CN118333217A - Power distribution cabinet information determining method and system - Google Patents
Power distribution cabinet information determining method and system Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02B—BOARDS, SUBSTATIONS OR SWITCHING ARRANGEMENTS FOR THE SUPPLY OR DISTRIBUTION OF ELECTRIC POWER
- H02B3/00—Apparatus specially adapted for the manufacture, assembly, or maintenance of boards or switchgear
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Abstract
The invention provides a method and a system for determining information of a power distribution cabinet, and relates to the technical field of power distribution cabinets, wherein the method comprises the steps of determining the size of the power distribution cabinet, a plurality of candidate positions of the power distribution cabinet, the energy loss degree of each candidate position of the power distribution cabinet and the electrical risk degree of each candidate position of the power distribution cabinet by using an equipment distribution processing model; determining the environmental risk degree of each candidate position by using a risk analysis model based on the size of the power distribution cabinet, a plurality of candidate positions of the power distribution cabinet and the environmental distribution diagram of the corresponding region of the power distribution cabinet; removing candidate positions with environmental risk exceeding a preset risk threshold to obtain a plurality of screened candidate positions; the method can accurately determine the installation position of the power distribution cabinet and avoid potential safety hazards based on the energy loss degree of each candidate position after screening, the electrical risk degree of each candidate position after screening and the environmental risk degree of each candidate position after screening.
Description
Technical Field
The invention relates to the technical field of power distribution cabinets, in particular to a method and a system for determining information of a power distribution cabinet.
Background
With the continuous development of power systems, power distribution cabinets are widely used in various fields as key equipment for power distribution and management. However, the choice of installation location of the power distribution cabinet has an important impact on the safe, stable operation and energy efficiency of the power system. Traditional power distribution cabinet installation position determining methods often depend on manual experience and simple field investigation, lack of scientificity and accuracy, and are difficult to comprehensively consider various factors, such as distribution of electrical equipment, environmental factors, potential risks and the like, and potential safety hazards are easily caused.
Therefore, how to accurately determine the installation position of the power distribution cabinet, and avoiding potential safety hazards is a current problem to be solved urgently.
Disclosure of Invention
The invention mainly solves the technical problem how to accurately determine the installation position of the power distribution cabinet and avoid potential safety hazards.
According to a first aspect, the present invention provides a method for determining information of a power distribution cabinet, including: acquiring an electrical equipment distribution diagram of a corresponding area of a power distribution cabinet and an environment distribution diagram of the corresponding area of the power distribution cabinet; determining the size of the power distribution cabinet, a plurality of candidate positions of the power distribution cabinet, the energy loss degree of each candidate position of the power distribution cabinet and the electrical risk degree of each candidate position of the power distribution cabinet by using an equipment distribution processing model based on an electrical equipment distribution diagram of a corresponding area of the power distribution cabinet; determining the environmental risk degree of each candidate position by using a risk analysis model based on the size of the power distribution cabinet, a plurality of candidate positions of the power distribution cabinet and an environmental distribution diagram of a corresponding region of the power distribution cabinet; comparing the environmental risk degree of each candidate position with a preset risk threshold, and eliminating candidate positions of which the environmental risk degree exceeds the preset risk threshold to obtain a plurality of screened candidate positions; acquiring the energy loss degree of each candidate position after screening, the electrical risk degree of each candidate position after screening and the environmental risk degree of each candidate position after screening; and determining a target mounting point based on the energy loss degree of each candidate position after screening, the electrical risk degree of each candidate position after screening and the environmental risk degree of each candidate position after screening.
Still further, the determining the target mounting point based on the energy loss degree of each candidate position after the screening, the electrical risk degree of each candidate position after the screening, and the environmental risk degree of each candidate position after the screening includes: and respectively giving different weights to the energy loss degree of each candidate position after screening, the electrical risk degree of each candidate position after screening and the environmental risk degree of each candidate position after screening, and then carrying out weighted summation to obtain the comprehensive risk degree of each candidate position after screening, wherein the candidate position after screening with the minimum comprehensive risk degree is used as a target mounting point of the power distribution cabinet.
Still further, the device distribution processing model is a convolutional neural network model.
Still further, the risk analysis model includes a generation countermeasure network layer and a risk determination layer, wherein an input of the generation countermeasure network layer is an environmental distribution diagram of a region corresponding to the power distribution cabinet, an output of the generation countermeasure network layer is an environmental risk degree prediction diagram of the region corresponding to the power distribution cabinet, an input of the risk determination layer is an environmental risk degree prediction diagram of the size of the power distribution cabinet, a plurality of candidate positions of the power distribution cabinet and the region corresponding to the power distribution cabinet, and an output of the risk determination layer is an environmental risk degree of each candidate position.
Still further, the risk determination layer is a convolutional neural network layer.
According to a second aspect, the present invention provides a power distribution cabinet information determining system, comprising:
The first acquisition module is used for acquiring an electrical equipment distribution diagram of a corresponding area of the power distribution cabinet and an environment distribution diagram of the corresponding area of the power distribution cabinet; the equipment distribution processing module is used for determining the size of the power distribution cabinet, a plurality of candidate positions of the power distribution cabinet, the energy loss degree of each candidate position of the power distribution cabinet and the electrical risk degree of each candidate position of the power distribution cabinet by using an equipment distribution processing model based on an electrical equipment distribution diagram of a corresponding area of the power distribution cabinet; the environment risk degree determining module is used for determining the environment risk degree of each candidate position by using a risk analysis model based on the size of the power distribution cabinet, the candidate positions of the power distribution cabinet and the environment distribution diagram of the corresponding area of the power distribution cabinet; the screening module is used for comparing the environmental risk degree of each candidate position with a preset risk threshold value, eliminating candidate positions of which the environmental risk degree exceeds the preset risk threshold value, and obtaining a plurality of screened candidate positions; the second acquisition module is used for acquiring the energy loss degree of each candidate position after screening, the electrical risk degree of each candidate position after screening and the environmental risk degree of each candidate position after screening; and the installation point determining module is used for determining a target installation point based on the energy loss degree of each candidate position after screening, the electrical risk degree of each candidate position after screening and the environmental risk degree of each candidate position after screening. Further, the heating information processing model is a gating cycle unit, and the time determination model is a gating cycle unit.
Still further, the mounting point determination module is further configured to:
And respectively giving different weights to the energy loss degree of each candidate position after screening, the electrical risk degree of each candidate position after screening and the environmental risk degree of each candidate position after screening, and then carrying out weighted summation to obtain the comprehensive risk degree of each candidate position after screening, wherein the candidate position after screening with the minimum comprehensive risk degree is used as a target mounting point of the power distribution cabinet.
Still further, the device distribution processing model is a convolutional neural network model.
Still further, the risk analysis model includes a generation countermeasure network layer and a risk determination layer, wherein an input of the generation countermeasure network layer is an environmental distribution diagram of a region corresponding to the power distribution cabinet, an output of the generation countermeasure network layer is an environmental risk degree prediction diagram of the region corresponding to the power distribution cabinet, an input of the risk determination layer is an environmental risk degree prediction diagram of the size of the power distribution cabinet, a plurality of candidate positions of the power distribution cabinet and the region corresponding to the power distribution cabinet, and an output of the risk determination layer is an environmental risk degree of each candidate position.
Still further, the risk determination layer is a convolutional neural network layer.
The invention provides a method and a system for determining information of a power distribution cabinet, wherein the method comprises the steps of obtaining an electrical equipment distribution diagram of a corresponding area of the power distribution cabinet and an environment distribution diagram of the corresponding area of the power distribution cabinet; determining the size of the power distribution cabinet, a plurality of candidate positions of the power distribution cabinet, the energy loss degree of each candidate position of the power distribution cabinet and the electrical risk degree of each candidate position of the power distribution cabinet by using an equipment distribution processing model based on an electrical equipment distribution diagram of a corresponding area of the power distribution cabinet; determining the environmental risk degree of each candidate position by using a risk analysis model based on the size of the power distribution cabinet, a plurality of candidate positions of the power distribution cabinet and an environmental distribution diagram of a corresponding region of the power distribution cabinet; comparing the environmental risk degree of each candidate position with a preset risk threshold, and eliminating candidate positions of which the environmental risk degree exceeds the preset risk threshold to obtain a plurality of screened candidate positions; acquiring the energy loss degree of each candidate position after screening, the electrical risk degree of each candidate position after screening and the environmental risk degree of each candidate position after screening; the method can accurately determine the installation position of the power distribution cabinet and avoid potential safety hazards based on the energy loss degree of each candidate position after screening, the electrical risk degree of each candidate position after screening and the environmental risk degree of each candidate position after screening.
Drawings
Fig. 1 is a schematic flow chart of a method for determining information of a power distribution cabinet according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a system for determining information of a power distribution cabinet according to an embodiment of the present invention.
Detailed Description
In the embodiment of the invention, a method for determining information of a power distribution cabinet shown in fig. 1 is provided, and the method for determining information of the power distribution cabinet comprises steps S1 to S6:
Step S1, acquiring an electrical equipment distribution diagram of a region corresponding to a power distribution cabinet and an environment distribution diagram of the region corresponding to the power distribution cabinet.
The electrical equipment distribution map is a drawing or image that can display the position and layout of electrical equipment within a particular area. The electrical devices may include switches, outlets, electrical boxes, wires, cables, air conditioning, lights, and the like.
The environmental distribution diagram of the corresponding region of the power distribution cabinet is a detailed chart, and the environmental distribution diagram of the corresponding region of the power distribution cabinet shows environmental characteristics in the region where the power distribution cabinet is located, including but not limited to temperature, humidity, dust concentration, ventilation conditions and the like. The environmental distribution diagram of the corresponding area of the power distribution cabinet can be obtained by installing various environmental monitoring sensors such as a temperature sensor, a humidity sensor, a dust sensor, an anemometer and the like in the area where the power distribution cabinet is located, sending the collected data to a data processing center, and processing the data by the data processing center. In some embodiments, the data processing center can generate an environmental profile of the corresponding area of the power distribution cabinet based on the data of the sensor through the deep neural network. The deep neural network may include a plurality of processing layers, each processing layer being composed of a plurality of neurons, each neuron matrixing data. The parameters used by the matrix may be obtained by training.
And S2, determining the size of the power distribution cabinet, a plurality of candidate positions of the power distribution cabinet, the energy loss degree of each candidate position of the power distribution cabinet and the electrical risk degree of each candidate position of the power distribution cabinet by using an equipment distribution processing model based on an electrical equipment distribution diagram of a corresponding area of the power distribution cabinet.
The equipment distribution processing model is a convolutional neural network model. The input of the equipment distribution processing model is an electrical equipment distribution diagram of a corresponding area of the power distribution cabinet, and the output of the equipment distribution processing model is the size of the power distribution cabinet, a plurality of candidate positions of the power distribution cabinet, the energy loss degree of each candidate position of the power distribution cabinet and the electrical risk degree of each candidate position of the power distribution cabinet.
The Convolutional Neural Network (CNN) may be a multi-layer neural network (e.g., comprising at least two layers). The at least two layers may include at least one of a convolutional layer (CONV), a modified linear unit (ReLU) layer, a pooling layer (POOL), or a fully-connected layer (FC). Convolutional neural networks are capable of extracting useful features from an image and progressively understanding and learning the contextual information of the image.
The electrical equipment distribution diagram shows information such as the position, distribution density, space utilization rate and the like of each electrical equipment in the area. Different types of electrical equipment place different demands on the size and configuration of the power distribution cabinet. The electrical equipment profile provides information about the type, number and location of equipment within the area that facilitates analysis of the equipment's requirements for the capacity and size of the power distribution cabinet to determine the appropriate power distribution cabinet size. As an example, the electrical equipment profile may be processed by a convolutional neural network to determine the number of circuit breakers and other components that the power distribution cabinet must accommodate, and thus the power distribution cabinet size.
By way of example, the convolutional neural network can identify an efficient area in space and output a plurality of candidate positions of the power distribution cabinet by analyzing an electrical equipment distribution diagram of a corresponding area of the power distribution cabinet, so that a cable path can be optimized, energy loss can be reduced, and power distribution efficiency can be improved.
The convolutional neural network can effectively extract the characteristics in the distribution diagram of the electrical equipment in the corresponding area of the power distribution cabinet through operations such as a convolutional layer, a pooling layer and the like, wherein the characteristics can comprise the distribution condition, the density and the position relation of the electrical equipment, so that the energy loss degree and the electrical risk degree of different candidate positions are evaluated.
The energy loss degree of each candidate position of the power distribution cabinet is used for representing the total energy loss degree of the electric equipment conveyed to the corresponding area of the power distribution cabinet by the power distribution cabinet.
The electrical risk level for each candidate location of the power distribution cabinet is used to assess the electrical safety risk that the power distribution cabinet installation location may face.
In some embodiments, the device distribution processing model includes a load analysis layer, a sizing layer, a candidate location determination layer, a risk determination layer. The load analysis layer, the size determination layer, the candidate position determination layer and the risk degree determination layer all comprise convolutional neural networks. The input of the load analysis layer is an electrical equipment distribution diagram of a corresponding area of the power distribution cabinet, the output of the load analysis layer is an electrical load distribution diagram of the corresponding area of the power distribution cabinet and an estimated electrical load increase rate, the input of the size determination layer is an electrical load distribution diagram of the corresponding area of the power distribution cabinet and an estimated electrical load increase rate, the output of the size determination layer is a power distribution cabinet size, the input of the candidate position determination layer is an electrical load distribution diagram of the corresponding area of the power distribution cabinet and an estimated electrical load increase rate, the output of the candidate position determination layer is a plurality of candidate positions of the power distribution cabinet, the input of the risk degree determination layer is an electrical risk degree of each candidate position of the power distribution cabinet.
The electric load distribution diagram is a diagram output by the load analysis layer and is used for displaying the distribution condition of the electric load in the corresponding area of the power distribution cabinet. The estimated power load increase rate is an estimated rate of possible increase of the power load in a period of time in the future, and the estimated power load increase rate is beneficial to long-term planning of the size of the power distribution cabinet. The cabinet size is a cabinet size determined based on the electrical load and future growth estimates to ensure that the desired electrical components can be accommodated. The candidate position determination layer outputs a series of candidate positions that may be suitable for installing the power distribution cabinet according to the size of the power distribution cabinet, the distribution of the power load and the estimated load increase rate.
By separating the device distribution processing model into a load analysis layer, a size determination layer, a candidate location determination layer, and a risk determination layer, each layer can focus on analyzing and solving specific problems, thereby improving the accuracy and reliability of the overall model. For example, the load analysis layer focuses on the calculation and prediction of electrical loads, while the sizing layer focuses on determining the appropriate switchgear size based on the load calculation results.
And S3, determining the environmental risk degree of each candidate position by using a risk analysis model based on the size of the power distribution cabinet, the plurality of candidate positions of the power distribution cabinet and the environmental distribution diagram of the corresponding area of the power distribution cabinet.
In some embodiments, the risk analysis model includes a generation of an countermeasure network layer and a risk determination layer, the input of the generation of the countermeasure network layer is an environmental distribution diagram of the region corresponding to the power distribution cabinet, the output of the generation of the countermeasure network layer is an environmental risk degree pre-estimated map of the region corresponding to the power distribution cabinet, the input of the risk determination layer is the size of the power distribution cabinet, a plurality of candidate positions of the power distribution cabinet, and the environmental risk degree pre-estimated map of the region corresponding to the power distribution cabinet, and the output of the risk determination layer is the environmental risk degree of each candidate position. The generating an countermeasure network layer includes generating an countermeasure network.
The generation countermeasure Network (GENERATIVE ADVERSARIAL Network, GAN for short) is composed of a Generator (Generator) and a arbiter (Discriminator). The two parts mutually oppose each other and learn each other, and jointly promote the training of the model. The generator that generates the countermeasure network is able to learn complex distributions and features of the input data. In this example, the environmental profile provides environmental characteristics of the power distribution cabinet area, such as temperature, humidity, etc., which the generator can learn and generate a corresponding environmental risk prediction graph. The generation of the countermeasure network layer can extract key information from the environmental distribution diagram so as to generate an environmental risk degree pre-estimated diagram, and the environmental risk degree pre-estimated diagram can be used for evaluating the preliminary environmental risk degree of each position.
The risk determination layer is a convolutional neural network layer, and the convolutional neural network layer comprises a convolutional neural network. Convolutional neural networks contain multiple convolutional layers that can capture features from local to global. This enables the convolutional neural network to take into account both local environmental risk details and the risk profile of the entire switchgear area. The convolutional neural network may fuse data from different sources (e.g., size information, location information, and environmental risk prediction graphs) together for comprehensive analysis to assess risk for each candidate location.
The environmental risk degree of each candidate location refers to a quantitative assessment of the environmental risk of each power distribution cabinet candidate location.
And S4, comparing the environmental risk degree of each candidate position with a preset risk threshold, and eliminating candidate positions of which the environmental risk degree exceeds the preset risk threshold to obtain a plurality of screened candidate positions.
By eliminating candidate positions with too high risks, the power distribution cabinet can be ensured to be installed in a relatively safe environment, and the potential safety accident risks are reduced.
And S5, acquiring the energy loss degree of each screened candidate position, the electrical risk degree of each screened candidate position and the environmental risk degree of each screened candidate position.
After the screened multiple candidate positions are obtained, the energy loss degree of each candidate position after screening, the electrical risk degree of each candidate position after screening and the environmental risk degree of each candidate position after screening can be obtained based on the energy loss degrees of the candidate positions, the electrical risk degrees of the candidate positions and the environmental risk degrees of the candidate positions obtained in the step S2 and the step S3.
And S6, determining a target mounting point based on the energy loss degree of each candidate position after screening, the electrical risk degree of each candidate position after screening and the environmental risk degree of each candidate position after screening.
In some embodiments, different weights may be respectively given to the energy loss degree of each candidate position after screening, the electrical risk degree of each candidate position after screening, and the environmental risk degree of each candidate position after screening, and then the comprehensive risk degree of each candidate position after screening is obtained after weighted summation, and the candidate position after screening with the minimum comprehensive risk degree is used as the target installation point of the power distribution cabinet.
In some embodiments, the target installation point location may also be determined by querying an installation point preset table. The installation point preset table comprises energy loss degrees of preset positions, electrical risk degrees of the preset positions, environment risk degrees of the preset positions and corresponding target installation points, and can be artificially constructed based on historical data.
Based on the same inventive concept, fig. 2 is a schematic diagram of a power distribution cabinet information determining system according to an embodiment of the present invention, where the power distribution cabinet information determining system includes:
A first obtaining module 21, configured to obtain an electrical equipment distribution diagram of a region corresponding to the power distribution cabinet and an environmental distribution diagram of the region corresponding to the power distribution cabinet;
The equipment distribution processing module 22 is configured to determine a size of the power distribution cabinet, a plurality of candidate positions of the power distribution cabinet, an energy loss degree of each candidate position of the power distribution cabinet, and an electrical risk degree of each candidate position of the power distribution cabinet by using an equipment distribution processing model based on an electrical equipment distribution diagram of a corresponding area of the power distribution cabinet;
An environmental risk degree determining module 23, configured to determine an environmental risk degree of each candidate location using a risk analysis model based on the size of the power distribution cabinet, a plurality of candidate locations of the power distribution cabinet, and an environmental profile of a corresponding area of the power distribution cabinet;
The screening module 24 is configured to compare the environmental risk degree of each candidate location with a preset risk threshold, and reject candidate locations where the environmental risk degree exceeds the preset risk threshold, so as to obtain a plurality of screened candidate locations;
A second obtaining module 25, configured to obtain the energy loss degree of each candidate location after screening, the electrical risk degree of each candidate location after screening, and the environmental risk degree of each candidate location after screening;
the installation point determining module 26 is configured to determine a target installation point based on the energy loss degree of each candidate location after screening, the electrical risk degree of each candidate location after screening, and the environmental risk degree of each candidate location after screening.
Claims (10)
1. The method for determining the information of the power distribution cabinet is characterized by comprising the following steps of:
acquiring an electrical equipment distribution diagram of a corresponding area of a power distribution cabinet and an environment distribution diagram of the corresponding area of the power distribution cabinet;
determining the size of the power distribution cabinet, a plurality of candidate positions of the power distribution cabinet, the energy loss degree of each candidate position of the power distribution cabinet and the electrical risk degree of each candidate position of the power distribution cabinet by using an equipment distribution processing model based on an electrical equipment distribution diagram of a corresponding area of the power distribution cabinet;
Determining the environmental risk degree of each candidate position by using a risk analysis model based on the size of the power distribution cabinet, a plurality of candidate positions of the power distribution cabinet and an environmental distribution diagram of a corresponding region of the power distribution cabinet;
Comparing the environmental risk degree of each candidate position with a preset risk threshold, and eliminating candidate positions of which the environmental risk degree exceeds the preset risk threshold to obtain a plurality of screened candidate positions;
acquiring the energy loss degree of each candidate position after screening, the electrical risk degree of each candidate position after screening and the environmental risk degree of each candidate position after screening;
And determining a target mounting point based on the energy loss degree of each candidate position after screening, the electrical risk degree of each candidate position after screening and the environmental risk degree of each candidate position after screening.
2. The method of claim 1, wherein the determining the target installation point based on the energy loss degree of each candidate location after the screening, the electrical risk degree of each candidate location after the screening, and the environmental risk degree of each candidate location after the screening comprises:
And respectively giving different weights to the energy loss degree of each candidate position after screening, the electrical risk degree of each candidate position after screening and the environmental risk degree of each candidate position after screening, and then carrying out weighted summation to obtain the comprehensive risk degree of each candidate position after screening, wherein the candidate position after screening with the minimum comprehensive risk degree is used as a target mounting point of the power distribution cabinet.
3. The method for determining information of a power distribution cabinet according to claim 1, wherein the equipment distribution processing model is a convolutional neural network model.
4. The method for determining information of a power distribution cabinet according to claim 1, wherein the risk analysis model includes a generation countermeasure network layer and a risk determination layer, an input of the generation countermeasure network layer is an environmental distribution map of a corresponding area of the power distribution cabinet, an output of the generation countermeasure network layer is an environmental risk degree prediction map of the corresponding area of the power distribution cabinet, an input of the risk determination layer is the size of the power distribution cabinet, a plurality of candidate positions of the power distribution cabinet, and an environmental risk degree prediction map of the corresponding area of the power distribution cabinet, and an output of the risk determination layer is an environmental risk degree of each candidate position.
5. The method for determining information of a power distribution cabinet according to claim 4, wherein the risk determination layer is a convolutional neural network layer.
6. A power distribution cabinet information determination system, comprising:
The first acquisition module is used for acquiring an electrical equipment distribution diagram of a corresponding area of the power distribution cabinet and an environment distribution diagram of the corresponding area of the power distribution cabinet;
The equipment distribution processing module is used for determining the size of the power distribution cabinet, a plurality of candidate positions of the power distribution cabinet, the energy loss degree of each candidate position of the power distribution cabinet and the electrical risk degree of each candidate position of the power distribution cabinet by using an equipment distribution processing model based on an electrical equipment distribution diagram of a corresponding area of the power distribution cabinet;
The environment risk degree determining module is used for determining the environment risk degree of each candidate position by using a risk analysis model based on the size of the power distribution cabinet, the candidate positions of the power distribution cabinet and the environment distribution diagram of the corresponding area of the power distribution cabinet;
The screening module is used for comparing the environmental risk degree of each candidate position with a preset risk threshold value, eliminating candidate positions of which the environmental risk degree exceeds the preset risk threshold value, and obtaining a plurality of screened candidate positions;
the second acquisition module is used for acquiring the energy loss degree of each candidate position after screening, the electrical risk degree of each candidate position after screening and the environmental risk degree of each candidate position after screening;
And the installation point determining module is used for determining a target installation point based on the energy loss degree of each candidate position after screening, the electrical risk degree of each candidate position after screening and the environmental risk degree of each candidate position after screening.
7. The power distribution cabinet information determination system of claim 6, wherein the mounting point determination module is further configured to:
And respectively giving different weights to the energy loss degree of each candidate position after screening, the electrical risk degree of each candidate position after screening and the environmental risk degree of each candidate position after screening, and then carrying out weighted summation to obtain the comprehensive risk degree of each candidate position after screening, wherein the candidate position after screening with the minimum comprehensive risk degree is used as a target mounting point of the power distribution cabinet.
8. The power distribution cabinet information determination system of claim 6, wherein the device distribution processing model is a convolutional neural network model.
9. The power distribution cabinet information determination system of claim 6, wherein the risk analysis model comprises a generation countermeasure network layer and a risk determination layer, wherein an input of the generation countermeasure network layer is an environmental profile of the power distribution cabinet corresponding area, an output of the generation countermeasure network layer is an environmental risk degree prediction graph of the power distribution cabinet corresponding area, an input of the risk determination layer is the power distribution cabinet size, a plurality of candidate positions of the power distribution cabinet, and an environmental risk degree prediction graph of the power distribution cabinet corresponding area, and an output of the risk determination layer is an environmental risk degree of each candidate position.
10. The electrical panel information determination system of claim 9, wherein the risk determination layer is a convolutional neural network layer.
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