DWRR scheduling optimization method for multi-terminal data transceiving of wind power plant

Abstract

The invention discloses a DWRR scheduling optimization method for multi-terminal data transceiving of a wind power plant, which is characterized by comprising the following steps: a plurality of terminals realize real-time data transceiving of the wind power plant based on memory management; the management chain table in the memory is emptied based on the scheduling bandwidth of the DWRR scheduling algorithm; wherein the management linked list is used for centrally storing the real-time data with the same size. Although memory management is efficient and saves memory in data transmission, recovery of the memory management is very troublesome. According to the method, the DWRR is creatively introduced, and the management linked list in the memory management is emptied, so that the DWRR and the memory management efficiently cooperate and support each other, and then scheduling optimization of multi-terminal data transceiving of the wind power plant is jointly realized.

Description

Technical Field

[0001] The present application relates to the technical field of data scheduling optimization, and in particular to a DWRR scheduling optimization method for multi-terminal data reception and transmission in a wind farm. Background Art

[0002] With the continuous expansion of wind farm scale and the improvement of intelligence, the demand for real-time monitoring and data collection of wind farms is growing. Each terminal needs to frequently send and receive a large amount of real-time data, such as wind speed, wind direction, blade pitch angle, generator speed, active power output, etc. These data are crucial for optimizing the operation status of wind turbines and improving power generation efficiency.

[0003] In the prior art, in order to fairly allocate resources among multiple tasks or terminals, the DWRR (Deficit Weighted Round Robin) algorithm is usually used. This algorithm dynamically adjusts bandwidth allocation according to the weight and deficiency value of the task to achieve fair scheduling among multiple tasks. However, when setting the scheduling bandwidth, the DWRR scheduling algorithm does not consider the actual remaining memory of each terminal, but uses a fixed or memory-independent scheduling bandwidth.

[0004] This defect leads to unreasonable scheduling in high-frequency data transmission and reception scenarios: since the DWRR scheduling algorithm fails to dynamically adjust the scheduling bandwidth according to the terminal's memory status, too much data may be allocated to terminals with tight memory, causing memory congestion; while terminals with sufficient memory are not fully utilized, resulting in unbalanced resource allocation and failure to meet the actual needs of each terminal. Summary of the invention

[0005] The present application provides a DWRR scheduling optimization method for multi-terminal data reception and transmission in a wind farm, which is used to solve the technical problem of insufficient actual memory allocation.

[0006] The present invention adopts the following technical solution.

[0007] The first aspect of the present invention discloses a DWRR scheduling optimization method for multi-terminal data transmission and reception in a wind farm, comprising: multi-terminals realize real-time data transmission and reception in the wind farm based on memory management; and clearing the management linked list in the memory based on the scheduling bandwidth of the DWRR scheduling algorithm; wherein the management linked list is used to centrally store real-time data of the same size.

[0008] Furthermore, based on the frequency of the real-time data, the type of real-time data received by each terminal is determined, and based on the size of the real-time data, a linked list in the terminal is initialized.

[0009] Further, various input parameters of the DWRR scheduling algorithm are initialized, including: the number of queues is equal to the sum of the total number of linked lists of each terminal; the weights of all queues are consistent; the scheduling bandwidth of each queue is set to the available remaining memory of the linked list corresponding to the queue.

[0010] Furthermore, the available remaining memory of the linked list is equal to the remaining memory in the first terminal minus the sum of the memory occupied by all data blocks corresponding to the second queue, plus the sum of the memory occupied by the data in the second queue; wherein the first terminal is the terminal where the linked list is located, the second queue is within the first terminal, and the values ​​of the head nodes are all smaller than the set of other queues in the linked list.

[0011] Furthermore, during the execution of the DWRR algorithm, the weight of the queue is set to be proportional to the vacancy rate of the queue; the vacancy rate is the ratio of the memory actually occupied in the queue to the memory of the data block.

[0012] Furthermore, if the scheduling bandwidth of queues of a certain category is less than a preset threshold, the third queues are sorted from high to low according to the vacancy rate, and steps S1 to S3 are executed;

[0013] Step S1, obtaining a fourth queue with the highest vacancy rate from the third queue, and removing the fourth queue from the third queue;

[0014] Step S2, outputting and merging all data in the fourth queue, and clearing the data blocks in the linked list corresponding to the fourth queue;

[0015] Step S3, recalculating the scheduling bandwidth of the queues of the category. If the scheduling bandwidth of the queues of the category is less than the preset threshold, return to step S1; otherwise, the step ends;

[0016] The categories of queues are distinguished according to the values ​​of the head nodes of the linked list, and the third queue refers to a set of other queues in all terminals whose head node values ​​are smaller than the queues of the certain category.

[0017] A second aspect of the present invention discloses a terminal, including a processor and a storage medium; the characteristics are:

[0018] The storage medium is used to store instructions;

[0019] The processor is used to operate according to the instructions to execute the steps of the method described in the first aspect.

[0020] The third aspect of the present invention discloses a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps of the method described in the first aspect.

[0021] Compared with the prior art, this application has the following beneficial effects:

[0022] (1) Although memory management is efficient in data transmission and saves memory, its recovery is extremely troublesome. The present invention creatively introduces DWRR to clear the management linked list in memory management. DWRR and memory management not only play their own roles, but also efficiently cooperate and support each other, thereby jointly realizing the scheduling optimization of multi-terminal data reception and transmission in wind farms.

[0023] (2) In the DWRR scheduling algorithm, the present invention sets the scheduling bandwidth of each queue to be proportional to its actual remaining memory, so that the scheduler can dynamically adjust the data allocation amount according to the memory status of each terminal. By adjusting the scheduling bandwidth according to the actual remaining memory, it avoids allocating too much data to terminals with insufficient memory and prevents memory congestion; at the same time, it makes full use of terminals with sufficient memory and improves resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0025] Figure 1 It is the form of a single data in memory.

[0026] Figure 2 It is the form of multiple data in memory.

[0027] Figure 3 It is a schematic diagram of the transmission and reception of multi-terminal data in the memory of a wind farm.

[0028] Figure 4A It is a schematic diagram of the memory of the first terminal before executing data transmission and reception.

[0029] Figure 4B It is a schematic diagram of the memory of the first terminal after executing data transmission and reception.

[0030] Figure 5 It is a flow chart of a DWRR scheduling optimization method for multi-terminal data reception and transmission in a wind farm according to an embodiment of the present invention.

[0031] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0032] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0033] The DWRR (Deficit Weighted Round Robin) algorithm is a weighted polling algorithm for network scheduling, which is mainly used to fairly allocate bandwidth among multiple tasks that share network resources. While allocating bandwidth, the DWRR scheduling algorithm dynamically adjusts resource allocation according to the task weight and deficiency value, so that even if there are differences in bandwidth requirements between different tasks, efficient and fair resource utilization can be guaranteed.

[0034] In the application scenario of the present invention, the key point to consider is not the network congestion of data transmission, but the memory congestion of each wind farm data transceiver terminal. Therefore, the present invention actually draws on the core principle of the DWRR scheduling algorithm to reasonably achieve traffic scheduling and load balancing among various terminals.

[0035] It is understandable that when dealing with network congestion, the scheduling bandwidth of each queue can be set to be proportional to the smoothness of the current network. Therefore, it is not difficult to imagine and expand that in the application scenario of the present invention, the scheduling bandwidth of each queue can be set to be proportional to the memory remaining of the corresponding terminal. However, this approach is not reasonable during the peak period of data transmission and reception. This is because in the use scenario of the present invention, the memory remaining amount and the available memory remaining amount (corresponding to the real-time data of different sizes themselves) are actually two completely different concepts.

[0036] To understand the above problem, we first need to clarify the specific form of real-time data transmission and reception in the use scenario of the present invention. Figure 1 As shown in the figure, when a piece of data is received, the memory space occupied by the data includes at least: 2 upper and lower cookies, debugger, pad and data. Among them, cookie is used to record the memory size occupied by the data; debugger is used for debugging, and its size is usually 0; pad does not need to be considered and cannot be improved, and its role is to make the memory space occupied by the data just an integer multiple of 8 bytes; data is the real-time data itself being sent and received.

[0037] Among them, the real-time data sent and received by multiple terminals in a wind farm can be: wind speed data: used to monitor the changes in wind speed to determine the optimal operating state and power generation efficiency of the wind turbine; wind direction data: used to determine the yaw angle of the wind turbine, so as to keep the wind turbine facing the wind direction and improve the energy capture rate; blade pitch angle data: used to adjust the angle of the wind turbine blades to adapt to different wind speeds and optimize energy conversion efficiency; generator speed data: real-time monitoring of the speed of the generator to ensure that it operates within a safe range to avoid efficiency loss or equipment damage caused by overspeed or underspeed; active power output data: used to monitor the real-time power output of the wind turbine to analyze the wind turbine. performance and system efficiency; reactive power output data: used to balance the reactive power demand of the power grid and ensure the voltage stability of the power system; cabin temperature data: monitor the temperature inside the wind turbine cabin to prevent equipment damage or reduced efficiency due to overheating; vibration data: monitor the vibration of the wind turbine tower and cabin to warn of potential mechanical failures; grid frequency data: monitor the frequency of the grid to ensure that the output frequency of the wind turbine is consistent with the grid, thereby maintaining the stability of the power system; fault alarm data: receive real-time fault alarm information of the wind turbine, including abnormal vibration, overheating, abnormal current, etc., so as to ensure timely maintenance and problem solving; and so on.

[0038] These real-time data are usually small, but the frequency is very high. Therefore, the terminal must use memory management to reduce the pressure of transmission merging.

[0039] Transmission refers to the data input from the on-site sensor or the unit itself to the terminal, while merging refers to the external output of the real-time data. In the context of the present invention, merging generally refers to the centralized output of the real-time data to a database.

[0040] It is not difficult to understand that in memory management, data of the same size is usually merged together to reduce the cookie overhead, which not only saves memory but also greatly speeds up transmission and merging. Figure 2 shown.

[0041] exist Figure 2 On the basis of Figure 3 As shown, first assume that the size of wind speed data d1 and wind direction data d2 is 32, the size of blade pitch angle data d3 and generator speed data d4 is 16, the size of active power output data d5 and reactive power output data d6 is 40, and there are exactly 2 terminals. Then, according to the output frequency, data of the same size can be allocated to the same terminal as much as possible. Figure 3In the example of terminal 1, position 32 or position 40 can be understood as a management linked list, that is, linked list 32 and linked list 40, 32 or 40 can be regarded as the value of the head node of the linked list. For convenience, the linked list and the management linked list in this article have the same meaning. The management linked list is used to centrally store real-time data of the same size.

[0042] Since cookies are removed between data of the same size, data of the same size will usually be grouped together to form a data block (for example: Figure 2 is a data block of length 3, i.e. it contains 3 data). Generally, the length of a data block must be a fixed value, usually 20 to 50, depending on the frequency of data transmission and reception. Figure 3 The length of the data block in is drawn as 4, and each data block is equivalent to a node in the linked list.

[0043] After receiving data, the terminal usually needs to store the received data in the database. However, in practice, when a terminal receives one or two pieces of data, it will not rush to compete for control of the database, but will wait until it receives enough data of a certain type before storing them in a centralized manner. In fact, data of different types but the same size can also be stored in a linked list of the same length, which results in the data in and out logic in the memory not being simply first-in, last-out or first-out, last-in.

[0044] Specifically, taking terminal 1 as an example, Figure 4A As shown in the figure, it is not difficult to see the first defect of this memory management method, that is, in a certain real-time data reception, the addresses 32EA2918, 78BC623F, 78BC626F, and 78BC629F are used to receive data of size 32. On the surface, 78BC623F, 78BC626F, and 78BC629F, as empty and continuous small blocks of memory, should be able to store real-time data of size up to 96, but in fact they cannot be used. With the increase in data transmission and reception, the number of empty small blocks of memory such as 78BC623F, 78BC626F, and 78BC629F may gradually increase, eventually leading to insufficient memory.

[0045] This section further explains the "you can't actually use them" in the previous section. When data is continuously received and destroyed, the pointers to each small block of memory will be completely randomized, which means that you can't even find two consecutive empty small blocks of memory in the memory, and you can't create data larger than the size of the small block of memory (but you can create data less than or equal to the size of the small block of memory). Figure 4ATake this as an example, on this basis, we create data data21, create data data22, create data data23, destroy data data22, destroy data data5, and destroy data data23. The final result will be as follows Figure 4B As shown. It is not difficult to see that only 7 data are created under linked list 32, and the pointers are already in a mess. Figure 4B In the example, although the three small blocks of memory 78BC623F, 78BC626F, and 78BC629F are all empty, they are no longer continuous empty small blocks of memory from a legal perspective. In other words, although we stand in the perspective of an observer, they are actually continuous empty small blocks of memory, but unfortunately, it is extremely difficult for us to prove this point: due to the loss of the "protection" of the cookie, when judging, for example, the small block of memory at 78BC626F, is it an empty small block of memory, or a data whose value happens to be all 0? This is extremely difficult to prove. In addition, when the pointers point to more and more chaos, these data blocks themselves cannot be recycled because they cannot be stripped from the linked list.

[0046] It is understandable that the technical solutions and technical defects mentioned above are common knowledge. In summary, the essence of overcoming the above problems lies in adjusting the weight of the DWRR scheduling algorithm so that it is truly proportional to the available remaining memory. Based on this, the DWRR scheduling optimization method for multi-terminal data reception and transmission in wind farms proposed in the present invention is essentially that multi-terminals are based on memory management to realize real-time data reception and transmission in wind farms; and the scheduling bandwidth based on the DWRR scheduling algorithm clears the management linked list in the memory.

[0047] In some embodiments, not every terminal needs to receive all types of real-time data, that is, "distribute data of the same size to the same terminal as much as possible" as mentioned above. Therefore, the type of real-time data received by each terminal can be determined based on the frequency of the real-time data, and the linked list in the terminal can be initialized based on the size of the real-time data, such as Figure 3 shown.

[0048] More specifically, the various input parameters of the DWRR scheduling algorithm are initialized, including: the number of queues is equal to the sum of the total number of linked lists of each terminal; the weights of all queues are consistent; and the scheduling bandwidth of each queue is set to the available remaining memory of the linked list corresponding to the queue.

[0049] In some embodiments, the weights of all queues may be initialized to 1.

[0050] Due to the existence of the first defect, the number of queues should not be the number of terminals, but the sum of the total number of linked lists of all terminals, that is, each queue corresponds to a linked list of a certain terminal.

[0051] Based on the first defect mentioned above, it is not difficult to infer the scheduling bandwidth of each queue: the available remaining memory of the linked list is equal to the remaining memory in the first terminal minus the sum of the memory occupied by all data blocks corresponding to the second queue, plus the sum of the memory occupied by the data in the second queue; wherein the first terminal is the terminal where the linked list is located, the second queue is within the first terminal, and the values ​​of the head nodes are all smaller than the set of other queues in the linked list.

[0052] In addition, it is not difficult to infer that during the execution of the DWRR algorithm (i.e., during the data transmission and reception process), if the vacancy rate in a queue is high, the weight corresponding to the queue should be increased. Therefore, the weight of the queue can be set to be proportional to the vacancy rate of the queue. The vacancy rate here refers to the ratio of the actual memory occupied in the queue to the memory of the data block.

[0053] After all these input parameters are determined, the problem that needs to be considered is how to clear the management linked list in the memory. In an embodiment of the present invention, if the scheduling bandwidth of a certain category of queues is less than a preset threshold, the third queue is sorted from high to low according to the vacancy rate, and steps S1 to S3 are executed.

[0054] Step S1, obtaining a fourth queue with the highest vacancy rate from the third queue, and removing the fourth queue from the third queue.

[0055] Step S2, output and merge all the data in the fourth queue, and clear the data blocks in the linked list corresponding to the fourth queue.

[0056] Step S3, recalculating the scheduling bandwidth of the queues of the category. If the scheduling bandwidth of the queues of the category is less than the preset threshold, return to step S1; otherwise, the step ends.

[0057] The categories of queues are distinguished according to the values ​​of the head nodes of the linked list, the third queue refers to a set of other queues in all terminals whose head node values ​​are smaller than those of queues of the certain category, and the fourth queue is one of the queues with the highest vacancy rate among the third queues. In the present invention, output merging of all data in the fourth queue may mean that the DWRR algorithm actively competes for the right to use the database based on the scheduling bandwidth, and then clears all data in the fourth queue from the memory.

[0058] It should be noted that the DWRR scheduling algorithm is a known algorithm. Therefore, in the application scenario of the present invention, it is only necessary to explain the various input parameters of the DWRR scheduling algorithm and clarify the specific changes of each input parameter during the execution process, and then its execution process is determined. Based on this, the execution process of realizing data transmission and reception of multiple terminals in a wind farm based on the DWRR scheduling algorithm will not be repeated.

[0059] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0060] It should be further noted that, although the various steps in the flowchart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0061] It should be understood that the above-mentioned device embodiments are only illustrative, and the device of the present application can also be implemented in other ways. For example, the division of units / modules in the above-mentioned embodiments is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0062] In addition, unless otherwise specified, each functional unit / module in each embodiment of the present application may be integrated into one unit / module, each unit / module may exist physically separately, or two or more units / modules may be integrated together. The above-mentioned integrated unit / module may be implemented in the form of hardware or in the form of a software program module.

[0063] In the above embodiments, the description of each embodiment has its own emphasis. For the part not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0064] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0065] It should be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A DWRR scheduling optimization method for multi-terminal data reception and transmission in a wind farm, characterized in that: include: Multi-terminals realize real-time data reception and transmission of wind farms based on memory management; and the management linked list in the memory is cleared based on the scheduling bandwidth of the DWRR scheduling algorithm; wherein the management linked list is used to centrally store real-time data of the same size.

2. A DWRR scheduling optimization method for multi-terminal data reception and transmission in a wind farm according to claim 1, characterized in that: Based on the frequency of the real-time data, the type of real-time data received by each terminal is determined, and based on the size of the real-time data, a linked list in the terminal is initialized.

3. A DWRR scheduling optimization method for multi-terminal data reception and transmission in a wind farm according to claim 1, characterized in that: Initialize the various input parameters of the DWRR scheduling algorithm, including: the number of queues is equal to the sum of the total number of linked lists of each terminal; the weights of all queues are consistent; the scheduling bandwidth of each queue is set to the available remaining memory of the linked list corresponding to the queue.

4. A DWRR scheduling optimization method for multi-terminal data reception and transmission in a wind farm according to claim 3, characterized in that: The available remaining memory of the linked list is equal to the remaining memory in the first terminal minus the sum of the memory occupied by all data blocks corresponding to the second queue, plus the sum of the memory occupied by the data in the second queue; wherein the first terminal is the terminal where the linked list is located, the second queue is the value of the head node in the first terminal, which is smaller than the set of other queues in the linked list.

5. The DWRR scheduling optimization method for multi-terminal data reception and transmission in a wind farm according to claim 1 is characterized in that: During the execution of the DWRR algorithm, the weight of the queue is set to be proportional to the vacancy rate of the queue; the vacancy rate is the ratio of the memory actually occupied in the queue to the memory of the data block.

6. A DWRR scheduling optimization method for multi-terminal data reception and transmission in a wind farm according to claim 1, characterized in that: If the scheduling bandwidth of queues of a certain category is less than the preset threshold, the third queues are sorted from high to low according to the vacancy rate, and steps S1 to S3 are executed; Step S1, obtaining a fourth queue with the highest vacancy rate from the third queue, and removing the fourth queue from the third queue; Step S2, outputting and merging all data in the fourth queue, and clearing the data blocks in the linked list corresponding to the fourth queue; Step S3, recalculating the scheduling bandwidth of the queues of the category, if the scheduling bandwidth of the queues of the category is less than the preset threshold, returning to step S1; Otherwise, the step ends; The categories of queues are distinguished according to the values ​​of the head nodes of the linked list, and the third queue refers to a set of other queues in all terminals whose head node values ​​are smaller than the queues of the certain category.

7. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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