CN112492265B - Uplink and downlink resource joint allocation method applied to smart grid - Google Patents
Uplink and downlink resource joint allocation method applied to smart grid Download PDFInfo
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- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
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- H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
- H04L65/80—Responding to QoS
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- H—ELECTRICITY
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- H04N21/234—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs
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- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/238—Interfacing the downstream path of the transmission network, e.g. adapting the transmission rate of a video stream to network bandwidth; Processing of multiplex streams
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- H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
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- H04N21/60—Network structure or processes for video distribution between server and client or between remote clients; Control signalling between clients, server and network components; Transmission of management data between server and client, e.g. sending from server to client commands for recording incoming content stream; Communication details between server and client
- H04N21/63—Control signaling related to video distribution between client, server and network components; Network processes for video distribution between server and clients or between remote clients, e.g. transmitting basic layer and enhancement layers over different transmission paths, setting up a peer-to-peer communication via Internet between remote STB's; Communication protocols; Addressing
- H04N21/647—Control signaling between network components and server or clients; Network processes for video distribution between server and clients, e.g. controlling the quality of the video stream, by dropping packets, protecting content from unauthorised alteration within the network, monitoring of network load, bridging between two different networks, e.g. between IP and wireless
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Abstract
The invention provides a joint allocation method of uplink and downlink resources applied to a smart grid, which is applied to video distribution and comprises the following steps: determining a multidimensional QoE index; and carrying out joint optimization on the QoE index by a multi-criterion multi-mode resource allocation method. The scheme of the invention can ensure QoE indexes such as time delay, quality, synchronization and the like of the video monitoring service and improve the reliability of a video monitoring system.
Description
Technical Field
The invention relates to the technical field of smart grids, in particular to a joint allocation method of uplink and downlink resources applied to a smart grid.
Background
With the development of management level of the south electric network company, higher requirements are continuously put on the monitoring level of the operation condition of the power distribution side. The urban scale is continuously expanded, the number of distribution rooms is continuously increased, the number and types of terminals at the distribution side are increased, at the moment, the environment at the distribution side and the operation parameters of equipment are managed and maintained in a manual mode, time and labor are wasted, real conditions cannot be reflected in time, problems are solved, and safe, reliable and high-quality power supply and distribution tasks are difficult to provide.
The intelligent power distribution monitoring system has the function of organically connecting the computer technology and the communication technology with each link of the power distribution engineering. The overall control, scientific measurement and guarantee of the safety and the progress smoothness of the power distribution engineering are carried out on the overall system of the power distribution engineering. In the prior art, an intelligent power distribution monitoring system is used in power distribution engineering, data are detected and processed in a unified platform, video and video can be carried out, functions of alarming and the like are given out to emergency accidents, and data processing and monitoring are integrated, so that the purposes of omnibearing monitoring and management of the power distribution engineering are achieved. In order to improve the automation level of a power distribution network, the prior art proposes and develops an image video-based remote control video linkage and intelligent patrol system for a switching station switch. The system realizes the anti-theft automatic monitoring of the switching station/power distribution room, the intelligent identification of the running states of equipment such as a switch, a control box and the like of the switching station/power distribution room, the in-station alarm linkage and the monitoring of the running states of the equipment. The image video monitoring system performs intelligent analysis on the image uploaded to the main station by the switching station, so that intelligent monitoring on the running states or readings of equipment such as a power distribution cabinet meter, a knob switch, an indicator lamp and the like in the switching station is realized; the intelligent inspection system judges whether the wearing conditions of the upper body of the staff meet the safety etiquette standard by detecting the wearing conditions of the upper body of the staff entering and exiting the switching station, thereby realizing effective supervision.
In order to ensure the reliability of a power distribution network, the prior art provides a monitoring client software system which integrates information of online data and offline data of the power distribution network, power distribution network data and user data, a power grid structure and a geographic graph, realizes offline and online intelligent monitoring management of the power distribution network and provides real-time data support for a power distribution management system. While the side-view monitoring system for power distribution is studied, the problems of related data processing and transmission are revealed: as nerve endings of an electrical power system, power distribution consists of a large number of power distribution devices, a large number of sensors are dispersed in a large distribution network, and continuous data is transmitted to a cloud platform where the data is stored and analyzed. With the increasing demands on real-time control of the power distribution network, etc., adopting cloud computing can lead to problems of prolonged time or low frame rate, etc. In addition to cloud computing, edge computing technology provides a new solution to these problems. The prior art provides an edge computing system of a smart grid based on the Internet of things, so as to overcome the defects of a cloud computing paradigm in the current power system, and the new system mainly introduces edge computing in the traditional cloud-based power system and establishes a new hardware and software architecture. Thus, the large amount of data generated in the grid will be analyzed, processed and stored at the network edge. The prior art proposes an edge computing framework for real-time monitoring that transfers computing from a centralized cloud to edge servers in the vicinity of the device. In order to maximize the benefit, it presents a scheduling problem to further optimize the framework and an efficient heuristic algorithm based on simulated annealing strategies. Both practical and simulation results show that the framework can increase the monitoring frame rate by up to 10 times and reduce the detection delay by up to 85% compared to the cloud monitoring solution.
In the monitoring of smart power grids, most of the current video stream content distribution decision optimization is aimed at the downlink or the downlink of the network, so that the QoE of users is improved, and the influence of uplink and downlink resources on the QoE is ignored. The end-to-end delay of the uplink device and the downlink user is a major factor in causing video lag.
Disclosure of Invention
The embodiment of the invention provides a joint allocation method of uplink and downlink resources applied to a smart grid, which is used for guaranteeing QoE indexes such as time delay, quality and synchronization of video monitoring service and improving the reliability of a video monitoring system.
In order to solve the technical problems, the embodiment of the invention provides the following technical scheme:
a joint allocation method of uplink and downlink resources applied to a smart grid is applied to video distribution and comprises the following steps:
determining a multidimensional QoE index;
and carrying out joint optimization on the QoE index by a multi-criterion multi-mode resource allocation method.
Optionally, the multi-dimensional QoE metrics include:
buffer status of client j;
average video quality downloaded by client j;
a QoE metric for the handover;
real-time performance of the video;
a transmission rate at which the data block is transmitted;
the time required for the server to process the video block;
downlink transmission rate.
Alternatively, the buffer status of the client j may be obtained by formula (1):
wherein ,Bj Representing the total bandwidth resources allocated to user j; a is that j and Lj Time nodes delta representing initial request and departure of user video respectively j Indicating the initial play-out delay of the user j buffer,
and
The downlink data throughput and video bit rate for user j are represented, respectively.
Alternatively, the average video quality downloaded by the client j may be obtained by the formula (2):
wherein i= {1,2, … I }, j= {1,2, … J }, s= {1,2, … S, … S }, v= {1,2, … V, … V }
Alternatively, the QoE metric of the handover may be obtained by equation (3):
alternatively, the real-time property of the video may be obtained by the formula (4):
T i,j (s) is the transmission time of the s-th video block of the uplink device i to the downlink user j, T m (s) represents the time required for the server to process the s-th video block.
Optionally, the T i,j (s) can be obtained by the formula (5):
alternatively, the transmission rate of the transmission data block may be obtained by formula (6);
wherein ,αi (S) represents the ratio of the bandwidth allocated by the uplink to user i, W i Representing the total bandwidth resources allocated to user i, P i and hi,m Representing the transmit power and channel gain of the user, respectively.
Alternatively, the time required for the server to process the video block can be obtained by equation (7):
wherein ,Ck Representing the number of CPU cycles, f, required by the edge computation server to compute unit bit data i (s) represents the calculation frequency allocated by the server when processing the s-th video block of user i.
Optionally, the multi-criterion multi-mode resource allocation method includes:
wherein ,zs,j (v) E {0,1} indicates whether downlink user j selects v version of the s-th video block, and
indicating that the user can only select one version of the same video block.
The embodiment of the invention has the beneficial effects that:
according to the embodiment of the invention, qoE indexes such as time delay, quality and synchronization of video monitoring service are ensured, and the reliability of a video monitoring system is improved.
Drawings
Fig. 1 is a schematic flow chart of a method for jointly allocating uplink and downlink resources applied to a smart grid;
FIG. 2 is a schematic diagram of the structure of the present invention;
FIG. 3 is a schematic flow chart of a first convex optimization theory phase of the present invention;
FIG. 4 is a schematic flow chart of a second stage of convex optimization theory according to the present invention;
FIG. 5 is a schematic flow chart of a convex optimization theory stage three of the present invention;
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
As shown in fig. 1, an embodiment of the present invention provides a method for jointly allocating uplink and downlink resources applied to a smart grid, which is applied to video distribution, and includes:
step 11, determining a multidimensional QoE index;
and step 22, performing joint optimization on the QoE index through a multi-criterion multi-mode resource allocation method.
In the embodiment of the invention, the heterogeneous resources are subjected to joint optimization by combining uplink and downlink, the relation analysis between the multidimensional QoE indexes and the heterogeneous resources is firstly carried out, and then the multidimensional QoE indexes are combined and optimized as targets, and a multi-criterion-based multi-mode resource allocation modeling method is provided; and finally, providing an uplink and downlink resource cooperation scheme based on a convex optimization theory.
In an optional embodiment of the present invention, in step 11, the multi-dimensional QoE indicator includes:
buffer status of client j;
average video quality downloaded by client j;
a QoE metric for the handover;
real-time performance of the video;
a transmission rate at which the data block is transmitted;
the time required for the server to process the video block;
downlink transmission rate.
In an optional embodiment of the present invention, in step 11, the buffer status of the client j may be obtained by equation (1):
wherein ,Bj Representing the total bandwidth resources allocated to user j; a is that j and Lj Time nodes delta representing initial request and departure of user video respectively j Indicating the initial play-out delay of the user j buffer,
and
The downlink data throughput and video bit rate for user j are represented, respectively.
Specifically, as shown in fig. 2, it is assumed that i= {1,2, … I } and j= {1,2, … J } represent user sets of uplink task transmission and downlink video browsing, respectively. Assume that the video data of the uplink is divided into a plurality of video blocks s= {1,2, … S, … S }, and each video block is decoded into a plurality of versions v= {1,2, … V, … V }, after being transmitted to the server. The downlink user may select a version of the corresponding video block according to the network conditions.
Video chunking may be considered that the buffer occupancy of the player is below a certain value, and video playback is buffered and interrupted. In particular, when the downlink download speed of the user is lower than the video play rate of the user, a jam and buffering phenomenon often occurs. Let assume that use A j and Lj Time nodes delta representing initial request and departure of user video respectively j Indicating the initial play-out delay of the user j buffer,
and
The downlink data throughput and video bit rate for user j are represented, respectively.
In an optional embodiment of the present invention, in step 11, the average video quality downloaded by the client j may be obtained by equation (2):
in particular, video bit rate has the greatest direct impact on customer quality of experience. There is a trade-off between video bit rate and pause: the higher the video bit rate, the higher the video quality, but a pause event may also be encountered.
In an alternative embodiment of the present invention, in step 11, the QoE metric of the handover may be obtained by equation (3):
in particular, frequent bit rate switching is also considered as an important factor affecting QoE. It is assumed that different video blocks of the same version have the same data size, but different versions of the same video block have the same data size. The difference between the bit rate levels of the video sequential blocks downloaded by the client can be used here as a QoE metric for the handover.
In an optional embodiment of the present invention, in step 11, the real-time performance of the video may be obtained by equation (4):
T i,j (s) is the transmission time of the s-th video block of the uplink device i to the downlink user j, T m (s) represents the time required for the server to process the s-th video block.
In particular, considering that the time consumed by uplink transmission to downlink of all video blocks can be represented as a real-time problem of video, the shorter the transmission delay, the higher the real-time of video received by the downlink user.
Optionally, the T i,j (s) can be obtained by the formula (5):
wherein ,
representing the time required for the uplink device i to upload the s-th video block to the nearby downlink server m, and +.>
The data size (bits) representing the highest version of the user i s video block.
In an optional embodiment of the present invention, in step 11, a transmission rate of the transmission data block may be obtained by equation (6);
wherein ,αi (S) represents the ratio of the bandwidth allocated by the uplink to user i, W i Representing the total bandwidth resources allocated to user i, P i and hi,m Representing the transmit power and channel gain of the user, respectively.
In an alternative embodiment of the present invention, in step 11, the time required for the server to process the video block may be obtained by equation (7):
wherein ,Tm (s) represents the time required for the downlink server m to process the s-th video block, where the processing mainly includes video codec and the like, T m The size of(s) is primarily related to the allocation of computing resources to the server. C (C) k Representing the number of CPU cycles, f, required by the edge computation server to compute unit bit data i (s) represents the calculation frequency allocated by the server when processing the s-th video block of user i.
Embodiments of the present invention further include, in step 11,
representing the time required for the downlink server m to distribute the s-th video block to user j, the downlink video distribution needs to consider the version of the video block, and its specific expression is as follows:
z s,j (v) E {0,1} indicates whether downlink user j selects v version of the s-th video block, and
indicating that the user can only select one version of the same video block.
Representing the data size of the v version of the s-th video block,
downlink transmission rate. The expression is as follows:
a j (s) represents the ratio of the bandwidth allocated by the downlink to user j, B j Representing the total bandwidth resources allocated to user j, P m and hm,i Representing the transmit power and channel gain of the server, respectively.
In particular, client-based video block adaptation algorithms may allocate bit rates in an unfair manner in some cases. This allocation causes serious resource maldistribution problems, so that the bit rate of some users is relatively low. To solve this problem, a fairness function is introduced that ensures video quality while giving consideration to inter-user fairness. The fairness function can be expressed as:
wherein ,ADq Representing the video quality of the downlink user q, which can be expressed in particular by the following formula:
considering that the video playing quality of the downlink user is improved and the switching times in the video playing of the user are reduced, the optimization target needs to be met simultaneously
and
In an optional embodiment of the present invention, in step 12, the multi-criterion multi-mode resource allocation method includes:
wherein ,zs,j (v) E {0,1} indicates whether downlink user j selects v version of the s-th video block, and
indicating that the user can only select one version of the same video block.
In the embodiment of the invention, C.1 can ensure the maximum fairness among users on the premise of reducing video jamming, C.2 can ensure that the users can only download one version when downloading the s-th video block, C.3 and C.4 ensure the selection of the version and the bandwidth allocation limit, and C.5 ensures the maximum resource allocation limit of the server when processing the s-th video block.
For ease of representation, it may also be assumed that:
where Z represents the optimal solution set, f=f 1 ,f 2 。
The above-described embodiments of the present invention, unlike single-objective optimization, require that multiple criteria be satisfied simultaneously for a viable solution to the multi-criteria multi-modal allocation problem. The feasible solutions of the multi-criterion multi-modal allocation problem are not unique but a set of optimal solutions z= { zi }, which satisfy pareto optimality. The example of the invention introduces a convex optimization theory design three-stage search algorithm to solve the problem.
Specifically, the three-phase search algorithm includes three phases, as shown in fig. 3, 4, and 5: first searching for a solution that satisfies the support non-dominant pole; secondly, searching solutions meeting the supporting non-pole and the non-supporting non-dominant point; and finally, searching a system optimal solution from the solution set according to the system requirements in the space of the solutions formed in the first two stages.
As described above, solving the multi-criterion multi-modal allocation problem makes it necessary for the problem to be solved by the present invention to satisfy a plurality of criteria simultaneously, unlike the single-objective optimization.
The optimal solution is a set and satisfies pareto optima. Assuming that a feasible solution z exists for problem (13) 1 ,z 2 E Z is such that:
the above holds, we call the point f 2 Is at point f 1 Dominating. If the resulting solution cannot be dominated, then the solution is the corresponding pareto optimal solution.
Set Z when the following condition is satisfied E Is an effective set of questions (13)
Z E = { Z e z|no existence
When condition (16) is satisfied, set f N Is a non-dominant point set
f N ＝{(f 1 ,f 2 )∈R 2 |f 1 ＝z 1 ,f 2 ＝z 2 ,z∈Z E } (16)
Non-dominant point set
May be divided into a set of supported and non-supported points. The set of support points is further divided into a set of poles and non-poles.
The specific implementation modes of the three phases of the algorithm are as follows:
as shown in fig. 3, two initial feasible solutions T are determined UL and TDR From these two possible solutions two supporting non-dominant poles f can be obtained UL F LR . Let f + :＝f UL ，f - :＝f DR The search direction λ=λ (f can be obtained + ,f - ). Step 5 solving the problem (13) in the search direction to obtain a solution T and a point at the same time
If->
The corresponding solution is to support non-dominant poles. Step 5, continuously cycling, and stopping cycling when the point meeting the condition cannot be found. The support non-dominant points thus found are incorporated into the solution space +.>
Is a kind of medium.
The support non-dominant points can be searched by stage one, but these points are all at the point space vertices, the non-poles at the boundary and the non-support non-dominant points inside the point space are not available. These two types of points exist in a triangle area delta (f + ,f - ) Can be searched through the stage two.
As shown in fig. 4, the triangle search area Δ (f + ,f - ) The three vertexes of the area are
(f) 1 - ,f 2 + ). Second by lambda (f + ,f - ) For searching the direction, searching a feasible solution by a KBest algorithm, and obtaining a parameter f λ (T) sorting; when f λ (T) reaching the upper bound->
When the search is stopped, the KBest algorithm returns to the optimal solution T t Parameter value f t ＝f λ (T t ) The method comprises the steps of carrying out a first treatment on the surface of the Substituting the parameter into the verification function non dom (f t ) If it is judged that f t For branch stay non-dominant point then f k Add to dot space->
In (a) corresponding solution is added to the solution space +.>
At the same time, the upper bound is updated to->
As shown in fig. 5, a solution space t= { T satisfying pareto conditions can be obtained by the stage one and the stage two i Space of corresponding points
The optimal solution that satisfies (13) is next searched for by stage three. Since the solution space has a one-to-one correspondence with the point space, then the solution space is +.>
Is equivalent to the search of the point space +.>
Is a search of (a).
The scheme of the embodiment of the invention can ensure QoE indexes such as time delay, quality, synchronization and the like of the video monitoring service and improve the reliability of a video monitoring system.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and changes can be made without departing from the principles of the present invention, and such modifications and changes are intended to be within the scope of the present invention.
Claims (6)
1. The uplink and downlink resource joint allocation method applied to the smart grid is applied to video distribution and is characterized by comprising the following steps of:
determining a multidimensional QoE index;
performing joint optimization on the QoE index by a multi-criterion multi-mode resource allocation method; wherein the multidimensional QoE metrics include:
buffer status of client j;
average video quality downloaded by client j;
a QoE metric for the handover;
real-time performance of the video;
a transmission rate at which the data block is transmitted;
the time required for the server to process the video block;
a transmission rate of the downlink;
wherein, the buffer status of the client j is obtained by the formula (1):
wherein ,Bj (t) represents the total bandwidth resources allocated to user j at time t; a is that j and Lj Time nodes delta representing initial request and departure of user video respectively j Indicating the initial play-out delay of the user j buffer,
and
Respectively representing the downlink data throughput and video bit rate of user j, Δt being the time interval;
wherein, the average video quality downloaded by the client j is obtained by the formula (2):
wherein i= {1,2, … I }, j= {1,2, … J }, s= {1,2, … S, … S }, v= {1,2, … V, … V };
i represents uplink task transmission;
j represents a user set for browsing downlink videos;
s denotes that the video data of the uplink is divided into a plurality of video block sets;
v denotes that each video block is decoded into multiple versions after being sent to the server;
z s,j (v) E {0,1} indicates whether downlink user j selects v version of the s-th video block, and
indicating that the user can only select one version of the same video block;
s j representing the s-th video block of user j.
2. The method for jointly allocating uplink and downlink resources applied to a smart grid according to claim 1, wherein the QoE metric of the handover is obtained by the formula (3):
z s,j (v) Indicating whether downlink user j selects v version of the s-th video block;
z s-1,j (v) Indicating whether downlink user j selects the v version of the s-1 video block.
3. The method for jointly allocating uplink and downlink resources applied to a smart grid according to claim 2, wherein the real-time performance of the video is obtained by the formula (4):
wherein ,Ti,j (s) is the transmission time of the video block s of the uplink device i to the downlink user j.
4. The method for jointly allocating uplink and downlink resources applied to smart grid according to claim 3, wherein the T is i,j (s) is obtained by the formula (5):
wherein ,
representing the time required for the downlink server m to distribute the s-th video block to user j;
T m (s) represents the time required for the downlink server m to process the s-th video block；
5. The joint allocation method of uplink and downlink resources applied to a smart grid according to claim 4, wherein the time required for the server to process the video block is obtained by equation (7):
wherein ,Ck Representing the number of CPU cycles, f, required by the edge computation server to compute unit bit data i (s) represents the computation frequency assigned by the server when processing s video clips for user i.
6. The method for jointly allocating uplink and downlink resources applied to a smart grid according to claim 5, wherein the method for allocating resources in multiple criteria and multiple modes comprises:
wherein min represents the minimum value, s.t represents the condition of being limited by z s,j (v) E {0,1} indicates whether downlink user j selects v version of the s-th video block, and
indicating that the user can only select one version of the same video block;
alpha represents the bandwidth proportion of the uplink allocation, a j (s) represents the ratio of bandwidth allocated by the downlink to user j;
beta represents the bandwidth proportion of the downlink allocation.
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