Multi-parameter power transmission line tree line distance prediction method based on improved PSO-BP

Abstract

The invention discloses a multi-parameter power transmission line tree line distance prediction method based on improved PSO-BP, and relates to the technical field of tree line distance prediction. The method comprises the following steps: calculating tower displacement data of a power transmission line in real time based on a center frame difference method; calculating a power transmission line tree line distance based on a monocular vision technology; acquiring multi-parameter data related to the tree line distance; preprocessing the tower displacement data, the power transmission line tree line distance and the multi-parameter data; and constructing a PSO-BP multi-parameter fusion prediction model for predicting the tree line distance, and generating auxiliary decision information according to the predicted tree line distance change trend to optimize the safety maintenance strategy of the power line. According to the method, the tower displacement, the tree line distance and multi-parameter data are fused and input, so that the prediction accuracy of the tree line distance is greatly improved, and errors caused by environmental changes are reduced; and a variation idea is introduced, so that the algorithm is prevented from falling into local optimum, and the prediction precision and response speed are remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of tree-line distance prediction, and in particular to a multi-parameter transmission line tree-line distance prediction method based on improved PSO-BP. Background Art

[0002] The safe operation of power transmission lines is closely related to the conditions of their surrounding environment, especially in complex terrain areas where trees and wires are too close to each other, which can easily cause power accidents. Traditional manual inspection methods not only consume a lot of manpower and material resources, but are also inefficient and time-consuming, and cannot meet the real-time monitoring needs of high-risk areas.

[0003] With the promotion of smart grids, some automated monitoring systems have been developed in recent years, such as LiDAR, ultrasonic rangefinders and other equipment, to monitor the distance of tree barriers around transmission lines. However, these systems are unable to cope with complex environmental conditions. For example, in complex terrain environments such as mountainous areas and forests, the growth rate of trees and environmental conditions are variable. Existing monitoring equipment shows a decrease in accuracy when facing changing conditions, and the system is not reliable enough to cope with high-frequency real-time monitoring needs.

[0004] In addition, traditional prediction models are single and usually only perform simple linear predictions based on a small number of parameters. They lack the ability to handle multi-parameter and nonlinear relationships, resulting in large prediction errors and inability to provide accurate warnings of future risks. Most ranging systems rely on regular data collection and processing and are unable to provide timely warnings and prediction information, especially when trees grow rapidly or weather changes drastically. Summary of the invention

[0005] In view of the problems of real-time, accuracy and dynamic multi-parameter processing faced in the existing transmission line tree-line distance monitoring and prediction, the present invention proposes a multi-parameter transmission line tree-line distance prediction method based on improved PSO-BP, which comprehensively considers various environments and line operation parameters and improves real-time response capabilities.

[0006] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0007] On the one hand, the present invention provides a multi-parameter transmission line tree-line distance prediction method based on improved PSO-BP, the method comprising:

[0008] Real-time calculation of transmission line tower displacement data based on the center frame difference method;

[0009] Calculate the distance between power transmission lines and trees based on monocular vision technology;

[0010] Acquire multi-parameter data related to the tree-line distance, including historical and real-time data of span, height difference between two towers, conductor inclination, swing angle, and conductor operating temperature;

[0011] Preprocessing the tower displacement data, transmission line tree-line distance and multi-parameter data;

[0012] The pre-processed tower displacement data, transmission line tree-line distance and multi-parameter data are fused, and the fused data is used as comprehensive input features and input into the BP neural network;

[0013] The number of nodes in the input layer of the BP neural network is matched with the dimension of the multi-parameter data, and the hidden layer uses a nonlinear activation function;

[0014] The Mutation-PSO algorithm is used to optimize the BP neural network, and the optimal weights and thresholds obtained by optimization are assigned to the BP neural network to form an optimized PSO-BP multi-parameter fusion prediction model for real-time prediction of tree line distance.

[0015] As a preferred solution of the present invention, the real-time calculation of the tower displacement data of the transmission line based on the center frame difference method specifically includes:

[0016] Collecting the image of the pole tower displacement and performing preprocessing, wherein the preprocessing includes distortion correction and color space conversion, and converting the image from the RGB color space to the HSV color space frame by frame;

[0017] The marked points in the image are clustered according to the DBSCAN clustering algorithm, and the center position of each marked point is identified by the centroid method, and the marked points are numbered according to their longitudinal coordinates;

[0018] The conversion factor is obtained through camera calibration, and the pixel displacement of the marking point is calculated based on the center frame difference method, that is, the number of pixels occupied by the tower displacement, thereby obtaining the tower displacement data.

[0019] As a preferred solution of the present invention, the conversion factor is obtained by camera calibration, and the pixel displacement of the marker point is calculated based on the center frame difference method, specifically:

[0020] The conversion factor sf is obtained through the plane homography, the formula is:

[0021]

[0022] In the formula, f pix is the focal length of the camera lens, d is the distance from the camera optical center to the structural surface;

[0023] Or the conversion factor sf can be obtained by the proportional factor method, the formula is:

[0024]

[0025] Where P1Q1 is the pixel length corresponding to the marker point in the image; P s Q s is the physical distance of the same marker point in world coordinates;

[0026] The initial frame in the video sequence is used as the reference frame. For each frame, the pixel displacement between the current position of the tower marker and the reference position is determined by performing pixel-level differential analysis with the reference frame. The formula is:

[0027] △x=x current -x reference , △y=y current -y reference ;

[0028] Where △x and △y are the pixel displacements in the horizontal and vertical directions; x current ,y current is the position of the marker in the current frame; x reference ,y reference is the position of the same marker in the reference frame;

[0029] Combined with the conversion factor sf, the calculated pixel displacement is converted into the actual physical distance.

[0030] As a preferred solution of the present invention, the calculation of the tree-line distance of the transmission line based on the monocular vision technology specifically includes:

[0031] After the monocular camera is installed and takes the first shot, use the first image as a sample reference image;

[0032] Analyze the tree features in the sample reference image and adjust the value range of the HSV color space to identify the trees in the image;

[0033] The HSV value range determined in the sample reference image is used as the HSV threshold value and used in subsequent monitoring images to automatically extract trees in the image;

[0034] Capturing a wide-angle image of the power transmission line and the surrounding environment through a monocular camera, and converting the wide-angle image into an HSV color space; identifying trees in the wide-angle image through image processing technology based on an HSV threshold;

[0035] The tree-line distance is calculated based on the camera imaging model and the spatial state parameters of the wire, including the vertical distance of the tree-line, the horizontal distance of the tree-line and the clearance distance of the tree-line, so as to determine the three-dimensional position of the trees. Specifically, a monocular camera is fixed on a tower to monitor the area between two adjacent towers, and geometric imaging models are established for the towers and trees respectively; the calculation formula of the tree-line distance is derived based on the geometric imaging model;

[0036] The calculation formula of the vertical distance Ty from the tree line is:

[0037]

[0038] Where GH is the vertical pixel height of the tree in the image; GI is the pixel height of the projection of the tree crown on the light screen to the center I of the image; Cy is the known camera installation height;

[0039] The calculation formula of the tree line horizontal distance Tx is:

[0040]

[0041] Where PQ and UV represent the known horizontal components and horizontal pixel distances on the tower, respectively; IG is the horizontal pixel distance from the tree crown G to the image center I; Pz is the known distance from the tower to the camera; Tz is the distance between the tree and the tower;

[0042]

[0043] Where Py is the known vertical height of the tower; CD is the pixel height of the image of the component Py with a known height on the tower;

[0044] Calculate the tree line clearance distance Tn:

[0045]

[0046] Where h1 is the actual height of the tree; x is the distance from the intersection of the wire and the tree to the tower; l OA 、h OA They represent the horizontal distance and vertical distance from the camera center point O to the bottom point A of the tower respectively; M is the scale factor used for the conversion between pixel distance and actual distance; θ is the camera shooting angle.

[0047] As a preferred solution of the present invention, the tower displacement data, the transmission line tree-line distance and the multi-parameter data are pre-processed, including:

[0048] Data verification and multi-source data synchronization: Use timestamps and spatial distribution characteristics to cross-check tower displacement data, tree line distance and multi-parameter data, set timestamp error threshold △T, if the multi-source data at the same time meets the condition |T k -T j |<△T, it is regarded as synchronous data, otherwise data compensation is performed; where T k 、T j Represent the timestamps of data source k and data source j respectively;

[0049] Abnormal data removal: Set the mean μ and standard deviation σ of each feature, and define the dynamic abnormal threshold as [μ-3σ,μ+3σ]. If the feature value of any data exceeds the dynamic abnormal threshold range, it will be regarded as abnormal data and removed;

[0050] Normalization and standardization processing: The maximum-minimum normalization combined with the standardization method is used to adjust the data to the range of [0,1]. In the standardization stage, the feature mean is adjusted to 0 and the standard deviation is adjusted to 1;

[0051] Feature enhancement: Feature extraction is performed based on principal component analysis. The original data matrix X is decomposed by principal components to obtain the feature matrix X PCA , select the first m principal components with the highest contribution rate to form a simplified data set, and assign feature weights in combination with the entropy weight method;

[0052] Multi-layer wavelet denoising: decompose the signal by wavelet to obtain sub-signals in different frequency bands, retain low-frequency signals to suppress high-frequency noise;

[0053] Missing data filling: The missing data is filled by dynamic interpolation method based on Bayesian estimation. Suppose the observed data is X obs , the missing data is X miss , and the posterior probability distribution is obtained through Bayesian inference:

[0054] p(X miss |X obs )∝p(X obs |X miss )p(X miss );

[0055] In the formula, p(X miss |X obs ) is the missing data X miss When observing data X obs The posterior probability under given conditions; p(X obs |X miss ) is the observed data X obs In the case of missing data X miss Likelihood function under given conditions; p(X miss ) is the missing data X miss The prior probability of

[0056] The missing values ​​are estimated by sampling from the posterior probability distribution to fill in the missing data.

[0057] As a preferred solution of the present invention, the fusion of the pre-processed tower displacement data, the transmission line tree-line distance and the multi-parameter data specifically includes:

[0058] Perform independent component analysis on the preprocessed data to decompose each feature into independent signal components, achieve feature decoupling by maximizing signal independence, and eliminate redundant information between features;

[0059] Introducing a context-aware mechanism, we can enhance the relevance of current data by modeling the memory of historical data. Let the current feature Z t The context fusion output is C t , the calculation formula is:

[0060] C t =f(Z t ,Z t-1 ,Z t-2 ,…,Z t-b );

[0061] Where f represents the context dependency function, Z t-1 represents the characteristics at time t-1, Z t-2 represents the characteristics at time t-2, Z t-b Indicates the characteristics at time tb;

[0062] The context of the current feature is constructed through historical data at b moments, improving the model's ability to capture dynamic changes;

[0063] Fuzzy logic is introduced into the data fusion process, and the weight of each feature is dynamically adjusted through the membership function. s The weight adjustment function is:

[0064] w s =t(Z s )·w base +(1-t(Z s ))·w context ;

[0065] In the formula, w s is the fused feature Z s The final weight of t(Z s ) represents feature Z s The fuzzy membership function has a range between [0,1]; w base is the base weight; w context Represents the context weight.

[0066] As a preferred solution of the present invention, the BP neural network is optimized by using the Mutation-PSO algorithm, specifically:

[0067] Initialize the particle group in the PSO algorithm, each particle represents the weight and threshold of the BP neural network, and randomly assign initial values ​​to the particle's position and velocity;

[0068] According to the prediction error of treeline distance, the fitness value of each particle is calculated using Huber-Loss function. The smaller the fitness value, the higher the prediction accuracy.

[0069] In each iteration, the current fitness of each particle is compared with its historical best fitness. If the current fitness is better, the individual optimal value of the particle is updated. At the same time, the global optimal value of the entire particle swarm is updated to ensure that the particle swarm gradually approaches the optimal solution.

[0070] Randomly select particles in the particle swarm to perform mutation operations, randomly perturb the position and velocity of the particles, expand the search range, avoid falling into the local optimal solution, and recalculate the fitness of the particles after the mutation operation to determine whether the mutation improves the prediction accuracy;

[0071] Adjust the position and velocity of all particles according to the following update formula:

[0072] v i (t+1)=ω·v i (t)+c1·rand1·(p best -x i (t))+c2·rand2·(g best -x i (t));

[0073] In the formula, v i (t+1) represents the velocity of particle i at the t+1th iteration, v i (t) represents the velocity of particle i at the tth iteration; ω is the inertia weight; c1 and c2 are both learning factors; rand1 and rand2 are both random factors; p best is the individual optimal position of the particle, g bes t is the global optimal position; x i (t) represents the current position of particle i at the tth iteration;

[0074] Repeat the above fitness calculation to the steps of adjusting the position and speed until the fitness reaches the preset accuracy requirement or the number of iterations reaches the maximum limit, and finally obtain the optimal weights and thresholds of the optimized BP neural network.

[0075] As a preferred solution of the present invention, the fitness value of each particle is calculated using the Huber-Loss function, which is defined as follows:

[0076]

[0077] In the formula, is the Huber-Loss function value; y is the actual tree line distance, is the treeline distance predicted by BP neural network; δ is the adjustment parameter.

[0078] As a preferred solution of the present invention, the method further includes constructing an expert system, which is integrated into the PSO-BP multi-parameter fusion prediction model to analyze the deviation between the prediction result and the real data. The steps of constructing the expert system are as follows:

[0079] Receive output data from the PSO-BP multi-parameter fusion prediction model and real-time monitoring data;

[0080] Compare predicted output data with real-time data to identify deviations between the data;

[0081] Based on the deviation analysis results, correction suggestions are generated through an expert rule base, where the rule base is composed of historical data and prior knowledge;

[0082] Output correction suggestions to the PSO-BP multi-parameter fusion prediction model and adjust model parameters in real time.

[0083] On the other hand, the present invention also provides a multi-parameter transmission line tree-line distance prediction system based on improved PSO-BP, which is applied to the multi-parameter transmission line tree-line distance prediction method based on improved PSO-BP as described above, and the system comprises:

[0084] The tower displacement calculation module is used to calculate the tower displacement data of the transmission line in real time based on the center frame difference method;

[0085] The tree-line distance calculation module is used to calculate the tree-line distance of the transmission line based on monocular vision technology;

[0086] A multi-parameter data acquisition module is used to acquire multi-parameter data related to the tree-line distance, wherein the multi-parameter data includes historical data and real-time data of the span, the height difference between the two towers, the inclination angle of the conductor, the swing angle and the operating temperature of the conductor;

[0087] A data preprocessing module, used for preprocessing the tower displacement data, transmission line tree-line distance and multi-parameter data;

[0088] Prediction model building module, used to build a PSO-BP multi-parameter prediction model for treeline distance prediction, including data fusion unit, dimension matching unit and optimization unit;

[0089] The data fusion unit is used to fuse the pre-processed pole tower displacement data, transmission line tree-line distance and multi-parameter data, and input the fused data as comprehensive input features into the BP neural network;

[0090] The dimension matching unit is used to match the number of input layer nodes of the BP neural network with the dimension of the multi-parameter data, and the hidden layer adopts a nonlinear activation function;

[0091] The optimization unit is used to optimize the BP neural network using the Mutation-PSO algorithm, assign the optimal weights and thresholds obtained by optimization to the BP neural network, and form an optimized PSO-BP multi-parameter fusion prediction model;

[0092] The treeline distance prediction module is used to make real-time predictions of the treeline distance through the optimized PSO-BP multi-parameter fusion prediction model.

[0093] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the multi-parameter transmission line tree distance prediction method based on improved PSO-BP as described above.

[0094] The beneficial effects of the present invention are as follows: the central frame difference method and monocular vision technology are used to calculate the pole tower displacement and tree-line distance of the transmission line in real time, which can monitor the line status in real time and effectively reduce the potential safety hazards caused by pole tower displacement or wire contact with trees, thereby improving the monitoring accuracy of the line; the preprocessing process of pole tower displacement data, tree-line distance and multi-parameter data reduces the misjudgment caused by data noise, ensuring the reliability of subsequent data fusion and prediction; multiple environmental factors such as conductor inclination, swing angle and operating temperature are integrated to improve the sensitivity to natural environmental changes; a PSO-BP multi-parameter fusion prediction model is constructed, The tower displacement, tree-line distance and multi-parameter data are input into the model as comprehensive input features. Compared with the single data prediction model, the accuracy of tree-line distance prediction is greatly improved, which helps to reduce the errors caused by environmental changes. Optimization is carried out on the basis of PSO-BP, and the idea of ​​mutation is introduced to prevent the algorithm from falling into local optimality, which significantly improves the prediction accuracy and response speed. Based on the real-time prediction results of tree-line distance, auxiliary decision-making information is generated, which can prompt line maintenance personnel to take measures such as pruning trees or adjusting lines in advance, thereby effectively optimizing the maintenance strategy of power lines and reducing the cost of daily inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0096] in:

[0097] Figure 1is a flow chart of the method of the present invention;

[0098] Figure 2 This is a flow chart of optimizing the BP neural network using the Mutation-PSO algorithm in an embodiment of the present invention;

[0099] Figure 3 It is a system modular structure diagram of the present invention. DETAILED DESCRIPTION

[0100] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0101] like Figure 1 , Figure 2 As shown in FIG. 1 , an embodiment of the present invention is provided, which provides a multi-parameter transmission line tree-line distance prediction method based on improved PSO-BP, comprising the following steps:

[0102] S1: Real-time calculation of the tower displacement data of the transmission line based on the center frame difference method, including:

[0103] S11: Collect the image of the tower displacement and perform preprocessing, including but not limited to distortion correction and color space conversion, and convert the image from RGB (Red, Green, Blue) color space to HSV (Hue, Saturation, Value) color space frame by frame;

[0104] S12: clustering the marker points in the image according to the DBSCAN clustering algorithm, identifying the center position of each marker point by the centroid method, and numbering the marker points according to their longitudinal coordinates;

[0105] S13: Obtain the conversion factor through camera calibration, and calculate the pixel displacement of the marking point based on the center frame difference method, that is, the number of pixels occupied by the pole tower displacement, so as to obtain the pole tower displacement data.

[0106] Further, step S13 includes:

[0107] The conversion factor sf is obtained through the plane homography, the formula is:

[0108]

[0109] In the formula, f pixis the focal length of the camera lens, d is the distance from the camera optical center to the structural surface;

[0110] Or the conversion factor sf can be obtained by the proportional factor method, the formula is:

[0111]

[0112] Where P1Q1 is the pixel length corresponding to the marker point in the image; P s Q s is the physical distance of the same marker point in world coordinates;

[0113] The initial frame in the video sequence is used as the reference frame. For each frame, the pixel displacement between the current position of the tower marker and the reference position is determined by performing pixel-level differential analysis with the reference frame. The formula is:

[0114] △x=x current -x reference , △y=y current -y reference ;

[0115] Where △x and △y are the pixel displacements in the horizontal and vertical directions; x current ,y current is the position of the marker in the current frame; x reference ,y reference is the position of the same marker in the reference frame;

[0116] Combined with the conversion factor sf, the calculated pixel displacement is converted into the actual physical distance.

[0117] S2: Calculate the distance between power transmission lines and trees based on monocular vision technology, including:

[0118] S21: After the monocular camera is installed and the first shot is taken, the first image is used as a sample reference image;

[0119] S22: analyzing tree features in the example reference image, and adjusting the value range of the HSV color space to identify the trees in the image;

[0120] S23: using the HSV value range determined in the example reference image as an HSV threshold value for use in subsequent monitoring images to automatically extract trees in the image;

[0121] S24: Capture wide-angle images of the power transmission line and its surroundings through a monocular camera and convert the wide-angle images into HSV color space; identify trees in the wide-angle images through image processing technology based on HSV thresholds;

[0122] S25: Calculate the tree-line distance according to the camera imaging model and the spatial state parameters of the wire, including the vertical distance of the tree-line, the horizontal distance of the tree-line and the clearance distance of the tree-line, so as to determine the three-dimensional position of the tree, specifically: fix a monocular camera on a pole tower to monitor the area between two adjacent pole towers, establish geometric imaging models for the pole towers and trees respectively; derive a calculation formula for the tree-line distance based on the geometric imaging model;

[0123] The calculation formula of the vertical distance Ty from the tree line is:

[0124]

[0125] Where GH is the vertical pixel height of the tree in the image; GI is the pixel height of the projection of the tree crown on the light screen to the center I of the image; Cy is the known camera installation height;

[0126] The calculation formula of the tree line horizontal distance Tx is:

[0127]

[0128] Where PQ and UV represent the known horizontal components and horizontal pixel distances on the tower, respectively; IG is the horizontal pixel distance from the tree crown G to the image center I; Pz is the known distance from the tower to the camera; Tz is the distance between the tree and the tower;

[0129]

[0130] Where Py is the known vertical height of the tower; CD is the pixel height of the image of the component Py with a known height on the tower;

[0131] Calculate the tree line clearance distance Tn:

[0132]

[0133] Where h1 is the actual height of the tree; x is the distance from the intersection of the wire and the tree to the tower; l OA 、h OA They represent the horizontal distance and vertical distance from the camera center point O to the bottom point A of the tower respectively; M is the scale factor used for the conversion between pixel distance and actual distance; θ is the camera shooting angle.

[0134] S3: Acquire multi-parameter data related to the tree-line distance, including but not limited to historical and real-time data of span, height difference between two towers, conductor inclination, swing angle and conductor operating temperature;

[0135] Specifically, multi-parameter data is collected in real time through sensors distributed along the transmission line, and recorded to form a historical database;

[0136] S4: Preprocess the tower displacement data, transmission line tree-line distance and multi-parameter data to eliminate data noise and dimensional differences and ensure the validity of the data input model;

[0137] In one specific embodiment, step S4 is implemented as follows:

[0138] S41: Data verification and multi-source data synchronization: Use timestamp and spatial distribution characteristics to cross-check tower displacement data, tree line distance and multi-parameter data, set timestamp error threshold △T, if the multi-source data at the same time meets the condition T k -T j |<△T, it is regarded as synchronous data, otherwise data compensation is performed; where T k 、T j Represent the timestamps of data source k and data source j respectively;

[0139] S42: Abnormal data removal: Set the mean μ and standard deviation σ of each feature, and define the dynamic abnormal threshold as

[0140] [μ-3σ,μ+3σ], if the characteristic value of any data exceeds the dynamic abnormal threshold range, it will be regarded as abnormal data and removed;

[0141] S43: Normalization and standardization processing: The maximum-minimum normalization combined with the standardization method is used to adjust the data to the range of [0,1]. In the standardization stage, the feature mean is adjusted to 0 and the standard deviation is adjusted to 1;

[0142] S44: Feature enhancement: Feature extraction is performed based on principal component analysis. The original data matrix X is decomposed by principal components to obtain the feature matrix X PCA , select the first m principal components with the highest contribution rate to form a simplified data set, and assign feature weights in combination with the entropy weight method;

[0143] S45: Multi-layer wavelet denoising: Decompose the signal by wavelet to obtain sub-signals of different frequency bands, retain low-frequency signals to suppress high-frequency noise;

[0144] S46: Missing data filling: The missing data are filled using a dynamic interpolation method based on Bayesian estimation. Suppose the observed data is X obs , the missing data is X miss , and the posterior probability distribution is obtained through Bayesian inference:

[0145] p(X miss |X obs )∝p(X obs |X miss )p(X miss );

[0146] In the formula, p(Xmiss |X obs ) is the missing data X miss When observing data X obs The posterior probability under given conditions; p(X obs |X miss ) is the observed data X obs In the case of missing data X miss Likelihood function under given conditions; p(X miss ) is the missing data X miss The prior probability of

[0147] The missing values ​​are estimated by sampling from the posterior probability distribution to fill in the missing data.

[0148] S5: Construct a PSO-BP (combining Particle Swarm Optimization (PSO) and Back Propagation (BP) neural network) multi-parameter fusion prediction model for tree line distance prediction, including:

[0149] S51: Fusing the pre-processed pole tower displacement data, transmission line tree-line distance and multi-parameter data, and using the fused data as comprehensive input features to input into a BP neural network (Back Propagation Neural Network);

[0150] Furthermore, the implementation steps of step S51 are:

[0151] Independent Component Analysis (ICA) is performed on the preprocessed data to decompose each feature into independent signal components, achieve feature decoupling by maximizing signal independence, and eliminate redundant information between features;

[0152] Introducing a context-aware mechanism, we can enhance the relevance of current data by modeling the memory of historical data. Let the current feature Z t The context fusion output is C t , the calculation formula is:

[0153] C t =f(Z t ,Z t-1 ,Z t-2 ,…,Z t-b );

[0154] Where f represents the context dependency function, Z t-1 represents the characteristics at time t-1, Z t-2 represents the characteristics at time t-2, Zt-b Indicates the characteristics at time tb;

[0155] The context of the current feature is constructed through historical data at b moments, improving the model's ability to capture dynamic changes;

[0156] Fuzzy logic is introduced into the data fusion process, and the weight of each feature is dynamically adjusted through the membership function. s The weight adjustment function is:

[0157] w s =t(Z s )·w base +(1-t(Z s ))·w context ;

[0158] In the formula, w s is the fused feature Z s The final weight of t(Z s ) represents feature Z s The fuzzy membership function has a range between [0,1]; w base is the base weight, i.e., feature Z s The default weight when there is no context influence; w context Represents the context weight.

[0159] S52: Match the number of nodes in the input layer of the BP neural network with the dimension of the multi-parameter data, and use a nonlinear activation function in the hidden layer;

[0160] S53: Optimizing the BP neural network using the Mutation-PSO algorithm (mutation particle swarm optimization algorithm), assigning the optimal weights and thresholds obtained by optimization to the BP neural network to form an optimized PSO-BP multi-parameter fusion prediction model, making real-time predictions of the tree-line distance, and generating auxiliary decision-making information according to the predicted tree-line distance change trend, so as to optimize the safety maintenance strategy of the power line;

[0161] The Mutation-PSO algorithm is used to optimize the BP neural network, specifically:

[0162] Particle swarm initialization: Initialize the particle swarm in the PSO algorithm. Each particle represents the weight and threshold of the BP neural network. The position and speed of the particle are randomly assigned initial values.

[0163] Fitness calculation: Based on the prediction error of the tree line distance, the Huber-Loss function is used to calculate the fitness value of each particle. The smaller the fitness value, the higher the prediction accuracy.

[0164] Update of individual optimal value and global optimal value: In each iteration, compare the current fitness of each particle with its historical best fitness. If the current fitness is better, update the individual optimal value of the particle. At the same time, update the global optimal value of the entire particle swarm to ensure that the particle swarm gradually approaches the optimal solution.

[0165] Mutation operation: Randomly select particles in the particle swarm to perform mutation operation, randomly perturb the position and speed of the particles, expand the search range, avoid falling into the local optimal solution, and recalculate the fitness of the particles after the mutation operation to determine whether the mutation improves the prediction accuracy;

[0166] Position and speed adjustment: Adjust the position and speed of all particles according to the following update formula:

[0167] v i (t+1)=ω·v i (t)+c1·rand1·(p best -x i (t))+c2·rand2·(g best -x i (t));

[0168] In the formula, v i (t+1) represents the velocity of particle i at the t+1th iteration, v i (t) represents the velocity of particle i at the tth iteration; ω is the inertia weight; c1 and c2 are both learning factors; rand1 and rand2 are both random factors; p best is the individual optimal position of the particle, g best is the global optimal position; x i (t) represents the current position of particle i at the tth iteration;

[0169] Iteration and convergence: Repeat the above fitness calculation, individual optimal value and global optimal value update, mutation operation and position and speed adjustment steps until the fitness reaches the preset accuracy requirement or the number of iterations reaches the maximum limit, and finally obtain the optimal weights and thresholds of the optimized BP neural network.

[0170] Furthermore, the Huber-Loss function is used to calculate the fitness value of each particle, which is defined as follows:

[0171]

[0172] In the formula, is the Huber-Loss function value, which is used to measure the error between the predicted value and the actual value; y is the actual tree line distance, is the treeline distance predicted by the BP neural network; δ is an adjustment parameter used to control the switching between MSE and MAE; when the prediction error is small, Huber Loss is similar to MSE; when the error is large, Huber-Loss is similar to MAE, thereby reducing the sensitivity to outliers and improving the stability of the model. The smaller the fitness value, the higher the prediction accuracy.

[0173] In another specific embodiment, the method of the present invention further includes constructing an expert system, which is integrated into the PSO-BP multi-parameter fusion prediction model to analyze the deviation between the prediction results and the real data. The steps of constructing the expert system are as follows:

[0174] Receive output data from the PSO-BP multi-parameter fusion prediction model and real-time monitoring data;

[0175] Compare predicted output data with real-time data to identify deviations between the data;

[0176] Based on the deviation analysis results, correction suggestions are generated through an expert rule base, where the rule base is composed of historical data and prior knowledge;

[0177] Output correction suggestions to the PSO-BP multi-parameter fusion prediction model and adjust the model parameters in real time to improve the accuracy of treeline distance prediction and the adaptability of the model.

[0178] like Figure 3 FIG. 1 is another embodiment of the present invention, which provides a multi-parameter transmission line tree-line distance prediction system based on improved PSO-BP, which is applied to the multi-parameter transmission line tree-line distance prediction method based on improved PSO-BP as described above, and the system includes:

[0179] The tower displacement calculation module is used to calculate the tower displacement data of the transmission line in real time based on the center frame difference method;

[0180] The tree-line distance calculation module is used to calculate the tree-line distance of the transmission line based on monocular vision technology;

[0181] Multi-parameter data acquisition module, used to obtain multi-parameter data related to tree-line distance, including historical and real-time data of span, height difference between two towers, conductor inclination, swing angle and conductor operating temperature;

[0182] Data preprocessing module, used to preprocess tower displacement data, transmission line tree-line distance and multi-parameter data;

[0183] The prediction model building module is used to build a PSO-BP multi-parameter prediction model for tree line distance prediction, including:

[0184] The data fusion unit is used to fuse the pre-processed pole tower displacement data, transmission line tree-line distance and multi-parameter data, and input the fused data as comprehensive input features into the BP neural network;

[0185] Dimension matching unit, used to match the number of input layer nodes of BP neural network with the dimension of multi-parameter data, and the hidden layer adopts nonlinear activation function;

[0186] The optimization unit is used to optimize the BP neural network using the Mutation-PSO algorithm, assign the optimal weights and thresholds obtained by optimization to the BP neural network, and form an optimized PSO-BP multi-parameter fusion prediction model;

[0187] The treeline distance prediction module is used to make real-time predictions of the treeline distance through the optimized PSO-BP multi-parameter fusion prediction model.

[0188] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the steps of the multi-parameter transmission line tree distance prediction method based on improved PSO-BP are implemented as described above.

[0189] In summary, the present invention uses the central frame difference method and monocular vision technology to calculate the pole tower displacement and tree-line distance of the transmission line in real time, which can monitor the line status in real time and effectively reduce the safety hazards caused by pole tower displacement or wire contact with trees, thereby improving the monitoring accuracy of the line; the preprocessing process of pole tower displacement data, tree-line distance and multi-parameter data reduces the misjudgment caused by data noise, ensuring the reliability of subsequent data fusion and prediction; integrates multiple environmental factors such as conductor inclination, swing angle and operating temperature to improve the sensitivity to changes in the natural environment; constructs a PSO-BP multi-parameter fusion prediction model Tower displacement, tree-line distance and multi-parameter data are input into the model as comprehensive input features. Compared with the single data prediction model, the accuracy of tree-line distance prediction is greatly improved, which helps to reduce the errors caused by environmental changes. Optimization is carried out on the basis of PSO-BP, and the idea of ​​mutation is introduced to prevent the algorithm from falling into local optimality, which significantly improves the prediction accuracy and response speed. Auxiliary decision-making information is generated based on the real-time prediction results of tree-line distance, which can prompt line maintenance personnel to take measures such as pruning trees or adjusting lines in advance, thereby effectively optimizing the maintenance strategy of power lines and reducing the cost of daily inspections.

[0190] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A multi-parameter transmission line tree-line distance prediction method based on improved PSO-BP is characterized by: The method comprises: calculating the displacement data of the tower of the power transmission line in real time based on the central frame difference method; Calculate the distance between power transmission lines and trees based on monocular vision technology; Acquire multi-parameter data related to the tree-line distance, including historical and real-time data of span, height difference between two towers, conductor inclination, swing angle, and conductor operating temperature; Preprocessing the tower displacement data, transmission line tree-line distance and multi-parameter data; The pre-processed tower displacement data, transmission line tree-line distance and multi-parameter data are fused, and the fused data is used as comprehensive input features and input into the BP neural network; The number of nodes in the input layer of the BP neural network is matched with the dimension of the multi-parameter data, and a nonlinear activation function is used in the hidden layer. The BP neural network is optimized using the Mutation-PSO algorithm, and the optimal weights and thresholds obtained by optimization are assigned to the BP neural network to form an optimized PSO-BP multi-parameter fusion prediction model for real-time prediction of tree line distance.

2. The multi-parameter transmission line tree-line distance prediction method based on improved PSO-BP according to claim 1 is characterized in that: The real-time calculation of the tower displacement data of the transmission line based on the center frame difference method specifically includes: Collecting the image of the pole tower displacement and performing preprocessing, wherein the preprocessing includes distortion correction and color space conversion, and converting the image from the RGB color space to the HSV color space frame by frame; The marked points in the image are clustered according to the DBSCAN clustering algorithm, and the center position of each marked point is identified by the centroid method, and the marked points are numbered according to their longitudinal coordinates; The conversion factor is obtained through camera calibration, and the pixel displacement of the marking point is calculated based on the center frame difference method, that is, the number of pixels occupied by the tower displacement, thereby obtaining the tower displacement data.

3. The multi-parameter transmission line tree-line distance prediction method based on improved PSO-BP according to claim 2 is characterized in that: The conversion factor is obtained by camera calibration, and the pixel displacement of the marker point is calculated based on the center frame difference method. Specifically, the conversion factor sf is obtained by plane homography, and the formula is: In the formula, f pix is the focal length of the camera lens, d is the distance from the camera optical center to the structural surface; Or the conversion factor sf can be obtained by the proportional factor method, the formula is: Where P1Q1 is the pixel length corresponding to the marker point in the image; P s Q s is the physical distance of the same marker point in the world coordinates; the initial frame in the video sequence is used as the reference frame. For each frame of the image, the pixel displacement between the current position of the tower marker and the reference position is determined by performing pixel-level differential analysis with the reference frame. The formula is: △x=x current -x reference ,△and=and current -and reference ; Where △x and △y are the pixel displacements in the horizontal and vertical directions; x current ,y current is the position of the marker in the current frame; x reference ,y reference is the position of the same marker in the reference frame; Combined with the conversion factor sf, the calculated pixel displacement is converted into the actual physical distance.

4. The multi-parameter transmission line tree-line distance prediction method based on improved PSO-BP according to claim 2 is characterized in that: The method of calculating the distance between a power transmission line and a tree line based on monocular vision technology specifically includes: After the monocular camera is installed and the first shot is taken, the first image is used as a sample reference image; Analyze the tree features in the sample reference image and adjust the value range of the HSV color space to identify the trees in the image; use the HSV value range determined in the sample reference image as the HSV threshold and use it in subsequent monitoring images to automatically extract the trees in the image; Capturing a wide-angle image of the power transmission line and the surrounding environment through a monocular camera, and converting the wide-angle image into an HSV color space; identifying trees in the wide-angle image through image processing technology based on an HSV threshold; The tree-line distance is calculated based on the camera imaging model and the spatial state parameters of the wire, including the vertical distance of the tree-line, the horizontal distance of the tree-line and the clearance distance of the tree-line, so as to determine the three-dimensional position of the trees. Specifically, a monocular camera is fixed on a tower to monitor the area between two adjacent towers, and geometric imaging models are established for the towers and trees respectively; the calculation formula of the tree-line distance is derived based on the geometric imaging model; The calculation formula of the vertical distance Ty from the tree line is: Where GH is the vertical pixel height of the tree in the image; GI is the pixel height of the projection of the tree crown on the light screen to the center I of the image; Cy is the known camera installation height; The calculation formula of the tree line horizontal distance Tx is: Where PQ and UV represent the known horizontal components and horizontal pixel distances on the tower, respectively; IG is the horizontal pixel distance from the tree crown G to the image center I; Pz is the known distance from the tower to the camera; Tz is the distance between the tree and the tower; Where Py is the known vertical height of the tower; CD is the pixel height of the image of the component Py with a known height on the tower; Calculate the tree line clearance distance Tn: Where h1 is the actual height of the tree; x is the distance from the intersection of the wire and the tree to the tower; l OA 、h OA They represent the horizontal distance and vertical distance from the camera center point O to the bottom point A of the tower respectively; M is the scale factor used for the conversion between pixel distance and actual distance; θ is the camera shooting angle.

5. The multi-parameter transmission line tree-line distance prediction method based on improved PSO-BP according to claim 1 is characterized in that: Preprocessing the tower displacement data, transmission line tree-line distance and multi-parameter data includes: Data verification and multi-source data synchronization: Use timestamps and spatial distribution characteristics to cross-check tower displacement data, tree line distance and multi-parameter data, set timestamp error threshold △T, if the multi-source data at the same time meets condition T k -T j <△T, it is regarded as synchronous data, otherwise data compensation is performed; where T k , T j Respectively represent the timestamps of data source k and data source j; Abnormal data removal: Set the mean μ and standard deviation σ of each feature, and define the dynamic abnormal threshold as [μ-3σ,μ+3σ]. If the feature value of any data exceeds the dynamic abnormal threshold range, it will be regarded as abnormal data and removed; Normalization and standardization processing: The maximum-minimum normalization combined with the standardization method is used to adjust the data to the range of [0,1]. In the standardization stage, the feature mean is adjusted to 0 and the standard deviation is adjusted to 1; Feature enhancement: Feature extraction is performed based on principal component analysis. The original data matrix X is decomposed by principal components to obtain the feature matrix X PCA , select the first m principal components with the highest contribution rate to form a simplified data set, and assign feature weights in combination with the entropy weight method; Multi-layer wavelet denoising: decompose the signal by wavelet to obtain sub-signals in different frequency bands, retain low-frequency signals to suppress high-frequency noise; Missing data filling: The missing data is filled by dynamic interpolation method based on Bayesian estimation. Suppose the observed data is X obs , the missing data is X miss , and the posterior probability distribution is obtained through Bayesian inference: p(X miss |X obs )∝p(X obs |X miss )p(X miss ); In the formula, p(X miss |X obs ) is the missing data X miss When observing data X obs The posterior probability under given conditions; p(X obs |X miss ) is the observed data X obs In the case of missing data X miss Likelihood function under given conditions; p(X miss ) is the missing data X miss Prior probability; estimate missing values ​​by sampling from the posterior probability distribution to complete the missing data.

6. The multi-parameter transmission line tree-line distance prediction method based on improved PSO-BP according to claim 1 is characterized in that: The fusion of the pre-processed tower displacement data, the transmission line tree-line distance and the multi-parameter data specifically includes: performing independent component analysis on the pre-processed data, decomposing each feature into mutually independent signal components, achieving feature decoupling by maximizing signal independence, and eliminating redundant information between features; Introducing a context-aware mechanism, we can enhance the relevance of current data by modeling the memory of historical data. Let the current feature Z t The context fusion output is C t , the calculation formula is: C t =f(Z t ,Z t-1 ,Z t-2 ,…,Z t-b ); Where f represents the context dependency function, Z t-1 represents the characteristics at time t-1, Z t-2 represents the characteristics at time t-2, Z t-b Indicates the characteristics at time tb; The context of the current feature is constructed through historical data at b moments, improving the model's ability to capture dynamic changes; Fuzzy logic is introduced into the data fusion process, and the weight of each feature is dynamically adjusted through the membership function. s The weight adjustment function is: w s =t(Z s )·w base +(1-t(Z s ))·w context ; In the formula, w s is the fused feature Z s The final weight of t(Z s ) represents feature Z s The fuzzy membership function has a range between [0,1]; w base is the base weight; w context Represents the context weight.

7. The multi-parameter transmission line tree-line distance prediction method based on improved PSO-BP according to claim 1 is characterized in that: The Mutation-PSO algorithm is used to optimize the BP neural network, specifically: Initialize the particle group in the PSO algorithm, each particle represents the weight and threshold of the BP neural network, and randomly assign initial values ​​to the particle's position and velocity; According to the prediction error of treeline distance, the fitness value of each particle is calculated using Huber-Loss function. The smaller the fitness value, the higher the prediction accuracy. In each iteration, the current fitness of each particle is compared with its historical best fitness. If the current fitness is better, the individual optimal value of the particle is updated. At the same time, the global optimal value of the entire particle swarm is updated to ensure that the particle swarm gradually approaches the optimal solution. Particles in the particle swarm are randomly selected to perform mutation operations, and the positions and velocities of the particles are randomly perturbed to expand the search range and avoid falling into the local optimal solution. The fitness of the particles after the mutation operation is recalculated to determine whether the mutation improves the prediction accuracy. Adjust the position and velocity of all particles according to the following update formula: v i (t+1)=ω·v i (t)+c1·rand1·(p best -x i (t))+c2·rand2·(g best -x i (t)); In the formula, v i (t+1) represents the velocity of particle i at the t+1th iteration, v i (t) represents the velocity of particle i at the tth iteration; ω is the inertia weight; c1 and c2 are both learning factors; rand1 and rand2 are both random factors; p bes t is the individual optimal position of the particle, g bes t is the global optimal position; x i (t) represents the current position of particle i at the tth iteration; Repeat the above fitness calculation to the steps of adjusting the position and speed until the fitness reaches the preset accuracy requirement or the number of iterations reaches the maximum limit, and finally obtain the optimal weights and thresholds of the optimized BP neural network.

8. The multi-parameter transmission line tree-line distance prediction method based on improved PSO-BP according to claim 7 is characterized in that: The Huber-Loss function is used to calculate the fitness value of each particle, which is defined as follows: In the formula, is the Huber-Loss function value; y is the actual tree line distance, is the treeline distance predicted by BP neural network; δ is the adjustment parameter.

9. The multi-parameter transmission line tree-line distance prediction method based on improved PSO-BP according to claim 1 is characterized in that: The method further includes constructing an expert system, which is integrated into the PSO-BP multi-parameter fusion prediction model and is used to analyze the deviation between the prediction result and the real data. The steps of constructing the expert system are as follows: Receive output data from the PSO-BP multi-parameter fusion prediction model and real-time monitoring data; Compare predicted output data with real-time data to identify deviations between the data; According to the deviation analysis results, correction suggestions are generated through the expert rule base, where the rule base is composed of historical data and prior knowledge; the correction suggestions are output to the PSO-BP multi-parameter fusion prediction model to adjust the model parameters in real time.

10. A multi-parameter transmission line tree-line distance prediction system based on improved PSO-BP, characterized in that: The method for predicting the tree-line distance of a multi-parameter transmission line based on an improved PSO-BP as claimed in any one of claims 1 to 9 comprises: The tower displacement calculation module is used to calculate the tower displacement data of the transmission line in real time based on the center frame difference method; The tree-line distance calculation module is used to calculate the tree-line distance of the transmission line based on monocular vision technology; A multi-parameter data acquisition module is used to acquire multi-parameter data related to the tree-line distance, wherein the multi-parameter data includes historical data and real-time data of the span, the height difference between the two towers, the inclination angle of the conductor, the swing angle and the operating temperature of the conductor; A data preprocessing module is used to preprocess the tower displacement data, the transmission line tree-line distance and multi-parameter data; a prediction model building module is used to build a PSO-BP multi-parameter prediction model for tree-line distance prediction, including a data fusion unit, a dimension matching unit and an optimization unit; The data fusion unit is used to fuse the pre-processed pole tower displacement data, transmission line tree-line distance and multi-parameter data, and input the fused data as comprehensive input features into the BP neural network; The dimension matching unit is used to match the number of input layer nodes of the BP neural network with the dimension of the multi-parameter data, and the hidden layer adopts a nonlinear activation function; The optimization unit is used to optimize the BP neural network using the Mutation-PSO algorithm, assign the optimal weights and thresholds obtained by optimization to the BP neural network, and form an optimized PSO-BP multi-parameter fusion prediction model; The treeline distance prediction module is used to make real-time predictions of the treeline distance through the optimized PSO-BP multi-parameter fusion prediction model.

11. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the multi-parameter transmission line tree distance prediction method based on improved PSO-BP as described in any one of claims 1 to 9 are implemented.

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