CN113792971A - Regional power dispatching networking method and system - Google Patents
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Abstract
The invention provides a regional power dispatching networking method and a system, comprising the following steps: constructing a regional cloud platform, and setting a communication connection between a regional scheduling node and the regional cloud platform; allocating initial resources of a cloud platform to the scheduling nodes according to the quarterly estimated workload of the scheduling nodes; monitoring the resource utilization rate of the scheduling node; predicting the resource utilization rate variation trend of the scheduling node according to the resource utilization rate by utilizing a pre-constructed Prophet model; and reallocating the cloud resources of the scheduling nodes according to the resource utilization rate change trend. According to the invention, through carrying out scheduling networking based on the cloud platform, the computing resources and the storage resources required by regional scheduling can be unified to the cloud platform, and the cloud platform carries out dynamic resource allocation on each scheduling node according to the resource utilization condition of each scheduling node, so that the problem of insufficient resources or excessive resources of each scheduling node is effectively avoided.
Description
Technical Field
The invention relates to the technical field of power dispatching, in particular to a regional power dispatching networking method and system.
Background
Power scheduling is an important link of power transmission, and regional power scheduling such as provincial power scheduling mostly sets multiple levels of scheduling nodes according to administrative regions or geographical divisions. Each scheduling node is a scheduling system and has local computing resources and storage resources, and different nodes carry out scheduling data transmission through local area network communication.
Along with the complexity of a power grid, more and more power dispatching tasks are performed, the problem that the resource expansion of dispatching nodes is not changed in the traditional power dispatching network exists, and if the resources are expanded blindly by all the dispatching nodes, the problems of resource surplus and cost waste are easily caused.
Disclosure of Invention
In view of the above-mentioned deficiencies of the prior art, the present invention provides a method and a system for regional power scheduling networking to solve the above-mentioned technical problems.
In a first aspect, the present invention provides a regional power scheduling networking method, including:
constructing a regional cloud platform, and setting a communication connection between a regional scheduling node and the regional cloud platform;
allocating initial resources of a cloud platform to the scheduling nodes according to the quarterly estimated workload of the scheduling nodes;
monitoring the resource utilization rate of the scheduling node;
predicting the resource utilization rate variation trend of the scheduling node according to the resource utilization rate by utilizing a pre-constructed Prophet model;
and reallocating the cloud resources of the scheduling nodes according to the resource utilization rate change trend.
Further, a regional cloud platform is constructed, and a regional scheduling node is set to be in communication connection with the regional cloud platform, including:
and setting a plurality of levels of scheduling nodes according to the regional power management level, and setting the operation authority of each scheduling node according to the level of the scheduling node.
Further, allocating the initial resource of the cloud platform to the scheduling node according to the quarterly estimated workload of the scheduling node includes:
estimating the workload of the scheduling node according to the service plan of the scheduling node;
and calculating the initial resources of the scheduling node according to the cloud resources required by the unit workload and the pre-estimated workload.
Further, monitoring the resource utilization of the scheduling node includes:
the method comprises the steps that resource utilization rates of the scheduling nodes are collected regularly according to a preset monitoring period, wherein the resource utilization rates comprise calculation resource utilization rates and storage resource utilization rates;
and sequencing the acquired resource utilization rates according to the acquisition time of the resource utilization rates to obtain a resource monitoring time sequence.
Further, predicting the resource utilization rate variation trend of the scheduling node according to the resource utilization rate by using a pre-constructed Prophet model, wherein the method comprises the following steps:
the method comprises the steps that historical resource utilization rate data of scheduling nodes in a specified period are stored in a data training set in advance;
constructing a Prophet holitray model, and training the Prophet holitray model by utilizing the data training set;
and training the resource monitoring time and inputting the resource monitoring time into a trained Prophet holitray model, and acquiring the prediction time when the resource utilization rate reaches a preset threshold value.
Further, the reallocation of the cloud resources of the scheduling node according to the resource utilization rate variation trend includes:
calculating the difference value between the predicted time and a preset time threshold value;
calculating the amount of the redistributed cloud resources according to a preset proportionality coefficient and the difference value;
and adjusting the cloud resources of the scheduling node according to the positive and negative attributes of the redistributed cloud resource amount and the redistributed cloud resource amount.
In a second aspect, the present invention provides a regional power dispatching networking system, including:
the communication setting unit is used for constructing a regional cloud platform and setting the communication connection between a regional scheduling node and the regional cloud platform;
the initial allocation unit is used for allocating initial resources of the cloud platform to the scheduling nodes according to the quarterly estimated workload of the scheduling nodes;
the resource monitoring unit is used for monitoring the resource utilization rate of the scheduling node;
the trend prediction unit is used for predicting the resource utilization rate change trend of the scheduling node according to the resource utilization rate by utilizing a pre-constructed Prophet model;
and the resource adjusting unit is used for reallocating the cloud resources of the scheduling node according to the resource utilization rate change trend.
Further, the resource monitoring unit includes:
the data acquisition module is used for regularly acquiring the resource utilization rate of the scheduling node according to a preset monitoring period, wherein the resource utilization rate comprises a calculation resource utilization rate and a storage resource utilization rate;
and the data sequencing module is used for sequencing the acquired resource utilization rate according to the acquisition time of the resource utilization rate to obtain a resource monitoring time sequence.
Further, the trend prediction unit includes:
the data preparation module is used for storing historical resource utilization rate data of the scheduling node in a specified period into a data training set in advance;
the model training module is used for constructing a Prophet holitray model and training the Prophet holitray model by utilizing the data training set;
and the time prediction module is used for inputting the resource monitoring time training into a trained Prophet holitray model and acquiring the prediction time when the resource utilization rate reaches a preset threshold value.
Further, the resource adjusting unit includes:
the difference value calculating module is used for calculating the difference value between the predicted time and a preset time threshold value;
the target calculation module is used for calculating the amount of the re-distributed cloud resources according to a preset proportionality coefficient and the difference value;
and the resource adjusting module is used for adjusting the cloud resources of the scheduling node according to the positive and negative attributes of the redistributed cloud resource amount and the redistributed cloud resource amount.
The beneficial effect of the invention is that,
according to the regional power dispatching networking method and system, the cloud platform is used for dispatching and networking, computing resources and storage resources required by regional dispatching can be unified to the cloud platform, the cloud platform performs dynamic resource allocation on each dispatching node according to the resource utilization condition of each dispatching node, and the problem that each dispatching node is insufficient in resources or surplus in resources is effectively solved.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
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In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention.
FIG. 2 is a schematic block diagram of a system of one embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention. The execution subject in fig. 1 may be a regional power dispatching networking system.
As shown in fig. 1, the method includes:
and 150, reallocating the cloud resources of the scheduling nodes according to the resource utilization rate change trend.
In order to facilitate understanding of the present invention, the regional power scheduling networking method provided in the present invention is further described below with reference to embodiments according to the principle of the regional power scheduling networking method of the present invention.
Specifically, the regional power dispatching networking method includes:
s1, constructing a region cloud platform, and setting a communication connection between the region scheduling node and the region cloud platform.
In the embodiment, a provincial cloud platform is constructed, and provincial scheduling nodes, city scheduling nodes and county scheduling nodes are arranged, wherein the three-level nodes are clients in communication connection with the cloud platform. The client side can send a request to the cloud platform and receive scheduling data issued by the cloud platform, and the client side controls the corresponding transformer substation to execute the corresponding scheduling plan according to the scheduling data.
In this embodiment, operation permissions of the provincial, municipal and county nodes are set on the cloud platform, for example, a scheduling target sent by a municipal scheduling node is subject to a scheduling plan of the provincial scheduling node, and the provincial scheduling node can view scheduling data of the municipal and county scheduling nodes.
And S2, distributing the initial resources of the cloud platform for the scheduling nodes according to the quarterly estimated workload of the scheduling nodes.
And estimating the workload of the scheduling node according to the service plan of the scheduling node, for example, the workload of a certain scheduling node in the past year is known, the service is increased after service adjustment is carried out, the sum of the workload in the past year and the newly increased service is the estimated workload, and the corresponding relation is the cloud resource required by unit workload according to the corresponding relation between the workload in the past and the data demand by using a big data analysis method. And then calculating the initial resources of the scheduling nodes according to the cloud resources required by the unit workload and the pre-estimated workload.
And S3, monitoring the resource utilization rate of the scheduling node.
And acquiring the resource utilization rate of the scheduling node periodically according to a preset monitoring period, wherein the resource utilization rate comprises a calculation resource utilization rate and a storage resource utilization rate, and the resource utilization rate is set to be acquired every 1h, for example. Sequencing the acquired resource utilization rates according to the acquisition time of the resource utilization rates to obtain a resource monitoring time sequence, wherein the resource monitoring implementation sequence taking the storage resources as an example is [ A1, A2, A3, A4], wherein A1 represents 1: the storage resource utilization rate collected at 00 hours, a2 represents the storage resource utilization rate collected at 2:00 hours, A3 represents the storage resource utilization rate collected at 3:00 hours, and a4 represents the storage resource utilization rate collected at 4:00 hours.
And S4, predicting the resource utilization rate change trend of the scheduling node according to the resource utilization rate by using a pre-constructed Prophet model.
Prophet is an open source library based on a resolvable (trend + season + holiday) model published by Facebook. The method can predict the time series with high precision by using simpler and more visual parameters and supports the influence of self-defined seasons and holiday factors. The overall framework of prophet is divided into four parts: modeling, Forecast Evaluation, Surface profiles, and visual inspection profiles. Overall, this is a cyclic structure which can be divided into an analyst's manipulation portion and an automation portion according to dashed lines. Therefore, the whole process is a circulation system combining an analyst and an automation process and is a process combining problem background knowledge and statistical analysis, the application range of the model is greatly enlarged by combining the problem background knowledge and the statistical analysis, and the accuracy of the model is improved. Firstly, Modeling: establishing a time series model; then carrying out Forecast Evaluation, namely model Evaluation, carrying out various attempts on parameters, and evaluating a more appropriate model according to a simulation effect; surface profiles are followed: presenting problems, presenting potential causes with larger errors to an analyst for manual intervention; the last part is the Visually Inspects forms: the whole prediction result is fed back in a visual mode, and after the problem is fed back to an analyst, the analyst considers whether to further adjust and construct the model.
In creating the Prophet holiday model, the national statutory holidays are customized because the power usage for holidays is large. The specific construction method is the prior art, and therefore, the detailed description is omitted.
And storing historical resource utilization rate data of the scheduling nodes in a specified year to a data training set in advance. And training the Prophet holiday model by using the data training set.
And (4) training and inputting the resource monitoring time obtained in the step (S3) into the trained Prophet holiday model, and acquiring the prediction time when the resource utilization rate reaches a preset threshold (such as 80%).
And S5, reallocating the cloud resources of the scheduling nodes according to the resource utilization rate change trend.
And calculating the difference value T between the predicted time T and a preset time threshold value T0, wherein T is T-T0. And calculating the redistributed cloud resource amount G according to a preset proportionality coefficient k and the difference value t, wherein G is kt. And when the predicted time T is less than the time threshold T0, T is a negative number, G is a negative number, the cloud resources are allocated to the scheduling node, and the allocated cloud resource amount is G. Similarly, if the predicted time T is greater than the time threshold T0, and T is a positive number, G is a positive number, and at this time, the cloud resource is recovered from the scheduling node, and the amount of the recovered cloud resource is G.
As shown in fig. 2, the system 200 includes:
the communication setting unit 210 is configured to construct a regional cloud platform, and set a communication connection between a regional scheduling node and the regional cloud platform;
an initial allocation unit 220, configured to allocate initial resources of the cloud platform to the scheduling node according to the quarterly estimated workload of the scheduling node;
a resource monitoring unit 230, configured to monitor a resource utilization rate of the scheduling node;
the trend prediction unit 240 is configured to predict a resource utilization rate change trend of the scheduling node according to the resource utilization rate by using a pre-constructed Prophet model;
and the resource adjusting unit 250 is configured to reallocate the cloud resources of the scheduling node according to the resource utilization rate variation trend.
Optionally, as an embodiment of the present invention, the resource monitoring unit includes:
the data acquisition module is used for regularly acquiring the resource utilization rate of the scheduling node according to a preset monitoring period, wherein the resource utilization rate comprises a calculation resource utilization rate and a storage resource utilization rate;
and the data sequencing module is used for sequencing the acquired resource utilization rate according to the acquisition time of the resource utilization rate to obtain a resource monitoring time sequence.
Optionally, as an embodiment of the present invention, the trend prediction unit includes:
the data preparation module is used for storing historical resource utilization rate data of the scheduling node in a specified period into a data training set in advance;
the model training module is used for constructing a Prophet holitray model and training the Prophet holitray model by utilizing the data training set;
and the time prediction module is used for inputting the resource monitoring time training into a trained Prophet holitray model and acquiring the prediction time when the resource utilization rate reaches a preset threshold value.
Optionally, as an embodiment of the present invention, the resource adjusting unit includes:
the difference value calculating module is used for calculating the difference value between the predicted time and a preset time threshold value;
the target calculation module is used for calculating the amount of the re-distributed cloud resources according to a preset proportionality coefficient and the difference value;
and the resource adjusting module is used for adjusting the cloud resources of the scheduling node according to the positive and negative attributes of the redistributed cloud resource amount and the redistributed cloud resource amount.
Claims (10)
1. A regional power dispatching networking method is characterized by comprising the following steps:
constructing a regional cloud platform, and setting a communication connection between a regional scheduling node and the regional cloud platform;
allocating initial resources of a cloud platform to the scheduling nodes according to the quarterly estimated workload of the scheduling nodes;
monitoring the resource utilization rate of the scheduling node;
predicting the resource utilization rate variation trend of the scheduling node according to the resource utilization rate by utilizing a pre-constructed Prophet model;
and reallocating the cloud resources of the scheduling nodes according to the resource utilization rate change trend.
2. The method of claim 1, wherein constructing a regional cloud platform and setting up a communication connection between a regional scheduling node and the regional cloud platform comprises:
and setting a plurality of levels of scheduling nodes according to the regional power management level, and setting the operation authority of each scheduling node according to the level of the scheduling node.
3. The method of claim 1, wherein allocating initial resources of a cloud platform for a scheduling node according to a quarterly predicted workload of the scheduling node comprises:
estimating the workload of the scheduling node according to the service plan of the scheduling node;
and calculating the initial resources of the scheduling node according to the cloud resources required by the unit workload and the pre-estimated workload.
4. The method of claim 1, wherein monitoring resource utilization of the scheduling node comprises:
the method comprises the steps that resource utilization rates of the scheduling nodes are collected regularly according to a preset monitoring period, wherein the resource utilization rates comprise calculation resource utilization rates and storage resource utilization rates;
and sequencing the acquired resource utilization rates according to the acquisition time of the resource utilization rates to obtain a resource monitoring time sequence.
5. The method according to claim 4, wherein predicting the resource utilization rate variation trend of the scheduling node according to the resource utilization rate by using a pre-constructed Prophet model comprises:
the method comprises the steps that historical resource utilization rate data of scheduling nodes in a specified period are stored in a data training set in advance;
constructing a Prophet holitray model, and training the Prophet holitray model by utilizing the data training set;
and training the resource monitoring time and inputting the resource monitoring time into a trained Prophet holitray model, and acquiring the prediction time when the resource utilization rate reaches a preset threshold value.
6. The method of claim 5, wherein re-allocating the cloud resources of the scheduling node according to the resource utilization rate trend comprises:
calculating the difference value between the predicted time and a preset time threshold value;
calculating the amount of the redistributed cloud resources according to a preset proportionality coefficient and the difference value;
and adjusting the cloud resources of the scheduling node according to the positive and negative attributes of the redistributed cloud resource amount and the redistributed cloud resource amount.
7. A regional power scheduling networking system, comprising:
the communication setting unit is used for constructing a regional cloud platform and setting the communication connection between a regional scheduling node and the regional cloud platform;
the initial allocation unit is used for allocating initial resources of the cloud platform to the scheduling nodes according to the quarterly estimated workload of the scheduling nodes;
the resource monitoring unit is used for monitoring the resource utilization rate of the scheduling node;
the trend prediction unit is used for predicting the resource utilization rate change trend of the scheduling node according to the resource utilization rate by utilizing a pre-constructed Prophet model;
and the resource adjusting unit is used for reallocating the cloud resources of the scheduling node according to the resource utilization rate change trend.
8. The system of claim 7, wherein the resource monitoring unit comprises:
the data acquisition module is used for regularly acquiring the resource utilization rate of the scheduling node according to a preset monitoring period, wherein the resource utilization rate comprises a calculation resource utilization rate and a storage resource utilization rate;
and the data sequencing module is used for sequencing the acquired resource utilization rate according to the acquisition time of the resource utilization rate to obtain a resource monitoring time sequence.
9. The system of claim 8, wherein the trend prediction unit comprises:
the data preparation module is used for storing historical resource utilization rate data of the scheduling node in a specified period into a data training set in advance;
the model training module is used for constructing a Prophet holitray model and training the Prophet holitray model by utilizing the data training set;
and the time prediction module is used for inputting the resource monitoring time training into a trained Prophet holitray model and acquiring the prediction time when the resource utilization rate reaches a preset threshold value.
10. The system of claim 9, wherein the resource adjusting unit comprises:
the difference value calculating module is used for calculating the difference value between the predicted time and a preset time threshold value;
the target calculation module is used for calculating the amount of the re-distributed cloud resources according to a preset proportionality coefficient and the difference value;
and the resource adjusting module is used for adjusting the cloud resources of the scheduling node according to the positive and negative attributes of the redistributed cloud resource amount and the redistributed cloud resource amount.
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