Method and system for predicting aerodynamic characteristics of inhaul cable based on PSO-GRU

Abstract

The invention discloses a PSO-GRU-based inhaul cable aerodynamic characteristic prediction method and system, and the method comprises the steps: carrying out the loop optimization of key hyper-parameters of a GRU network according to a particle swarm algorithm, calculating a fitness function, obtaining optimal parameters, and building a PSO-GRU model according to the optimal parameters; the method comprises the following steps: acquiring multiple pieces of inhaul cable data, performing weighted fusion on the multiple pieces of inhaul cable data according to a preset multi-channel information weighted fusion model to obtain target inhaul cable data, inputting the target inhaul cable data into a PSO-GRU model, acquiring point prediction probability intervals under different quantiles by adopting a quantile regression algorithm, and performing point prediction on the target inhaul cable data according to the point prediction probability intervals. And the PSO-GRU model outputs to obtain inhaul cable aerodynamic characteristics corresponding to the target inhaul cable data. The aerodynamic characteristics of the inhaul cable under the complex working condition can be effectively predicted, and the prediction accuracy is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of cable-stayed cable detection, and in particular relates to a method and system for predicting aerodynamic characteristics of a cable based on PSO-GRU. Background Art

[0002] As the core load-bearing component of a cable-stayed bridge, the cable is responsible for transferring the bridge deck load to the main tower. Its performance is directly related to the overall stability and safety of the cable-stayed bridge. The cable itself has a large slenderness ratio, which makes it relatively slender in geometry. When subjected to external loads, it is easy to produce large deformations. The cable has a small stiffness, which means that its ability to resist deformation is relatively weak. A small external force may cause a significant displacement of the cable. The cable has low damping, which results in a limited ability of the cable to consume vibration energy when vibration occurs. The vibration decays slowly, and it is easy to produce large-amplitude and long-duration vibrations, posing a potential threat to the safety of the bridge structure. In the research of cable-stayed bridges, In research and engineering practice, the aerodynamic characteristics of cables under complex working conditions is a very challenging and critical research field. The climatic conditions of the geographical location of cable-stayed bridges are complex and diverse, which adds many variables to the study of aerodynamic characteristics of cables, such as seasonal temperature differences and icing conditions. Changes in wind direction will change the relative angle between the cables and the airflow, thereby affecting the size and direction of aerodynamic forces. Fluctuations in wind speed will not only change the amplitude of aerodynamic forces, but may also induce different types of aerodynamic vibrations, such as vortex-induced vibrations and flutter. Different wind field characteristics, such as uniform flow and turbulent flow, also have completely different effects on the aerodynamic characteristics of cables. These complex aerodynamic conditions are intertwined, making it extremely difficult to accurately grasp the aerodynamic characteristics of cables. Summary of the invention

[0003] The present invention provides a method and system for predicting aerodynamic characteristics of cables based on PSO-GRU, which overcomes several shortcomings of traditional aerodynamic characteristics prediction, improves the accuracy of aerodynamic characteristics prediction of cables and the precision of classification, and is conducive to predicting aerodynamic characteristics of cables under complex working conditions.

[0004] In a first aspect, the present invention provides a method for predicting aerodynamic characteristics of a cable based on PSO-GRU, comprising: According to the particle swarm algorithm, the key hyperparameters of the GRU network are cyclically optimized and the fitness function is calculated to obtain the optimal parameters, and a PSO-GRU model is constructed according to the optimal parameters; Acquire multiple cable data, and perform weighted fusion on the multiple cable data according to a preset multi-channel information weighted fusion model to obtain target cable data; The target cable data is input into the PSO-GRU model, and the point prediction probability intervals under different quantiles are obtained by using the quantile regression algorithm. The PSO-GRU model outputs the aerodynamic characteristics of the cable corresponding to the target cable data.

[0005] In a second aspect, the present invention provides a cable aerodynamic characteristics prediction system based on PSO-GRU, comprising: An optimization module is configured to cyclically optimize key hyperparameters of the GRU network according to a particle swarm algorithm and calculate a fitness function to obtain optimal parameters, and construct a PSO-GRU model according to the optimal parameters; A fusion module is configured to obtain a plurality of cable data, and perform weighted fusion on the plurality of cable data according to a preset multi-channel information weighted fusion model to obtain target cable data; The output module is configured to input the target cable data into the PSO-GRU model, use the quantile regression algorithm to obtain the point prediction probability interval under different quantiles, and the PSO-GRU model outputs the cable aerodynamic characteristics corresponding to the target cable data.

[0006] According to a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the method for predicting aerodynamic characteristics of a cable based on PSO-GRU according to any embodiment of the present invention.

[0007] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the steps of the method for predicting aerodynamic characteristics of a cable based on PSO-GRU according to any embodiment of the present invention.

[0008] The PSO-GRU-based cable aerodynamic characteristics prediction method and system of the present application obtains multiple cable data, and performs weighted fusion on the multiple cable data according to a preset multi-channel information weighted fusion model to obtain target cable data, and inputs the target cable data into the PSO-GRU model, and uses the quantile regression algorithm to obtain the point prediction probability interval under different quantiles. The PSO-GRU model outputs the cable aerodynamic characteristics corresponding to the target cable data, which can effectively predict the aerodynamic characteristics of the cable under complex working conditions and improve the accuracy of the prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A flow chart of a method for predicting aerodynamic characteristics of a cable based on PSO-GRU provided in one embodiment of the present invention; Figure 2 A structural block diagram of a cable aerodynamic characteristics prediction system based on PSO-GRU provided in one embodiment of the present invention; Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0011] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] See also Figure 1 , which shows a flow chart of a method for predicting aerodynamic characteristics of a cable based on PSO-GRU of the present application.

[0013] like Figure 1 As shown in FIG. 1 , the cable aerodynamic characteristics prediction method based on PSO-GRU specifically includes the following steps: Step S101, according to the particle swarm algorithm, the key hyperparameters of the GRU network are cyclically optimized and the fitness function is calculated to obtain the optimal parameters, and a PSO-GRU model is constructed according to the optimal parameters.

[0014] In this step, during the iteration process, each particle will adjust its path according to its identified best position and the overall recognized best position. As the iteration proceeds, the particle will update its own coordinates according to its momentum. Indicates The particle finds the best position, and represents the best position among all particles, represents the number of iterations, then in the In the iteration, Particles in the The expression of velocity change in dimension is: , In the formula, Indicates In the next iteration, Particles in the The speed in dimension, is the inertia weight, which affects the continuity of particle velocity. and are the individual learning factor and the group learning factor, respectively, and their values ​​are within (0,2). and For two independent random numbers, take values ​​in [0,1], Indicates -1 iteration, Particles in the The position in the dimension, the particle position update expression is:

[0015] In the formula, For the In the +1 iteration, Particles in the The position in dimension, For the In the next iteration, Particles in the The position in dimension, For the In the next iteration, Particles in the Speed ​​in dimension; Set the minimum value of the fitness function ,Right now: , In the formula, is the aerodynamic characteristic weight. When the adaptation value is lower, the solution of the problem is closer to the optimal solution.

[0016] It should be noted that according to the parameter double-harmonic optimization decision equation, the key hyperparameter mapping relationship of the GRU network is constructed, and the parameters related to the decision are set considering the complex working conditions and constraints to obtain the optimal parameters. The expression of the parameter double-harmonic optimization decision equation is: , , , , , In the formula, is a biharmonic operator, is the feasible domain projection operator, is the sensitivity operator, is the parameter biharmonic optimization decision equation, is the parameter modulus threshold, is the parameter modulus accuracy, is the parameter modulus bias term, is the parameter weight, is the parameter sequence resolution, is the local resolution of the parameter sequence, is the parameter sequence displacement resolution, is a hyperparameter, is the hyperparameter prediction component, is the actual component of the hyperparameter, is the hyperparameter mixture component, is the hyperparameter weight, Forgetting data for hyperparameter prediction, is the actual forgotten data for the hyperparameter, Forget data for hyperparameter mixing, is the hyperparameter data sequence, is the hyperparameter prediction error, is the actual error of the hyperparameter, is the hyperparameter mixing error, is the parameter modulus threshold weight, is the parameter modulus threshold correction parameter, is the parameter modulus threshold sensitivity, is the parameter modulus threshold mean square error, is the parameter modulus threshold correlation coefficient, is the parameter modulus threshold smoothing factor, is the parameter modulus threshold correction factor, is the parameter modulus threshold high-order mode, is the parameter modulus threshold low-order mode, is the parameter modulus threshold hole width, is the parameter modulus threshold mixed order mode, is the parameter modulus precision time entropy sequence, is the parameter modulus precision energy entropy sequence, is the parameter modulus precision sub-signal time entropy sequence weight, is the parameter modulus precision angle sequence, is the parameter modulus precision mixed signal time entropy sequence weight, is the numerical sequence of parameter modulus precision, is the discrete coefficient of the parameter modulus bias term, is the parameter modulus bias discrete weight, is the discrete angle of the parameter modulus bias term, is the discrete gradient of the parameter modulus bias term, is the discrete boundary threshold of the parameter modulus bias term, is the parameter modulus bias node value, is the parameter modulus bias term boundary value, is the parameter modulus bias error, is the main index of the parameter modulus bias term, is the total index of parameter modulus bias term, is the first-order index of the parameter modulus bias term, is the second-order index of the parameter modulus bias term.

[0017] Specifically, constructing a PSO-GRU model according to the optimal parameters includes: According to the multi-fractal nonlinear cable aerodynamic characteristic association strategy, the deep correlation between different working conditions and the cable aerodynamic characteristics is extracted, and the GRU network configured with the optimal parameters is trained according to the deep correlation to obtain the PSO-GRU model. The expression of the multi-fractal nonlinear cable aerodynamic characteristic association strategy is: , In the formula, It is a multi-fractal nonlinear cable aerodynamic characteristics correlation strategy. is the aerodynamic feature extractor, is a nonlinear mapping function, is the lower bound of the label threshold, is the upper bound of the label threshold, is the lower bound of the domain discriminator, is the upper bound of the domain discriminator; in, , , , In the formula, represents the objective function of aerodynamic feature extraction, represents a single interval of function, represents a function mixing interval, represents the aerodynamic characteristic weight, represents a long vector, represents the nonlinear feature mapping coefficient, represents the nonlinear aerodynamic characteristic coefficient, represents the linear feature mapping coefficient, represents the linear aerodynamic characteristic coefficient, represents the adversarial bias term, represents the multi-branch bias term, represents the reverse bias term, represents the superimposed bias term, represents the regularization coefficient of nonlinear aerodynamic characteristics, represents the Boolean coefficient of nonlinear aerodynamic characteristics, represents the significance coefficient of nonlinear aerodynamic characteristics, represents the original coefficient of nonlinear aerodynamic characteristics, represents the nonlinear aerodynamic characteristic prediction coefficient, represents the nonlinear aerodynamic characteristic density coefficient, represents the random variable coefficient of nonlinear aerodynamic characteristics, represents the reliability coefficient of nonlinear aerodynamic characteristics, represents the linear aerodynamic characteristic bounding coefficient, represents the linear aerodynamic characteristic distribution vector, represents the linear aerodynamic characteristic singular vector, represents the one-dimensional standard vector of linear aerodynamic characteristics, represents the linear aerodynamic characteristic state vector, is the torque, is the resistance coefficient.

[0018] Step S102 , acquiring a plurality of cable data, and performing weighted fusion on the plurality of cable data according to a preset multi-channel information weighted fusion model to obtain target cable data.

[0019] In this step, multiple vibration sensors of the same type collect data simultaneously to construct a data set. The data collected by each sensor is normalized to become dimensionless data, providing conditions for subsequent weighted fusion. The expression of dimensionless data is: , In the formula, is dimensionless data, is the data collected by sensor i within time T; for The mean of for The maximum value of for The minimum value of Calculate this time period and The correlation coefficient value of the two time data series is It can be defined as: , In the formula, is the Pearson correlation coefficient between variables X and Y; is the covariance of variables X and Y; represents the variance of variable X; Represents the variance of variable Y, and further constructs the related support matrix S, which is expressed as: , In the formula, is the relevant support of the mth row and mth column; Therefore, in the time period T, the support of all sensors except sensor i to sensor i is: , The support indicates the degree of correlation between each sensor data and other sensor data. During the entire detection time period T, it is necessary to evaluate the reliability of the vibration sensor node itself to ensure the accuracy and stability of the detection results. The mean and standard deviation of the i-th sensor node data in the detection time period T are defined as follows: , , Define the variation factor as:

[0020] The final weighted fusion expression is: , , In the formula, is the final weighted fusion expression, is the weighted value corresponding to the time series data collected by sensor node i within the detection time period T. Data with high support and high reliability should be given a larger weight. is the number of sensor nodes, is dimensionless data, is the support of all sensors except sensor i to sensor i, is the variation factor, To define the mean value of the data of the i-th sensor node in the detection time period T, is the standard deviation, is the number of acquisition sequences, is the data collected by sensor i within time T, is the sensor node data, is the correlation support between two time data series.

[0021] A topology-guided abnormal data filtering framework (ADFF) is established. By suppressing the abnormal data collected by the sensor, the abnormal data is cleaned using topology information. A judgment and filtering module is used to fuse the Euclidean structure features with enhanced correlation and the graph topology features of non-Euclidean structure, which effectively improves the accuracy of anomaly recognition under complex data forms and dynamically optimizes high-quality output. The expression of the topology-guided abnormal data filtering framework is: , , , , , In the formula, is the abnormal data filtering equation guided by topological information, is the data margin, is the strong impact data component, is the band energy, is the third-order fault data, is a single data component, is the interference data weight, is the interference data component, is the fuzzy data weight, is the fuzzy data component, is the data component sequence, is the peak value of strong impact data, is the effective value of strong impact data, is the data energy envelope component weight, is the modal mixing component, is the envelope transition component, is the modal aliasing weight, is the number of categories, is the data entropy, is the data spreading entropy, is the mode overlap component, is the number of overlaps, is the number of data, is a coarse-grained sequence, is the probability mean, is the embedded component, and All are fault information. is the numerical accuracy of fault data, is the fault data numerical singular value, is the fault expansion numerical energy ratio, is the data density, is the neighborhood density.

[0022] Step S103, inputting the target cable data into the PSO-GRU model, using a quantile regression algorithm to obtain point prediction probability intervals under different quantiles, and the PSO-GRU model outputs the aerodynamic characteristics of the cable corresponding to the target cable data.

[0023] In this step, quantile regression is to regress the independent variable based on the conditional quantile of the dependent variable to obtain the regression model under the given quantile. , dependent variable , the linear quantile regression model can be expressed as: , In the formula, The dependent variable conditional quantiles, The range is (0,1), is the i-th dimension vector, is the vector of regression coefficients. When the training data of the model is known, find the regression coefficient vectors of different quantile points; The problem can be transformed into minimizing the loss function L, that is: , In the formula, is the tilt absolute value function, and its calculation formula is: , , Since the linear quantile regression model is only applicable to studying the linear relationship between independent variables and dependent variables, the actual wind power data has more nonlinear relationships: , In the formula, and is the regularization parameter, v and w are weight parameters; i is the network input layer node, k is the hidden layer node, and the nonlinear quantile regression loss function of the PSO-GRU model is expressed as: , In the formula, is the nonlinear quantile regression loss function, is the weight parameter, is the quantile number, is the element-wise minimum, For different quantiles, is the response correction amount, is the parameter model compensation, Quantify fuzzy existence.

[0024] In summary, the method of the present application obtains multiple cable data, and performs weighted fusion on the multiple cable data according to a preset multi-channel information weighted fusion model to obtain target cable data, and inputs the target cable data into the PSO-GRU model. The quantile regression algorithm is used to obtain the point prediction probability interval under different quantiles. The PSO-GRU model outputs the aerodynamic characteristics of the cable corresponding to the target cable data, which can effectively predict the aerodynamic characteristics of the cable under complex working conditions and improve the accuracy of the prediction.

[0025] See also Figure 2 , which shows a structural block diagram of a cable aerodynamic characteristics prediction system based on PSO-GRU in the present application.

[0026] like Figure 2 As shown, the cable aerodynamic characteristic prediction system 200 includes an optimization module 210 , a fusion module 220 and an output module 230 .

[0027] The optimization module 210 is configured to perform cyclic optimization on the key hyperparameters of the GRU network according to the particle swarm algorithm and calculate the fitness function to obtain the optimal parameters, and construct the PSO-GRU model according to the optimal parameters; A fusion module 220 is configured to obtain a plurality of cable data, and perform weighted fusion on the plurality of cable data according to a preset multi-channel information weighted fusion model to obtain target cable data; The output module 230 is configured to input the target cable data into the PSO-GRU model, and use the quantile regression algorithm to obtain the point prediction probability interval under different quantiles. The PSO-GRU model outputs the cable aerodynamic characteristics corresponding to the target cable data.

[0028] It should be understood that Figure 2 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 2 The modules in it will not be described in detail here.

[0029] In other embodiments, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the cable aerodynamic characteristics prediction method based on PSO-GRU in any of the above method embodiments; As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows: According to the particle swarm algorithm, the key hyperparameters of the GRU network are cyclically optimized and the fitness function is calculated to obtain the optimal parameters, and a PSO-GRU model is constructed according to the optimal parameters; Acquire multiple cable data, and perform weighted fusion on the multiple cable data according to a preset multi-channel information weighted fusion model to obtain target cable data; The target cable data is input into the PSO-GRU model, and the point prediction probability intervals under different quantiles are obtained by using the quantile regression algorithm. The PSO-GRU model outputs the aerodynamic characteristics of the cable corresponding to the target cable data.

[0030] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created according to the use of the cable aerodynamic characteristics prediction system based on PSO-GRU, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the cable aerodynamic characteristics prediction system based on PSO-GRU via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0031] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 3 The example of the connection via bus is taken. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the cable aerodynamic characteristics prediction method based on PSO-GRU in the above-mentioned method embodiment is realized. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the cable aerodynamic characteristics prediction system based on PSO-GRU. The output device 340 may include display devices such as display screens.

[0032] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0033] As an implementation mode, the electronic device is applied to a cable aerodynamic characteristic prediction system based on PSO-GRU, and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: According to the particle swarm algorithm, the key hyperparameters of the GRU network are cyclically optimized and the fitness function is calculated to obtain the optimal parameters, and a PSO-GRU model is constructed according to the optimal parameters; Acquire multiple cable data, and perform weighted fusion on the multiple cable data according to a preset multi-channel information weighted fusion model to obtain target cable data; The target cable data is input into the PSO-GRU model, and the point prediction probability intervals under different quantiles are obtained by using the quantile regression algorithm. The PSO-GRU model outputs the aerodynamic characteristics of the cable corresponding to the target cable data.

[0034] Through the description of the above implementation modes, those skilled in the art can clearly understand that each implementation mode can be implemented by means of software plus a necessary general hardware platform, or of course by hardware. Based on such an understanding, the above technical solution can essentially or in other words be embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting aerodynamic characteristics of cables based on PSO-GRU, characterized in that: include: According to the particle swarm algorithm, the key hyperparameters of the GRU network are cyclically optimized and the fitness function is calculated to obtain the optimal parameters, and the PSO-GRU model is constructed according to the optimal parameters, wherein the key hyperparameters of the GRU network are cyclically optimized and the fitness function is calculated according to the particle swarm algorithm to obtain the optimal parameters including: According to the parameter double-harmonic optimization decision equation, the key hyperparameter mapping relationship of the GRU network is constructed, and the parameters related to the decision are set considering the complex working conditions and constraints to obtain the optimal parameters. The expression of the parameter double-harmonic optimization decision equation is: ， ， ， ， ， In the formula, is a biharmonic operator, is the feasible domain projection operator, is the sensitivity operator, is the parameter biharmonic optimization decision equation, is the parameter modulus threshold, is the parameter modulus accuracy, is the parameter modulus bias term, is the parameter weight, is the parameter sequence resolution, is the local resolution of the parameter sequence, is the parameter sequence displacement resolution, is a hyperparameter, is the hyperparameter prediction component, is the actual component of the hyperparameter, is the hyperparameter mixture component, is the hyperparameter weight, Forgetting data for hyperparameter prediction, is the actual forgotten data for the hyperparameter, Forget data for hyperparameter mixing, is the hyperparameter data sequence, is the hyperparameter prediction error, is the actual error of the hyperparameter, is the parameter modulus threshold weight, is the parameter modulus threshold correction parameter, is the parameter modulus threshold sensitivity, is the parameter modulus threshold mean square error, is the parameter modulus threshold correlation coefficient, is the parameter modulus threshold smoothing factor, is the parameter modulus threshold correction factor, is the parameter modulus threshold high-order mode, is the parameter modulus threshold hole width, is the parameter modulus precision time entropy sequence, is the parameter parameter modulus precision energy entropy sequence, is the parameter modulus precision sub-signal time entropy sequence weight, is the parameter modulus precision angle sequence, is the parameter modulus precision mixed signal time entropy sequence weight, is the numerical sequence of parameter modulus precision, is the discrete coefficient of the parameter modulus bias term, is the parameter modulus bias discrete weight, is the discrete angle of the parameter modulus bias term, is the discrete gradient of the parameter modulus bias term, is the discrete boundary threshold of the parameter modulus bias term, is the parameter modulus bias node value, is the parameter modulus bias term boundary value, is the parameter modulus bias error, is the main index of the parameter modulus bias term, is the total index of parameter modulus bias term, is the first-order index of the parameter modulus bias term, is the second-order index of the parameter modulus bias term; Acquire multiple cable data, and perform weighted fusion on the multiple cable data according to a preset multi-channel information weighted fusion model to obtain target cable data; The target cable data is input into the PSO-GRU model, and the point prediction probability intervals under different quantiles are obtained by using the quantile regression algorithm. The PSO-GRU model outputs the aerodynamic characteristics of the cable corresponding to the target cable data.

2. The method for predicting aerodynamic characteristics of a cable based on PSO-GRU according to claim 1, characterized in that: The constructing of the PSO-GRU model according to the optimal parameters comprises: According to the multi-fractal nonlinear cable aerodynamic characteristic association strategy, the deep correlation between different working conditions and the cable aerodynamic characteristics is extracted, and the GRU network configured with the optimal parameters is trained according to the deep correlation to obtain the PSO-GRU model. The expression of the multi-fractal nonlinear cable aerodynamic characteristic association strategy is: ， In the formula, It is a multi-fractal nonlinear cable aerodynamic characteristics correlation strategy. is the aerodynamic feature extractor, is a nonlinear mapping function, is the lower bound of the label threshold, is the upper bound of the label threshold, is the lower bound of the domain discriminator, is the upper bound of the domain discriminator; in, , ， ， In the formula, represents a single interval of function, represents a function mixing interval, represents the aerodynamic characteristic weight, represents a long vector, represents the nonlinear feature mapping coefficient, represents the nonlinear aerodynamic characteristic coefficient, represents the linear feature mapping coefficient, represents the linear aerodynamic characteristic coefficient, represents the adversarial bias term, represents the multi-branch bias term, represents the reverse bias term, represents the superposition bias term, represents the random variable coefficient of nonlinear aerodynamic characteristics, represents the reliability coefficient of nonlinear aerodynamic characteristics, represents the linear aerodynamic characteristic bounding coefficient, represents the linear aerodynamic characteristic distribution vector, represents the linear aerodynamic characteristic singular vector, represents the one-dimensional standard vector of linear aerodynamic characteristics, represents the linear aerodynamic characteristic state vector, is the torque, is the resistance coefficient.

3. The method for predicting aerodynamic characteristics of a cable based on PSO-GRU according to claim 1, characterized in that: in, The expression of weighted fusion is: ， ， ， ， ， ， In the formula, is the final weighted fusion expression, is the weighted value corresponding to the time series data collected by sensor node i within the detection time period T. Data with high support and high reliability should be given a larger weight. is the number of sensor nodes, is dimensionless data, is the support of all sensors except sensor i to sensor i, is the variation factor, To define the mean value of the data of the i-th sensor node in the detection time period T, is the standard deviation, is the number of acquisition sequences, is the data collected by sensor i within time T, is the sensor node data, is the correlation support between two time data series.

4. The method for predicting aerodynamic characteristics of a cable based on PSO-GRU according to claim 1, characterized in that: Before weighted fusion of the plurality of cable data is performed according to a preset multi-channel information weighted fusion model, the method further includes: The plurality of cable data are processed according to a preset abnormal data filtering framework guided by topology information, wherein the expression of the abnormal data filtering framework guided by topology information is: ， ， ， ， ， In the formula, is the abnormal data filtering equation guided by topological information, is the data margin, is the strong impact data component, is the band energy, is the third-order fault data, is a single data component, is the interference data weight, is the interference data component, is the fuzzy data weight, is the fuzzy data component, is the data component sequence, is the peak value of strong impact data, is the effective value of strong impact data, is the data energy envelope component weight, is the modal mixing component, is the envelope transition component, is the modal aliasing weight, is the number of categories, is the data entropy, is the data spreading entropy, is the mode overlap component, is the number of overlaps, is the number of data, is a coarse-grained sequence, is the probability mean, is the embedded component, and All are fault information. is the numerical accuracy of fault data, is the fault data numerical singular value, is the fault expansion numerical energy ratio, is the data density, is the neighborhood density.

5. The method for predicting aerodynamic characteristics of a cable based on PSO-GRU according to claim 1, characterized in that: The expression of the nonlinear quantile regression loss function of the PSO-GRU model is: ， In the formula, is the nonlinear quantile regression loss function, is the weight parameter, is the quantile number, is the element-wise minimum, For different quantiles, is the response correction amount, is the parameter model compensation, Quantify fuzzy existence.

6. A cable aerodynamic characteristics prediction system based on PSO-GRU, characterized in that: include: The optimization module is configured to perform cyclic optimization on the key hyperparameters of the GRU network according to the particle swarm algorithm and calculate the fitness function to obtain the optimal parameters, and construct the PSO-GRU model according to the optimal parameters, wherein the cyclic optimization on the key hyperparameters of the GRU network according to the particle swarm algorithm and calculate the fitness function to obtain the optimal parameters include: According to the parameter double-harmonic optimization decision equation, the key hyperparameter mapping relationship of the GRU network is constructed, and the parameters related to the decision are set considering the complex working conditions and constraints to obtain the optimal parameters. The expression of the parameter double-harmonic optimization decision equation is: ， ， ， ， ， In the formula, is a biharmonic operator, is the feasible domain projection operator, is the sensitivity operator, is the parameter biharmonic optimization decision equation, is the parameter modulus threshold, is the parameter modulus accuracy, is the parameter modulus bias term, is the parameter weight, is the parameter sequence resolution, is the local resolution of the parameter sequence, is the parameter sequence displacement resolution, is a hyperparameter, is the hyperparameter prediction component, is the actual component of the hyperparameter, is the hyperparameter mixture component, is the hyperparameter weight, Forgetting data for hyperparameter prediction, is the actual forgotten data for the hyperparameter, Forget data for hyperparameter mixing, is the hyperparameter data sequence, is the hyperparameter prediction error, is the actual error of the hyperparameter, is the parameter modulus threshold weight, is the parameter modulus threshold correction parameter, is the parameter modulus threshold sensitivity, is the parameter modulus threshold mean square error, is the parameter modulus threshold correlation coefficient, is the parameter modulus threshold smoothing factor, is the parameter modulus threshold correction factor, is the parameter modulus threshold high-order mode, is the parameter modulus threshold hole width, is the parameter modulus precision time entropy sequence, is the parameter parameter modulus precision energy entropy sequence, is the parameter modulus precision sub-signal time entropy sequence weight, is the parameter modulus precision angle sequence, is the parameter modulus precision mixed signal time entropy sequence weight, is the numerical sequence of parameter modulus precision, is the discrete coefficient of the parameter modulus bias term, is the parameter modulus bias discrete weight, is the discrete angle of the parameter modulus bias term, is the discrete gradient of the parameter modulus bias term, is the discrete boundary threshold of the parameter modulus bias term, is the parameter modulus bias node value, is the parameter modulus bias term boundary value, is the parameter modulus bias error, is the main index of the parameter modulus bias term, is the total index of parameter modulus bias term, is the first-order index of the parameter modulus bias term, is the second-order index of the parameter modulus bias term; A fusion module is configured to obtain a plurality of cable data, and perform weighted fusion on the plurality of cable data according to a preset multi-channel information weighted fusion model to obtain target cable data; The output module is configured to input the target cable data into the PSO-GRU model, use the quantile regression algorithm to obtain the point prediction probability interval under different quantiles, and the PSO-GRU model outputs the cable aerodynamic characteristics corresponding to the target cable data.

7. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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