A state evaluation method of a gas-insulated environmentally-friendly ring main unit

Abstract

The application relates to the technical field of ring main unit engineering, in particular to a state evaluation method and system of a gas-insulated environmentally-friendly ring main unit, which comprises the following steps: acquiring spatial probe distribution coordinates and gas concentration data in the environmentally-friendly ring main unit, generating a three-dimensional concentration matrix of a leakage gas according to the three-dimensional concentration matrix of the leakage gas; constructing a gas concentration distribution prediction model matched with the environmentally-friendly ring main unit, inputting the three-dimensional concentration matrix of the leakage gas and cabinet geometric parameters into the gas concentration distribution prediction model, and outputting to obtain a predicted gas concentration distribution; generating a gas leakage behavior parameter, generating an insulation failure probability and an electric sparking risk index; generating an insulation performance evaluation index, and generating a state evaluation of the environmentally-friendly ring main unit according to the insulation performance evaluation index. The application accurately simulates the gas leakage behavior, quantifies the insulation failure probability and the electric sparking risk index, and greatly improves the accuracy of the state evaluation of the ring main unit.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of ring main unit engineering, in particular to a state evaluation method of a gas-insulated environmentally-friendly ring main unit. BACKGROUND

[0002] In a power system, as a key environmentally-friendly ring main unit, the accurate evaluation of the running state of the environmentally-friendly ring main unit is directly related to the stability and safety of the power grid. In the running process of the environmentally-friendly ring main unit, gas leakage is an important safety hazard, which may cause the insulation performance of the environmentally-friendly ring main unit to decrease and further cause serious accidents such as electric sparking.

[0003] The state evaluation method of the gas-insulated environmentally-friendly ring main unit in the prior art mainly depends on limited gas concentration data, and it is difficult to accurately evaluate the influence of gas leakage on the insulation performance and overall running state of the ring main unit, thereby causing the problem that the state evaluation of the environmentally-friendly ring main unit is inaccurate and further causing serious accidents such as electric sparking. SUMMARY

[0004] Therefore, it is necessary to provide a state evaluation method of a gas-insulated environmentally-friendly ring main unit which can solve the problem that the state evaluation of the environmentally-friendly ring main unit is inaccurate in the prior art and further causes serious accidents such as electric sparking, by accurately simulating gas leakage behavior and further quantifying insulation failure probability and electric sparking risk index, so as to greatly improve the accuracy of the state evaluation of the ring main unit.

[0005] The technical scheme of the application is as follows:

[0006] A state evaluation method of a gas-insulated environmentally-friendly ring main unit, the method comprising:

[0007] obtaining spatial probe distribution coordinates and gas concentration data in the environmentally-friendly ring main unit, and generating a three-dimensional concentration matrix of the leaked gas according to the three-dimensional concentration matrix of the leaked gas;

[0008] constructing a gas concentration distribution prediction model matched with the environmentally-friendly ring main unit, inputting the three-dimensional concentration matrix of the leaked gas and the geometric parameters of the cabinet body into the gas concentration distribution prediction model, and outputting to obtain a predicted gas concentration distribution;

[0009] generating gas leakage behavior parameters according to the predicted gas concentration distribution, and generating insulation failure probability and electric sparking risk index according to the gas leakage behavior parameters;

[0010] generating an insulation performance evaluation index according to the insulation failure probability and the electric sparking risk index, and generating a state evaluation of the environmentally-friendly ring main unit according to the insulation performance evaluation index.

[0011] Specifically, the spatial probe distribution coordinates and the gas concentration data in the environmentally-friendly ring main unit are acquired, and a three-dimensional concentration matrix of the leaked gas is generated according to the three-dimensional concentration matrix of the leaked gas; comprising:

[0012] The spatial probe distribution coordinates and the gas concentration data in the environmentally-friendly ring main unit are acquired based on the preset probe;

[0013] The spatial probe distribution coordinates and the gas concentration data are combined to calculate and generate a three-dimensional concentration matrix of the leaked gas.

[0014] Specifically, the spatial probe distribution coordinates and the gas concentration data in the environmentally-friendly ring main unit are acquired based on the preset probe; comprising:

[0015] The spatial probe distribution coordinates of the environmentally-friendly ring main unit are recorded based on the preset probe to obtain the probe distribution coordinates;

[0016] The gas concentration data collected by the probe in the environmentally-friendly ring main unit is collected to obtain the gas concentration data.

[0017] Specifically, the spatial probe distribution coordinates and the gas concentration data are combined to calculate and generate a three-dimensional concentration matrix of the leaked gas, comprising:

[0018] The grid size is determined based on the actual size of the environmentally-friendly ring main unit;

[0019] The spatial probe distribution coordinates and the gas concentration data are combined to generate a concentration estimation value of each grid point, and a three-dimensional concentration matrix of the leaked gas is generated according to the concentration estimation value.

[0020] Specifically, a gas concentration distribution prediction model matched with the environmentally-friendly ring main unit is constructed, the three-dimensional concentration matrix of the leaked gas and the cabinet geometric parameters are input into the gas concentration distribution prediction model, and a predicted gas concentration distribution is output; comprising:

[0021] The geometric parameters of the environmentally-friendly ring main unit are collected to obtain the cabinet geometric parameters;

[0022] A gas concentration distribution prediction model is constructed based on the cabinet geometric parameters and historical gas concentration distribution data at multiple time points;

[0023] The three-dimensional concentration matrix of the leaked gas and the cabinet geometric parameters are input into the gas concentration distribution prediction model, and a predicted gas concentration distribution is output.

[0024] Specifically, a gas leakage behavior parameter is generated according to the predicted gas concentration distribution, and an insulation failure probability and an electric spark risk index are generated according to the gas leakage behavior parameter; comprising:

[0025] analyze the predicted gas concentration distribution to obtain a gas leakage behavior parameter, wherein the gas leakage behavior parameter includes a leakage speed, a diffusion range, and a local concentration extreme value;

[0026] generate an insulation failure probability and an electric sparking risk index according to the leakage speed, the diffusion range, and the local concentration extreme value.

[0027] Specifically, generate an insulation performance evaluation index according to the insulation failure probability and the electric sparking risk index, and generate a state evaluation of the environmentally-friendly ring main unit according to the insulation performance evaluation index, including:

[0028] generate an insulation performance evaluation index according to the insulation failure probability and the electric sparking risk index, and compare the insulation performance evaluation index with a preset threshold to obtain a safety state level of the environmentally-friendly ring main unit;

[0029] complete the state evaluation of the environmentally-friendly ring main unit based on the safety state level of the environmentally-friendly ring main unit.

[0030] Specifically, the system also provides a state evaluation system of a gas-insulated environmentally-friendly ring main unit, and the system includes:

[0031] a gas leakage matrix generation module, configured to obtain spatial probe distribution coordinates and gas concentration data in the environmentally-friendly ring main unit, and generate a leakage gas three-dimensional concentration matrix according to the leakage gas three-dimensional concentration matrix;

[0032] a gas concentration distribution generation module, configured to construct a gas concentration distribution prediction model matched with the environmentally-friendly ring main unit, input the leakage gas three-dimensional concentration matrix and cabinet geometry parameters into the gas concentration distribution prediction model, and output to obtain a predicted gas concentration distribution;

[0033] a sparking risk index generation module, configured to generate a gas leakage behavior parameter according to the predicted gas concentration distribution, and generate an insulation failure probability and an electric sparking risk index according to the gas leakage behavior parameter;

[0034] a ring main unit state evaluation generation module, configured to generate an insulation performance evaluation index according to the insulation failure probability and the electric sparking risk index, and generate a state evaluation of the environmentally-friendly ring main unit according to the insulation performance evaluation index.

[0035] Specifically, the gas leakage matrix generation module is also configured to obtain spatial probe distribution coordinates and gas concentration data in the environmentally-friendly ring main unit based on a preset probe, combine the spatial probe distribution coordinates and the gas concentration data, and calculate to generate a leakage gas three-dimensional concentration matrix.

[0036] Specifically, the gas leakage matrix generation module is further configured to: record spatial probe distribution coordinates of the probe based on a preset probe, to obtain probe distribution coordinates; and collect gas concentration data collected by the probe in the environmentally-friendly ring main unit, to obtain gas concentration data.

[0037] Specifically, the gas leakage matrix generation module is further configured to: determine a grid size based on an actual size of the environmentally-friendly ring main unit; combine the spatial probe distribution coordinates and the gas concentration data, to generate a concentration estimation value of each grid point, and generate a three-dimensional concentration matrix of the leaked gas according to the concentration estimation value.

[0038] Specifically, the gas concentration distribution generation module is further configured to: collect geometric parameters of the environmentally-friendly ring main unit, to obtain cabinet geometric parameters; construct a gas concentration distribution prediction model based on the cabinet geometric parameters and historical gas concentration distribution data at multiple time points; input the three-dimensional concentration matrix of the leaked gas and the cabinet geometric parameters into the gas concentration distribution prediction model, to output a predicted gas concentration distribution.

[0039] Specifically, the arc risk index generation module is further configured to: analyze the predicted gas concentration distribution, to obtain gas leakage behavior parameters, wherein the gas leakage behavior parameters include a leakage speed, a diffusion range and a local concentration extreme value; and calculate and generate an insulation failure probability and an electric arc risk index according to the leakage speed, the diffusion range and the local concentration extreme value.

[0040] Specifically, the ring main unit state evaluation generation module is further configured to: generate an insulation performance evaluation index according to the insulation failure probability and the electric arc risk index, compare the insulation performance evaluation index with a preset threshold, to obtain a safety state grade of the environmentally-friendly ring main unit, and complete state evaluation of the environmentally-friendly ring main unit based on the safety state grade of the environmentally-friendly ring main unit.

[0041] Optionally, a computer device is also provided, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the state evaluation method of the gas-insulated environmentally-friendly ring main unit when executing the computer program.

[0042] Optionally, a computer readable storage medium is also provided, which stores a computer program, and the computer program implements the steps of the state evaluation method of the gas-insulated environmentally-friendly ring main unit when executed by a processor.

[0043] The present application relates to machine learning and deep learning technology, which achieves the following technical effects:

[0044] (1) The state evaluation method and system of the gas insulated environmentally-friendly ring main unit, by acquiring the spatial probe distribution coordinates and gas concentration data in the environmentally-friendly ring main unit, generating a three-dimensional concentration matrix of the leaked gas according to the three-dimensional concentration matrix of the leaked gas; realizing the combination of recording the spatial distribution coordinates and gas concentration data of the probe and using the spatial interpolation method to construct the three-dimensional concentration matrix of the leaked gas, accurately reflecting the three-dimensional spatial distribution of the gas in the environmentally-friendly ring main unit, making up for the shortcomings of the traditional method in the evaluation of the spatial distribution of the gas concentration, and providing more accurate basic data for subsequent analysis of the gas leakage behavior and its influence on the insulation performance;

[0045] (2) By constructing a gas concentration distribution prediction model matched with the environmentally-friendly ring main unit, inputting the three-dimensional concentration matrix of the leaked gas and the cabinet geometry parameters into the gas concentration distribution prediction model, and outputting the predicted gas concentration distribution, the gas leakage behavior parameters are generated, the insulation failure probability and the electric spark risk index are generated, the insulation performance evaluation index is generated according to the insulation failure probability and the electric spark risk index, and the state evaluation of the environmentally-friendly ring main unit is generated according to the insulation performance evaluation index, realizing the more accurate evaluation of the influence of the gas leakage on the insulation capacity of the environmentally-friendly ring main unit, including the calculation of the insulation failure probability and the electric spark risk index, and providing a more comprehensive and detailed quantitative basis for the insulation performance evaluation of the environmentally-friendly ring main unit;

[0046] (3) By multi-dimensional data acquisition, the spatial probe gas concentration data, the cabinet geometry parameters, and the running state data are organically combined, scientific algorithms such as the spatial interpolation method and the fluid dynamics method are used to construct the gas concentration distribution prediction model, compared with the traditional method, the gas leakage behavior can be more accurately simulated, and then the insulation failure probability and the electric spark risk index are quantified, greatly improving the accuracy of the state evaluation of the ring main unit. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 It is a flowchart of the state evaluation method of the gas insulated environmentally-friendly ring main unit in one embodiment;

[0048] Figure 2 It is a structural block diagram of the state evaluation system of the gas insulated environmentally-friendly ring main unit in one embodiment;

[0049] Figure 3 It is a structural block diagram of the computer device in one embodiment. DETAILED DESCRIPTION

[0050] In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular architectures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known methods, devices, circuits, and

[0051] It will be understood that the term "includes," "including," "has," "having," "comprises," "comprising" and the like when used in the present specification and throughout the claims, specify the presence of stated features, integers, steps, operations, elements, and / or components but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0052] It will be understood that the term "and / or," when used in the present specification and throughout the claims, refers to one or more of the associated listed items, and all possible combinations of one or more of the associated listed items.

[0053] As used in the present specification and throughout the claims, the term "if" can be interpreted as meaning "when" or "once" or "in response to a determination" or "in response to a detection" depending on the context. Similarly, the phrase "if it is determined" or "if [a described condition or event] is detected" can be interpreted as meaning "once it is determined" or "in response to the determination" or "once [the described condition or event] is detected" or "in response to the detection [of the described condition or event]" depending on the context.

[0054] In addition, the terms "first," "second," "third," etc. are used herein only to describe different instances of an element, and do not imply relative importance of the elements.

[0055] The terms "one embodiment," "an embodiment," "some embodiments," "other embodiments," "another embodiment," "one implementation," "an implementation," "some implementations," "other implementations," "another implementation," etc. have the same meaning and can be used interchangeably. Each of the expressions "in one embodiment" or "in some embodiments" and the like appearing in various places of the specification are not necessarily referring to the same embodiment or embodiments, but are intended to convey that the feature so described can be present in one or more embodiments.

[0056] In one embodiment, a terminal is provided, which is configured to: acquire spatial probe distribution coordinates and gas concentration data in an environmentally-friendly ring main unit, generate a three-dimensional concentration matrix of a leakage gas according to the three-dimensional concentration matrix of the leakage gas; construct a gas concentration distribution prediction model matched with the environmentally-friendly ring main unit, input the three-dimensional concentration matrix of the leakage gas and cabinet geometry parameters into the gas concentration distribution prediction model, and output a predicted gas concentration distribution; generate a gas leakage behavior parameter according to the predicted gas concentration distribution, and generate an insulation failure probability and an electric spark risk index according to the gas leakage behavior parameter; generate an insulation performance evaluation index according to the insulation failure probability and the electric spark risk index, and generate a state evaluation of the environmentally-friendly ring main unit according to the insulation performance evaluation index.

[0057] The terminal can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices.

[0058] In one embodiment, as shown in Figure 1 A state evaluation method of a gas-insulated environmentally-friendly ring main unit is provided, which comprises:

[0059] Step S100: acquiring spatial probe distribution coordinates and gas concentration data in an environmentally-friendly ring main unit, and generating a three-dimensional concentration matrix of a leakage gas according to the three-dimensional concentration matrix of the leakage gas;

[0060] Step S200: constructing a gas concentration distribution prediction model matched with the environmentally-friendly ring main unit, inputting the three-dimensional concentration matrix of the leakage gas and cabinet geometry parameters into the gas concentration distribution prediction model, and outputting a predicted gas concentration distribution;

[0061] Step S300: generating a gas leakage behavior parameter according to the predicted gas concentration distribution, and generating an insulation failure probability and an electric spark risk index according to the gas leakage behavior parameter;

[0062] Step S400: generating an insulation performance evaluation index according to the insulation failure probability and the electric spark risk index, and generating a state evaluation of the environmentally-friendly ring main unit according to the insulation performance evaluation index.

[0063] In this embodiment, by acquiring the spatial probe distribution coordinates and gas concentration data within the environmental protection ring main unit, a three-dimensional concentration matrix of leaking gas is generated based on the leaking gas three-dimensional concentration matrix. By recording the spatial distribution coordinates of the probes and the gas concentration data, and combining this with spatial interpolation, a three-dimensional concentration matrix of leaking gas can be constructed, accurately reflecting the three-dimensional spatial distribution of gas within the environmental protection ring main unit. This overcomes the shortcomings of traditional methods in assessing the spatial distribution of gas concentration, providing more accurate basic data for subsequent analysis of gas leakage behavior and its impact on insulation performance. By constructing a gas concentration distribution prediction model that matches the environmental protection ring main unit, the three-dimensional concentration matrix of leaking gas and the cabinet geometry are integrated. The parameters are input into the gas concentration distribution prediction model, and the predicted gas concentration distribution is output. Gas leakage behavior parameters are generated, along with insulation failure probability and electrical spark risk index. An insulation performance evaluation index is generated based on the insulation failure probability and electrical spark risk index, and a status evaluation of the environmental protection ring main unit is generated based on the insulation performance evaluation index. This allows for a more accurate assessment of the impact of gas leakage on the insulation capability of the environmental protection ring main unit, including the calculation of insulation failure probability and electrical spark risk index, providing a more comprehensive and detailed quantitative basis for the insulation performance evaluation of the environmental protection ring main unit. Through multi-dimensional data acquisition, spatial probe gas concentration data, cabinet geometric parameters, and operating status data are organically combined. Using scientific algorithms such as spatial interpolation and fluid dynamics, a gas concentration distribution prediction model is constructed. Compared with traditional methods, this model can more accurately simulate gas leakage behavior, thereby quantifying the insulation failure probability and electrical spark risk index, significantly improving the accuracy of the ring main unit status evaluation.

[0064] In one embodiment, step S100: acquiring the spatial probe distribution coordinates and gas concentration data within the environmental protection ring network cabinet, and generating a three-dimensional concentration matrix of the leaking gas based on the leaking gas three-dimensional concentration matrix; including:

[0065] Step S110: Based on the preset probes, acquire the spatial probe distribution coordinates and gas concentration data within the environmental protection ring network cabinet;

[0066] Step S120: Combine the spatial probe distribution coordinates with the gas concentration data to calculate and generate a three-dimensional concentration matrix of the leaked gas.

[0067] In this embodiment, the spatial probe distribution coordinates and gas concentration data within the environmental protection ring network cabinet are obtained based on preset probes. The spatial probe distribution coordinates and gas concentration data are combined to calculate and generate a three-dimensional concentration matrix of leaked gas, providing reliable data support for subsequent prediction of gas concentration distribution.

[0068] In one embodiment, step S110: acquiring spatial probe distribution coordinates and gas concentration data within the environmental protection ring network cabinet based on preset probes; including:

[0069] Step S111: Record the spatial probe distribution coordinates of the environmental protection ring network cabinet based on the preset probes to obtain the probe distribution coordinates;

[0070] Step S112: Collect gas concentration data collected by the probe inside the environmental protection ring network cabinet to obtain gas concentration data.

[0071] In this embodiment, multi-point micro-leakage detection units are pre-installed at several key sealing locations of the ring main unit, such as joints, busbar interfaces, and sensor lead inlets and outlets. These detection units are equipped with high-sensitivity non-dispersive infrared gas probes, which can accurately and directionally detect specific environmentally friendly insulating gases, such as fluoroketones or fluorocarbon mixtures, at extremely low concentrations. Each probe has a unique identification code, and its coordinates are recorded using laser rangefinder technology. For example, when each probe is installed, its coordinates relative to the bottom corner of the ring main unit are measured using a laser rangefinder and stored as a three-dimensional vector, i.e., the spatial probe distribution coordinates.

[0072] After installation, the probe collects real-time gas concentration data from the environment. The corresponding gas concentration data is as follows: , The coordinate information and detection data are simultaneously uploaded to the data processing terminal via wireless transmission, providing raw data support for subsequent analysis.

[0073] It should be noted that the critical sealing areas are inside the ring main unit. The ring main unit contains various electrical connections and environmental protection components, such as disconnect joints and busbar interfaces. These areas need to be sealed to prevent the internal electrical components from being affected by the external environment (such as moisture, dust, corrosive gases, etc.), ensuring the safe and stable operation of the electrical and environmental protection ring main unit. The sensor lead-in / out points also originate from the internal environmental protection ring main unit and require sealing treatment on the cabinet.

[0074] Therefore, by recording the spatial probe distribution coordinates of the environmental protection ring network cabinet based on preset probes, the probe distribution coordinates are obtained; by collecting the gas concentration data collected by the probes in the environmental protection ring network cabinet, the gas concentration data is obtained. Through a high-precision probe network with multiple points, the spatial positioning and real-time monitoring of gas concentration in the ring network cabinet are realized, providing original data support for the subsequent construction of a three-dimensional concentration field and solving the limitations of traditional single-point monitoring.

[0075] In one embodiment, step S120: combining the spatial probe distribution coordinates with gas concentration data to calculate and generate a three-dimensional concentration matrix of the leaked gas includes:

[0076] Step S121: Determine the grid size based on the actual dimensions of the environmental protection ring network cabinet;

[0077] Step S122: Combine the spatial probe distribution coordinates with the gas concentration data to generate the concentration estimate of each grid point, and generate a three-dimensional concentration matrix of the leaked gas based on the concentration estimate.

[0078] In this embodiment, the spatial probe distribution coordinates are combined with the gas concentration data using spatial interpolation to calculate the three-dimensional concentration matrix of the leaked gas.

[0079] Specifically, data preprocessing is performed first.

[0080] Outlier removal: The IQR (interquartile range) method is used. First, concentration data is calculated. First quartile and the third and fourth quartiles ;

[0081]

[0082] Where IQR is the interquartile range. It is the third quartile. It is the first quartile; it is retained in the interval Data points within the specified range are excluded. The IQR method is robust to outliers and is suitable for handling sudden jumps in gas concentration data caused by transient sensor interference in this application. Compared with the standard deviation method, it is more adaptable to the characteristics of non-normally distributed field data.

[0083] Next, determine the grid size:

[0084] Based on the actual dimensions of the ring main unit (length L, width W, height H), select an appropriate mesh size. ),

[0085] Assuming the cabinet dimensions are L×W×H = 1m×0.8m×1.5m, the following are reference dimensions for key components of the ring main unit: sealing strip width: 10mm; sensor size: 20mm. Based on these key component dimensions, the recommended minimum grid step size is... .

[0086] Calculate the number of grid cells in each direction:

[0087]

[0088] Determine the grid size as .

[0089] Calculate the coordinates of the grid points:

[0090] for direction: , ;

[0091] for direction: , ;

[0092] for direction: , .

[0093] in,( , ) represents the coordinates of grid points, where M represents the total number of points in the x-direction. It is the first in the x direction The coordinates of N points; here N represents the total number of points in the y-direction. It is the first in the y direction The coordinates of the points; here P represents the total number of points in the z-direction. It is the coordinate of the l-th point in the z-direction.

[0094] Then, the grid point concentration is calculated using spatial interpolation (IDW):

[0095] For each grid point Calculate the distance from the grid point to all valid probe points (after outlier removal). :

[0096]

[0097] in, It is the distance from the grid point to all valid probe points; These are the coordinates of each grid point. , ) represents the coordinates of the i-th valid probe point, where i represents the probe index;

[0098] Weights are calculated based on distance. :

[0099]

[0100] in, The weight is calculated based on distance. Distance weighting index (usually) When p=2, the weight decays at a moderate rate with distance, avoiding local fluctuations caused by excessive weighting of nearby points and preventing errors introduced by excessive participation of distant points in the calculation. This is a relatively optimal empirical value in gas diffusion scenarios. It is the distance from the grid point to all valid probe points.

[0101] right Normalization is performed, that is... Divide by the sum of the weights of its m neighboring points:

[0102]

[0103] in, The weights are calculated based on the normalized distance. This is the distance calculation weight, where m is an integer representing the number of all selected neighboring points; j is the subscript of the summation symbol, and the summation range is from j=1 to j=m.

[0104] Calculate the concentration estimate at the grid points :

[0105]

[0106] Here, c(x,y,z) is the concentration estimate of the grid points in the three-dimensional space (x,y,z). m is the upper limit of the summation, representing the number of points involved in the calculation. These are the normalized weights. This is the concentration value at the i-th point. The meaning of this formula is that by analyzing the concentration values ​​at m points... By performing a weighted summation, the concentration estimate for grid point (x,y,z) is obtained.

[0107] Repeat the above calculation steps for all grid points to obtain the three-dimensional concentration matrix of the leaked gas. The grid points are virtual points uniformly divided within the cabinet space. Using concentration data from the probe points (associated with spatial coordinates), IDW interpolation is employed to estimate the concentration at each grid point, thus constructing the overall three-dimensional concentration distribution. In other words, spatial interpolation technology transforms discrete probe data into a continuous three-dimensional concentration distribution matrix, intuitively presenting the spatial diffusion characteristics of gas leaks and laying a spatial data foundation for subsequent leak behavior analysis.

[0108] In one embodiment, step S200: Constructing a gas concentration distribution prediction model matching the environmental protection ring main unit, inputting the three-dimensional concentration matrix of the leaked gas and the geometric parameters of the unit into the gas concentration distribution prediction model, and outputting the predicted gas concentration distribution; including:

[0109] Step S210: Collect the geometric parameters of the environmental protection ring network cabinet to obtain the cabinet's geometric parameters;

[0110] Step S220: Based on the cabinet's geometric parameters and historical gas concentration distribution data at multiple times, construct a gas concentration distribution prediction model;

[0111] Step S230: Input the three-dimensional concentration matrix of the leaked gas and the geometric parameters of the cabinet into the gas concentration distribution prediction model, and output the predicted gas concentration distribution.

[0112] In this embodiment, the geometric parameters of the environmental protection ring network cabinet are collected to obtain the cabinet's geometric parameters:

[0113] Collection of ventilation opening locations: Using tools such as laser rangefinders and measuring tapes, measure the location of ventilation openings on the cabinet surface (distance relative to the bottom or corner of the cabinet) and record their specific location on the front, side or top of the cabinet;

[0114] Obstacle distribution collection: Open the cabinet (power off and follow safety regulations), observe the layout of internal electrical components (such as circuit breakers, busbars, cable joints, etc.), and record their positions, dimensions, and distances from the cabinet walls.

[0115] By comparing the design drawings with the actual cabinet structure, we can correct any deviations in the position of obstacles caused by manufacturing processes or modifications, thus ensuring data accuracy.

[0116] Construct a gas concentration distribution prediction model:

[0117] This model adopts a hybrid architecture of 3D Convolutional Neural Network (3DCNN) + LSTM, and is divided into four main parts: spatial feature extraction module, geometric encoding module, spatiotemporal fusion module, and temporal prediction module.

[0118] Spatial Feature Extraction Module: This module uses a 3D convolutional neural network (3DCNN) to extract features from the input 3D concentration matrix of the leaked gas. The core formula is as follows:

[0119]

[0120] in, It is a spatial feature. Let be the three-dimensional concentration matrix of the leaked gas at time t; It is a 3D convolution kernel; Represents a 3D convolution operation; For bias terms, Using tiny random numbers breaks the symmetry and accelerates convergence; ReLU is the activation function, which enhances the model's expressive power by introducing nonlinearity. After multiple convolutions, the original concentration distribution is transformed into more abstract spatial features.

[0121] Geometric Encoding Module: The cabinet's geometric parameters contain key information such as the location of ventilation openings and the distribution of obstacles. These parameters are encoded into static feature vectors using a multilayer perceptron (MLP).

[0122]

[0123] in, It is an eigenvector. It is a vector of geometric parameters. For multilayer perceptrons, geometric parameters are mapped to fixed-dimensional feature vectors through linear transformations and activation functions. , for subsequent fusion.

[0124] Spatiotemporal fusion module:

[0125] Spatial features extracted by a 3D convolutional neural network (3DCNN) With geometric coding Perform splicing and fusion:

[0126]

[0127] in, It is a feature of fusion. It is a spatial feature. It is an eigenvector. The function is used to... and By stitching along the channel dimension, spatial features and geometric codes are combined, for example... The dimensions are [A×B×C×D] (D is the number of feature channels). Dimensions [1×1×1×E], after splicing The dimensions are [A×B×C×(D+E)]. By stitching together the channel dimensions, spatial features and geometric information are integrated to form a fused feature that contains both spatial and structural information. , as input to LSTM.

[0128] The time-series prediction module uses an LSTM network to learn the temporal evolution of concentration distribution. The core formula is as follows:

[0129]

[0130] in, It is a fusion feature of spatial and structural information, used as input to LSTM. yes The hidden state at the previous moment, where t is the time step. The input gate controls the entry of new information into the memory unit; It's the forgetting gate, which controls the proportion of old memories retained; It is a candidate cell state; It is an output gate that controls the intensity of the memory's output. It represents the cell state at the previous moment; In cellular state, It is the sigmoid function; Multiplication of elements; and These are model parameters; It is in a hidden state.

[0131] , , and These are the bias terms for the input gate, forget gate, candidate cell state, and output gate, respectively.

[0132] These are the weight matrices associated with the input, used to calculate the input gate, forget gate, candidate states, and output gate. , , , These are weight matrices related to the hidden state at the previous time step, used to calculate the input gate, forget gate, candidate state, and output gate.

[0133] LSTM selectively retains and updates information through a gating mechanism to capture the temporal dependence of concentration distribution. Finally, a decoder maps the hidden states of the LSTM to the predicted concentration distribution.

[0134]

[0135] in, This represents the predicted gas concentration distribution for the next time step t+1, where t is the time step. These are the final input features. It is a decoder function; its function is to decode the input features. Convert to predicted values.

[0136] Model loss function: To ensure that the model's predictions conform to physical laws, a loss function incorporating physical constraints is used.

[0137]

[0138] in, It is a loss function. It is the mean squared error loss, which measures the pixel-level difference between the predicted concentration distribution and the actual concentration distribution. It is a mass conservation constraint loss, which ensures that the gas mass is approximately conserved during the prediction process; It is a diffusion direction constraint loss, which ensures that the diffusion direction conforms to physical laws by comparing the gradient direction difference between the predicted concentration distribution and the actual concentration distribution.

[0139] and For hyperparameters, and Adjusted through training, for example The value is 0.1. The value is set to 0.05 to balance the weights of various loss terms. This constraint is achieved by calculating the normalized value of the difference in total gas mass before and after prediction, ensuring that the model output conforms to the basic laws of fluid mechanics and avoiding the phenomenon of "concentration increasing or decreasing out of thin air" that violates physical laws.

[0140] Mean squared error loss:

[0141]

[0142] Where N is the total number of grid points, This is the predicted concentration at the a-th grid point. Let be the actual concentration at the a-th grid point.

[0143] Loss due to mass conservation constraint:

[0144]

[0145] in, It is the set of predicted concentrations for all grid points at time t. It is the set of the true concentrations of all grid points at time t; It is a summation symbol; the mass conservation constraint ensures that the predicted total gas mass remains unchanged.

[0146] Diffusion directionality constraint loss:

[0147]

[0148] in, It is a gradient operator; It is the gradient field for predicting the concentration at time t; It is the gradient field of the actual concentration at time t, and the diffusion direction constraint ensures that the diffusion process conforms to fluid dynamics.

[0149] Training process:

[0150] Data preparation: historical gas concentration distribution data at multiple time points (3D concentration matrix), cabinet geometric parameters;

[0151] Data preprocessing: Normalize the gas concentration distribution data at multiple historical moments, encode the geometric parameters into feature vectors, and divide the data into training, validation, and test sets.

[0152] Model training: Set the optimizer (such as Adam) and learning rate, iteratively train the model, minimize the physical constraint loss function, and adjust the hyperparameters through the validation set.

[0153] Model evaluation: Evaluate model performance on the test set and calculate metrics such as mean squared error (MSE) and mass conservation error.

[0154] Input data: The current three-dimensional concentration matrix of leaked gas. Input the cabinet's geometric parameters into the model.

[0155] Model prediction: The model outputs the concentration distribution prediction results for multiple future time steps: predicting the gas concentration distribution. .

[0156] By combining the geometric structure and spatiotemporal characteristics of the ring main unit, a deep learning model is used to predict the evolution trend of gas concentration, thereby achieving a forward-looking simulation of the dynamic process of leakage and improving the accuracy and physical regularity of leakage behavior prediction.

[0157] In one embodiment, step S300: generating gas leakage behavior parameters based on the predicted gas concentration distribution, and generating insulation failure probability and electrical arcing risk index based on the gas leakage behavior parameters; including:

[0158] Step S310: Analyze the predicted gas concentration distribution to obtain gas leakage behavior parameters, wherein the gas leakage behavior parameters include leakage rate, diffusion range and local concentration extreme value;

[0159] Step S320: Calculate and generate the insulation failure probability and electrical arcing risk index based on the leakage rate, diffusion range, and local concentration extreme value.

[0160] In this embodiment, the predicted gas concentration distribution is analyzed, and leakage behavior parameters such as leakage velocity, diffusion range, duration, and local concentration extremes are calculated:

[0161] Leakage rate:

[0162] Based on predicted concentration distribution Let's calculate. and This refers to the predicted concentration distribution between two adjacent time steps. First, a concentration threshold is determined. This is typically set as the minimum safe critical concentration for environmentally friendly insulating gases. The concentration can be adjusted based on the gas type (e.g., fluoroketones or fluorocarbon mixtures) and the ring main unit design standards. The area is considered a high-concentration area. Let... and They are at time t and The volume of the high-concentration region.

[0163] Gas volume leakage rate The calculation formula is:

[0164]

[0165] in, It is the gas volume leakage rate. It is the time step. and They are at time t and The volume of the high-concentration region.

[0166] Dispersion range:

[0167] Also based on the predicted concentration distribution, at time t+1, the diffusion range Is the concentration higher than The volume of the region.

[0168] Right now equal to concentration greater than The number of voxels multiplied by the volume of a single voxel, where a voxel is the smallest cubic unit of a regular mesh in three-dimensional space, and the volume of a single voxel is... Then the diffusion range for:

[0169]

[0170] in, It is the diffusion range, in units of ; It is the volume of a single voxel, in units of ; Is the concentration greater than The number of voxels is dimensionless.

[0171] Local concentration extremes:

[0172] Local concentration extremes It is about predicting concentration distribution. Maximum concentration value in:

[0173]

[0174] in, It is a local concentration extreme. Indicates all Take the maximum value on the coordinate. It is the predicted concentration distribution at time t+1;

[0175] Based on the leakage behavior parameters such as leakage velocity, diffusion range, duration, and local concentration extremes, several insulation performance evaluation indicators were calculated:

[0176] Insulation failure probability, based on the Weibull distribution model:

[0177]

[0178] in, It is the probability of insulation failure. and The parameters are calibrated based on the properties of the gas and the insulating material. This represents a localized extreme concentration. The insulation failure probability reflects the likelihood of insulation failure in the environmental protection ring main unit due to gas leakage.

[0179] Electric spark risk index:

[0180]

[0181] in It is the risk index for electrical sparks. It is the gas volume leakage rate. It refers to the extent of the spread. It is a local concentration extreme. , , The correlation coefficient β is determined based on the operating environment of the environmental protection ring main unit. The specific value of β can be set by those skilled in the art according to the actual situation. For example, =0.5, =0.3, =0.2, the system risk hazard was fitted using training data. The electrical spark risk index is used to comprehensively assess the magnitude of the risk of electrical sparks caused by leakage.

[0182] By combining physical parameters with risks, the physical behavior of gas leakage is transformed into quantifiable indicators of insulation failure probability and electrical arcing risk, providing a scientific quantitative basis for the safety assessment of ring main units.

[0183] In one embodiment, step S400: generating an insulation performance evaluation index based on the insulation failure probability and electrical arcing risk index, and generating a status evaluation of the environmental protection ring main unit based on the insulation performance evaluation index, including:

[0184] Step S410: Generate an insulation performance evaluation index based on the insulation failure probability and electrical arcing risk index, and compare the insulation performance evaluation index with a preset threshold to obtain the safety status level of the environmental protection ring main unit;

[0185] Step S420: Based on the safety status level of the environmental protection ring main unit, complete the status evaluation of the environmental protection ring main unit.

[0186] In this embodiment, the electrical arcing risk index and the insulation failure probability are combined using the Sigmoid function to obtain the insulation performance evaluation index, as shown in the formula:

[0187]

[0188] in, It is an insulation performance evaluation index. It is the risk index for electrical sparks. It is the probability of insulation failure. and These are parameters that need to be determined. The Sigmoid function can map input values ​​to... The range is steep, with a large slope in the middle, making it sensitive to changes in input values. The electrical arcing risk index and insulation failure probability are nonlinearly transformed using the Sigmoid function, and then summed to obtain the insulation performance evaluation index. .

[0189] Determine parameters a and b:

[0190] Data collection: Collect a large amount of historical operating data of ring main units, including known insulation performance data and operating status data, as well as the corresponding actual status of environmental protection ring main units (e.g., normal operation, pre-fault status, fault status, etc.).

[0191] Parameter fitting: Using optimization algorithms to determine parameters and The goal is to make the calculated... It can accurately reflect the actual status of the environmental protection ring network cabinet.

[0192] For example, the known environmental protection ring network cabinet fault status can be used to... and Substitute into the formula and adjust and ,make Approximately 1 (indicating a fault state); substitute the data from the normal operating state of the environmental protection ring network cabinet into the input, so that... Approaching 0 (indicating a normal state).

[0193] Determining the threshold for the safety status level of environmental protection ring main units: This is achieved through statistical analysis of random historical fault data, for example, analyzing 100 ring main unit cases. Failure rate <1%; Failure rate <50%; Failure rate <20% when the fault rate is >0.8; failure rate >50% when the fault rate is >0.8.

[0194] when The environmental protection ring main unit is in normal condition; it is operating normally with no obvious risks or signs of malfunction.

[0195] Maintenance strategy: Maintain a regular inspection frequency. This means inspecting the environmental protection ring main unit according to a pre-established, periodic inspection plan to ensure its continued normal operation. Inspection content may include basic performance checks and visual inspections.

[0196] when At that time, the safety status level of the environmental protection ring network cabinet is: Caution (potential risks exist).

[0197] At this time, although the environmental protection ring network cabinet is still operating normally, some potential risk factors have emerged, which may affect the long-term stable operation of the environmental protection ring network cabinet.

[0198] Maintenance strategy: Increase the frequency of inspections and perform some simple tests and maintenance. In this situation, not only should the number of inspections be increased, for example from once a week to three times a week, but some simple tests should also be performed during the inspection process, such as measuring key parameters, checking the wear of vulnerable parts, etc., and some basic maintenance operations such as cleaning and tightening should also be performed.

[0199] when At that time, the environmental protection ring network cabinet's safety status level was: abnormal (high probability of failure).

[0200] At this point, the operation of the environmental protection ring network cabinet has shown obvious abnormalities, and the possibility of failure is high, requiring close monitoring.

[0201] Maintenance Strategy: Immediately arrange for professional personnel to conduct a detailed inspection and repair. This will involve activating the miniature gas replenishment unit located inside the cabinet. This unit, connected to a sealed gas storage tank via a miniature differential pressure valve, can precisely inject a matched proportion of environmentally friendly insulating gas to maintain insulation pressure balance. Once the environmental protection ring main unit enters this state, it is necessary to quickly assemble professional maintenance personnel to conduct a comprehensive inspection of the unit, identify the specific cause of the abnormality, and perform targeted repairs. Repair work may include replacing faulty components and adjusting the parameters of the environmental protection ring main unit.

[0202] when At this time, the environmental protection ring main unit's safety status level is: critical (the environmental protection ring main unit is in a high-risk state and may malfunction at any time).

[0203] Description: The environmental protection ring main unit is in a very dangerous state and may malfunction at any time, which may lead to damage to the environmental protection ring main unit or a safety accident.

[0204] Maintenance Strategy: Shut down the environmental protection ring main unit and conduct emergency repairs. In this situation, the environmental protection ring main unit is extremely dangerous and must be shut down immediately to prevent potential serious accidents. Then, organize a professional repair team and use all necessary means to repair the environmental protection ring main unit as quickly as possible and restore its normal operation. The repair process may include replacing more severely faulty components and performing complex troubleshooting operations.

[0205] Therefore, this embodiment transforms complex risk assessments into clear safety status levels through multi-indicator fusion and threshold discrimination, providing operation and maintenance personnel with an intuitive basis for decision-making and realizing the upgrade of ring main unit status evaluation from qualitative to quantitative.

[0206] In summary, this application organically combines gas concentration data from spatial probes, cabinet geometric parameters, and operational status data through multi-dimensional data acquisition. Utilizing scientific algorithms such as spatial interpolation and fluid dynamics, a gas concentration distribution prediction model is constructed. Compared to traditional methods, this model can more accurately simulate gas leakage behavior, thereby quantifying the probability of insulation failure and the risk index of electrical arcing, significantly improving the accuracy of ring main unit condition assessment.

[0207] In one embodiment, such as Figure 2 As shown, a condition assessment system for gas-insulated environmentally friendly ring main units is also provided, the system comprising:

[0208] The gas leakage matrix generation module is used to obtain the spatial probe distribution coordinates and gas concentration data within the environmental protection ring network cabinet, and generate a three-dimensional concentration matrix of the leaking gas based on the leaking gas three-dimensional concentration matrix.

[0209] The gas concentration distribution generation module is used to construct a gas concentration distribution prediction model that matches the environmental protection ring network cabinet. The three-dimensional concentration matrix of the leaked gas and the geometric parameters of the cabinet are input into the gas concentration distribution prediction model, and the predicted gas concentration distribution is output.

[0210] The fire risk index generation module is used to generate gas leakage behavior parameters based on the predicted gas concentration distribution, and to generate insulation failure probability and electrical fire risk index based on the gas leakage behavior parameters.

[0211] The network cabinet status evaluation generation module is used to generate an insulation performance evaluation index based on the insulation failure probability and electrical arcing risk index, and to generate a status evaluation of the environmental protection network cabinet based on the insulation performance evaluation index.

[0212] In one embodiment, the gas leakage matrix generation module is further configured to: acquire spatial probe distribution coordinates and gas concentration data within the environmental protection ring network cabinet based on preset probes; and combine the spatial probe distribution coordinates with the gas concentration data to calculate and generate a three-dimensional concentration matrix of the leaking gas.

[0213] In one embodiment, the gas leakage matrix generation module is further used to: record the spatial probe distribution coordinates of the environmental protection ring network cabinet based on a preset probe to obtain the probe distribution coordinates; and collect gas concentration data collected by the probes in the environmental protection ring network cabinet to obtain gas concentration data.

[0214] In one embodiment, the gas leakage matrix generation module is further configured to: determine the grid size based on the actual dimensions of the environmental protection ring network cabinet; combine the spatial probe distribution coordinates with the gas concentration data to generate a concentration estimate for each grid point, and generate a three-dimensional concentration matrix of the leaked gas based on the concentration estimate.

[0215] In one embodiment, the gas concentration distribution generation module is further configured to: collect the geometric parameters of the environmental protection ring network cabinet to obtain the cabinet's geometric parameters; construct a gas concentration distribution prediction model based on the cabinet's geometric parameters and historical gas concentration distribution data at multiple times; input the leaked gas three-dimensional concentration matrix and the cabinet's geometric parameters into the gas concentration distribution prediction model, and output the predicted gas concentration distribution.

[0216] In one embodiment, the arcing risk index generation module is further configured to: analyze the predicted gas concentration distribution to obtain gas leakage behavior parameters, wherein the gas leakage behavior parameters include leakage rate, diffusion range, and local concentration extreme value; and calculate and generate insulation failure probability and arcing risk index based on the leakage rate, diffusion range, and local concentration extreme value.

[0217] In one embodiment, the network cabinet status evaluation generation module is further configured to: generate an insulation performance evaluation index based on the insulation failure probability and the electrical arcing risk index; compare the insulation performance evaluation index with a preset threshold to obtain the safety status level of the environmental protection network cabinet; and complete the status evaluation of the environmental protection network cabinet based on the safety status level of the environmental protection network cabinet.

[0218] In one embodiment, such as Figure 3 As shown, a computer device is also provided, including a memory and a processor. The memory stores a computer program and an operating system. When the processor executes the computer program, it implements the steps described in the machine vision-based separator sieve surface separation and detection method. The computer device also includes a system bus, internal memory, network structure, display screen, and input devices.

[0219] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps described in the above-described method for evaluating the condition of a gas-insulated environmentally friendly ring main unit.

[0220] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0221] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0222] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0223] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0224] This application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0225] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0226] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0227] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-described embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographic device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0228] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0229] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0230] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0231] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0232] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

[0233] One embodiment of this application also provides a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the embodiments of the above methods.

[0234] The computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above description is an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than described above, or a combination of certain components, or different components, such as input / output devices, network access devices, etc.

[0235] The processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0236] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard drive or RAM. In other embodiments, the memory may be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory may include both internal and external storage units of the computer device. The memory is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory can also be used to temporarily store data that has been output or will be output.

[0237] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are 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.

[0238] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A condition assessment method for gas-insulated environmentally friendly ring main unit, characterized in that, The method includes: Acquire the spatial probe distribution coordinates and gas concentration data within the environmental protection ring network cabinet, and generate a three-dimensional concentration matrix of the leaking gas based on the spatial probe distribution coordinates and gas concentration data. A gas concentration distribution prediction model matching the environmental protection ring network cabinet is constructed. The three-dimensional concentration matrix of the leaked gas and the geometric parameters of the cabinet are input into the gas concentration distribution prediction model, and the predicted gas concentration distribution is output. The gas concentration distribution prediction model adopts a hybrid architecture of 3D Convolutional Neural Network (3DCNN) + LSTM, which includes a spatial feature extraction module, a geometric encoding module, a spatiotemporal fusion module, and a temporal prediction module. Specifically, the spatial feature extraction module uses a 3DCNN to extract features from the input three-dimensional concentration matrix of the leaked gas; the geometric encoding module encodes the cabinet's geometric parameters, including the location of ventilation openings and the distribution of obstacles, into static feature vectors using a Multilayer Perceptron (MLP); the spatiotemporal fusion module concatenates and fuses the spatial features extracted by the 3DCNN with the geometric encoding; the temporal prediction module uses an LSTM network to learn the temporal evolution of the concentration distribution; the LSTM selectively retains and updates information through a gating mechanism to capture the temporal dependence of the concentration distribution; finally, a decoder maps the hidden states of the LSTM to the predicted concentration distribution. Gas leakage behavior parameters are generated based on the predicted gas concentration distribution, and insulation failure probability and electrical arcing risk index are generated based on the gas leakage behavior parameters. An insulation performance evaluation index is generated based on the insulation failure probability and electrical arcing risk index, and a status evaluation of the environmental protection ring main unit is generated based on the insulation performance evaluation index.

2. The condition assessment method for gas-insulated environmentally friendly ring main unit according to claim 1, characterized in that, Acquire the spatial probe distribution coordinates and gas concentration data within the environmental protection ring network cabinet, and generate a three-dimensional concentration matrix of the leaking gas based on the spatial probe distribution coordinates and gas concentration data; including: Based on preset probes, the spatial probe distribution coordinates and gas concentration data within the environmental protection ring network cabinet are obtained; By combining the spatial probe distribution coordinates with the gas concentration data, a three-dimensional concentration matrix of the leaked gas is calculated and generated.

3. The condition assessment method for gas-insulated environmentally friendly ring main unit according to claim 2, characterized in that, Based on preset probes, the spatial probe distribution coordinates and gas concentration data within the environmental protection ring network cabinet are acquired; including: Based on the preset probes, the spatial probe distribution coordinates of the environmental protection ring network cabinet are recorded to obtain the probe distribution coordinates; The gas concentration data is obtained by collecting gas concentration data from the probe inside the environmental protection ring network cabinet.

4. The condition assessment method for gas-insulated environmentally friendly ring main unit according to claim 1, characterized in that, By combining the spatial probe distribution coordinates with gas concentration data, a three-dimensional concentration matrix of the leaked gas is calculated and generated, including: The grid size is determined based on the actual dimensions of the environmental protection ring network cabinet; The spatial probe distribution coordinates are combined with gas concentration data to generate concentration estimates for each grid point, and a three-dimensional concentration matrix of the leaked gas is generated based on the concentration estimates.

5. The condition assessment method for gas-insulated environmentally friendly ring main unit according to claim 1, characterized in that, A gas concentration distribution prediction model matching the environmental protection ring main unit is constructed. The three-dimensional concentration matrix of the leaked gas and the geometric parameters of the unit are input into the gas concentration distribution prediction model, and the predicted gas concentration distribution is output. This includes: Collect the geometric parameters of the environmental protection ring network cabinet to obtain the cabinet's geometric parameters; Based on the cabinet's geometric parameters and historical gas concentration distribution data at multiple times, a gas concentration distribution prediction model is constructed. The three-dimensional concentration matrix of the leaked gas and the geometric parameters of the cabinet are input into the gas concentration distribution prediction model, and the predicted gas concentration distribution is output.

6. The condition assessment method for gas-insulated environmentally friendly ring main unit according to claim 1, characterized in that, Gas leakage behavior parameters are generated based on the predicted gas concentration distribution, and insulation failure probability and electrical spark risk index are generated based on the gas leakage behavior parameters; including: The predicted gas concentration distribution is analyzed to obtain gas leakage behavior parameters, which include leakage rate, diffusion range, and local concentration extremes. Based on the leakage rate, diffusion range, and local concentration extremes, the insulation failure probability and electrical arcing risk index are calculated and generated.

7. The condition assessment method for gas-insulated environmentally friendly ring main unit according to claim 1, characterized in that, An insulation performance evaluation index is generated based on the insulation failure probability and electrical arcing risk index. A status evaluation of the environmental protection ring main unit is then generated based on the insulation performance evaluation index, including: An insulation performance evaluation index is generated based on the insulation failure probability and electrical arcing risk index. The insulation performance evaluation index is then compared with a preset threshold to obtain the safety status level of the environmental protection ring main unit. Based on the safety status level of the environmental protection ring main unit, a status evaluation of the environmental protection ring main unit is completed.

8. A condition assessment system for a gas-insulated environmentally friendly ring main unit, characterized in that, The system includes: The gas leakage matrix generation module is used to acquire the spatial probe distribution coordinates and gas concentration data in the environmental protection ring network cabinet, and generate a three-dimensional concentration matrix of leaked gas based on the spatial probe distribution coordinates and gas concentration data. A gas concentration distribution generation module is used to construct a gas concentration distribution prediction model that matches the environmental protection ring network cabinet. The model inputs the three-dimensional concentration matrix of the leaked gas and the cabinet's geometric parameters, and outputs the predicted gas concentration distribution. The gas concentration distribution prediction model adopts a hybrid architecture of a three-dimensional convolutional neural network (3DCNN) and LSTM, including a spatial feature extraction module, a geometric encoding module, a spatiotemporal fusion module, and a temporal prediction module. Specifically, the spatial feature extraction module uses a 3DCNN to extract features from the input three-dimensional concentration matrix of the leaked gas; the geometric encoding module encodes the cabinet's geometric parameters, including the location of ventilation openings and the distribution of obstacles, into static feature vectors using a multilayer perceptron (MLP); the spatiotemporal fusion module combines the spatial features extracted by the 3DCNN with the geometric encoding; and the temporal prediction module uses an LSTM network to learn the temporal evolution of the concentration distribution. The LSTM selectively retains and updates information through a gating mechanism to capture the temporal dependence of the concentration distribution; finally, a decoder maps the hidden states of the LSTM to the predicted concentration distribution. The fire risk index generation module is used to generate gas leakage behavior parameters based on the predicted gas concentration distribution, and to generate insulation failure probability and electrical fire risk index based on the gas leakage behavior parameters. The network cabinet status evaluation generation module is used to generate an insulation performance evaluation index based on the insulation failure probability and electrical arcing risk index, and to generate a status evaluation of the environmental protection network cabinet based on the insulation performance evaluation index.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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