CN114693141B - Transformer substation inspection method based on end edge cooperation - Google Patents
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Abstract
A transformer substation inspection method based on end edge coordination relates to the technical field of power Internet of things, establishes a transformer substation inspection system model based on end edge coordination, provides a task unloading and resource management scheme for balancing system average information age and wireless equipment energy consumption indexes, and improves the timeliness of transformer substation inspection task data processing. Compared with the existing method that data are collected by equipment and then are analyzed manually, the method saves manual investment, obviously improves the data processing efficiency, does not depend too much on the experience of inspection personnel in the data processing process, and is more scientific. Compared with the existing method for transmitting the polling task data back to the cloud center for processing, the method saves network bandwidth resources, relieves the computing pressure of the cloud center, and solves the problem of low timeliness of data processing caused by overlong transmission links.
Description
Technical Field
The invention relates to the technical field of power internet of things, in particular to a transformer substation inspection method based on end edge coordination.
Background
The inspection of the transformer substation is one of key links of the operation management of the transformer substation and is a foundation for ensuring the safe and stable operation of each level of power grid. According to the intelligent inspection technology of the transformer substation, an infrared camera, an ultraviolet camera and a visible light camera are carried on intelligent equipment to acquire the multi-element data of the transformer substation equipment, and then the acquired data are transmitted to an operation and maintenance management center to be processed. The intelligent inspection system of the transformer substation can solve the problems of high time consumption of manual inspection, high requirement on professional knowledge of inspection personnel, complex inspection flow, low inspection efficiency and the like, and enables the operation management of the transformer substation to be systematic, standardized and scientific. With the continuous development and construction of power grids, the intensity of the routing inspection task and the data volume of the routing inspection task of the transformer substation are increased continuously, and the application characteristics of intensive calculation and time delay sensitivity are presented gradually, so that the data processing pressure is huge. The conventional substation inspection method adopts a manual processing or cloud center processing task data mode, so that the manual processing efficiency is low, the inspection method depends on the experience and professional knowledge of personnel, and inspection holes are easy to appear; the cloud center processing occupies a large amount of backhaul network bandwidth resources, and the link length is difficult to ensure the timeliness of data processing. The edge calculation and edge-side cooperation technology is introduced into the transformer substation inspection, so that the data processing efficiency can be improved, and the transformer substation high-efficiency, high-precision and high-frequency inspection can be realized. With the continuous and deep application of the intelligent inspection technology, the edge side resources are limited, how to integrate the inspection terminal and the edge side resources, and designing an efficient task unloading scheme and a resource management scheme become key problems faced by the intelligent inspection system of the transformer substation.
Common indexes for measuring the performance of the task unloading scheme and the resource management scheme comprise time delay and energy consumption, wherein the time delay can represent the timeliness of the scheme to a certain extent, but the time delay cannot reflect the freshness of data in a system. The Information freshness is of great importance to the real-time monitoring system and the state updating system of the internet of things, and if the Information received by the system is outdated Information, the accuracy and reliability of system decision can be reduced, so that the fact that the Age of Information (AoI) is introduced into the transformer substation system to measure the Information freshness of the system is of great significance to guarantee the safety and stability of the power system.
In conclusion, the important trend of the substation inspection method is as follows: an edge computing technology is introduced into the substation inspection system, a reasonable and efficient task unloading scheme and a resource management scheme are designed to realize end edge cooperation, the substation inspection efficiency and the inspection quality are promoted to be improved, and the development requirements of a future digital power grid are met.
Prior art 1: hujin Lei, zhu Yang Feng, lin Zhu, li Yang sheep, liu Jian, shenhuan a substation unmanned aerial vehicle patrol edge calculation frame design and resource scheduling method [ J ] high voltage technology, 2021,47 (02): 425-433. The technology designs an unmanned aerial vehicle inspection edge calculation framework of the transformer substation, provides a resource scheduling method under the framework, and provides a basis for promoting efficient and safe transformer substation unmanned operation and maintenance. However, the unmanned aerial vehicle has limited computing resources and energy resources, and long-term large sample data processing is difficult to realize by using the unmanned aerial vehicle as an edge computing server.
Prior art 2: information age [ J ].2019 for compute-intensive messages in Kuang Q, jie G, xiang C, et al. The technology researches an information age calculation method under the conditions of local calculation and remote calculation of calculation-intensive data in mobile edge calculation, and the zero-waiting strategy is adopted to simplify a system model but is not enough to embody an actual data service flow.
Under the background, the invention provides a transformer substation inspection method based on end edge cooperation.
Disclosure of Invention
The invention provides a transformer substation inspection method based on end edge coordination, which establishes a transformer substation inspection system model based on end edge coordination, provides a task unloading and resource management scheme for balancing system average information age and wireless equipment energy consumption indexes, improves transformer substation inspection task data processing timeliness, and provides a solution for solving the problem that the existing transformer substation inspection method is difficult to adapt to future high-efficiency, high-precision and high-frequency inspection requirements.
A transformer substation inspection method based on end edge coordination is specifically realized by the following steps:
acquiring state information, channel state information and port information of intelligent inspection equipment in an inspection system;
the intelligent patrol equipment state information comprises: the device type, the device number, the access mode and the current working state; the setting system comprises M intelligent inspection devices to form a device set M SE =1, ·, M,. And M, and devices are classified into wired devices and wireless devices according to different access modes, b m ∈{0,1},
Indicates the type of equipment, wherein b m =1 denotes a wireless device, b m =0 represents a wired device;
step two, establishing a transformer substation inspection system model based on end edge coordination according to the information acquired in the step one, and specifically realizing the three steps of establishing a transformer substation inspection task data calculation model, calculating average AoI and calculating wireless equipment energy consumption;
step two, establishing a transformer substation inspection task data calculation model;
randomly generating fixed inseparable Task data packet Task by equipment m in inspection process m ＝{d m ,c m }
Wherein d is m Representing the length of data generated by device m, c m Indicating the amount of computation required to process the unit bit data, then the Task is completed m The total calculation amount required is d m c m ；
The task data packet has two calculation modes: local computing or off-loading computing;
determining task data packet calculation mode as unloading decision process, and recording m ∈{0,1},
For decision variables, representing the way in which the device m is calculated, y m =0 for local calculation, y m Where =1 denotes an offload computation, the decision variables of the plurality of devices constitute a set of decision variables, denoted as Y = { Y = { Y } m ,m∈M SE }；
Secondly, calculating average AoI;
analyzing the calculation process of the average AoI according to two conditions in a calculation mode, namely the calculation process of the average AoI in local calculation and the calculation process of the average AoI in unloading calculation;
the calculation process of the average AoI in the local calculation is as follows:
the data packet generated by the intelligent inspection equipment m has the arrival rate of lambda m0 The local computation process of the device m is modeled as having an arrival rate lambda m0 And service rate mu mc FCFS rule M/M/1 queuing system of (1);
setting the generation time interval of the ith data packet to
Then the
Ε[·]Represents a mathematical expectation; service time is
Then
Queue latency of
Residence time of
The locally calculated average AoI is:
in the formula (I), the compound is shown in the specification,
and satisfy
The system stability is ensured;
the computing power of the intelligent patrol equipment m during local computing is recorded as a computing power set
The calculation process of the average AoI in the unloading calculation is as follows:
the service process of the task data packet during the unloading calculation is divided into two phases Phase1 and Phase2, which are expressed as follows:
phase1 includes three processes of task data packet generation, entering local data transmission queue and data transmission, and is modeled as having an arrival rate lambda m0 And service rate mu mt FCFS rule M/M/1 queuing system of (1), where μ mt ＝R m /d m ，R m For data transmission rate, it is expressed by the following equation:
wherein R is m0 For wired transmission rate, p m For wireless transmission power, the transmission power set is P = { P = m ,m∈M SE }，h m Is the channel gain, σ 2 Is the Gaussian white noise power of the channel, and B is the bandwidth of the channel;
phase2 modeling as arrival rate λ m0 And a service rate of mu ms FCFS rule M/M/1 queuing system of, wherein
To offload the computing power allocated to device m by the edge computing center during computation, note
Allocating a set for computing power, wherein the set represents a computing resource allocation scheme of an edge computing center;
setting the generation time interval of the ith data packet during the unloading calculation as
Then
The waiting time at Phase1 and Phase2 is
And
service times are respectively
And
then
Residence times are respectively
And
unload calculation Total residence time
Comprises the following steps:
the average AoI at the time of the offload calculation is:
wherein the content of the first and second substances,
and
and satisfy
And
the system stability is ensured;
step two, calculating the energy consumption of the wireless equipment, and analyzing the calculation process of the energy consumption of the wireless equipment according to two conditions of a calculation mode, namely the calculation process of the energy consumption of the wireless equipment during local calculation and the calculation process of the energy consumption of the wireless equipment during unloading calculation;
wireless device energy consumption during said local computation
Comprises the following steps:
in the formula (I), the compound is shown in the specification,
the wireless device runs power for local computing,
is the calculated energy consumption of the wireless device at the time of local calculation, epsilon is the energy consumption coefficient,
is the energy consumption value of the wireless device in unit time;
wireless device energy consumption during the offloading of computing
Comprises the following steps:
in the formula (I), the compound is shown in the specification,
to offload power consumption of the wireless device operation when computing,
transmitting energy consumption for the wireless device in unloading the calculation;
step three, weighing the average AoI of the system and the energy consumption of the wireless equipment of the system, establishing a weighting and cost function, and establishing an optimization problem; the method comprises the following specific steps:
step three, determining the average AoI of the system and the energy consumption of wireless equipment of the system, and establishing a weighting and cost function after standardization; the system average AoI is:
the energy consumption of wireless equipment of the system is as follows:
standardizing delta and E by min-max standardization method, and establishing weighting and cost function G (Y, F) l ,F e P), represented by the formula:
G(Y,F l ,F e ,P)＝λ 1 Δ * +(1-λ 1 )E *
in the formula, the weight coefficient lambda 1 ∈[0,1]Showing the bias degree, delta, of the cost function to the two indexes of the average AoI of the system and the average energy consumption of the wireless equipment of the system * Average AoI, E for normalized System * Wireless device energy consumption for standardized systems;
step two, establishing a constraint optimization problem according to the weighting and cost function and the constraint condition;
Problem:
s.t.C1:
C2:
C3:
C4:
C5:
C6:
C7:p m ≤p max ,
C8:
where constraints C1, C2, C3 indicate that the queuing system should be stable; the constraints C4, C5, C7 and C8 are value range constraints of the relevant parameters; constraint C6 indicates the sum of the computing power allocated to each device by the edge computing service center;
solving an optimization problem by adopting an improved wolf algorithm to obtain an unloading decision and a resource allocation scheme; i.e. the solution of the optimization problem; the key steps of the improved gray wolf algorithm are as follows:
fourthly, initializing the population by adopting a Logistic chaotic mapping method, and recording the current position vector of the wolf of China as
Step two, introducing interference factors and increasing the randomness of the position updating process;
step four, weighing global search and local search by adopting a nonlinear convergence factor;
in the formula, a initial An initial value of a,2,a final The final value of a is 0, kappa is the nonlinear degree, the nonlinear change rule of a is changed by adjusting kappa, iter is the current iteration number, and maxiter is the maximum iteration number;
fifthly, distributing computing resources for the inspection equipment according to the unloading decision and the resource distribution scheme obtained in the fourth step;
and step six, finishing data processing and feeding back the inspection result to the user interaction interface.
The invention has the beneficial effects that:
(1) In the method, the routing inspection task data processing has two processing modes: and local calculation and uninstalling calculation, and the end side equipment completes the inspection work of the transformer substation in a coordinated manner. Compared with the existing method that data are collected by equipment and then are analyzed manually, the method saves manual investment, obviously improves the data processing efficiency, does not depend too much on the experience of inspection personnel in the data processing process, and is more scientific. Compared with the existing method for transmitting the polling task data back to the cloud center for processing, the method saves network bandwidth resources, relieves the computing pressure of the cloud center, and solves the problem of low timeliness of data processing caused by too long transmission link.
(2) Compared with the existing M/M/1 queuing system which describes the unloading calculation service flow as zero-waiting strategy first-come first-serve, the system better conforms to the processing process of the data packet, comprises the whole projects of data packet generation, transmission queue entering, transmission, queue entering waiting service and task data processing, and has more reference and application values.
(3) In the system modeling process, the cost function is constructed by balancing the average AoI of the system and the energy consumption index of the wireless equipment of the system, specific expressions of the average AoI of the system and the energy consumption index of the wireless equipment of the system are determined according to the local computing service process and the unloading computing service process respectively, and the cost function is established after standardization. Compared with the existing method for establishing the cost function by taking the time delay as the main index, the average AoI comprises the waiting time of the information at the source node and the staying time of the information at the destination node, so that the timeliness of data processing can be better reflected, and the freshness of system information is ensured.
(4) In the system modeling process, the average AoI of the wireless equipment and the average AoI of the wired equipment are considered, and compared with the existing scheme of only considering the indexes of the wireless equipment to obtain the unloading decision and resource allocation, the model provided by the invention is more consistent with the actual scene of the transformer substation and has stronger applicability.
(5) When the optimization problem is solved, an improved grey wolf algorithm is provided, population initialization is carried out by adopting a Logistic chaotic mapping method, the uniformity of population distribution is improved, interference factors and nonlinear convergence factors are introduced, and the global search capability of the algorithm is improved. The improved grayish wolf algorithm can obtain a better approximate solution within a similar number of iterations as compared to the basic grayish wolf algorithm. Compared with particle swarm and genetic algorithms, the improved Hui wolf algorithm has better convergence performance.
In conclusion, the transformer substation inspection method disclosed by the invention is more suitable for actual transformer substation scenes, can effectively improve the data processing efficiency of the transformer substation inspection method, relieves the pressure of the bandwidth of a return network and the pressure of cloud center calculation, ensures the freshness of system information, improves the scientificity of transformer substation inspection, and provides a new solution for ensuring the safe and stable operation of the transformer substation.
Drawings
FIG. 1 is a schematic diagram of a multi-device substation inspection scene;
FIG. 2 is a flow chart of a transformer substation inspection method based on end edge coordination according to the invention;
FIG. 3 is a schematic diagram of a system service process for locally computing task data;
FIG. 4 is a schematic diagram of a system service process for offloading computing task data;
FIG. 5 is a flow chart of the improved Grey wolf algorithm of the present invention;
FIG. 6 is a comparison graph of the results of the convergence performance simulation performed with the 4 algorithms.
Detailed Description
The present embodiment, a substation inspection method based on end edge coordination, is described with reference to fig. 1 to 6, and fig. 1 is a schematic diagram of a multi-device substation inspection scene provided by the present embodiment. The intelligent patrol inspection system comprises a base station, an edge calculation center and intelligent patrol inspection equipment, wherein the intelligent patrol inspection equipment comprises a plurality of wireless equipment and a plurality of wired equipment, the wireless equipment is connected into the edge calculation center through the base station, and the base station is connected with the edge calculation center in a wired mode. The edge computing center comprises an access control unit, a decision management unit, a resource management unit and a computing unit. The access control unit is responsible for managing the intelligent inspection equipment accessed to the edge computing center and acquiring the state information, the channel state information, the port information and the like of the intelligent inspection equipment in the system; the decision management unit is responsible for system model construction, model solution and unloading decision making; the resource management unit allocates computing resources for the access equipment according to the unloading decision; the computing unit is responsible for task data processing and feeds back a computing result to the user interaction interface, so that inspection personnel can conveniently check the inspection result.
Fig. 2 is a flowchart of a method for a substation based on edge-side coordination according to the embodiment. The method specifically comprises the following steps:
step 1: and acquiring state information, channel state information and port information of intelligent inspection equipment in the inspection system.
The device state information includes: device type, number of devices, access mode, current working state, etc. Suppose that the system contains M intelligent inspection devices in total, and is recorded as a set M SE = {1,. Lam, M }; according to the access mode, the devices are divided into wired devices and wireless devices, note b m ∈{0,1},
Represents a device type, wherein b m =1 denotes a wireless device, b m =0 denotes a wired device.
And 2, step: establishing a transformer substation inspection system model based on end edge cooperation according to the information acquired in the step 1, and specifically, realizing three steps of establishing a transformer substation inspection task data calculation model, calculating average AoI and calculating energy consumption of wireless equipment;
step 21: establishing a transformer substation inspection task data calculation model;
randomly generating a fixed inseparable Task data packet Task by the equipment m in the polling process m ＝{d m ,c m },
Wherein d is m Indicates the length of data (in bit), c, generated by device m m Indicating the amount of computation (unit: cycles/bit) required to process the unit bit data, the Task is completed m The total calculation amount required is d m c m (unit: cycles). The data packet generation follows the poisson distribution, and the data packets generated by different devices are different. The task data packet has two calculation modes: local calculation or unloading calculation, wherein the local calculation means that a task data packet is calculated by a processor of the device m, and the unloading calculation means that the task data packet is unloaded to an edge calculation center and the edge calculation center completes the calculation; since the data packets are inseparable, only the choice is madeOne of the calculation methods. The process of determining the data packet calculation mode is the unloading decision process, the unloading decision is given by the edge calculation center according to the subsequent steps of the method of the invention, and the memory y is used for recording m ∈{0,1},
For decision variables, representing the way in which the device m is calculated, y m =0 for local calculation, y m Where =1 denotes an offload computation, the decision variables of the plurality of devices constitute a set of decision variables, denoted as Y = { Y = { Y } m ,m∈M SE }。
Step 22: calculating an average AoI;
the age of information (AoI) is a performance index that evaluates the freshness of information received by a destination, and is defined as:
Δ(t)＝t-g(t) (1)
wherein t represents the current time, g (t) represents the time stamp of the generation time of the latest data received by the user platform, namely AoI refers to the time difference from the generation of the information to the current time, and AoI and the time delay are the most different in that the age of the information not only includes the transmission time delay of the information, but also includes the waiting time of the information at the source node and the staying time at the destination node. Therefore, the AoI can better reflect the information circulation process in the system than the time delay, and the good AoI index can ensure the freshness of the system information, so that the system has good timeliness, the accuracy and the reliability of system decision are improved, and the method has important significance for ensuring the safety and the stability of the power system.
The AoI indicator covers the whole process from the generation of a task packet by a data source to the end of service waiting in a queue, so the average AoI formula in the normal case is introduced first. Assume that the observation start time is t =0, the queue is empty, and the information age initial value is Δ (0) = Δ 0 . Note t = t i I =1, 2.. And n is the generation time of the ith task data packet, the task data packet generation interval is X i ＝t i -t i-1 I =1, 2. Note t = t i' I =1, 2.. N is the service completion time of the ith task data packet, and the lingering time of the ith task data packet is T i ＝t i' -t i ＝W i +S i Wherein W is i For the waiting time of the ith task data packet in the queue, S i The service time of the ith task data packet is calculated locally. Until the nth task data packet is processed, the system observation time length is tau = t n' -0＝t n' At time (0, t) n' ) Within, the average AoI can be expressed as:
in the embodiment, the task queue obeys a First Come First Serve (FCFS) queuing rule, and the calculation process of the average AoI is analyzed in two cases according to the calculation mode, namely the calculation process of the average AoI during local calculation and the calculation process of the average AoI during unloading calculation.
(1) Calculating the average AoI in local calculation;
the local computation task data packet service flow is as shown in fig. 3, after a task data packet is generated by a data source, the task data packet enters a queue to wait for service, after the service is finished, result data is sent to a user interaction interface, and a data service process is terminated. Preferably, the data amount is generally small after the calculation is completed, so that the calculation service is approximately considered to be completed, that is, the result data is successfully received by the user interaction interface. The data packet generated by the intelligent inspection equipment m has the arrival rate of lambda m0 The local computational process is modeled as having an arrival rate of λ m0 And a service rate of mu mc FCFS rule M/M/1 queuing system. Recording the generation time interval of the ith data packet as
Then the
Service time is
Then
Ε[·]Represents a mathematical expectation; queue latency of
According to the flow of the data packet service,
can be expressed as:
residence time of
Then
The probability density function of (a) is:
wherein, the first and the second end of the pipe are connected with each other,
and satisfy
To ensure that the system is stable;
the computing power of the intelligent inspection equipment m is recorded as a computing power set
The locally calculated average AoI is expressed as:
and the following steps:
then:
substitution of formula (5) gives:
(2) Unloading the calculation process of the average AoI in calculation;
when the calculation is unloaded, the equipment m establishes connection with the edge calculation center in a wired or wireless mode, and the service center allocates calculation resources for the equipment m according to the equipment state information
And establishes a virtual machine V m Virtual machine V m Forming a one-to-one service relationship with device m. Preferably, as shown in fig. 4, the service flow of the data packet for offloading the computation task is that after the computation data packet is generated by the terminal, the computation data packet enters a local data transmission queue, and then is transmitted to the virtual machine V through the device m m Service queue, waiting for service, and finally by virtual machine V m And finishing the data packet processing, sending the calculation result to a user interaction interface, and feeding back a finishing signal to the equipment m until the service flow is terminated. Preferably, the data volume of the calculation result and the feedback signal is generally small, so that the completion of the calculation service is approximately considered as the success of receiving the result data for the user interaction interface.
Dividing the service flow of the task data packet during the unloading calculation into two phases Phase1 and Phase2:
phase1: comprises 3 processes of task data packet generation, entering a local data transmission queue and data transmission, and the process is modeled to have an arrival rate lambda m0 And service rate mu mt FCFS rules of (M/1) queuing system, where μ mt ＝R m /d m ，R m Is the data transmission rate.
Wherein R is m0 For wired transmission rate, p m For wireless transmission power, the transmission power set is P = { P = m ,m∈M SE }，h m For channel gain, σ 2 Is the gaussian white noise power of the channel and B is the channel bandwidth.
Phase2: involving arrival of task data into virtual machine V m Service queue, waiting for service, task data processing 3 processes. From (9), the output process of Phase1 is the input process of Phase2, and Phase1 is the arrival rate λ m0 The output process of the M/M/1 queuing system under the statistical balance is still the parameter of lambda m0 So Phase2 is the arrival rate of λ m0 And a service rate of mu ms FCFS rules of (M/M/1) queuing system, in which
The computing power (in cycles/s) of the edge computing center to the device m is shown
And allocating a set for computing power, wherein the computing power allocation set represents a computing resource allocation scheme of the edge computing center.
The generation time interval of the ith data packet during the unloading calculation is recorded as
Then the
The waiting times at Phase1 and Phase2 are respectively
And
service times are respectively
And
then
Residence times are respectively
And
residence time of Phase1
The probability density function is:
wherein:
and satisfy
To ensure that the system is stable.
Residence time of Phase2
The probability density function is:
wherein:
and satisfy
To ensure that the system is stable.
Then the total dwell time T at the time of the offload calculation i e Comprises the following steps:
the average AoI at the time of the offload calculation is:
according to formula (7):
in the belt-in (14), the average AoI at the time of the unload calculation can be found to be:
step 23: calculating the energy consumption of the wireless device;
the intelligent inspection equipment comprises wired equipment and wireless equipment, wherein the wireless equipment is powered by a limited-capacity battery when executing an inspection task, so that the effective working time of the wireless equipment is determined by the energy consumption, especially mobile equipment such as an unmanned aerial vehicle, an inspection robot and the like need to keep a motion attitude and process calculation-intensive and delay-sensitive tasks, the problem of energy consumption is more prominent, and the inspection distance, the inspection efficiency and the inspection frequency of the equipment are directly influenced. In view of the above, the present invention takes wireless device energy consumption as an important indicator for system modeling. The wired equipment is usually supplied with power by cables, and the energy supply is stable, so the energy consumption of the wired equipment such as a fixed camera and the like is not considered in the system model.
The longer the average stay time of the task data packet in the system, the lower the service efficiency of the system is, and the higher the energy consumption of the wireless device is. According to the packet service flow, the energy consumption of the wireless device m during the packet stay mainly includes the operation energy consumption, the calculation energy consumption and the transmission energy consumption.
The operation energy consumption mainly refers to the energy consumption generated when the wireless device maintains the working state or the moving state. The main wireless equipment involved in substation inspection comprises an unmanned aerial vehicle, intelligent inspection robot mobile equipment and other non-mobile wireless acquisition equipment. The non-mobile wireless acquisition equipment generally has a fixed working mode, and the energy consumption of the non-mobile wireless acquisition equipment in unit time is approximate to a fixed value; the energy consumption of a mobile device is mainly related to parameters such as moving speed, device quality and the like, and for the convenience of description, the invention assumes that the mobile device is at a velocity v m Moving at a constant speed, wherein the quality of the equipment is unchanged in the process of inspection, namely the energy consumption of the equipment in unit time is approximate to a fixed value; further, it can be assumed that the power consumption of the wireless device per unit time is constant
(there are differences between the devices). Whether the local computing mode or the unloading computing mode is adopted, the wireless device needs to maintain an operating state or a moving state, and the local computing mode and the unloading computing mode can calculate the operation energy consumption of the wireless device according to the rule.
The computing energy consumption is local meterThe time computing device m consumes energy generated by computing service, and the transmission energy consumption
Which refers to the energy consumption of device m due to data transmission when offloading the computation. The wireless device energy consumption calculation is related to the selected calculation mode, and the calculation process of the wireless device energy consumption in the local calculation and the calculation process of the wireless device energy consumption in the unloading calculation are respectively explained below.
(1) The energy consumption of the wireless device during local computing mainly comprises the operation energy consumption of the wireless device during local computing
And calculating energy consumption
According to the local calculation data service flow, the average residence time of the local calculation data packet is as follows:
then the wireless device operating energy consumption at the time of local computation can be defined as:
when in local calculation, the calculation energy consumption is as follows:
where ε is the coefficient of energy consumption, which is related to the device chip architecture.
The local computing wireless device energy consumption is therefore:
(2) The wireless device energy consumption during the off-load calculation mainly comprises the operation energy consumption of the wireless device during the off-load calculation
And transmission energy consumption
Preferably, since the execution result and the feedback signal data amount are generally small, the execution result and the feedback signal data transmission process can be omitted in the case of a higher transmission rate.
According to the offloading computing data service process, the latency distribution function of Phase1 is:
the latency distribution function for Phase2 is:
then the offload calculates the total latency W e ＝W (1) +W (2) The distribution function is:
the average latency is:
offload computation service time of S e ＝S (1) +S (2) And Phase1 and Phase2 service processes are independent of each other, so
According to (25) (26), the average residence time of the offload computation packets is:
the operating energy consumption of the wireless device in offloading the computation may be defined as:
the transmission energy consumption of the wireless device is as follows:
so the wireless device energy consumption when offloading the computation is:
and 3, step 3: and (4) weighing the system average information age and the system wireless equipment energy consumption index, establishing a weighting and cost function, and establishing an optimization problem.
Step 31: and establishing the average AoI of the system and the energy consumption of wireless equipment of the system, and establishing a weighting and cost function after standardization.
The average AoI of the system obtained from (8) and (17) is:
the energy consumption of the wireless equipment of the system according to (21) and (30) is as follows:
the two indexes of the average AoI delta of the system and the energy consumption E of the wireless equipment of the system have different dimensions and dimension units, and the influence of the dimensions needs to be eliminated when comprehensive comparison evaluation is carried out so as to solve the comparability between data indexes. To this end the invention normalizes Δ and E using the min-max normalization method.
Wherein, delta min And Δ max Respectively minimum and maximum values of Delta in the obtained scheme, E min And E max Respectively representing the minimum and maximum values of E in the obtained scheme.
Establishing a weight sum cost function G (Y, F) based on the above l ,F e ,P)：
G(Y,F l ,F e ,P)＝λ 1 Δ * +(1-λ 1 )E * (35)
Wherein the weight coefficient lambda 1 ∈[0,1]The bias degree of the cost function to two indexes of the average AoI of the system and the average energy consumption of the wireless equipment of the system is shown.
Step 32: and establishing a constraint optimization problem according to the cost function and the constraint condition:
Problem:
s.t.C1:
C2:
C3:
C4:
(36)
C5:
C6:
C7:p m ≤p max ,
C8:
in the equation, constraints C1, C2, C3 indicate that the queuing system should be stable. And the constraints C4, C5, C7 and C8 are value range constraints of the relevant parameters. Constraint C6 indicates the sum of the computing power allocated by the edge computing service center to each device, which should not exceed its upper computing power limit.
And 4, step 4: and solving the optimization problem by adopting an improved wolf algorithm to obtain an unloading decision and resource allocation scheme, namely a solution of the optimization problem.
As can be seen from the formula (36), the Problem includes both continuous variables and discrete variables, that is, the Problem is a mixed integer nonlinear programming Problem (MINLP), and the solution of such a Problem often has certain difficulty. The intelligent optimization algorithm is one of the common methods for solving the problems, can find an approximate solution in a short time, and has the advantages of wide application range, high efficiency and the like, and the common intelligent optimization algorithm comprises a genetic algorithm, a particle swarm algorithm, a wolf algorithm and the like. The Grey Wolf optimization algorithm (GWOlf Optimizer, GWOO) is a group intelligent optimization algorithm proposed by Mirjalili and the like in 2014, GWOO is inspired from the Grey Wolf group predation behavior in the nature, the basic GWOO algorithm divides a Wolf group into 4 social levels, 3 most suitable individuals in the Wolf group are sequentially marked as alpha Wolf, beta Wolf and delta Wolf according to the individual fitness value of the group, the rest individuals are omega wolfs, the alpha wolfs command the hunting action, the beta wolfs and the delta wolfs assist the alpha wolfs to command the hunting action, the omega wolfs are subjected to the command, and the Wolf group is updated for searching an optimal target through multiple iterations.
The grey wolf position updating process comprises the following steps: firstly, the distance between the gray wolf and the prey is calculated according to the formula (37)
Wherein:
is a prey position vector;
is the gray wolf location vector, t represents the current iteration;
the coefficient vector is usually a random value, so that the situation that the gray wolf approaches the target too fast and falls into local optimum is avoided. And then sequentially updating the gray wolf positions according to an equation (38), wherein:
is a vector of the coefficients of the image data,
when the system is used, the wolf attacks the prey, namely local search is carried out;
and when the wolf individual is far away from the current target, searching for a better target, namely carrying out global search. Calculating coefficient vectors from equations (39) and (40)
Is [0,1 ]]A represents the attack range of the wolf, and the iterative process a decreases linearly from 2 to 0.
According to the mathematical model, the position updating formula in the algorithm iteration process is as follows:
wherein the content of the first and second substances,
representing the current iteration alpha wolf, beta wolf, delta wolf and omega wolf position vectors,
the effect of alpha wolf, beta wolf and delta wolf on position updating is explained,
indicating the wolf pack location vector after location update.
The basic GWOL algorithm has the advantages of few parameters, simple structure and clear principle, but the convergence process of the GWOL algorithm is mainly guided by the alpha wolf, the beta wolf and the delta wolf together, so that the convergence precision is easily influenced due to the fact that the population diversity is insufficient and the local optimization is easily caused.
The basic Husky algorithm randomly initializes the population, and is difficult to ensure the distribution uniformity of the population.
In the embodiment, an interference factor is introduced in the position updating process, the randomness of the position updating process is increased, and the condition that the basic wolf algorithm is converged to the local optimal solution and is difficult to jump out is improved. The specific method comprises the following steps:
first, the interference probability p is preset 0 ∈[0,1]
Secondly, calculating fitness values, determining alpha wolf, beta wolf and delta wolf according to the optimal three fitness values, and storing the positions of the alpha wolf, the beta wolf and the delta wolf;
then, a random number r is generated 0 ∈[0,1]Randomly generating a wolf individual gamma as an interference factor;
finally, judge r 0 ≥p 0 If yes, calculating the distance from the gray wolf to the interference factor gamma according to a formula (44), calculating the influence of the interference factor on position updating according to a formula (45), updating the position of the gray wolf according to a formula (46), then calculating a fitness value, and replacing the worst 3 gray wolfs with alpha wolfs, beta wolfs and delta wolfs; further, λ in the formula (46) 2 ∈[0,1]Indicating the degree of influence of the interference factors on the position updating;
if r 0 ＜p 0 The gray wolf location is updated according to the basic gray wolf algorithm given by equation (43).
Further, the existing gray wolf algorithm balances the global search and the local search by utilizing the value A, when the value A is greater than 1, the global search is favored, when the value A is less than 1, the local search is favored, according to a formula (39), the value of A is related to a convergence factor a, namely the value A belongs to [ -2a,2a ], and the convergence factor a of the existing gray wolf algorithm is linearly decreased from 2 to 0 along with the iteration process.
In view of the non-linear nature of the algorithm search process, the present embodiment employs a non-linear convergence factor, as shown in equation (47), where a initial The initial value of a is 2,a final And (3) representing the final value of a, taking the value as 0, wherein kappa represents the nonlinear degree, the nonlinear change rule of a can be changed by adjusting kappa, iter is the current iteration number, and maxiter is the maximum iteration number.
And 5: and distributing the computing resources for the inspection equipment according to the unloading decision and the resource distribution scheme in the step 4.
And 6: and finishing data processing and feeding back the inspection result to the user interaction interface.
The method of the embodiment is adopted to carry out simulation experiments, and the simulation area is set to be 200 x 300m 2 The transformer substation is provided with 1 edge computing service center in an area, and the area contains 20 intelligent patrol data acquisition devices, wherein the area contains 4 wireless devices. The computing power of the edge computing center is not less than 30GHz, the upper limit of the computing power of the energy terminal is 2GHz, and the energy coefficient epsilon =10 -26 The maximum transmitting power upper limit of the intelligent terminal is 23dBm, the transmission rate of a wireless channel is not less than 20Mbit/s, andthe line channel rate is not less than 40Mbit/s. The data size is 8Mbit, the required calculation amount per bit is 1000cycles, and the interference probability p 0 =0.2, weight λ 1 ＝0.6，λ 2 =0.5, degree of non-linearity κ =4. The particle swarm algorithm, the genetic algorithm, the basic grayling algorithm and the improved grayling algorithm are respectively adopted to solve the optimization problem provided by the invention, and the convergence curves of the 4 algorithms are shown in figure 6. Compared with the improved wolf algorithm, the convergence speed of the genetic algorithm and the particle swarm algorithm is low, and the genetic algorithm and the particle swarm algorithm are easy to fall into local optimum; the convergence speed of the basic gray wolf algorithm is higher than that of the improved gray wolf algorithm in the iteration starting stage, mainly because the improved gray wolf algorithm introduces interference factors in the gray wolf position updating process, the population is more diverse, the search range is wider, and the convergence speed is influenced.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is specific and detailed, but not to be understood as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent should be subject to the appended claims.
Claims (4)
1. A transformer substation inspection method based on end edge coordination is characterized in that: the method is realized by the following steps:
acquiring state information, channel state information and port information of intelligent inspection equipment in an inspection system;
the intelligent patrol equipment state information comprises: the device type, the device number, the access mode and the current working state; the setting system comprises M intelligent inspection devices to form a device set M SE =1, ·, M,. And M, and devices are classified into wired devices and wireless devices according to different access modes, b m E {0,1}, where b m =1 denotes a wireless device, b m =0 represents a wired device;
step two, establishing a transformer substation inspection system model based on end edge coordination according to the information acquired in the step one, and specifically realizing the three steps of establishing a transformer substation inspection task data calculation model, calculating average AoI and calculating wireless equipment energy consumption;
step two, establishing a transformer substation inspection task data calculation model;
randomly generating fixed and inseparable task data packets by equipment m in inspection process
Wherein d is m Indicating the length of data generated by device m, c m Indicating the amount of computation required to process the unit bit data, the Task is completed m The total calculation amount required is d m c m ；
The task data packet has two calculation modes: local computing or off-loading computing;
determining task data packet calculation mode as unloading decision process, and recording
For decision variables, representing the way in which the device m is calculated, y m =0 for local calculation, y m Where =1 denotes an offload computation, the decision variables of the plurality of devices constitute a set of decision variables, denoted as Y = { Y = { Y } m ,m∈M SE }；
Secondly, calculating average AoI;
analyzing the calculation process of the average AoI in two situations according to a calculation mode, namely the calculation process of the average AoI in local calculation and the calculation process of the average AoI in unloading calculation;
the calculation process of the average AoI in the local calculation is as follows:
the data packet generated by the intelligent inspection equipment m has the arrival rate of lambda m0 The local computational process of device m is modeled as having an arrival rate λ m0 And service rate mu mc FCFS rule M/M/1 queuing system of (1);
setting the generation interval of the ith data packet to
Then
Ε[·]Represents a mathematical expectation; service time is
Then
Queue latency of W i l The residence time is
The locally calculated average AoI is:
wherein tau is the system observation time length,
and satisfy
The system stability is ensured;
the computing power of the intelligent patrol equipment m during local computing is recorded as a computing power set
The calculation process of the average AoI in the unloading calculation is as follows:
the service process of the task data packet during the unloading calculation is divided into two phases Phase1 and Phase2, which are expressed as follows:
t i for the generation time, t, of the ith task packet i' For the service completion time of the ith task data packet, phase1 comprises three processes of task data packet generation, entering into a local data transmission queue and data transmission and is modeled as having an arrival rate lambda m0 And service rate mu mt FCFS rule M/M/1 queuing system of (1), where μ mt ＝R m /d m ，R m For data transmission rate, it is expressed by the following equation:
wherein R is m0 For wired transmission rate, p m For wireless transmission power, the transmission power set is P = { P = m ,m∈M SE }，h m For channel gain, σ 2 Is the Gaussian white noise power of the channel, and B is the bandwidth of the channel;
phase2 modeling as arrival rate λ m0 And a service rate of mu ms FCFS rule M/M/1 queuing system of, wherein
For offloading computing power assigned to device m by the edge computing center during computation, note
Allocating a set for computing capacity, wherein the set represents a computing resource allocation scheme of an edge computing center;
setting the generation time interval of the ith data packet during the unloading calculation as
Then
The waiting time at Phase1 and Phase2 is W i (1) And W i (2) (ii) a Service times are respectively
And
then
Residence times are respectively T i (1) And T i (2) Off-load calculation of Total residence time T i e Comprises the following steps:
the average AoI at the time of the offload calculation is:
wherein, the first and the second end of the pipe are connected with each other,
and
and satisfy
And
the system stability is ensured;
step two, calculating the energy consumption of the wireless equipment, and analyzing the calculation process of the energy consumption of the wireless equipment according to two conditions of a calculation mode, namely the calculation process of the energy consumption of the wireless equipment during local calculation and the calculation process of the energy consumption of the wireless equipment during unloading calculation;
wireless device energy consumption during said local computation
Comprises the following steps:
in the formula (I), the compound is shown in the specification,
the wireless device runs power for local computing,
is the calculated energy consumption of the wireless device at the time of local calculation, epsilon is the energy consumption coefficient,
is the energy consumption value of the wireless device in unit time;
wireless device energy consumption during the offload computation
Comprises the following steps:
in the formula (I), the compound is shown in the specification,
to offload power consumption of the wireless device operation when computing,
transmitting energy consumption for the wireless device in unloading the calculation;
step three, weighing the average AoI of the system and the energy consumption of the wireless equipment of the system, establishing a weighting and cost function, and establishing an optimization problem; the method comprises the following specific steps:
step three, determining the average AoI of the system and the energy consumption of wireless equipment of the system, and establishing a weighting and cost function after standardization;
the system average AoI is:
the energy consumption of the wireless equipment of the system is as follows:
standardizing delta and E by min-max standardization method, and establishing weighting and cost function G (Y, F) l ,F e P), represented by the formula:
G(Y,F l ,F e ,P)＝λ 1 Δ * +(1-λ 1 )E *
in the formula, the weight coefficient lambda 1 ∈[0,1]Shows the bias degree of the cost function to two indexes of the average AoI of the system and the average energy consumption of the wireless equipment of the system, delta * Average AoI, E for normalized System * Wireless device energy consumption for standardized systems;
step two, establishing a constraint optimization problem according to the weighting and cost function and the constraint condition;
in the formula, constraints C1, C2, C3 indicate that the queuing system should be stable; the constraints C4, C5, C7 and C8 are value range constraints of the relevant parameters; constraint C6 indicates the sum of the computing power allocated to each device by the edge computing service center;
solving the optimization problem by adopting an improved wolf algorithm to obtain an unloading decision and a resource allocation scheme; i.e. the solution to the optimization problem; the key steps of the improved grey wolf algorithm are as follows:
fourthly, initializing the population by adopting a Logistic chaotic mapping method, and recording the current gray wolf position vector as
Step two, introducing interference factors and increasing the randomness of the position updating process;
step three, weighing global search and local search by adopting a nonlinear convergence factor;
wherein a is the attack range of the gray wolf, a initial An initial value of a,2,a final The final value of a is 0, kappa is the nonlinear degree, the nonlinear change rule of a is changed by adjusting kappa, iter is the current iteration number, and maxiter is the maximum iteration number;
fifthly, distributing computing resources for the inspection equipment according to the unloading decision and the resource distribution scheme obtained in the fourth step;
and step six, finishing data processing and feeding back the inspection result to the user interaction interface.
2. The substation inspection method based on end edge coordination according to claim 1, characterized in that: the inspection system comprises a base station, an edge calculation center and a plurality of intelligent inspection devices;
the wireless equipment is accessed to the edge computing center through a base station, and the base station is connected with the edge service center in a wired mode; the edge computing center comprises an access control unit, a decision management unit, a resource management unit and a computing unit;
the access control unit is responsible for managing the intelligent inspection equipment accessed to the edge computing center and acquiring the state information, the channel state information and the port information of the intelligent inspection equipment in the system;
the decision management unit is responsible for system model construction, model solution and unloading decision making;
the resource management unit allocates computing resources to the access equipment according to the unloading decision;
and the computing unit is responsible for task data processing and feeding back a computing result to the user interaction interface, and the computing result is used for inspection personnel to check the inspection result.
3. The substation inspection method based on end edge coordination according to claim 1, characterized in that: in the second step, the wireless equipment operation energy consumption is reduced during the unloading calculation
Comprises the following steps:
in the formula (I), the compound is shown in the specification,
average residence time of packets in calculation for offloading;
wireless device transmission power consumption
Comprises the following steps:
4. the substation inspection method based on end edge coordination according to claim 1, characterized in that: in the second step, interference factors are introduced, and the specific process of increasing the randomness of the position updating process is as follows:
first, an interference probability p is set 0 ∈[0,1]；
Secondly, calculating fitness values, determining alpha wolf, beta wolf and delta wolf according to the optimal three fitness values, and storing the positions of the alpha wolf, the beta wolf and the delta wolf
Updating formula calculation according to basic Grey wolf algorithm position, wherein
The effect of alpha wolf, beta wolf and delta wolf on position updating is explained;
then, a random number r is generated 0 ∈[0,1]Randomly generating the gray wolf individual gamma as the interference factor, the position vector of which is
Finally, judge r 0 ≥p 0 If yes, then according to the formula
Calculating the distance from the wolf to the interference factor gamma
A coefficient vector; then according to the formula
The effect of the interference factor on the location update is calculated,
is a coefficient vector; then, according to the following formula, the gray wolf position is updated to
Calculating the fitness value, and replacing the worst 3 grey wolves with alpha wolves, beta wolves and delta wolves to complete position updating;
in the formula (I), the compound is shown in the specification,
represents the position vector of the wolf cluster after the position update, t represents the current iteration, and lambda 2 Indicating the degree of influence of the interference factor on the location update, if r 0 ＜p 0 And updating the grey wolf position according to the existing grey wolf algorithm.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107846704A (en) * | 2017-10-26 | 2018-03-27 | 北京邮电大学 | A kind of resource allocation and base station service arrangement method based on mobile edge calculations |
| CN111262947A (en) * | 2020-02-10 | 2020-06-09 | 深圳清华大学研究院 | Calculation-intensive data state updating implementation method based on mobile edge calculation |
| CN111756812A (en) * | 2020-05-29 | 2020-10-09 | 华南理工大学 | Energy consumption perception edge cloud cooperation dynamic unloading scheduling method |
| WO2020216135A1 (en) * | 2019-04-25 | 2020-10-29 | 南京邮电大学 | Multi-user multi-mec task unloading resource scheduling method based on edge-end collaboration |
| CN113422803A (en) * | 2021-05-19 | 2021-09-21 | 武汉工程大学 | Seamless migration method for intelligent unmanned aerial vehicle inspection task based on end edge cloud cooperation |
| CN113660303A (en) * | 2021-07-02 | 2021-11-16 | 山东师范大学 | Task unloading method and system based on end side network cloud cooperation |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112115505A (en) * | 2020-08-07 | 2020-12-22 | 北京工业大学 | New energy automobile charging station charging data transmission method based on mobile edge calculation and block chain technology |
| CN113452956B (en) * | 2021-02-26 | 2023-03-28 | 深圳供电局有限公司 | Intelligent distribution method and system for power transmission line inspection tasks |
-
2022
- 2022-04-06 CN CN202210354442.0A patent/CN114693141B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107846704A (en) * | 2017-10-26 | 2018-03-27 | 北京邮电大学 | A kind of resource allocation and base station service arrangement method based on mobile edge calculations |
| WO2020216135A1 (en) * | 2019-04-25 | 2020-10-29 | 南京邮电大学 | Multi-user multi-mec task unloading resource scheduling method based on edge-end collaboration |
| CN111262947A (en) * | 2020-02-10 | 2020-06-09 | 深圳清华大学研究院 | Calculation-intensive data state updating implementation method based on mobile edge calculation |
| CN111756812A (en) * | 2020-05-29 | 2020-10-09 | 华南理工大学 | Energy consumption perception edge cloud cooperation dynamic unloading scheduling method |
| CN113422803A (en) * | 2021-05-19 | 2021-09-21 | 武汉工程大学 | Seamless migration method for intelligent unmanned aerial vehicle inspection task based on end edge cloud cooperation |
| CN113660303A (en) * | 2021-07-02 | 2021-11-16 | 山东师范大学 | Task unloading method and system based on end side network cloud cooperation |
Non-Patent Citations (3)
| Title |
|---|
| Smart grid encounters edge computing: opportunities and applications;Cheng Feng 等;《Advances in Applied Energy》;20201223;第1卷;第1-17页 * |
| 基于NOMA-MEC系统的信息年龄最小化研究;李保罡 等;《通信学报》;20211031;第42卷(第10期);第222-231页 * |
| 基于大规模MIMO系统下的移动边缘计算研究;王斌;《中国优秀硕士学位论文全文数据库 信息科技辑》;20220315(第03期);第I136-867页 * |
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