Cross interconnection cable fault diagnosis method and system based on improved Bayesian optimization XGBoost

Abstract

The invention provides a cross interconnection cable fault diagnosis method and system based on improved Bayesian optimization XGBoost, and aims to solve the problems of insufficient precision and low efficiency of an existing cable fault detection method in a complex environment. Firstly, a Bayesian optimization algorithm is improved, for a Gaussian process in a prior function, a Marton kernel function and a square index kernel function are multiplied and compounded to serve as a kernel function, an improved Markov chain Monte Carlo method is used for optimizing parameters of the kernel function, and secondly, for an acquisition function, a kernel function is obtained. And if yes, adjusting parameters in the traditional confidence boundary strategy are improved. And finally, performing intelligent search and optimization on the hyper-parameters of the XGBoost by using an improved Bayesian optimization algorithm, thereby effectively improving the prediction performance and generalization ability of the model, and realizing efficient and accurate diagnosis of the fault of the cross interconnection cable. According to the implementation of the method, the automation level of cable fault processing can be improved, the operation and maintenance cost is reduced, and the method has important practical application value and popularization prospects.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment fault diagnosis, and in particular to a cross-connection cable fault diagnosis method and system based on improved Bayesian optimization XGBoost. Background Art

[0002] In modern power systems, cross-connect cables are an important part of power transmission, and their reliability is directly related to the safe and stable operation of the power grid. However, due to the long-term operation of cables in complex and changeable environments, they are affected by various external factors such as soil moisture, temperature changes, mechanical damage and aging, resulting in frequent cable failures. Rapidly and accurately diagnosing and locating cable faults is of great significance for reducing power outage time, ensuring power supply reliability and reducing maintenance costs.

[0003] Traditional cable fault detection methods, such as the bridge method and low-voltage pulse reflection method, can complete fault detection to a certain extent, but when faced with complex and changeable fault types, they often have problems such as low detection accuracy and complex operation. In recent years, XGBoost, as an efficient gradient boosting algorithm, has performed well in data classification and regression prediction and has been widely used in various fault diagnosis fields.

[0004] However, XGBoost has many hyperparameters, and how to efficiently determine its optimal parameter combination is the key to improving the model's prediction performance. As an advanced optimization method, the Bayesian optimization algorithm has become an effective means of optimizing complex machine learning models by constructing a probabilistic model of the objective function and efficiently searching for the optimal parameter combination based on the model.

[0005] For example, the invention patent with the publication number CN117434388A discloses a method for identifying the fault mode of the sheath grounding of a cross-connected high-voltage cable. The method selects the factor with the greatest impact on the fault as the characteristic index of the input sample, and uses the Bayesian method to determine the hyperparameter. The size of the hyperparameter can be adaptively adjusted during the training process of the network and optimized, thereby improving the generalization ability of the neural network. The patent mentions the use of high-voltage pulse emission and high-energy audio emission as detection units, but different cable faults may require signals of different frequencies or intensities for detection, so there may be problems that some specific faults are difficult to detect accurately. Under different environmental conditions (such as extreme temperature, humidity, soil conditions, etc.), the performance of the detection unit may be affected, resulting in a decrease in the accuracy of the detection results. The setting of the threshold may need to be adjusted according to the actual situation to ensure the accuracy and reliability of fault identification. However, the selection of the threshold may be affected by many factors, such as cable type, operating conditions, etc., so there may be certain difficulties and uncertainties. As the operating conditions of the cable change (such as aging, environmental factors, etc.), the threshold may need to be updated and adjusted regularly to ensure the continued effectiveness of fault identification.

[0006] The invention patent with the publication number CN117630562A discloses a superconducting cable fault detection method based on the joint feature of current amplitude and phase, which uses a machine learning algorithm to construct a classifier model, and trains the classifier based on the data in the sample set based on the joint feature of current amplitude and phase to obtain a maturely trained classifier model; multiple test points are selected for the superconducting cable to be tested, and tests are performed at each test point and a joint feature based on current amplitude and phase is constructed; the joint features of each test point are input into a mature classifier model, and the fault detection of the superconducting cable to be tested is performed according to the output of the classifier model. The present invention performs quench detection based on the joint feature of current amplitude and phase, has better accuracy, and can determine the location of the quench fault when the superconducting cable quenches. However, this method mainly focuses on quench detection based on current amplitude and phase, and may not cover all types of superconducting cable faults, such as mechanical damage or insulation aging. The operating environment of the superconducting cable is complex and changeable, and factors including temperature, humidity, electromagnetic interference, etc. may affect the detection results. Although the patent performs detection based on the joint characteristics of current amplitude and phase, it does not explicitly mention how to eliminate or reduce the impact of these environmental factors. Therefore, the category and accuracy of fault detection need to be improved. Summary of the invention

[0007] Purpose of the invention: In view of the limitations of traditional fault detection methods in the face of complex fault types and the insufficient accuracy and low efficiency of existing cable fault detection methods in complex environments, the present invention provides a cross-connected cable fault diagnosis method based on improved Bayesian optimization XGBoost. The present invention also discloses a cross-connected cable fault diagnosis system based on improved Bayesian optimization XGBoost.

[0008] Technical solution: The present invention provides a cross-connection cable fault diagnosis method based on improved Bayesian optimization XGBoost, the method comprising:

[0009] The collected circulation samples are used to construct a multidimensional feature vector I, and the circulation sample data is constructed according to multiple multidimensional feature vectors I, and the circulation sample data is divided into a training set and a test set according to a certain ratio. The circulation samples are obtained from the monitoring background by installing an online monitoring device for sheath circulation at the circulation monitoring point of the cable line, and then all the measured circulation amplitudes are used to construct a multidimensional feature vector I;

[0010] Setting the range of XGBoost model hyperparameters, and pre-training the XGBoost model based on the training set using an improved Bayesian optimization algorithm to find the optimal hyperparameter combination;

[0011] The improved Bayesian optimization algorithm includes optimizing the parameters in the prior function and optimizing the acquisition function, wherein optimizing the parameters in the prior function includes: using a Gaussian process to model the objective function, the function obeying the distribution of the objective function includes a mean function and a kernel function, the kernel function is a composite kernel function obtained by multiplying two single kernel functions, namely, a Marton kernel function and a square exponential kernel function, and optimizing one or more parameters corresponding to the composite kernel function using an improved Markov chain Monte Carlo method based on flower pollination, calculating the distribution of the parameters to be optimized, and then calculating the corresponding expected values, thereby obtaining the optimized parameter values; the optimization of the acquisition function includes: optimizing and improving the adjustment parameters therein using an adaptive confidence boundary strategy;

[0012] The optimal hyperparameter combination is used as the parameters in the final model, and the training set is reused to train the XGBoost model;

[0013] The trained XGBoost model is used to predict the fault type of the test set, output the prediction results and analyze the diagnostic effect.

[0014] Further, including:

[0015] The Gaussian Process (GP) is selected to model the objective function, and the function that obeys the distribution of the objective function includes a mean function and a kernel function, specifically including:

[0016] The Gaussian process GP is used to model the objective function. GP is regarded as a generalized extension of the multivariate Gaussian distribution in infinite dimensions. The function that obeys its distribution consists of a mean function and a kernel function, namely:

[0017] f(x)~GP(m(x),k(x,x′));

[0018] Wherein, mean function m(x) = E[f(x)], kernel function k(x,x′) = E[(f(x)-m(x))(f(x′)-m(x′))], which is used to indicate the similarity between two data. For the hyperparameter optimization process, x is a series of hyperparameter combinations, and f(x) is the final evaluation index score. Therefore, without loss of generality, let m(x) = 0;

[0019] If the output value on the training set is f=(f1,f2,...,f n ) T , the predicted function value f(x * )=f * , then there is the following joint distribution in the properties of the Gaussian process:

[0020]

[0021] Where K is the Gram matrix k(X,X), k * ={k(x * ,x1),k(x * ,x2),...,k(x * ,x n )} T ,

[0022] But the objective function f(x) and the observed value y=(y1,y2,...,y n ) T There is usually an error between them, which is represented by noise ∈, that is:

[0023] y=f(x)+∈;

[0024] And assume that the noise ∈ i independent and identically distributed, and Then the prior of the kernel function becomes, the prior of the kernel function, that is, after considering the noise, the prior of the kernel function needs to be adjusted to include the influence of noise:

[0025]

[0026] Then we get y and f * The joint distribution of is:

[0027]

[0028] Where I is the unit matrix, and They represent the variance of the objective function and the variance of the noise respectively,

[0029] The variance of the objective function is part of the kernel function in the Gaussian process, which measures how much the objective function varies between different points in the absence of observation noise. This variance together with the kernel function defines the smoothness of the objective function. The value means that the objective function changes more drastically in the input space, while a smaller A value of means that the objective function is smoother. In the context of Gaussian processes, Often works together with the kernel function's scale parameter to control the smoothness and variability of the function.

[0030] The variance of the noise represents the random error or noise in the observed data and describes the distribution of the random error. In Gaussian process regression, the noise variance is an important hyperparameter that affects how well the model fits the observed data. If the noise variance is set too large, the model may be too smooth and fail to capture the true changes in the objective function; if it is set too small, the model may overfit the noise in the observed data.

[0031] According to the properties of multivariate normal distribution, we get:

[0032] f * |x * ,y~N(μ(x * ),σ 2 (x * ));

[0033] μ(x * ) is the final predicted value, σ 2 (x * ) is the prediction variance,

[0034] The traditional Marton kernel function and the square exponential kernel function are multiplied and compounded to obtain a more accurate fitting result. The Marton-square exponential composite function is:

[0035]

[0036] Where r = xx′, represents the Euclidean distance between sample points, l is the scale parameter, and α is the smoothing parameter.

[0037] Among them, the scale parameter controls the smoothness and "speed" of change of the function in the input space. It affects the rate at which the function value changes with the input point. A smaller scale parameter value means that the function changes faster in the input space, that is, the function is more sensitive to small changes in the input point, which may cause the model to overfit the data. A larger scale parameter value means that the function changes more slowly and more smoothly, which helps the model generalize to unseen data, but may cause the model to underfit the data.

[0038] Further, including:

[0039] One or more parameters corresponding to the composite kernel function are optimized using an improved Markov chain Monte Carlo method based on flower pollination, including:

[0040] Arbitrarily select the initial parameter θ0 to be optimized and execute it in a loop i=1,2,...,k times;

[0041] Let the value of the parameter to be optimized for the current number of visits be θ i-1 =θ, randomly select the next candidate parameter value θ′ according to the proposed distribution;

[0042] According to the flower pollination FPA, when the conversion probability between global optimization and local optimization p>0.8, the next candidate parameter θ′ is calculated globally as:

[0043]

[0044] In the formula, is the sample generated by the mth Markov chain at the i-th iteration, is the optimal sampling point among the currently generated samples, L is the search step length, and its calculation formula is as follows:

[0045]

[0046] In the formula, u=i -λ , Γ is the standard gamma function, λ is usually 1-5, v~N(0,1);

[0047] If p≤0.8, the next candidate parameter θ′ is:

[0048]

[0049] Where ξ is a random value that follows a uniform distribution on [0,1], and are the samples generated by the m-th Markov chain at the j-th and k-th iterations, respectively. and are equivalent to random solutions in the model.

[0050] Among them, the flower pollination algorithm is an optimization algorithm inspired by the pollination process of flowers in nature. It is mainly divided into two stages: global pollination and local pollination. Global pollination: simulates the process of pollen spreading over a large range through pollinators such as insects. In global pollination, pollen can spread over a long distance, which helps the pollination and reproduction of the fittest. The global pollination in the algorithm is simulated by Lévy flight, which is a random walk with a heavy-tailed distribution, which means that there is a relatively high probability of large strides in the random walk process, thereby achieving global search. Local pollination: simulates the process of self-pollination, that is, pollen spreads between flowers that are physically close. Local pollination is achieved through local random walks, that is, searching in the neighborhood of the current solution, which helps the algorithm to perform fine searches near the found high-quality solutions. The flower pollination algorithm introduces a conversion probability p to weigh the proportion of global search and local search, thereby balancing global search and local search.

[0051] Using the updated acceptance probability, we get the posterior samples θ1, θ2..., θ k ;

[0052] Therefore, the expected value of the distribution of the parameters to be optimized is expressed as:

[0053]

[0054] Further, including:

[0055] The acceptance probability of the update is expressed as:

[0056]

[0057] Where p(·) is the given prior distribution and q(·) is the given proposed distribution;

[0058] Randomly select u from uniform distribution, u∈(0,1);

[0059] If u≤α(θ,θ′), then accept the transfer θ i =θ′, otherwise, the transfer is not accepted, and θ i =θ i-1 After the loop is executed, we can get the posterior samples θ1, θ2..., θ k .

[0060] Further, including:

[0061] The adaptive confidence boundary strategy is used to optimize and improve the adjustment parameters, specifically including:

[0062] The acquisition function adopts an adaptive confidence boundary strategy to sample in unknown areas or explore near the currently considered global optimal solution, which is expressed as:

[0063] AUCB(x)=μ(x)+εσ(x);

[0064] In the formula, the adaptive adjustment parameter s=|y max +D max | / (y max +D max ), y max Indicates the maximum value in the current fitting curve, D max It represents the maximum value in the current observation set, and s is the coefficient of difference, which is used to further represent y max With D max degree of difference.

[0065] Among them, the fitting curve: In the context of Bayesian optimization, the fitting curve usually refers to the prediction curve obtained by modeling the objective function using the Gaussian Process (GP). This curve represents the predicted behavior of the objective function in the input space, including its mean and uncertainty. The Gaussian process estimates the value of the objective function at unobserved points through the existing observed data points, thus forming a smooth curve, which is the fitting curve.

[0066] Among them, the observation set: The observation set refers to the set of parameter points that have been evaluated during the optimization process and their corresponding objective function values. These data points are used to build a Gaussian process model, that is, to learn the behavior of the objective function through these points. In the iterative process of Bayesian optimization, new observation points are continuously added to this set to update and improve the fitting curve. In the adaptive confidence boundary strategy, the maximum value of the fitting curve and the maximum value in the observation set are used to calculate the difference coefficient, which in turn affects the value of the adaptive adjustment parameter. This parameter determines the tendency of the acquisition function between exploration and development. When y max and D max When they are close, the acquisition function tends to explore; when they are far away, the acquisition function tends to exploit.

[0067] Further, including:

[0068] The adaptive adjustment parameters and y max , D max The relationships include:

[0069] When max With D max When it is infinitely close, ε tends to 1, and the acquisition function based on the adaptive confidence boundary strategy is more inclined to the exploration state;

[0070] When max With D max When it is infinitely far away, ε tends to 0, and the acquisition function is more inclined to the development state.

[0071] In a second aspect, the present invention further provides a cross-connection cable fault diagnosis method system based on improved Bayesian optimization XGBoost, the system comprising:

[0072] The acquisition module is used to construct a multidimensional feature vector I using the collected circulation samples, construct circulation sample data according to multiple multidimensional feature vectors I, and divide the circulation sample data into a training set and a test set according to a certain ratio. The circulation samples are obtained from the monitoring background by installing an online monitoring device for sheath circulation at the circulation monitoring point of the cable line, and then constructing a multidimensional feature vector I with all the measured circulation amplitudes;

[0073] A model pre-training module, used to set the range of XGBoost model hyperparameters, and pre-train the XGBoost model based on the training set using an improved Bayesian optimization algorithm to find the optimal hyperparameter combination;

[0074] The improved Bayesian optimization algorithm includes optimizing the parameters in the prior function and optimizing the acquisition function, wherein optimizing the parameters in the prior function includes: using a Gaussian process to model the objective function, the function obeying the distribution of the objective function includes a mean function and a kernel function, the kernel function is a composite kernel function obtained by multiplying two single kernel functions, namely, a Marton kernel function and a square exponential kernel function, and optimizing one or more parameters corresponding to the composite kernel function using an improved Markov chain Monte Carlo method based on flower pollination, calculating the distribution of the parameters to be optimized, and then calculating the corresponding expected values, thereby obtaining the optimized parameter values; the optimization of the acquisition function includes: optimizing and improving the adjustment parameters therein using an adaptive confidence boundary strategy;

[0075] The model training module is used to use the optimal hyperparameter combination as the parameters in the final model and reuse the training set to train the XGBoost model;

[0076] The prediction module is used to use the trained XGBoost model to predict the fault type of the test set, output the prediction results and analyze the diagnosis effect.

[0077] Further, including:

[0078] In the model pre-training module, a Gaussian process is selected to model the objective function, and the function that obeys the distribution of the objective function includes a mean function and a kernel function, specifically including:

[0079] The Gaussian process GP is used to model the objective function. GP is regarded as a generalized extension of the multivariate Gaussian distribution in infinite dimensions. The function that obeys its distribution consists of a mean function and a kernel function, namely:

[0080] f(x)~GP(m(x),k(x,x′));

[0081] Wherein, mean function m(x) = E[f(x)], kernel function k(x,x′) = E[(f(x)-m(x))(f(x′)-m(x′))], which is used to indicate the similarity between two data. For the hyperparameter optimization process, x is a series of hyperparameter combinations, and f(x) is the final evaluation index score. Therefore, without loss of generality, let m(x) = 0;

[0082] If the output value on the training set is f=(f1,f2,...,f n ) T , the predicted function value f(x * ) = f * , then there is the following joint distribution in the properties of the Gaussian process:

[0083]

[0084] Where K is the Gram matrix k(X,X), k * ={k(x * ,x1),k(x * ,x2),...,k(x * ,x n )} T ,

[0085] But the objective function f(x) and the observed value y=(y1,y2,...,y n ) T There is usually an error between them, which is represented by noise ∈, that is:

[0086] y=f(x)+∈;

[0087] And assume that the noise ∈ i independent and identically distributed, and Then the prior of the kernel function becomes:

[0088]

[0089] Then we get y and f * The joint distribution of is:

[0090]

[0091] Where I is the unit matrix, and They represent the variance of the objective function and the variance of the noise respectively,

[0092] According to the properties of multivariate normal distribution, we get:

[0093] f * |x * ,y~N(μ(x * ),σ 2 (x * ));

[0094] μ(x * ) is the final predicted value, σ 2 (x * ) is the prediction variance,

[0095] The traditional Marton kernel function and the square exponential kernel function are multiplied and compounded to obtain a more accurate fitting result. The Marton-square exponential composite function is:

[0096]

[0097] Where r = xx′, represents the Euclidean distance between sample points, l is the scale parameter, and α is the smoothing parameter.

[0098] Further, including:

[0099] One or more parameters corresponding to the composite kernel function are optimized using an improved Markov chain Monte Carlo method based on flower pollination, including:

[0100] Arbitrarily select the initial parameter θ0 to be optimized and execute it in a loop i=1,2,...,k times;

[0101] Let the value of the parameter to be optimized for the current number of visits be θ i-1 =θ, randomly select the next candidate parameter value θ′ according to the proposed distribution;

[0102] According to the flower pollination FPA, when the conversion probability between global optimization and local optimization p>0.8, the next candidate parameter θ′ is calculated globally as:

[0103]

[0104] In the formula, is the sample generated by the mth Markov chain at the i-th iteration, is the optimal sampling point among the currently generated samples, L is the search step length, and its calculation formula is as follows:

[0105]

[0106] In the formula, u=i -λ , Γ is the standard gamma function, λ is usually 1-5, v~N(0,1);

[0107] If p≤0.8, the next candidate parameter θ′ is:

[0108]

[0109] Where ξ is a random value that follows a uniform distribution on [0,1], and are the samples generated by the m-th Markov chain at the j-th and k-th iterations, respectively. and are equivalent to random solutions in the model;

[0110] Using the updated acceptance probability, we get the posterior samples θ1, θ2..., θ k ;

[0111] Therefore, the expected value of the distribution of the parameters to be optimized is expressed as:

[0112]

[0113] Further, including:

[0114] The acceptance probability of the update is expressed as:

[0115]

[0116] Where p(·) is the given prior distribution and q(·) is the given proposed distribution;

[0117] Randomly select u from uniform distribution, u∈(0,1);

[0118] If u≤α(θ,θ′), then accept the transfer θ i =θ′, otherwise, the transfer is not accepted, and θ i =θ i-1 After the loop is executed, we can get the posterior samples θ1, θ2..., θ k .

[0119] Further, including:

[0120] The adaptive confidence boundary strategy is used to optimize and improve the adjustment parameters, specifically including:

[0121] The acquisition function adopts an adaptive confidence boundary strategy to sample in unknown areas or explore near the currently considered global optimal solution, which is expressed as:

[0122] AUCB(x)=μ(x)+εσ(x);

[0123] In the formula, the adaptive adjustment parameter s=|y max +D max | / (y max +D max ), y max Indicates the maximum value in the current fitting curve, D max It represents the maximum value in the current observation set, and s is the coefficient of difference, which is used to further represent y max With D max degree of difference.

[0124] In a third aspect, the present invention further provides a cross-connect cable fault diagnosis device based on improved Bayesian optimization XGBoost, wherein the cross-connect cable fault diagnosis device based on improved Bayesian optimization XGBoost comprises: a memory, a processor, and a cross-connect cable fault diagnosis program stored in the memory and capable of running on the processor based on improved Bayesian optimization XGBoost, wherein the fault diagnosis program implements the steps of the cross-connect cable fault diagnosis method based on improved Bayesian optimization XGBoost as described above when executed by the processor.

[0125] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0126] The present invention optimizes the hyperparameters of XGBoost using an improved Bayesian algorithm to obtain an optimized XGBoost model, and uses the trained XGBoost model to diagnose faults. Specifically, first, the present invention uses a Gaussian process to model the objective function, and the function that obeys its distribution is composed of a mean function and a kernel function. The present invention first abandons the use of a single kernel function and optimizes the improved kernel function, that is, the traditional Marton kernel function and the square exponential kernel function, two more typical single kernel functions, are multiplied and compounded to obtain a more accurate fitting result; the optimization method used is to use the Markov chain Monte Carlo method (MCMC) improved based on flower pollination (FPA); secondly, the acquisition function in the Bayesian optimization algorithm is optimized, thereby effectively improving the accuracy and efficiency of model diagnosis.

[0127] Compared with other mainstream classification models, XGBoost has better fault diagnosis effect, and combined with the improved Bayesian algorithm to optimize XGBoost's hyperparameters, the accuracy and efficiency of fault detection can be improved.

[0128] In summary, the present application first improves the Bayesian optimization algorithm. For the Gaussian process in the prior function, the Marton kernel function and the square exponential kernel function are multiplied and compounded as the kernel function, and the parameters of the kernel function are optimized using the improved Markov chain Monte Carlo method. Secondly, for the acquisition function, the adjustment parameters in the traditional confidence boundary strategy are improved. Finally, the improved Bayesian optimization algorithm is used to intelligently search and optimize the hyperparameters of XGBoost, which effectively improves the prediction performance and generalization ability of the model, and realizes efficient and accurate diagnosis of various faults of cross-connected cables. The implementation of the present invention will help to improve the automation level of cable fault handling and reduce operation and maintenance costs. It has important practical application value and promotion prospects and is of great significance to ensuring the safe and stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0129] Figure 1 A flow chart of a cross-connect cable fault diagnosis method based on improved Bayesian optimization XGBoost according to an embodiment of the present invention;

[0130] Figure 2 The present invention is a flowchart of a method for diagnosing cross-connect cable faults according to an embodiment of the present invention. DETAILED DESCRIPTION

[0131] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0132] like Figure 1 As shown, the present invention provides a cross-connection cable fault diagnosis method based on improved Bayesian optimization XGBoost, the method comprising:

[0133] S1 constructs a multidimensional feature vector I using the collected circulation samples, constructs circulation sample data according to multiple multidimensional feature vectors I, and divides the circulation sample data into a training set and a test set according to a certain ratio. The circulation samples are obtained from the monitoring background by installing an online monitoring device for sheath circulation at the circulation monitoring point of the cable line, and then constructing a multidimensional feature vector I with all the measured circulation amplitudes;

[0134] S2 sets the range of the XGBoost model hyperparameters, and pre-trains the XGBoost model based on the training set using an improved Bayesian optimization algorithm to find the optimal hyperparameter combination;

[0135] The improved Bayesian optimization algorithm includes optimizing the parameters in the prior function and optimizing the acquisition function, wherein optimizing the parameters in the prior function includes: using a Gaussian process to model the objective function, the function obeying the distribution of the objective function includes a mean function and a kernel function, the kernel function is a composite kernel function obtained by multiplying two single kernel functions, namely, a Marton kernel function and a square exponential kernel function, and optimizing one or more parameters corresponding to the composite kernel function using an improved Markov chain Monte Carlo method based on flower pollination, calculating the distribution of the parameters to be optimized, and then calculating the corresponding expected values, thereby obtaining the optimized parameter values; the optimization of the acquisition function includes: optimizing and improving the adjustment parameters therein using an adaptive confidence boundary strategy;

[0136] S3 uses the optimal hyperparameter combination as the parameters in the final model and reuses the training set to train the XGBoost model;

[0137] S4 uses the trained XGBoost model to predict the fault type of the test set, outputs the prediction results and analyzes the diagnosis effect.

[0138] In the above-mentioned cross-connection cable fault diagnosis method based on improved Bayesian optimization XGBoost, in step 2, the construction of the multidimensional feature vector is achieved by installing an online monitoring device for the sheath circulation at the cable line circulation monitoring point, obtaining relevant circulation data from the monitoring background, and measuring the circulation on the left and right sides of the metal sheath lead-out line in the cross-connection box at the same time, and constructing a multidimensional feature vector with all the measured circulation amplitudes.

[0139] In the above-mentioned cross-connection cable fault diagnosis method based on improved Bayesian optimization XGBoost, in step 2, the XGBoost model uses classification regression tree as the base learner, the objective function includes loss function and regularization term, the regularization term is used to punish the model complexity to avoid overfitting, and the overall prediction effect is improved by integrating the prediction results of a series of CARTs. Among them, the Bayesian optimization algorithm is an optimization algorithm based on probability distribution, that is, the prior distribution model of the objective function is assumed in advance, and then the model is continuously optimized using sample data, and finally the posterior distribution model of the objective function is obtained. Among them, for the prior function, the Marton kernel function and the square exponential kernel function are multiplied and compounded as the kernel function of the Gaussian process to model the objective function. GP can be regarded as a generalized extension of multivariate Gaussian distribution in infinite dimensions. The function that obeys its distribution is composed of a mean function and a kernel function. The kernel function is used to represent the similarity of two data; the acquisition function adopts an adaptive confidence boundary strategy to sample in unknown areas or explore near the currently considered global optimal solution.

[0140] In the above-mentioned cross-connection cable fault diagnosis method based on improved Bayesian optimization XGBoost, in step 2, the acquisition function adopts an adaptive confidence boundary strategy. When the adaptive adjustment parameter tends to 1, the acquisition function based on the adaptive confidence boundary strategy is more inclined to the exploration state; when it tends to 0, the acquisition function is more inclined to the development state.

[0141] Specifically, in order to implement the above method, this embodiment provides the following specific implementation methods, such as Figure 2 As shown, this method can effectively diagnose the fault of the cross-connection cable. The following are detailed descriptions:

[0142] The first step is to use the collected circulation sample data to construct a multidimensional feature vector I, and divide the sample data into a training set and a test set according to a certain ratio (such as 4:1).

[0143] The construction of the multidimensional feature vector in the first step method is to install an online monitoring device for the sheath circulating current at the cable line circulating current monitoring point, obtain relevant circulating current data from the monitoring background, and measure the circulating current on both sides of the metal sheath lead-out line in the cross-connection box at the same time in order to effectively distinguish the fault type, and construct a multidimensional feature vector I with all the measured circulating current amplitudes.

[0144] The second step is to set the range of XGBoost model hyperparameters, use the improved Bayesian optimization algorithm to pre-train the XGBoost model, and find the optimal hyperparameter combination by continuously optimizing the acquisition function of the Gaussian process.

[0145] In the second step, the Extreme Gradient Boosting (XGBoost) algorithm uses the Classification and Regression Tree (CART) as the base learner, and improves the overall prediction effect by integrating a series of CART prediction results. The classification principle of XGBoost is introduced in detail below:

[0146] Assume that the training data set D = {(x i ,y i )}(x i ∈R m ,y i ∈R), where x i is a feature vector with m dimensions, y i is the sample label. For cross-connection system fault diagnosis, x i That is, the characteristic vector of each sheath circulation, y i is the corresponding fault category, which is composed of K CART trees. Then the XGBoost model is:

[0147]

[0148] F={f(x)=ω q(x)},q:R m →T,ω∈R T (2)

[0149] In the formula, is the final prediction value of the XGBoost model for the i-th sample; f k is the kth tree. Each CART will map each sample to a leaf node according to the sample characteristics and classification rules. Each leaf node corresponds to a weight score ω, which is the predicted value of the sample falling on the corresponding leaf node. q is the structural vector corresponding to each CART, ω q(x) is the predicted value of CART for sample x; F is the set formed by K CART trees; T is the total number of leaf nodes; for sample x i , when all CART trees have completed classification, i The weights of the leaf nodes are added together to get the sample x i The final predicted value.

[0150] In order to train each CART tree well and make the XGBoost model have excellent performance, a regularization term (penalty term) is added to the objective function of XGBoost. The specific function is as follows:

[0151]

[0152]

[0153] The above objective function consists of two parts. The first term is the loss function, which is a differentiable convex function that can represent the error between the model prediction value and the true value. The second term is the regularization term, which is used to penalize the model complexity to avoid overfitting. φ is the set of functions in the model. In the formula, γ and λ are two parameters that control complexity. The larger the values ​​of the two, the less likely it is to overfit. Among them, γ suppresses the complexity of the model by controlling the number of leaf nodes. The second term on the right is the L2 regularization term, and λ reduces the complexity by controlling the weight score of the leaf nodes.

[0154] XGBoost starts to optimize from the first sub-model during model training, finds the optimal sub-model under the current conditions, and then optimizes the subsequent sub-models in sequence. When training the t-th sub-model f t , the first t-1 sub-models are known. Therefore, the goal of the t-th round of optimization is to find an optimal f t , so that the objective function value is minimized, then the new model after the tth round of training is the t-1th round model plus the optimal f found tThe predicted value during model training and the objective function L of the tth round of optimization (t) It can be expressed as follows:

[0155]

[0156] In the formula, is the predicted value of the model after the tth round of training.

[0157] Perform a second-order Taylor expansion on the objective function, ignoring the constant term, and the objective function formula is:

[0158]

[0159] f t (x i ) is essentially a tree model, so x i After mapping, it will definitely fall on a node in the tree. Define a sample set I of leaf node j j ,Right now

[0160] I j ={i|q(x i )=j}(7)

[0161] Rewrite the objective function as

[0162]

[0163] For a fixed tree structure, the optimal weight of its leaf node j is

[0164]

[0165] The leaf node weight depends on the first-order and second-order gradient statistics and the L2 regularization coefficient λ. After finding the optimal weight, it is easy to get the optimal objective function value:

[0166]

[0167] The above formula is a scoring function for evaluating the quality of a tree. The smaller the value, the better the tree model. Therefore, the best tree model can be selected by calculating the score of each tree model. XGBoost starts from the root node, calculates the score before and after the node split, and traverses all the split points of all features to find the best split point and the best feature.

[0168] The present invention will improve the Bayesian Optimization (BO) algorithm. First, the algorithm is an optimization algorithm based on probability distribution, that is, the prior distribution model of the objective function is assumed in advance, and then the model is continuously optimized using sample data, and finally the posterior distribution model of the objective function is obtained. Its main process is the construction of the prior function and the acquisition function.

[0169] The prior function in the improved Bayesian optimization algorithm uses Gaussian Process (GP) to model the objective function. GP can be regarded as a generalized extension of multivariate Gaussian distribution in infinite dimensions. The function that obeys its distribution consists of a mean function and a kernel function, that is,

[0170] f(x)~GP(m(x),k(x,x′))(11)

[0171] Where, mean function m(x) = E[f(x)], kernel function k(x,x′) = E[(f(x)-m(x))(f(x′)-m(x′))], for the hyperparameter optimization process, x is a series of hyperparameter combinations, and f(x) is the final evaluation index score. Without loss of generality, let m(x) = 0.

[0172] If the output value on the training set is f=(f1,f2,...,f n ) T , the predicted function value f(x * ) = f * , then there is the following joint distribution in the properties of the Gaussian process:

[0173]

[0174] Where K is the Gram matrix k(X,X), k * ={k(x * ,x1),k(x * ,x2),...,k(x * ,x n )} T ,

[0175] But the objective function f(x) and the observed value y=(y1,y2,...,y n ) T There is usually a certain error between them, which is represented by noise ∈, that is:

[0176] y=f(x)+∈(13)

[0177] And assume that the noise ∈ i independent and identically distributed, and Then the prior of the kernel function becomes:

[0178]

[0179] Then we get y and f * The joint distribution of is:

[0180]

[0181] Where I is the unit matrix, and They represent the variance of the objective function and the variance of the noise respectively,

[0182] According to the properties of multivariate normal distribution, we can get:

[0183] f * |x * ,y~N(μ(x * ),σ 2 (x * ))(16)

[0184] μ(x * ) is the final predicted value, σ 2 (x * ) is the prediction variance,

[0185] The Gaussian process observes the real data points on the unknown target function and uses the kernel function to measure the relationship between any two observation points to infer the distribution of the unknown target function. Compared with the single kernel function, the composite kernel function has more characteristics and can better fit different types of data. The traditional Marton kernel function and the square exponential kernel function, two more typical single kernel functions, are multiplied and compounded to obtain a more accurate fitting result. The Marton-square exponential composite function is:

[0186]

[0187] Where r = xx′, represents the Euclidean distance between sample points, l is the scale parameter, and α is the smoothing parameter.

[0188] In this embodiment, the parameters of the kernel function, such as the scale parameter, the objective function variance and the noise variance, are optimized using the Markov chain Monte Carlo method (MCMC) improved based on flower pollination (FPA), the distribution of the parameters is obtained by the acceptance-rejection method, and then their expected values ​​are calculated to obtain the optimized parameter values.

[0189] The acceptance probability is:

[0190]

[0191] Where p(·) is the given prior distribution and q(·) is the given proposal distribution.

[0192] The specific steps for optimizing the parameter θ are:

[0193] (1) Arbitrarily select the initial parameter θ0;

[0194] (2) Loop execution for i=1,2,...,k:

[0195] Let the currently accessed parameter value θ i-1 =θ, randomly extract the next candidate parameter value θ′ according to the proposed distribution.

[0196] According to the basic principle of FPA, when the conversion probability between global optimization and local optimization p>0.8, the next candidate parameter θ′ is calculated globally as:

[0197]

[0198] In the formula, is the sample generated by the mth Markov chain at the i-th iteration, is the optimal sampling point among the currently generated samples, L is the search step length, and its calculation formula is as follows:

[0199]

[0200] In the formula, u=i -λ , Γ is the standard gamma function, λ is usually 1-5, v~N(0,1).

[0201] If p≤0.8, the next candidate parameter θ′ is:

[0202]

[0203] Where ξ is a random value that follows a uniform distribution on [0,1], and are the samples generated by the m-th Markov chain at the j-th and k-th iterations, respectively. and are equivalent to random solutions in the model.

[0204] Update acceptance probability:

[0205]

[0206] Randomly select u from uniform distribution, u∈(0,1). If u≤α(θ,θ′), then accept the transfer θ i =θ′, otherwise the transfer is not accepted, θ i =θ i-1 After the loop is executed, we can get the posterior samples θ1, θ2..., θ k .

[0207] (3) The expected value of the parameter distribution is calculated as:

[0208]

[0209] The improved Markov Chain Monte Carlo method can increase the efficiency of optimizing the candidate parameters θ′ and improve the acceptance rate.

[0210] At the same time, the present invention also improves the acquisition function. Specifically, the acquisition function in the improved Bayesian optimization algorithm adopts the adaptive upper confidence boundary strategy (The Adaptive Upper Confidence Bbound Algorithm, AUCB). By improving the adjustment parameters in the traditional confidence boundary strategy, the improved adjustment parameters have the ability of self-adaptive updating during the calculation process. The acquisition function is expressed as:

[0211] AUCB(x)=μ(x)+εσ(x); (24)

[0212] In the formula, the adaptive adjustment parameter s=|y max +D max | / (y max +D max ). max Indicates the maximum value in the current fitting curve, D max It represents the maximum value in the current observation set, and s is the coefficient of difference, which is used to further represent y max With D max degree of difference.

[0213] When max With D max When it is infinitely close, ε tends to 1. At this time, the acquisition function based on the adaptive confidence boundary strategy is more inclined to the exploration state; when y max With D max When it is infinitely far away, ε tends to 0, and the acquisition function is more inclined to the development state.

[0214] The third step is to use the optimal hyperparameter combination obtained in the second step as the parameters in the final model, and re-use the training set to train the XGBoost model.

[0215] Step 4: Use the trained XGBoost model to predict the fault type of the test set, output the prediction results and analyze the diagnostic effect.

[0216] The present invention proposes a cross-connected high-voltage cable fault diagnosis method based on improved Bayesian optimization XGBoost. The implementation principle of the XGBoost algorithm and the improved Bayesian optimization algorithm is introduced in detail. First, according to the actual project and the fault circulation characteristics, a multidimensional feature vector based on the circulation amplitude of the monitoring point is constructed, and the improved Bayesian algorithm is used to optimize the hyperparameters of XGBoost to obtain the optimized XGBoost model. Finally, the trained XGBoost model is used to diagnose the fault. Therefore, this diagnostic model can effectively identify various types of faults and can effectively improve the accuracy of model diagnosis.

[0217] Secondly, the present invention also provides a cross-connection cable fault diagnosis method system based on improved Bayesian optimization XGBoost, the system comprising:

[0218] The acquisition module is used to construct a multidimensional feature vector I using the collected circulation sample data, and divide the sample data into a training set and a test set according to a certain ratio. The circulation sample data is obtained from the monitoring background by installing an online monitoring device for sheath circulation at the cable line circulation monitoring point, and then constructing a multidimensional feature vector I with all the measured circulation amplitudes;

[0219] A model pre-training module, used to set the range of XGBoost model hyperparameters, and pre-train the XGBoost model based on the training set using an improved Bayesian optimization algorithm to find the optimal hyperparameter combination;

[0220] The improved Bayesian optimization algorithm includes optimizing the parameters in the prior function and optimizing the acquisition function, wherein optimizing the parameters in the prior function includes: using a Gaussian process to model the objective function, the function obeying the distribution of the objective function includes a mean function and a kernel function, the kernel function is a composite kernel function obtained by multiplying two single kernel functions, namely, a Marton kernel function and a square exponential kernel function, and optimizing one or more parameters corresponding to the composite kernel function using an improved Markov chain Monte Carlo method based on flower pollination, calculating the distribution of the parameters to be optimized, and then calculating the corresponding expected values, thereby obtaining the optimized parameter values; the optimization of the acquisition function includes: optimizing and improving the adjustment parameters therein using an adaptive confidence boundary strategy;

[0221] The model training module is used to use the optimal hyperparameter combination as the parameters in the final model and reuse the training set to train the XGBoost model;

[0222] The prediction module is used to use the trained XGBoost model to predict the fault type of the test set, output the prediction results and analyze the diagnosis effect.

[0223] Further, including:

[0224] In the model pre-training module, a Gaussian process is selected to model the objective function, and the function that obeys the distribution of the objective function includes a mean function and a kernel function, specifically including:

[0225] The Gaussian process GP is used to model the objective function. GP is regarded as a generalized extension of the multivariate Gaussian distribution in infinite dimensions. The function that obeys its distribution consists of a mean function and a kernel function, namely:

[0226] f(x)~GP(m(x),k(x,x′));

[0227] Wherein, mean function m(x) = E[f(x)], kernel function k(x,x′) = E[(f(x)-m(x))(f(x′)-m(x′))], which is used to indicate the similarity between two data. For the hyperparameter optimization process, x is a series of hyperparameter combinations, and f(x) is the final evaluation index score. Therefore, without loss of generality, let m(x) = 0;

[0228] If the output value on the training set is f=(f1,f2,...,f n ) T , the predicted function value f(x * ) = f * , then there is the following joint distribution in the properties of the Gaussian process:

[0229]

[0230] Where K is the Gram matrix k(X,X), k * ={k(x * ,x1),k(x * ,x2),...,k(x * ,x n )} T ,

[0231] But the objective function f(x) and the observed value y=(y1,y2,...,y n ) T There is usually an error between them, which is represented by noise ∈, that is:

[0232] y=f(x)+∈;

[0233] And assume that the noise ∈ i independent and identically distributed, and Then the prior of the kernel function becomes:

[0234]

[0235] Then we get y and f * The joint distribution of is:

[0236]

[0237] Where I is the unit matrix, and They represent the variance of the objective function and the variance of the noise respectively,

[0238] According to the properties of multivariate normal distribution, we get:

[0239] f * |x * ,y~N(μ(x * ),σ 2 (x * ));

[0240] μ(x * ) is the final predicted value, σ 2 (x * ) is the prediction variance,

[0241] The traditional Marton kernel function and the square exponential kernel function are multiplied and compounded to obtain a more accurate fitting result. The Marton-square exponential composite function is:

[0242]

[0243] Where r = xx′, represents the Euclidean distance between sample points, l is the scale parameter, and α is the smoothing parameter.

[0244] Further, including:

[0245] One or more parameters corresponding to the composite kernel function are optimized using an improved Markov chain Monte Carlo method based on flower pollination, including:

[0246] Arbitrarily select the initial parameter θ0 to be optimized and execute it in a loop i=1,2,...,k times;

[0247] Let the value of the parameter to be optimized for the current number of visits be θ i-1 =θ, randomly select the next candidate parameter value θ′ according to the proposed distribution;

[0248] According to the flower pollination FPA, when the conversion probability between global optimization and local optimization p>0.8, the next candidate parameter θ′ is calculated globally as:

[0249]

[0250] In the formula, is the sample generated by the mth Markov chain at the i-th iteration, is the optimal sampling point among the currently generated samples, L is the search step length, and its calculation formula is as follows:

[0251]

[0252] In the formula, u=i -λ , Γ is the standard gamma function, λ is usually 1-5, v~N(0,1);

[0253] If p≤0.8, the next candidate parameter θ′ is:

[0254]

[0255] Where ξ is a random value that follows a uniform distribution on [0,1], and are the samples generated by the m-th Markov chain at the j-th and k-th iterations, respectively. and are equivalent to random solutions in the model;

[0256] Using the updated acceptance probability, we get the posterior samples θ1, θ2..., θ k ;

[0257] Therefore, the expected value of the distribution of the parameters to be optimized is expressed as:

[0258]

[0259] Further, including:

[0260] The acceptance probability of the update is expressed as:

[0261]

[0262] Where p(·) is the given prior distribution and q(·) is the given proposed distribution;

[0263] Randomly select u from uniform distribution, u∈(0,1);

[0264] If u≤α(θ,θ′), then accept the transfer θ i =θ′, otherwise, the transfer is not accepted, and θ i =θ i-1 After the loop is executed, we can get the posterior samples θ1, θ2..., θ k .

[0265] Further, including:

[0266] The adaptive confidence boundary strategy is used to optimize and improve the adjustment parameters, specifically including:

[0267] The acquisition function adopts an adaptive confidence boundary strategy to sample in unknown areas or explore near the currently considered global optimal solution, which is expressed as:

[0268] AUCB(x)=μ(x)+εσ(x);

[0269] In the formula, the adaptive adjustment parameter s=|y max +D max | / (y max +D max ), y max Indicates the maximum value in the current fitting curve, D max It represents the maximum value in the current observation set, and s is the coefficient of difference, which is used to further represent y max With D max degree of difference.

[0270] Finally, the present invention also provides a cross-connected cable fault diagnosis device based on improved Bayesian optimization XGBoost, and the cross-connected cable fault diagnosis device based on improved Bayesian optimization XGBoost includes: a memory, a processor, and a cross-connected cable fault diagnosis program stored in the memory and based on improved Bayesian optimization XGBoost on the processor. When the fault diagnosis program is executed by the processor, the steps of the cross-connected cable fault diagnosis method based on improved Bayesian optimization XGBoost as described above are implemented.

[0271] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0272] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0273] Obviously, those skilled in the art can make various changes and modifications to the embodiments of the present invention without departing from the spirit and scope of the embodiments of the present invention. Thus, if these modifications and variations of the embodiments of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A cross-connection cable fault diagnosis method based on improved Bayesian optimization XGBoost, characterized in that: The method includes: The collected circulation samples are used to construct a multidimensional feature vector, and multiple multidimensional feature vectors I constitute the entire circulation sample data, and the circulation sample data is divided into a training set and a test set according to a certain ratio; Setting the range of XGBoost model hyperparameters, and pre-training the XGBoost model based on the training set using an improved Bayesian optimization algorithm to find the optimal hyperparameter combination; The improved Bayesian optimization algorithm includes optimizing the parameters in the prior function and optimizing the acquisition function, wherein optimizing the parameters in the prior function includes: using a Gaussian process to model the objective function, the function obeying the distribution of the objective function includes a mean function and a kernel function, the kernel function is a composite kernel function obtained by multiplying two single kernel functions, namely, a Marton kernel function and a square exponential kernel function, and optimizing one or more parameters corresponding to the composite kernel function using an improved Markov chain Monte Carlo method based on flower pollination, calculating the distribution of the parameters to be optimized, and then calculating the corresponding expected values, thereby obtaining the optimized parameter values; the optimization of the acquisition function includes: optimizing and improving the adjustment parameters therein using an adaptive confidence boundary strategy; The optimal hyperparameter combination is used as the parameters in the final model, and the training set is reused to train the XGBoost model; The trained XGBoost model is used to predict the fault type of the test set, output the prediction results and analyze the diagnostic effect.

2. The cross-connect cable fault diagnosis method based on improved Bayesian optimization XGBoost according to claim 1 is characterized in that: The Gaussian process is used to model the objective function, and the function that obeys the distribution of the objective function includes a mean function and a kernel function, specifically including: The Gaussian process GP is used to model the objective function. GP is regarded as a generalized extension of the multivariate Gaussian distribution in infinite dimensions. The function that obeys its distribution consists of a mean function and a kernel function, namely: f(x)~GP(m(x),k(x,x′)); Wherein, mean function m(x) = E[f(x)], kernel function k(x,x′) = E[(f(x)-m(x))(f(x′)-m(x′))], which is used to indicate the similarity between two data. For the hyperparameter optimization process, x is a series of hyperparameter combinations, and f(x) is the final evaluation index score. Therefore, without loss of generality, let m(x) = 0; If the output value on the training set is f=(f1,f2,...,f n ) T , the predicted function value f(x * ) = f * , then there is the following joint distribution in the properties of the Gaussian process: Where K is the Gram matrix k(X,X), k * ={k(x * ,x1),k(x * ,x2),...,k(x * ,x n )} T , But the objective function f(x) and the observed value y=(y1,y2,...,y n ) T There is usually an error between them, which is represented by noise ∈, that is: y=f(x)+∈; And assume that the noise ∈ i independent and identically distributed, and Then the prior of the kernel function becomes: Then we get y and f * The joint distribution of is: Where I is the unit matrix, and They represent the variance of the objective function and the variance of the noise respectively, According to the properties of multivariate normal distribution, we get: f * |x * ,y~N(μ(x * ),s 2 (x * )); μ(x * ) is the final predicted value, σ 2 (x * ) is the prediction variance, The traditional Marton kernel function and the square exponential kernel function are multiplied and compounded to obtain a more accurate fitting result. The Marton-square exponential composite function is: Where r = xx′, represents the Euclidean distance between sample points, l is the scale parameter, and α is the smoothing parameter.

3. The cross-connect cable fault diagnosis method based on improved Bayesian optimization XGBoost according to claim 2 is characterized in that: One or more parameters corresponding to the composite kernel function are optimized using an improved Markov chain Monte Carlo method based on flower pollination, including: Arbitrarily select the initial parameter θ0 to be optimized and execute it in a loop i=1,2,...,k times; Let the value of the parameter to be optimized for the current number of visits be θ i-1 =θ, randomly select the next candidate parameter value θ′ according to the proposed distribution; According to the flower pollination FPA, when the conversion probability between global optimization and local optimization p>0.8, the next candidate parameter θ′ is calculated globally as: In the formula, is the sample generated by the mth Markov chain at the i-th iteration, is the optimal sampling point among the currently generated samples, L is the search step length, and its calculation formula is as follows: In the formula, u=i -λ , Γ is the standard gamma function, λ is usually 1-5, v~N(0,1); If p≤0.8, the next candidate parameter θ′ is: Where ξ is a random value that follows a uniform distribution on [0,1], and are the samples generated by the m-th Markov chain at the j-th and k-th iterations, respectively. and are equivalent to random solutions in the model; Using the updated acceptance probability, we get the posterior samples θ1, θ2..., θ k ; Therefore, the expected value of the distribution of the parameters to be optimized is expressed as:

4. The cross-connection cable fault diagnosis method based on improved Bayesian optimization XGBoost according to claim 3 is characterized in that: The acceptance probability of the update is expressed as: Where p(·) is the given prior distribution and q(·) is the given proposed distribution; Randomly select u from uniform distribution, u∈(0,1); If u≤α(θ,θ′), then accept the transfer θ i =θ′, otherwise, the transfer is not accepted, and θ i =θ i-1 After the loop is executed, we can get the posterior samples θ1, θ2..., θ k .

5. The cross-connect cable fault diagnosis method based on improved Bayesian optimization XGBoost according to claim 4 is characterized in that: The adaptive confidence boundary strategy is used to optimize and improve the adjustment parameters, specifically including: The acquisition function adopts an adaptive confidence boundary strategy to sample in unknown areas or explore near the currently considered global optimal solution, which is expressed as: AUCB(x)=μ(x)+εσ(x); In the formula, the adaptive adjustment parameter s=|y max +D max | / (y max +D max ), y max Indicates the maximum value in the current fitting curve, D max It represents the maximum value in the current observation set, and s is the coefficient of difference, which is used to further represent y max With D max The observation set refers to the set of adjustment parameters and their corresponding objective function values ​​that have been evaluated during the optimization process. In the context of Bayesian optimization, the fitting curve usually refers to the prediction curve obtained after modeling the objective function using a Gaussian process.

6. The cross-connection cable fault diagnosis method based on improved Bayesian optimization XGBoost according to claim 5 is characterized in that: The adaptive adjustment parameters and y max , D max The relationships include: When max With D max When it is infinitely close, ε tends to 1, and the acquisition function based on the adaptive confidence boundary strategy is more inclined to the exploration state; When max With D max When it is infinitely far away, ε tends to 0, and the acquisition function is more inclined to the development state.

7. A cross-connection cable fault diagnosis method system based on improved Bayesian optimization XGBoost, characterized in that: The system includes: The acquisition module is used to construct a multidimensional feature vector using the collected circulation samples, and multiple multidimensional feature vectors I constitute the entire circulation sample data, and the circulation sample data is divided into a training set and a test set according to a certain ratio; A model pre-training module, used to set the range of XGBoost model hyperparameters, and pre-train the XGBoost model based on the training set using an improved Bayesian optimization algorithm to find the optimal hyperparameter combination; The improved Bayesian optimization algorithm includes optimizing the parameters in the prior function and optimizing the acquisition function, wherein optimizing the parameters in the prior function includes: using a Gaussian process to model the objective function, the function obeying the distribution of the objective function includes a mean function and a kernel function, the kernel function is a composite kernel function obtained by multiplying two single kernel functions, namely, a Marton kernel function and a square exponential kernel function, and optimizing one or more parameters corresponding to the composite kernel function using an improved Markov chain Monte Carlo method based on flower pollination, calculating the distribution of the parameters to be optimized, and then calculating the corresponding expected values, thereby obtaining the optimized parameter values; the optimization of the acquisition function includes: optimizing and improving the adjustment parameters therein using an adaptive confidence boundary strategy; The model training module is used to use the optimal hyperparameter combination as the parameters in the final model and reuse the training set to train the XGBoost model; The prediction module is used to use the trained XGBoost model to predict the fault type of the test set, output the prediction results and analyze the diagnosis effect.

8. The cross-connection cable fault diagnosis method system based on improved Bayesian optimization XGBoost according to claim 7 is characterized in that: In the model pre-training module, a Gaussian process is selected to model the objective function, and the function that obeys the distribution of the objective function includes a mean function and a kernel function, specifically including: The Gaussian process GP is used to model the objective function. GP is regarded as a generalized extension of the multivariate Gaussian distribution in infinite dimensions. The function that obeys its distribution consists of a mean function and a kernel function, namely: f(x)~GP(m(x),k(x,x′)); Wherein, mean function m(x) = E[f(x)], kernel function k(x,x′) = E[(f(x)-m(x))(f(x′)-m(x′))], which is used to indicate the similarity between two data. For the hyperparameter optimization process, x is a series of hyperparameter combinations, and f(x) is the final evaluation index score. Therefore, without loss of generality, let m(x) = 0; If the output value on the training set is f=(f1,f2,...,f n ) T , the predicted function value f(x * ) = f * , then there is the following joint distribution in the properties of the Gaussian process: Where K is the Gram matrix k(X,X), k * ={k(x * ,x1),k(x * ,x2),...,k(x * ,x n )} T , But the objective function f(x) and the observed value y=(y1,y2,...,y n ) T There is usually an error between them, which is represented by noise ∈, that is: y=f(x)+∈; And assume that the noise ∈ i independent and identically distributed, and Then the prior of the kernel function becomes: Then we get y and f * The joint distribution of is: Where I is the unit matrix, and They represent the variance of the objective function and the variance of the noise respectively, According to the properties of multivariate normal distribution, we get: μ(x * ) is the final predicted value, σ 2 (x * ) is the prediction variance, The traditional Marton kernel function and the square exponential kernel function are multiplied and compounded to obtain a more accurate fitting result. The Marton-square exponential composite function is: Where r = xx′, represents the Euclidean distance between sample points, l is the scale parameter, and α is the smoothing parameter.

9. The cross-connect cable fault diagnosis system based on improved Bayesian optimization XGBoost according to claim 8, characterized in that: One or more parameters corresponding to the composite kernel function are optimized using an improved Markov chain Monte Carlo method based on flower pollination, including: Arbitrarily select the initial parameter θ0 to be optimized and execute it in a loop i=1,2,...,k times; Let the value of the parameter to be optimized for the current number of visits be θ i-1 =θ, randomly select the next candidate parameter value θ′ according to the proposed distribution; According to the flower pollination FPA, when the conversion probability between global optimization and local optimization p>0.8, the next candidate parameter θ′ is calculated globally as: In the formula, is the sample generated by the mth Markov chain at the i-th iteration, is the optimal sampling point among the currently generated samples, L is the search step length, and its calculation formula is as follows: In the formula, u=i -λ , Γ is the standard gamma function, λ is usually 1-5, v~N(0,1); If p≤0.8, the next candidate parameter θ′ is: Where ξ is a random value that follows a uniform distribution on [0,1], and are the samples generated by the m-th Markov chain at the j-th and k-th iterations, respectively. and are equivalent to random solutions in the model; Using the updated acceptance probability, we get the posterior samples θ1, θ2..., θ k ; Therefore, the expected value of the distribution of the parameters to be optimized is expressed as:

10. The cross-connect cable fault diagnosis system based on improved Bayesian optimization XGBoost according to claim 9, characterized in that: The acceptance probability of the update is expressed as: Where p(·) is the given prior distribution and q(·) is the given proposed distribution; Randomly select u from uniform distribution, u∈(0,1); If u≤α(θ,θ′), then accept the transfer θ i =θ′, otherwise, the transfer is not accepted, and θ i =θ i-1 After the loop is executed, we can get the posterior samples θ1, θ2..., θ k .

11. The cross-connect cable fault diagnosis system based on improved Bayesian optimization XGBoost according to claim 10, characterized in that: The adaptive confidence boundary strategy is used to optimize and improve the adjustment parameters, specifically including: The acquisition function adopts an adaptive confidence boundary strategy to sample in unknown areas or explore near the currently considered global optimal solution, which is expressed as: AUCB(x)=μ(x)+εσ(x); In the formula, the adaptive adjustment parameter s=|y max +D max | / (y max +D max ), y max Indicates the maximum value in the current fitting curve, D max It represents the maximum value in the current observation set, and s is the coefficient of difference, which is used to further represent y max With D max The observation set refers to the set of adjustment parameters and their corresponding objective function values ​​that have been evaluated during the optimization process. In the context of Bayesian optimization, the fitting curve usually refers to the prediction curve obtained after modeling the objective function using a Gaussian process.

12. A cross-connection cable fault diagnosis device based on improved Bayesian optimization XGBoost, characterized in that: The cross-connect cable fault diagnosis device based on improved Bayesian optimization XGBoost includes: a memory, a processor, and a cross-connect cable fault diagnosis program stored in the memory and based on improved Bayesian optimization XGBoost on the processor. When the fault diagnosis program is executed by the processor, the steps of the cross-connect cable fault diagnosis method based on improved Bayesian optimization XGBoost as described in any one of claims 1 to 6 are implemented.

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