of the updated sub-group S A , and updating individual optimality
p
best
_
A
t
of the sub-group S A according to a fitness competition mechanism;
a vaccination step, extracting vaccine information according to a vaccine selection method, and vaccinating the subgroup S B to obtain vaccinated sub-group S B ;
a second calculation step, calculating fitness of the vaccinated sub-group S B , and updating individual optimality
p
best
_
B
t
of the vaccinated sub-group S B according to the fitness competition mechanism; a second update step: synthesizing the updated sub-group S A and the vaccinated sub-group S B into a population S C , performing position updating on the population S C to obtain updated population S C , calculating multiple population fitness of the updated population S C , and updating individual optimality
p
best
t
and global optimality
p
gbest
t
of the updated population S C according to the fitness competition mechanism;
repeating the first calculation step, the division step, the first update step, the vaccination step, the second calculation step and the second update step at least once in sequence until the individual optimal
p
best
t
and the global optimal
p
gbest
t
meet a target requirement or the number of iterations t is greater than a maximum number of iterations, and exiting cycle and outputting the optimal population particle.
11 . The method according to claim 5 , wherein determining the network user flow minimum control function as the fitness function, and optimizing a plurality of population particles by using the immune particle swarm optimization algorithm, so as to obtain the optimal population particle comprises:
calculating fitness of each of the population particles;
setting a number of iterations t to be 1, and starting iteration calculation;
a first calculation step: calculating concentration of the population particles;
a division step: dividing the population particles of which the fitness is higher than an average value of fitness of all the population particles and the concentration is lower than an average value of concentration of all the population particles into sub-group S A , and dividing remaining population particles into sub-group S B ;
a first update step: performing position update on the sub-group S A to obtain updated sub-group S A , calculating multiple population particle fitness of the updated sub-group S A , and updating individual optimality
p
best
_
A
t
of the sub-group S A according to a fitness competition mechanism;
a vaccination step, extracting vaccine information according to a vaccine selection method, and vaccinating the subgroup S B to obtain vaccinated sub-group S B ;
a second calculation step, calculating fitness of the vaccinated sub-group S B , and updating individual optimality
p
best
_
B
t
of the vaccinated sub-group S B according to the fitness competition mechanism;
a second update step: synthesizing the updated sub-group S A and the vaccinated sub-group S B into a population S C , performing position updating on the population S C to obtain updated population S C , calculating multiple population fitness of the updated population S C , and updating individual optimality
p
best
t
and global optimality
p
gbest
t
of the updated population S C according to the fitness competition mechanism;
repeating the first calculation step, the division step, the first update step, the vaccination step, the second calculation step and the second update step at least once in sequence until the individual optimal
p
best
t
and the global optimal
p
gbest
t
meet a target requirement or the number of iterations t is greater than a maximum number of iterations, and exiting cycle and outputting the optimal population particle.
12 . The method according to claim 2 , wherein performing power Internet of Things flow control according to the variables of the optimal population particle comprises:
determining the flow control quantity of each network user according to the parameter of the optimal population particle, so that a sum of all the flow control quantity of the network users is the smallest.
13 . The method according to claim 3 , wherein performing power Internet of Things flow control according to the variables of the optimal population particle comprises:
determining the flow control quantity of each network user according to the parameter of the optimal population particle, so that a sum of all the flow control quantity of the network users is the smallest.
14 . The method according to claim 4 , wherein performing power Internet of Things flow control according to the variables of the optimal population particle comprises:
determining the flow control quantity of each network user according to the parameter of the optimal population particle, so that the sum of all the flow control quantity of the network users is the smallest.
15 . The method according to claim 5 , wherein performing power Internet of Things flow control according to the variables of the optimal population particle comprises:
determining the flow control quantity of each network user according to the parameter of the optimal population particle, so that a sum of all the flow control quantity of the network users is the smallest.
16 . A non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium comprises a stored program, wherein when the program runs, a device where the non-transitory computer readable storage medium is located is controlled to execute a method, wherein the method comprising: determining a probability formula for reduction of network user flow according to a probability formula of signal power over-limit, a probability formula of poor quality of service, a probability formula of channel congestion and a probability formula of signal transmission delay occurring in each channel, the probability formula for reduction of network user flow is a formula for calculating probability of reducing network user flow; determining a probability formula for cascading flow reduction of N network users according to the probability formula for reduction of network user flow, wherein 1≤N≤N v , and N v is a number of network users, the probability formula for cascading flow reduction of N network users is a formula for calculating probability of reducing cascading flow of N network users; establishing a network user flow minimum control quantity function according to the probability formula for cascading flow reduction of N network users, wherein the network user flow minimum control quantity function is a function for calculating a minimum value of network user flow control quantity when cascading flow of N network users is reduced; determining a plurality of population particles according to value ranges corresponding to parameters of the network user flow minimum control quantity function, wherein variables of the population particles comprise all the parameters of the network user flow minimum control quantity function and the values of the parameters in the population particles are different; determining the network user flow minimum control quantity function as a fitness function, and optimizing a plurality of population particles by using an immune particle swarm optimization algorithm, so as to obtain an optimal population particle; performing power Internet of Things flow control according to the parameters of the optimal population particle.
17 . A computer program product, comprising a computer program stored on a non-transitory computer readable storage medium, wherein the computer program implements a method when being executed by a processor, wherein the method comprising: determining a probability formula for reduction of network user flow according to a probability formula of signal power over-limit, a probability formula of poor quality of service, a probability formula of channel congestion and a probability formula of signal transmission delay occurring in each channel, the probability formula for reduction of network user flow is a formula for calculating probability of reducing network user flow; determining a probability formula for cascading flow reduction of N network users according to the probability formula for reduction of network user flow, wherein 1≤N≤N v , and N v is a number of network users, the probability formula for cascading flow reduction of N network users is a formula for calculating probability of reducing cascading flow of N network users; establishing a network user flow minimum control quantity function according to the probability formula for cascading flow reduction of N network users, wherein the network user flow minimum control quantity function is a function for calculating a minimum value of network user flow control quantity when cascading flow of N network users is reduced; determining a plurality of population particles according to value ranges corresponding to parameters of the network user flow minimum control quantity function, wherein variables of the population particles comprise all the parameters of the network user flow minimum control quantity function and the values of the parameters in the population particles are different; determining the network user flow minimum control quantity function as a fitness function, and optimizing a plurality of population particles by using an immune particle swarm optimization algorithm, so as to obtain an optimal population particle; performing power Internet of Things flow control according to the parameters of the optimal population particle.
18 . The non-transitory computer readable storage medium according to claim 16 , wherein before determining the probability formula for reduction of network user flow according to the probability formula of signal power over-limit, the probability formula of poor quality of service, the probability formula of the channel congestion and the probability formula of signal transmission delay occurring in each channel comprises:
establishing the probability formula of signal power over-limit occurring in each channel
p
Li
,
t
P
=
exp
[
K
Li
,
t
P
(
P
Li
,
t
-
P
Li
,
t
re
)
P
Li
,
t
re
]
-
k
Li
,
t
P
,
wherein
k
Li
,
t
P
is an influence coefficient of a signal power of an i-th channel at a moment t on flow control, P Li,t is a signal power of the i-th channel at the moment t,
P
Li
,
t
re
is a maximum signal power allowed for the i-th channel at the moment t, and
K
Li
,
t
P
is a signal power control coefficient of the i-th channel at the moment t; establishing the probability formula of poor quality of service occurring in each channel
p
Li
,
t
S
=
exp
[
K
Li
,
t
S
(
S
Li
,
t
-
S
Li
,
t
re
)
S
Li
,
t
re
]
-
k
Li
,
t
S
,
wherein
k
Li
,
t
S
is an influence coefficient of quality of service of the i-th channel on flow control at a moment t, S Li,t is quality of service of the i-th channel at the moment t,
S
Li
,
t
re
is normal quality of service of the i-th channel at the moment t, and
K
Li
,
t
S
is a quality of service control coefficient of the i-th channel at the moment t; establishing the probability formula of channel congestion occurring in each channel
p
Li
,
t
ZS
=
exp
(
V
Li
,
t
K
Li
,
t
ZS
-
1
)
-
k
Li
,
t
ZS
,
wherein
k
Li
,
t
ZS
is an influence coefficient of the i-th channel blocking state on flow control at a moment t, V Li,t a blocking degree of the i-th channel at the moment t, and
K
Li
,
t
ZS
is a bandwidth coefficient related to a channel design parameter and flow control;
establishing the probability formula of signal transmission delay occurring in each channel
p
Li
,
t
SY
=
exp
(
t
Li
,
t
K
Li
,
t
SY
-
1
)
-
k
Li
,
t
SY
,
wherein
k
Li
,
t
SY
is an influence coefficient of the i-th channel transmission time at a moment t on flow control, t Li,t is a signal transmission time of the i-th channel at the moment t, and
K
Li
,
t
SY
is a coefficient related to a channel design parameter and flow control.
19 . The non-transitory computer readable storage medium according to claim 16 , wherein determining the probability formula for reduction of network user flow according to the probability formula of signal power over-limit, the probability formula of poor quality of service, the probability formula of channel congestion and the probability formula of signal transmission delay occurring in each channel, the method comprising:
calculating the probability of one or more of signal power over-limit, poor quality of service, channel blockage and signal transmission delay occurring in any channel according to the probability formula of the signal power over-limit, the quality of service under-limit, the channel blockage and the signal transmission delay occurring in each channel, so as to obtain the probability formula for reduction of network user flow
p
L
=
1
-
∏
i
=
1
N
L
(
1
-
p
Li
,
t
P
)
-
∏
i
=
1
N
L
(
1
-
p
Li
,
t
S
)
-
∏
i
=
1
N
L
(
1
-
p
Li
,
t
ZS
)
-
∏
i
=
1
N
L
(
1
-
p
Li
,
t
SY
)
,
N L is a number of channel.
20 . The non-transitory computer readable storage medium according to claim 16 , wherein determining the probability formula for cascading flow reduction of N network users according to the probability formula for reduction of network user flow comprises:
according to the probability formula for reduction of network user flow p L , determining the probability formula for cascading flow reduction of N network users,
p
v
MU
=
{
p
L
1
network
user
traffic
is
decreased
k
v
2
U
p
L
2
network
user
traffic
is
decreased
k
v
3
U
p
L
3
network
user
traffic
is
decreased
⋮
k
v
N
v
U
p
L
N
v
network
user
traffic
is
decreased
,
p
v
MU
is a probability of network users suffering cascading flow is reduced, and
k
v
2
U
,
k
v
3
U
,
⋯
,
k
v
N
V
U
are respectively coefficients of 2, 3, . . . , N v network users suffering cascading flow is reduced.