1. A power grid resource deployment analysis method based on Bayesian back propagation is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,

acquiring power grid resource data;

constructing a network tag according to the position information corresponding to the power grid resource data;

and importing the network tag into a simulation analysis platform to read key information and outputting an analysis report.

2. The Bayesian back propagation-based power grid resource deployment analysis method according to claim 1, wherein: and calling the power grid resource data stored in the Internet by using a cloud database.

3. The Bayesian back propagation-based power grid resource deployment analysis method according to claim 1 or 2, wherein: and cleaning, screening, filtering and standardizing the power grid resource data to form a data sample set.

4. The Bayesian back propagation-based power grid resource deployment analysis method according to claim 3, wherein: the data sample set comprises a test data set, a verification data set and a comparison data set.

5. The Bayesian back propagation-based power grid resource deployment analysis method according to claim 4, wherein: the network tag comprises a network code, an identification symbol and an elevation code;

the network code comprises a grid code determined by equipment position information of the power grid resource data;

the identification symbol comprises a coding mode used for the elevation coding;

the elevation coding comprises coding elevation information of the power grid resource data.

6. The Bayesian back propagation-based power grid resource deployment analysis method of claim 5, wherein: comprises the steps of (a) preparing a mixture of a plurality of raw materials,

constructing and analyzing a target function of power grid resource deployment based on a Bayesian back propagation principle;

coding the target function to form a program package and importing the program package into the simulation analysis platform;

and the simulation analysis platform finely adjusts the program package test operation, and starts to import the test data set for testing when the program package test operation is read and finished.

7. The Bayesian back propagation-based power grid resource deployment analysis method of claim 6, wherein: and starting the simulation analysis platform to operate, and observing and recording analysis data in real time.

8. The Bayesian back propagation-based power grid resource deployment analysis method of claim 7, wherein: and forming excel tables by the recorded analysis data, and summarizing to obtain the analysis report.

Background

At present, a high-performance platform meeting the requirement of an energy internet for power grid resource deployment construction needs to face the problems of huge number of managed elements, frequent change, many users and high response real-time performance, and in a power grid scene, power grid resource data (namely power grid space position data and space data which are index-managed power grid resource data) has the characteristics of many sources, huge data volume and complex processing and analysis, so that the current power grid resource deployment planning has extremely high difficulty.

Disclosure of Invention

This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.

The present invention has been made in view of the above-mentioned conventional problems.

Therefore, the technical problem solved by the invention is as follows: the source is many, the data volume is huge, and processing analysis is complicated.

In order to solve the technical problems, the invention provides the following technical scheme: the method comprises the steps of obtaining power grid resource data; constructing a network tag according to the position information corresponding to the power grid resource data; and importing the network tag into a simulation analysis platform to read key information and outputting an analysis report.

As an optimal scheme of the power grid resource deployment analysis method based on bayesian back propagation, the method comprises the following steps: and calling the power grid resource data stored in the Internet by using a cloud database.

As an optimal scheme of the power grid resource deployment analysis method based on bayesian back propagation, the method comprises the following steps: and cleaning, screening, filtering and standardizing the power grid resource data to form a data sample set.

As an optimal scheme of the power grid resource deployment analysis method based on bayesian back propagation, the method comprises the following steps: the data sample set comprises a test data set, a verification data set and a comparison data set.

As an optimal scheme of the power grid resource deployment analysis method based on bayesian back propagation, the method comprises the following steps: the network tag comprises a network code, an identification symbol and an elevation code; the network code comprises a grid code determined by equipment position information of the power grid resource data; the identification symbol comprises a coding mode used for the elevation coding; the elevation coding comprises coding elevation information of the power grid resource data.

As an optimal scheme of the power grid resource deployment analysis method based on bayesian back propagation, the method comprises the following steps: constructing and analyzing a target function of power grid resource deployment based on a Bayesian back propagation principle; coding the target function to form a program package and importing the program package into the simulation analysis platform; and the simulation analysis platform finely adjusts the program package test operation, and starts to import the test data set for testing when the program package test operation is read and finished.

As an optimal scheme of the power grid resource deployment analysis method based on bayesian back propagation, the method comprises the following steps: and starting the simulation analysis platform to operate, and observing and recording analysis data in real time.

As an optimal scheme of the power grid resource deployment analysis method based on bayesian back propagation, the method comprises the following steps: and forming excel tables by the recorded analysis data, and summarizing to obtain the analysis report.

The invention has the beneficial effects that: the invention can eliminate the problems of complex mass data, complex analysis and untimely processing, and greatly improves the efficiency and the real-time property.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:

fig. 1 is a schematic flowchart of a power grid resource deployment analysis method based on bayesian back propagation according to a first embodiment of the present invention;

fig. 2 is a schematic diagram of a comparison curve of performance tests of a power grid resource deployment analysis method based on bayesian back propagation according to a second embodiment of the present invention.

Detailed Description

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.

Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.

The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.

Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.

The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.

Example 1

Referring to fig. 1, a power grid resource deployment analysis method based on bayesian back propagation is provided as a first embodiment of the present invention, and specifically includes:

and S1, acquiring the power grid resource data. Wherein, it is required to be noted that:

calling power grid resource data stored in the internet by using a cloud database;

cleaning, screening, filtering and standardizing the power grid resource data to form a data sample set;

the data sample set includes a test data set, a validation data set, and a comparison data set.

And S2, constructing a network label according to the position information corresponding to the power grid resource data. The steps to be explained are as follows:

the network tag comprises a network code, an identification symbol and an elevation code;

the network code comprises a grid code determined by equipment position information of the power grid resource data;

the identification symbol comprises a coding mode used for elevation coding;

the elevation coding comprises coding elevation information of the power grid resource data.

And S3, importing the network label into the simulation analysis platform to read the key information and outputting an analysis report. Among them, it is also to be noted that:

constructing and analyzing a target function of power grid resource deployment based on a Bayesian back propagation principle;

coding the target function to form a program package and importing the program package into a simulation analysis platform;

the simulation analysis platform fine-tunes the program package for test run, and starts to import a test data set for test when the test run is finished and the reading is finished;

starting a simulation analysis platform to operate, and observing and recording analysis data in real time;

and forming excel tables by the recorded analysis data, and summarizing to obtain an analysis report.

Preferably, in this embodiment, it should be further noted that, when the simulation analysis platform provided in this embodiment runs and writes a package to perform analysis test on the deployment of the power grid resource, the related part of the running code is as follows:

example 2

Referring to fig. 2, a second embodiment of the present invention is different from the first embodiment in that a verification process of a power grid resource deployment analysis method based on bayesian back propagation is provided, which specifically includes:

in order to better verify and explain the technical effects adopted in the method of the present invention, in this embodiment, a comparison test is performed by using a traditional power grid resource analysis method and the method of the present invention, and the test results are compared by using a scientific demonstration method to verify the real effect of the method of the present invention.

The traditional power grid resource analysis method cannot process massive data information, and in order to verify that the method has higher efficiency and real-time performance compared with the traditional method, the traditional method and the method are adopted for real-time measurement and comparison in the embodiment in the face of complicated analysis of massive information with unknown sources, low efficiency and large errors.

And (3) testing environment: (1) the system comprises a cloud server, an Http data packet, a wireshark and a service program;

(2) the simulation platform obtains a cloud database, and 10000 sets of historical data are found to serve as a standard database test sample set;

(3) the machine learning algorithm of the traditional method and the Bayes back propagation algorithm of the invention are respectively imported into simulation software for state simulation through MATLB compiling codes.

Referring to fig. 2, a solid line is a curve output by the method of the present invention, a dotted line is a curve output by a conventional method, and according to the schematic diagram of fig. 2, it can be seen intuitively that the solid line and the dotted line show different trends along with the increase of time, the solid line shows a stable rising trend in the former period compared with the dotted line, although the solid line slides down in the latter period, the fluctuation is not large and is always above the dotted line and keeps a certain distance, and the dotted line shows a large fluctuation trend and is unstable, so that the calculation efficiency of the solid line is always greater than that of the dotted line, i.e. the real effect of the method of the present invention is verified.

It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.