A demand response data scheduling method and device

Abstract

The present invention relates to a demand response data scheduling method and device, comprising: determining the dynamic weight of each demand response data queue according to the queue length of each demand response data queue; determining the virtual completion time of each demand response data queue according to the dynamic weight of each demand response data queue; selecting the demand response data queue to be scheduled according to the virtual completion time of each demand response data queue, and forwarding the header data of the demand response data queue to be scheduled; the present invention determines the demand response data queue to be scheduled based on the queue length, dynamic weight and virtual completion time of each demand response data queue, and forwards its header data, so as to meet the real-time requirements of the demand response data, reduce the delay of the header data of the demand response data queue, and improve the service quality of the demand response data.

Description

Technical Field

[0001] The present invention relates to the technical field of power system communications, and in particular to a demand response data scheduling method and device. Background Art

[0002] In recent years, various regions have successively implemented pilot projects of power demand response (DR), interacting with power users through the government-led and grid enterprise-implemented approach, and appropriately introducing the participation of new third-party entities such as load aggregators and service providers in the implementation process. However, in the process of residential users participating in DR data, due to the inadequacy and backwardness of information interfaces and communication protocols, the DR data interaction process is still relatively cumbersome, and it is difficult to adapt to the requirements of future power demand response data autonomy and flexibility in terms of real-time transmission, reliability, and cost-effectiveness. It is impossible to give full play to the role of user DR resources, and it is even more impossible to further promote the implementation of DR data.

[0003] In order to solve the traffic conflict problem during DR data transmission and ensure the service quality of DR data, it is particularly important to select an appropriate demand response data scheduling strategy while ensuring the service quality of real-time and emergency DR data and taking into account the service quality of various types of non-real-time and conventional DR data. At present, due to the lack of an effective DR data queuing strategy scheduling method, the real-time transmission of DR data information cannot be met when DR data is carried out on a large scale. In the prior art, the queuing strategy scheduling method based on strict priority, in an emergency, the high-priority data communication volume is large and occupies more bandwidth, resulting in an increase in the waiting delay of low-priority data and a decrease in service quality. If high-priority communication equals or exceeds the transmission rate of the line for a period of time, it may cause the loss of low-priority data and even affect important network control communications.

[0004] Therefore, it is of great significance to conduct research on scheduling methods that can take into account the service quality of various types of DR data and meet the real-time requirements of DR data. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a demand response data scheduling method and device, which determines the demand response data queue to be scheduled based on the queue length, dynamic weight, and virtual completion time of each demand response data queue, and forwards its header data to meet the real-time requirements of the demand response data, reduce the delay of the header data of the demand response data queue, and improve the service quality of the demand response data.

[0006] The purpose of the present invention is achieved by adopting the following technical solutions:

[0007] The present invention provides a demand response data scheduling method, the improvement of which lies in that the method comprises:

[0008] Determining a dynamic weight of each demand response data queue according to the queue length of each demand response data queue;

[0009] Determining a virtual completion time of each demand response data queue according to the dynamic weight of each demand response data queue;

[0010] A demand response data queue to be scheduled is selected according to the virtual completion time of each demand response data queue, and the header data of the demand response data queue to be scheduled is forwarded.

[0011] Preferably, determining the dynamic weight of each demand response data queue according to the queue length of each demand response data queue includes:

[0012] If the queue length L of the i-th demand response data queue i satisfy Then the service burst degree and dynamic priority of the i-th demand response data queue are obtained, and the dynamic weight of the i-th demand response data queue at the current system virtual moment is determined according to the queue length, service burst degree and dynamic priority of the i-th demand response data queue; otherwise, the dynamic weight of the i-th demand response data queue at the previous system virtual moment is used as the dynamic weight of the i-th demand response data queue at the current system virtual moment;

[0013] Among them, L i The initialization value is 0, L i_max is the maximum queue length of the i-th demand response data queue, i∈[1,N], and N is the total number of demand response data queues.

[0014] Furthermore, obtaining the service burst degree and dynamic priority of the i-th demand response data queue includes:

[0015] The service burst level of the i-th demand response data queue is determined by the following formula: i and dynamic priority p i :

[0016]

[0017] Where, L i_avg is the average queue length of the i-th demand response data queue, a and b are the first dynamic priority adjustment coefficient and the second dynamic priority adjustment coefficient, respectively, T i l is the queuing time of the head data l in the i-th demand response data queue, L l i is the length of the header data l of the i-th demand response data queue, r i' is the service rate of the i-th demand response data queue at the previous system virtual moment, Burst i 、p i The initialization value of is 0;

[0018] The average queue length L of the i-th demand response data queue is determined by the following formula: i_avg :

[0019] L i_avg =(1-k)L' i_avg +kL i

[0020] Where k is the low-pass filter coefficient, L' i_avg is the average queue length of the i-th demand response data queue at the last system virtual time, L i_avg The initialization value is 0.

[0021] Further, the determining of the dynamic weight of the i-th demand response data queue at the current system virtual moment according to the queue length, service burst degree and dynamic priority of the i-th demand response data queue includes:

[0022] Determine the queue length, service burstiness and fuzzy value of the dynamic priority of the i-th demand response data queue by using a fuzzy membership function;

[0023] Based on a pre-given fuzzy rule, the Mamdami algorithm is used to obtain the fuzzy value of the dynamic weight of the i-th demand response data queue at the current system virtual moment corresponding to the fuzzy value of the queue length, service burst degree and dynamic priority of the i-th demand response data queue;

[0024] The center of gravity method is used to obtain the dynamic weight of the ith demand response data queue corresponding to the fuzzy value of the dynamic weight of the ith demand response data queue at the current system virtual moment.

[0025] Preferably, determining the virtual completion time of each demand response data queue according to the dynamic weight of each demand response data queue includes:

[0026] The service rate of the ith demand response data queue is determined according to the dynamic weight of the ith demand response data queue, and the virtual completion time F of the ith demand response data queue is determined according to the following formula: i :

[0027]

[0028] In the formula, S i is the virtual start time of the ith demand response data queue, r i is the service rate of the i-th demand response data queue, is the length of the header data l of the demand response data queue with the highest dynamic priority among the N demand response data queues, is the service loss ratio of the head data l of the i-th demand response data queue;

[0029] Furthermore, the virtual start time S of the i-th demand response data queue is determined as follows: i :

[0030]

[0031] Where V i l is the system virtual time corresponding to the time when the head data l of the i-th demand response data queue reaches the head, F i ' is the virtual completion time of the previous system virtual time of the i-th demand response data queue.

[0032] Furthermore, the service rate r of the i-th demand response data queue is determined according to the dynamic weight of the i-th demand response data queue. i include:

[0033] The service rate r of the i-th demand response data queue is determined as follows: i :

[0034]

[0035] In the formula, w i is the dynamic weight of the i-th demand response data queue, w i The initialization value of is 0, N is the total number of demand response data queues, and C is the total bandwidth of the scheduling system.

[0036] Furthermore, the service loss ratio of the head data l of the i-th demand response data queue is determined as follows:

[0037]

[0038] Where RLV i l is the business loss value of the head data l of the i-th demand response data queue, N is the total number of demand response data queues, B i The initialization value of is 0;

[0039] The service loss value RLV of the head data l of the i-th demand response data queue is determined as follows: i l :

[0040]

[0041] In the formula, k 0 is the business loss value adjustment factor, RLV i l The initialization value is 0. is the waiting time t of the head data l in the i-th demand response data queue i,l p i Power, is the maximum tolerable delay T of the head data l of the i-th demand response data queue i,l p i +1 power.

[0042] Preferably, the selecting a demand response data queue to be scheduled according to the virtual completion time of each demand response data queue, and forwarding the header data of the demand response data queue to be scheduled includes:

[0043] Selecting a demand response data queue from among the demand response data queues whose virtual start time is not greater than the current system virtual time;

[0044] Selecting the demand response data queue with the smallest virtual completion time from the demand response data queues whose virtual start time is not greater than the current system virtual time as the demand response data queue to be scheduled;

[0045] Forward the header data of the demand response data queue to be scheduled.

[0046] The present invention provides a demand response data scheduling device, the improvement of which lies in that the device comprises:

[0047] A weight updating unit, used to determine the dynamic weight of each demand response data queue according to the queue length of each demand response data queue;

[0048] a determining unit, configured to determine a virtual completion time of each demand response data queue according to a dynamic weight of each demand response data queue;

[0049] A scheduling unit is used to select a demand response data queue to be scheduled according to the virtual completion time of each demand response data queue, and forward the header data of the demand response data queue to be scheduled.

[0050] Compared with the closest prior art, the present invention has the following beneficial effects:

[0051] The present invention provides a demand response data scheduling method and device, including: determining the dynamic weight of each demand response data queue according to the queue length of each demand response data queue; determining the virtual completion time of each demand response data queue according to the dynamic weight of each demand response data queue; selecting the demand response data queue to be scheduled according to the virtual completion time of each demand response data queue, and forwarding the header data of the demand response data queue to be scheduled; the present invention determines the demand response data queue to be scheduled based on the queue length, dynamic weight and virtual completion time of each demand response data queue, and forwards its header data, so as to meet the real-time requirements of the demand response data, reduce the delay of the header data of the demand response data queue, and improve the service quality of the demand response data. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flow chart of the demand response data scheduling method of the present invention;

[0053] Figure 2 is a line graph of a membership function in an embodiment of a demand response data scheduling method of the present invention;

[0054] Figure 3 It is a schematic diagram of a demand response data scheduling device according to the present invention. DETAILED DESCRIPTION

[0055] The specific implementation modes of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0056] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] The present invention provides a demand response data scheduling method, such as Figure 1 As shown, the method includes:

[0058] Determining a dynamic weight of each demand response data queue according to the queue length of each demand response data queue;

[0059] Determining a virtual completion time of each demand response data queue according to the dynamic weight of each demand response data queue;

[0060] A demand response data queue to be scheduled is selected according to the virtual completion time of each demand response data queue, and the header data of the demand response data queue to be scheduled is forwarded.

[0061] In an embodiment of the present invention, demand response data queue types include: emergency demand response data queue, general demand response data queue, registered demand response data queue, polling demand response data queue, reporting demand response data queue and selected demand response data queue.

[0062] In an embodiment of the present invention, determining the dynamic weight of each demand response data queue according to the queue length of each demand response data queue in the above method includes:

[0063] If the queue length L of the i-th demand response data queue i satisfy Then the service burst degree and dynamic priority of the i-th demand response data queue are obtained, and the dynamic weight of the i-th demand response data queue at the current system virtual moment is determined according to the queue length, service burst degree and dynamic priority of the i-th demand response data queue; otherwise, the dynamic weight of the i-th demand response data queue at the previous system virtual moment is used as the dynamic weight of the i-th demand response data queue at the current system virtual moment;

[0064] Among them, L i The initialization value is 0, L i_max is the maximum queue length of the i-th demand response data queue, i∈[1,N], and N is the total number of demand response data queues.

[0065] Specifically, the above-mentioned acquisition of the service burst degree and dynamic priority of the i-th demand response data queue includes:

[0066] The service burst level of the i-th demand response data queue is determined by the following formula: i and dynamic priority p i :

[0067]

[0068] Where, L i_avg is the average queue length of the i-th demand response data queue, a and b are the first dynamic priority adjustment coefficient and the second dynamic priority adjustment coefficient, respectively, T i l is the queuing time of the head data l in the i-th demand response data queue, L l i is the length of the header data l of the i-th demand response data queue, r i ' is the service rate of the i-th demand response data queue at the previous system virtual moment, Burst i 、p i The initialization value of is 0;

[0069] The average queue length L of the i-th demand response data queue is determined by the following formula: i_avg :

[0070] L i_avg =(1-k)L' i_avg +kL i

[0071] Where k is the low-pass filter coefficient, L' i_avg is the average queue length of the i-th demand response data queue at the last system virtual time, L i_avg The initialization value of is 0. In the embodiment of the present invention, the low-pass filter coefficient is 0.01.

[0072] Specifically, the above-mentioned determination of the dynamic weight of the i-th demand response data queue at the current system virtual moment according to the queue length, service burst degree and dynamic priority of the i-th demand response data queue includes:

[0073] Determine the queue length, service burstiness and fuzzy value of the dynamic priority of the i-th demand response data queue by using a fuzzy membership function;

[0074] Based on a pre-given fuzzy rule, the Mamdami algorithm is used to obtain the fuzzy value of the dynamic weight of the i-th demand response data queue at the current system virtual moment corresponding to the fuzzy value of the queue length, service burst degree and dynamic priority of the i-th demand response data queue;

[0075] The dynamic weight of the i-th demand response data queue corresponding to the fuzzy value of the dynamic weight of the i-th demand response data queue at the current system virtual moment is obtained by using the center of gravity method;

[0076] Among them, in the embodiments of the present invention, Figure 2 As shown, the fuzzy membership function is a trigonometric function and a semi-trapezoidal function; the fuzzy value set of the queue length of the demand response data queue is small S, medium M and large L, and the domain is [0,1]; the fuzzy value set of the service burst degree of the demand response data queue is very low VL, relatively low RL, medium M, relatively high RH and very high VH, [-1,1]; the fuzzy value set of the dynamic priority of the demand response data queue is low L, medium M, high H, and the domain is [0,1]; the fuzzy value set of the dynamic weight of the demand response data queue is very small VS, relatively small RS, medium M, relatively high RL and very high VL;

[0077] The fuzzy rules in the embodiment of the present invention are shown in Table 1:

[0078]

[0079]

[0080] Determining the virtual completion time of each demand response data queue according to the dynamic weight of each demand response data queue in the above method includes:

[0081] The service rate of the ith demand response data queue is determined according to the dynamic weight of the ith demand response data queue, and the virtual completion time F of the ith demand response data queue is determined according to the following formula: i :

[0082]

[0083] In the formula, S i is the virtual start time of the ith demand response data queue, r i is the service rate of the i-th demand response data queue, is the length of the header data l of the demand response data queue with the highest dynamic priority among the N demand response data queues, is the service loss ratio of the head data l of the i-th demand response data queue;

[0084] Furthermore, the virtual start time S of the i-th demand response data queue is determined as follows: i :

[0085]

[0086] Where V i l is the system virtual time corresponding to the time when the head data l of the i-th demand response data queue reaches the head, F i ' is the virtual completion time of the previous system virtual time of the i-th demand response data queue.

[0087] Furthermore, the service rate r of the i-th demand response data queue is determined according to the dynamic weight of the i-th demand response data queue. i include:

[0088] The service rate r of the i-th demand response data queue is determined as follows: i :

[0089]

[0090] In the formula, w i is the dynamic weight of the i-th demand response data queue, w i The initialization value of is 0, N is the total number of demand response data queues, and C is the total bandwidth of the scheduling system.

[0091] Furthermore, the service loss ratio of the head data l of the i-th demand response data queue is determined as follows:

[0092]

[0093] Where RLV i l is the business loss value of the head data l of the i-th demand response data queue, N is the total number of demand response data queues, B i The initialization value of is 0;

[0094] The service loss value RLV of the head data l of the i-th demand response data queue is determined as follows: i l :

[0095]

[0096] In the formula, k 0 is the business loss value adjustment factor, RLV i l The initialization value is 0. is the waiting time t of the head data l in the i-th demand response data queue i,l p i Power, is the maximum tolerable delay T of the head data l of the i-th demand response data queue i,l p i +1 power.

[0097] In the above method, selecting a demand response data queue to be scheduled according to the virtual completion time of each demand response data queue, and forwarding the header data of the demand response data queue to be scheduled includes:

[0098] Selecting a demand response data queue from among the demand response data queues whose virtual start time is not greater than the current system virtual time;

[0099] Selecting the demand response data queue with the smallest virtual completion time from the demand response data queues whose virtual start time is not greater than the current system virtual time as the demand response data queue to be scheduled;

[0100] Forward the header data of the demand response data queue to be scheduled.

[0101] Based on the same inventive concept, the present invention provides a demand response data scheduling device, such as Figure 3 As shown, the device comprises:

[0102] A weight updating unit, used to determine the dynamic weight of each demand response data queue according to the queue length of each demand response data queue;

[0103] a determining unit, configured to determine a virtual completion time of each demand response data queue according to a dynamic weight of each demand response data queue;

[0104] A scheduling unit is used to select a demand response data queue to be scheduled according to the virtual completion time of each demand response data queue, and forward the header data of the demand response data queue to be scheduled.

[0105] The weight updating unit of the above device is specifically used for:

[0106] If the queue length L of the i-th demand response data queue i satisfy Then the service burst degree and dynamic priority of the i-th demand response data queue are obtained, and the dynamic weight of the i-th demand response data queue at the current system virtual moment is determined according to the queue length, service burst degree and dynamic priority of the i-th demand response data queue; otherwise, the dynamic weight of the i-th demand response data queue at the previous system virtual moment is used as the dynamic weight of the i-th demand response data queue at the current system virtual moment;

[0107] Among them, L i The initialization value is 0, L i_max is the maximum queue length of the i-th demand response data queue, i∈[1,N], and N is the total number of demand response data queues.

[0108] Furthermore, obtaining the service burst degree and dynamic priority of the i-th demand response data queue includes:

[0109] The service burst level of the i-th demand response data queue is determined by the following formula: i and dynamic priority p i :

[0110]

[0111] Where, L i_avg is the average queue length of the i-th demand response data queue, a and b are the first dynamic priority adjustment coefficient and the second dynamic priority adjustment coefficient, respectively, T i l is the queuing time of the head data l in the i-th demand response data queue, L l i is the length of the header data l of the i-th demand response data queue, r i ' is the service rate of the i-th demand response data queue at the previous system virtual moment, Burst i 、p i The initialization value of is 0;

[0112] The average queue length L of the i-th demand response data queue is determined by the following formula: i_avg :

[0113] L i_avg =(1-k)L' i_avg +kL i

[0114] Where k is the low-pass filter coefficient, L' i_avg is the average queue length of the i-th demand response data queue at the last system virtual time, L i_avg The initialization value is 0.

[0115] Further, the determining of the dynamic weight of the i-th demand response data queue at the current system virtual moment according to the queue length, service burst degree and dynamic priority of the i-th demand response data queue includes:

[0116] Determine the queue length, service burstiness and fuzzy value of the dynamic priority of the i-th demand response data queue by using a fuzzy membership function;

[0117] Based on a pre-given fuzzy rule, the Mamdami algorithm is used to obtain the fuzzy value of the dynamic weight of the i-th demand response data queue at the current system virtual moment corresponding to the fuzzy value of the queue length, service burst degree and dynamic priority of the i-th demand response data queue;

[0118] The center of gravity method is used to obtain the dynamic weight of the ith demand response data queue corresponding to the fuzzy value of the dynamic weight of the ith demand response data queue at the current system virtual moment.

[0119] The determining unit in the above device is specifically used for:

[0120] The service rate of the ith demand response data queue is determined according to the dynamic weight of the ith demand response data queue, and the virtual completion time F of the ith demand response data queue is determined according to the following formula: i :

[0121]

[0122] In the formula, S i is the virtual start time of the ith demand response data queue, r i is the service rate of the i-th demand response data queue, is the length of the header data l of the demand response data queue with the highest dynamic priority among the N demand response data queues, is the service loss ratio of the head data l of the i-th demand response data queue;

[0123] Furthermore, the virtual start time S of the i-th demand response data queue is determined as follows: i :

[0124]

[0125] Where V i l is the system virtual time corresponding to the time when the head data l of the i-th demand response data queue reaches the head, F i ' is the virtual completion time of the previous system virtual time of the i-th demand response data queue.

[0126] Furthermore, the service rate r of the i-th demand response data queue is determined according to the dynamic weight of the i-th demand response data queue. i include:

[0127] The service rate r of the i-th demand response data queue is determined as follows: i :

[0128]

[0129] In the formula, w i is the dynamic weight of the i-th demand response data queue, w i The initialization value of is 0, N is the total number of demand response data queues, and C is the total bandwidth of the scheduling system.

[0130] Furthermore, the service loss ratio of the head data l of the i-th demand response data queue is determined as follows:

[0131]

[0132] Where RLV i l is the business loss value of the head data l of the i-th demand response data queue, N is the total number of demand response data queues, B i The initialization value of is 0;

[0133] The service loss value RLV of the head data l of the i-th demand response data queue is determined as follows: i l :

[0134]

[0135] In the formula, k 0 is the business loss value adjustment factor, RLV i l The initialization value is 0. is the waiting time t of the head data l in the i-th demand response data queuei,l p i Power, is the maximum tolerable delay T of the head data l of the i-th demand response data queue i,l p i +1 power.

[0136] The scheduling unit in the above device is specifically used for:

[0137] Selecting a demand response data queue from among the demand response data queues whose virtual start time is not greater than the current system virtual time;

[0138] Selecting the demand response data queue with the smallest virtual completion time from the demand response data queues whose virtual start time is not greater than the current system virtual time as the demand response data queue to be scheduled;

[0139] Forward the header data of the demand response data queue to be scheduled.

[0140] Compared with the closest prior art, the present invention has the following beneficial effects:

[0141] The present invention provides a demand response data scheduling method and device, including: determining the dynamic weight of each demand response data queue according to the queue length of each demand response data queue; determining the virtual completion time of each demand response data queue according to the dynamic weight of each demand response data queue; selecting the demand response data queue to be scheduled according to the virtual completion time of each demand response data queue, and forwarding the header data of the demand response data queue to be scheduled; the present invention determines the demand response data queue to be scheduled based on the queue length, dynamic weight and virtual completion time of each demand response data queue, and forwards its header data, so as to meet the real-time requirements of the demand response data, reduce the delay of the header data of the demand response data queue, and improve the service quality of the demand response data.

[0142] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0143] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0144] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0145] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A demand response data scheduling method, characterized in that: The method comprises: Determining a dynamic weight of each demand response data queue according to the queue length of each demand response data queue; Determining a virtual completion time of each demand response data queue according to the dynamic weight of each demand response data queue; Selecting a demand response data queue to be scheduled according to the virtual completion time of each demand response data queue, and forwarding the header data of the demand response data queue to be scheduled; Determining the dynamic weight of each demand response data queue according to the queue length of each demand response data queue includes: If the queue length L of the i-th demand response data queue i satisfy Then the service burst degree and dynamic priority of the i-th demand response data queue are obtained, and the dynamic weight of the i-th demand response data queue at the current system virtual moment is determined according to the queue length, service burst degree and dynamic priority of the i-th demand response data queue; otherwise, the dynamic weight of the i-th demand response data queue at the previous system virtual moment is used as the dynamic weight of the i-th demand response data queue at the current system virtual moment; Among them, L i The initialization value is 0, L i_max is the maximum queue length of the i-th demand response data queue, i∈[1,N], N is the total number of demand response data queues; The obtaining of the service burst degree and dynamic priority of the i-th demand response data queue includes: The service burst level of the i-th demand response data queue is determined by the following formula: i and dynamic priority p i : Where, L i_avg is the average queue length of the i-th demand response data queue, a and b are the first dynamic priority adjustment coefficient and the second dynamic priority adjustment coefficient, respectively, T i l is the queuing time of the head data l in the i-th demand response data queue, L l i is the length of the header data l of the i-th demand response data queue, r i ' is the service rate of the i-th demand response data queue at the previous system virtual moment, Burst i 、p i The initialization value of is 0; The average queue length L of the i-th demand response data queue is determined by the following formula: i_avg : L i_avg =(1-k)L' i_avg +kL i Where k is the low-pass filter coefficient, L' i_avg is the average queue length of the i-th demand response data queue at the last system virtual time, L i_avg The initialization value is 0.

2. The method according to claim 1, characterized in that Determining the dynamic weight of the i-th demand response data queue at the current system virtual moment according to the queue length, service burst degree and dynamic priority of the i-th demand response data queue includes: Determine the queue length, service burstiness and fuzzy value of dynamic priority of the i-th demand response data queue by using a fuzzy membership function; Based on a pre-given fuzzy rule, the Mamdami algorithm is used to obtain the fuzzy value of the dynamic weight of the i-th demand response data queue at the current system virtual moment corresponding to the fuzzy value of the queue length, service burst degree and dynamic priority of the i-th demand response data queue; The center of gravity method is used to obtain the dynamic weight of the ith demand response data queue corresponding to the fuzzy value of the dynamic weight of the ith demand response data queue at the current system virtual moment.

3. The method according to claim 1, characterized in that The determining of the virtual completion time of each demand response data queue according to the dynamic weight of each demand response data queue comprises: The service rate of the ith demand response data queue is determined according to the dynamic weight of the ith demand response data queue, and the virtual completion time F of the ith demand response data queue is determined according to the following formula: i : In the formula, S i is the virtual start time of the ith demand response data queue, r i is the service rate of the i-th demand response data queue, is the length of the header data l of the demand response data queue with the highest dynamic priority among the N demand response data queues, is the service loss ratio of the head data l of the i-th demand response data queue.

4. The method according to claim 3, characterized in that The virtual start time S of the i-th demand response data queue is determined as follows: i : Where V i l is the system virtual time corresponding to the time when the head data l of the i-th demand response data queue reaches the head, F i ' is the virtual completion time of the previous system virtual time of the i-th demand response data queue.

5. The method according to claim 3, characterized in that The service rate r of the i-th demand response data queue is determined according to the dynamic weight of the i-th demand response data queue. i include: The service rate r of the i-th demand response data queue is determined as follows: i : In the formula, w i is the dynamic weight of the i-th demand response data queue, w i The initialization value of is 0, N is the total number of demand response data queues, and C is the total bandwidth of the scheduling system.

6. The method according to claim 3, characterized in that The service loss ratio of the head data l of the i-th demand response data queue is determined by the following formula: Where RLV i l is the business loss value of the head data l of the i-th demand response data queue, N is the total number of demand response data queues, B i The initialization value of is 0; The service loss value RLV of the head data l of the i-th demand response data queue is determined as follows: i l : Where k0 is the business loss value adjustment coefficient, RLV i l The initialization value is 0. is the waiting time t of the head data l in the i-th demand response data queue i,l p i Power, is the maximum tolerable delay T of the head data l of the i-th demand response data queue i,l p i +1 power.

7. The method according to claim 1, characterized in that The selecting a demand response data queue to be scheduled according to the virtual completion time of each demand response data queue, and forwarding the header data of the demand response data queue to be scheduled includes: Selecting a demand response data queue from among the demand response data queues whose virtual start time is not greater than the current system virtual time; Selecting the demand response data queue with the smallest virtual completion time from the demand response data queues whose virtual start time is not greater than the current system virtual time as the demand response data queue to be scheduled; Forward the header data of the demand response data queue to be scheduled.

8. A demand response data scheduling device, characterized in that: The device comprises: A weight updating unit, used to determine the dynamic weight of each demand response data queue according to the queue length of each demand response data queue; a determining unit, configured to determine a virtual completion time of each demand response data queue according to a dynamic weight of each demand response data queue; A scheduling unit, configured to select a demand response data queue to be scheduled according to the virtual completion time of each demand response data queue, and forward the header data of the demand response data queue to be scheduled; The weight updating unit is specifically used for: If the queue length L of the i-th demand response data queue i satisfy Then the service burst degree and dynamic priority of the i-th demand response data queue are obtained, and the dynamic weight of the i-th demand response data queue at the current system virtual moment is determined according to the queue length, service burst degree and dynamic priority of the i-th demand response data queue; otherwise, the dynamic weight of the i-th demand response data queue at the previous system virtual moment is used as the dynamic weight of the i-th demand response data queue at the current system virtual moment; Among them, L i The initialization value is 0, L i_max is the maximum queue length of the i-th demand response data queue, i∈[1,N], N is the total number of demand response data queues; The obtaining of the service burst degree and dynamic priority of the i-th demand response data queue includes: The service burst level of the i-th demand response data queue is determined by the following formula: i and dynamic priority p i : Where, L i_avg is the average queue length of the i-th demand response data queue, a and b are the first dynamic priority adjustment coefficient and the second dynamic priority adjustment coefficient, respectively, T i l is the queuing time of the head data l in the i-th demand response data queue, L l i is the length of the header data l of the i-th demand response data queue, r i ' is the service rate of the i-th demand response data queue at the previous system virtual moment, Burst i 、p i The initialization value of is 0; The average queue length L of the i-th demand response data queue is determined by the following formula: i_avg : L i_avg =(1-k)L' i_avg +kL i Where k is the low-pass filter coefficient, L' i_avg is the average queue length of the i-th demand response data queue at the last system virtual time, L i_avg The initialization value is 0.

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