Catenary status assessment method and system based on responsive multivariable weighted optimization

Abstract

The present invention discloses a contact network state assessment method and system based on responsive multivariable weighted optimization; the method comprises collecting dynamic and static parameters of the contact network, calculating the subjective and objective weights of the parameters by adopting a hierarchical analysis method and an entropy weight method, and calculating the static composite weight by adopting a combined weight method; designing a dynamic response mechanism, and if the response conditions are met, dynamically optimizing the static composite weight to obtain a dynamic composite weight optimization result, and optimizing and updating an adaptive gain matrix; if the response conditions are not met, maintaining the dynamic composite weight optimization result and the adaptive gain matrix at the last response moment; and performing state assessment based on the dynamic composite weight optimization result; the method and system of the present invention are not only applicable to contact network state assessment, but can also be extended to other state assessment fields with parameter coupling relationships.

Description

Technical Field

[0001] The present invention belongs to the field of state assessment, and in particular relates to a contact network state assessment method and system based on responsive multivariable weighted optimization. Background Art

[0002] In the field of contact network health status monitoring, the existing evaluation system faces technical bottlenecks in two dimensions: one is the insufficient evaluation accuracy caused by parameter coupling, and the other is the insufficient real-time performance under resource constraints.

[0003] First, traditional static weight assignment methods (such as principal component analysis and entropy weighting) have inherent flaws when dealing with multi-parameter collaborative degradation. Because key parameters reflecting catenary evaluation indicators exhibit nonlinear coupling, current methods that weight individual parameters individually ignore this coupling, affecting the accuracy of the weighting and, consequently, the safety and reliability of railway operations.

[0004] Secondly, condition assessment calculation systems are limited by system bandwidth and communication resources. Traditional periodic sampling consumes significant network bandwidth and energy, even when the parameter states reflecting the condition assessment indicators change slightly, resulting in wasted communication resources and increased system operating costs. Therefore, a rational response mechanism is introduced to update the dynamic composite weight results only when significant changes in parameter states occur. This effectively reduces unnecessary communication and computation, thereby conserving system resources. Therefore, a method for contact network condition assessment is urgently needed that can integrate multi-source data, dynamically adjust weights, and consider the coupling relationships between parameters reflecting evaluation indicators, while minimizing computational and communication resource consumption to improve assessment accuracy and operational efficiency. Summary of the Invention

[0005] In order to solve the problems existing in the background technology, the purpose of the present invention is to provide a contact network state assessment method based on responsive multivariable weighted optimization, the method comprising the following steps:

[0006] Step (1): Establish a contact network status evaluation system, which includes an indicator level and a parameter level. The indicator level includes a safety indicator X1, a smoothness indicator X2, a current-collecting performance indicator X3, and an electrical performance indicator X4. The parameter level includes several parameters reflecting each indicator level.

[0007] Step (2): collecting dynamic and static parameters of the contact network and processing the dynamic and static parameters of the contact network;

[0008] Step (3): Obtain the subjective and objective weights of each parameter at the parameter level, and calculate the static composite weight of each parameter at the parameter level;

[0009] Step (4): collecting historical degradation data of the dynamic and static parameters of the contact network, using a time series neural network to establish a multivariate degradation rate prediction model for each indicator of the indicator level, and obtaining the expected degradation rate of each parameter through the multivariate degradation rate prediction model; designing a dynamic response mechanism, if the response condition is met, calculating the real-time degradation rate of the dynamic and static parameters of the contact network, based on the real-time degradation rate and the expected degradation rate, using a multivariate adaptive method to dynamically optimize the static composite weights of each parameter of the parameter level in step (3), and obtaining a dynamic composite weight optimization result, the multivariate adaptive method includes a first adaptive gain matrix and a second adaptive gain matrix; if the response condition is not met, maintaining the dynamic composite weight optimization result of the previous response moment;

[0010] Step (5): Based on the dynamic composite weight optimization results, the state of the contact network at the data collection point is evaluated;

[0011] Step (6): constructing a cost function based on the second-order degradation rate differential, combined with the dynamic response mechanism, if the response condition is met, then using the function extreme value method to solve the cost function, and optimizing and updating the first adaptive gain matrix and the second adaptive gain matrix in step (4); if the response condition is not met, then maintaining the adaptive gain matrix at the previous response moment;

[0012] Repeat the above steps (4) to (6) to obtain the contact network status evaluation results at the data collection points at different times.

[0013] Furthermore, in step (3), the subjective and objective weights of each parameter at the parameter level are obtained, and the static composite weights of each parameter at the parameter level are calculated, including the following steps:

[0014] Step (3.1): Use the hierarchical analysis method to calculate the parameter level reflection index The subjective weight of each parameter is used to quantify the relative importance of the parameters through expert scoring, and a judgment matrix is ​​constructed. ;

[0015] in, Indicates reflection indicators The parameter level judgment matrix, Indicates that the parameter level reflects the index No. Parameters and The relative importance of the parameters, , n Reflects indicators for the parameter level The number of parameters;

[0016] After the judgment matrix passes the consistency test, the eigenvector of the judgment matrix is ​​calculated and normalized to obtain the parameter level reflection index. No. Parameter AHP weights ;

[0017] Step (3.2): Use the entropy weight method to calculate the parameter level reflection index The objective weights of each parameter are constructed and the evaluation matrix is ​​normalized to obtain ;

[0018] in, Represents the reflection index after normalization The parameter level evaluation matrix of Indicates the Catenary section reflection index No. The normalized measurement values ​​of the parameters, , m is the number of catenary sections;

[0019] Calculate the parameter level reflection index No. Information entropy of parameters :

[0020] , ;

[0021] in, Indicates the Catenary section reflection index No. The probability of a parameter appearing;

[0022] Calculate the parameter level reflection index No. Entropy weight method weight of parameters :

[0023] ;

[0024] Step (3.3): Calculate the parameter level reflection index using the combined weight method No. Static composite weights of parameters :

[0025] .

[0026] Furthermore, the method for calculating the real-time degradation rate of the dynamic and static parameters of the contact network in step (4) is:

[0027] ;

[0028] in, Indicates that the parameter level reflects the index No. Parameters in The real-time degradation rate at the moment, express Time parameter level reflection indicators No. The measured values ​​of the parameters, , n Indicates that the parameter level reflects the index The number of parameters, Indicates the data collection time interval.

[0029] Furthermore, in step (4), a dynamic response mechanism is designed to determine the response mechanism for the next response time:

[0030] ;

[0031] in, Reflects indicators for the parameter level No. The first parameter i +1 response time, i is a positive integer; inf is the lower bound, is a set of integers; Indicates the data collection time, Reflects indicators for the parameter level No. The first parameter i Response time, Reflects indicators for the parameter level No. The response error of the parameters, Reflects indicators for the parameter level No. Parameters in The real-time degradation rate at the moment, Reflects indicators for the parameter level No. The parameter in i The real-time degradation rate of each response moment, is the response mechanism parameter; n Indicates that the parameter level reflects the index The number of parameters; is the preset threshold parameter; is an internal dynamic variable, and the update rule is:

[0032] ;

[0033] in, is the step size parameter, satisfying , ; is the initial value of the internal dynamic variable, is a constant;

[0034] Introducing indicator factors Indicates whether the response condition is met:

[0035] .

[0036] Furthermore, if the response condition is met in step (4), the real-time degradation rate of the dynamic and static parameters of the contact network is calculated. Based on the real-time degradation rate and the expected degradation rate, a multivariable adaptive method is used to dynamically optimize the static composite weights of the parameters of the parameter level in step (3). The method for obtaining the dynamic composite weight optimization result is as follows:

[0037] ;

[0038] in, Indicates the data collection time, express Time parameter level reflection indicators The weight vector of each parameter, express Time parameter level reflection indicators No. n The weight vector of the parameters, Indicates the Response time parameter level reflection index The weight vector of each parameter, Indicates the Response time parameter level reflection index No. n The weight vector of the parameters; n Indicates that the parameter level reflects the index The number of parameters; 、 are the first adaptive gain matrix and the second adaptive gain matrix respectively, 、 Indicates that the parameter level reflects the index No. The first parameter N The first adaptive gain, N Order second adaptive gain; Represented by vector , , , common N A vector of vectors, Indicated by , , , common n A vector consisting of elements, Indicates that the parameter level reflects the index No. The expected degradation rate and real-time degradation rate of the parameters are The degradation rate difference at each moment, express The first-order backward difference of Indicates that the parameter level reflects the index No. Parameters in The expected degradation rate at time t, Indicates that the parameter level reflects the index No. Parameters in The real-time degradation rate at the moment, , N is the adaptive method order, N is a positive integer; Represented by vector , , , common N A vector of vectors, Indicated by , , , common n A vector consisting of elements, express The first-order backward difference of Indicates that the parameter level reflects the index No. Parameters in Environmental factors at all times;

[0039] Parameter level reflection indicators No. The dynamic composite weight optimization results of the parameters are:

[0040] ;

[0041] in, Indicates that the parameter level reflects the index No. Dynamic composite weight optimization results of parameters; express No. elements, , Reflect indicators at the parameter level No. The static composite weight of the parameters.

[0042] Furthermore, in step (6), the cost function based on the second-order degradation rate differential is constructed, and combined with the dynamic response mechanism, if the response condition is met, the cost function is solved by the function extreme value method, and the method for optimizing and updating the first adaptive gain matrix and the second adaptive gain matrix in step (4) is:

[0043] Step (6.1): Construct a cost function based on the second-order degradation rate differential :

[0044] ;

[0045] in, Indicates that the parameter level reflects the index The parameters are The expected degradation rate vector at time , Indicates that the parameter level reflects the index No. Parameters in The expected degradation rate at time t; Indicates that the parameter level reflects the index The parameters are The real-time degradation rate vector at time , Indicates that the parameter level reflects the index No. Parameters in Real-time degradation rate at the moment; Indicates that the parameter level reflects the index The parameters are The degradation rate error at time The difference in degradation rate error at time , is a constant, is a constant, represents the two-norm;

[0046] Step (6.2): ​​Combined with the dynamic response mechanism, if the response conditions are met, the cost function in step (6.1) is solved using the function extreme value method to optimize and update the first adaptive gain matrix. , the second adaptive gain matrix :

[0047] ;

[0048] ;

[0049] in, and Respectively , In the The adaptive gain matrix of the response time; indicator factors representing response conditions; 、 , respectively the first and second step length factors; is a constant, is a constant; is the pseudo partial derivative matrix; Indicates that the parameter level reflects the index The parameters are The expected degradation rate vector at time t; Indicates that the parameter level reflects the index The parameters are The real-time degradation rate vector at time t; Indicates that the parameter level reflects the index The parameters are The degradation rate error at time The difference in degradation rate error at the moment; ; ; Indicates that the parameter level reflects the index The parameters are The degradation rate error at time The difference in degradation rate error at the moment; Indicates that the parameter level reflects the index The parameters are Environmental factors at all times and The difference in environmental factors at each moment; , N is the order of the adaptive method; express t Time parameter level reflection indicators The weight vector increments of each parameter;

[0050] Specifically, The calculation formula is as follows:

[0051] ;

[0052] The calculation formula is as follows:

[0053] .

[0054] Furthermore, the pseudo partial derivative matrix The calculation method is:

[0055] ;

[0056] in, for t The pseudo partial derivative matrix at time -1, Indicates that the parameter level reflects the index The parameters are The real-time degradation rate vector at time , , The calculation formula is , is a constant, is a constant.

[0057] The catenary condition assessment system based on responsive multivariable weighted optimization includes:

[0058] Catenary status assessment system establishment module: used to establish a catenary status assessment system, the assessment system includes an indicator level and a parameter level, the indicator level includes a safety indicator X1, a smoothness indicator X2, a current-collecting performance indicator X3, and an electrical performance indicator X4, and the parameter level includes several parameters reflecting each indicator level;

[0059] Data processing module: used for collecting dynamic and static parameters of the contact network and processing the dynamic and static parameters of the contact network;

[0060] Initial weight calculation module: used to obtain the subjective and objective weights of each parameter in the parameter level, and calculate the static composite weights of each parameter in the parameter level;

[0061] Responsive multivariable weight optimization module: used to collect historical degradation data of the dynamic and static parameters of the contact network, use time series neural network to establish multivariable degradation rate prediction models for each indicator of the indicator level, and obtain the expected degradation rate of each parameter through the multivariable degradation rate prediction model; design a dynamic response mechanism, if the response conditions are met, calculate the real-time degradation rate of the dynamic and static parameters of the contact network, based on the real-time degradation rate and the expected degradation rate, use a multivariable adaptive method to dynamically optimize the static composite weights of each parameter of the parameter level to obtain a dynamic composite weight optimization result, the multivariable adaptive method includes a first adaptive gain matrix and a second adaptive gain matrix; if the response conditions are not met, maintain the dynamic composite weight optimization result of the previous response moment;

[0062] Comprehensive status assessment module: used to assess the status of the contact network at the data collection point based on the dynamic composite weight optimization results;

[0063] Responsive gain matrix update module: used to construct a cost function based on the second-order degradation rate differential, combined with the dynamic response mechanism. If the response condition is met, the cost function is solved using the function extreme value method to optimize and update the first adaptive gain matrix and the second adaptive gain matrix in the responsive multivariable weight optimization module; if the response condition is not met, the adaptive gain matrix at the previous response moment is maintained;

[0064] Among them, the first adaptive gain matrix is ​​optimized and updated , the second adaptive gain matrix The method is:

[0065] ;

[0066] ;

[0067] in, and Respectively , In the The adaptive gain matrix of the response time; indicator factors representing response conditions; 、 , respectively the first and second step length factors; is a constant, is a constant, is the pseudo partial derivative matrix; Indicates that the parameter level reflects the index The parameters are The expected degradation rate vector at time t; Indicates that the parameter level reflects the index The parameters are The real-time degradation rate vector at time t; Indicates that the parameter level reflects the index The parameters are The degradation rate error at time The difference in degradation rate error at the moment; ; ; Indicates that the parameter level reflects the index The parameters are The degradation rate error at time The difference in degradation rate error at the moment; Indicates that the parameter level reflects the index The parameters are Environmental factors at all times and The difference in environmental factors at each moment; , N is the order of the adaptive method; express t Time parameter level reflection indicators The weight vector increments of each parameter .

[0068] Furthermore, the present invention adopts the following technical solutions:

[0069] A non-transitory computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned contact network state assessment method based on responsive multivariable weighted optimization.

[0070] Furthermore, the present invention adopts the following technical solutions:

[0071] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned contact network status assessment method based on responsive multivariable weighted optimization is implemented.

[0072] The beneficial technical effects of the present invention are:

[0073] (1) Reduce computing and communication resource consumption: On the one hand, the multivariable adaptive weight optimization method of the present invention has low computing resource consumption and is easy to apply in practice; on the other hand, a response mechanism is introduced to control the weight update timing through dynamic threshold judgment and internal variables. The dynamic composite weight result is updated only when the conditions in the response mechanism are met, which effectively reduces unnecessary communication and computing, thereby saving system resources;

[0074] (2) Improved assessment adaptability: First, the proposed second-order degradation rate differential cost function takes into account for the first time the influence of all associated parameters of a certain indicator at the parameter level, making the weight adjustment process dynamically stable; second, the dynamic degradation characteristics of the contact network parameters during long-term operation are fully utilized to optimize the weight adjustment, thereby improving the adaptability of the contact network status assessment;

[0075] (3) Reflecting the coupling relationship between parameters: The method of the present invention establishes a multivariate prediction model to predict the degradation rate, which solves the defect of the single variable method that ignores the correlation between parameters and improves the accuracy of weighting. The pseudo partial derivative matrix is ​​introduced to capture the time-varying characteristics of the parameter coupling reflecting the state evaluation index, and the coupling relationship between parameters is updated in real time, so that the model can adapt to the changes in parameter correlation caused by long-term aging of the contact network.

[0076] (4) Broaden the scope of application: The method and system proposed in the present invention are not only suitable for contact network status assessment, but can also be extended to other status assessment fields with parameter coupling relationships, and have broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 A contact network status assessment system provided by an embodiment of the present invention;

[0078] Figure 2 A schematic diagram of a process for obtaining dynamic composite weight optimization results in a method for evaluating contact network status based on responsive multivariable weighted optimization provided by an embodiment of the present invention;

[0079] Figure 3 A schematic diagram of the connection modules of the overhead line status assessment system based on responsive multivariable weighted optimization provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0080] The present invention discloses a contact network state assessment method and system based on responsive multivariable weighted optimization; the method comprises the following steps: collecting dynamic and static parameters of the contact network, calculating the subjective and objective weights of the parameters by adopting a hierarchical analysis method and an entropy weight method, and calculating the static composite weight by adopting a combined weight method; establishing a multivariable degradation rate prediction model for each indicator of the indicator hierarchy by adopting a time series neural network, and obtaining the expected degradation rate of each parameter by means of the multivariable degradation rate prediction model; designing a dynamic response mechanism, and if the response conditions are met, dynamically optimizing the static composite weight by adopting the method of the present invention to obtain a dynamic composite weight optimization result, and optimizing and updating the first adaptive gain matrix and the second adaptive gain matrix; if the response conditions are not met, maintaining the dynamic composite weight optimization result and the adaptive gain matrix at the previous response moment; and performing a state assessment on the contact network based on the dynamic composite weight optimization result until the assessment task is completed; the method and system of the present invention are not only applicable to contact network state assessment, but can also be extended to other state assessment fields with parameter coupling relationships.

[0081] The following is a further clear and complete description of the contact network state assessment method and system based on responsive multivariable weighted optimization provided by the present invention in conjunction with the accompanying drawings: Example 1

[0082] Figure 1 The contact network status assessment system provided for this embodiment; Figure 2 A schematic diagram of a process for obtaining dynamic composite weight optimization results in a contact network condition assessment method based on responsive multivariable weighted optimization provided in this embodiment; this embodiment provides a contact network condition assessment method based on responsive multivariable weighted optimization, the method comprising the following steps:

[0083] Step (1): Establish a contact network status evaluation system, the evaluation system includes an indicator level and a parameter level, the indicator level includes a safety indicator X1, a smoothness indicator X2, a current receiving performance indicator X3, and an electrical performance indicator X4, the parameter level includes a number of parameters reflecting each indicator level; specifically, the parameter level includes a pull-out value parameter X1 reflecting the safety indicator X1, 11, guide height parameter X 12 , reflecting the hard point parameter X2 of the smoothness index 21 , Span height parameter X 22 , reflecting the contact force parameter X3 of the current-carrying performance index 31 , offline rate parameter X 32 , reflecting the grid voltage parameter X4 of the electrical performance index X 41 , insulation resistance parameter X 42 ;

[0084] Step (2): collecting dynamic and static parameters of the contact network and processing the dynamic and static parameters of the contact network;

[0085] Step (3): Calculate the subjective and objective weights of each parameter at the parameter level using the analytic hierarchy process and entropy weight method respectively, and calculate the static composite weight of each parameter at the parameter level using the combined weight method;

[0086] Step (4): collecting historical degradation data of the dynamic and static parameters of the contact network, using a time series neural network to establish a multivariate degradation rate prediction model for each indicator of the indicator level, and obtaining the expected degradation rate of each parameter through the multivariate degradation rate prediction model; designing a dynamic response mechanism, if the response condition is met, calculating the real-time degradation rate of the dynamic and static parameters of the contact network, based on the real-time degradation rate and the expected degradation rate, using a multivariate adaptive method to dynamically optimize the static composite weights of each parameter of the parameter level in step (3), and obtaining a dynamic composite weight optimization result, the multivariate adaptive method includes a first adaptive gain matrix and a second adaptive gain matrix; if the response condition is not met, maintaining the dynamic composite weight optimization result of the previous response moment;

[0087] Step (5): Based on the dynamic composite weight optimization results, the state of the contact network at the data collection point is evaluated;

[0088] Step (6): constructing a cost function based on the second-order degradation rate differential, combined with the dynamic response mechanism, if the response condition is met, then using the function extreme value method to solve the cost function, and optimizing and updating the first adaptive gain matrix and the second adaptive gain matrix in step (4); if the response condition is not met, then maintaining the adaptive gain matrix at the previous response moment;

[0089] Repeat steps (4) to (6) to obtain the contact network status assessment results at all data collection points.

[0090] According to the contact network state assessment method based on responsive multivariable weighted optimization provided by this embodiment, in step (4), historical degradation data of the dynamic and static parameters of the contact network are collected, and a multivariable degradation rate prediction model for each indicator of the indicator hierarchy is established respectively using a time series neural network, and the expected degradation rate of each parameter is obtained through the multivariable degradation rate prediction model; the establishment process of the multivariable degradation rate prediction model specifically includes: establishing a multivariable degradation rate prediction model for the dynamic and static parameter groups with physical coupling relationships, specifically, establishing a safety index multivariable degradation rate prediction model for the pull-out value parameter and the conductor height parameter group reflecting the safety index, establishing a smoothness index multivariable degradation rate prediction model for the hard point parameter and the span height parameter group reflecting the smoothness index, establishing a current collecting performance index multivariable degradation rate prediction model for the contact force parameter and the offline rate parameter group reflecting the current collecting performance index, and establishing a current collecting performance index multivariable degradation rate prediction model for the grid voltage parameter and the insulation resistance parameter group reflecting the electrical performance index. The group established a multivariate degradation rate prediction model for electrical performance indicators; collected historical time series data of dynamic and static parameter groups and their environmental factor data, aligned these data according to a unified time step, and constructed a multidimensional training data set containing time series features; the time series neural network adopted a multivariate LSTM-attention mechanism hybrid model, the input layer of which received a multidimensional tensor composed of degradation rate data and environmental factor data calculated from the historical time series data of the parameter group, extracted spatiotemporal features and captured the correlation between parameters through a bidirectional LSTM layer, and then used the attention mechanism to dynamically focus on key time nodes, and finally the fully connected layer output the expected degradation rate of the parameter group; in the real-time prediction stage, the real-time degradation rate data and real-time environmental factor data in the current sliding window were input into the trained degradation rate prediction model, and the expected degradation rate of the parameter group was output; in this way, the coupling relationship between parameters reflecting the state evaluation indicators can be fully considered, thereby improving the accuracy of the empowerment;

[0091] It should be noted that the processing of the dynamic and static parameters of the contact network in step (2) includes cleaning and normalizing the parameters to remove noise and abnormal values.

[0092] Specifically, in step (3), the subjective and objective weights of the parameters of the parameter level are calculated using the hierarchical analysis method and the entropy weight method respectively, and the static composite weight of the parameters of the parameter level is calculated using the combined weight method, which includes the following steps:

[0093] Step (3.1): Use the hierarchical analysis method to calculate the parameter level reflection index The subjective weight of each parameter is used to quantify the relative importance of the parameters through expert scoring, and a judgment matrix is ​​constructed. ;

[0094] in, Indicates reflection indicators The parameter level judgment matrix, Indicates that the parameter level reflects the index No. Parameters and The relative importance of the parameters, , n Reflects indicators for the parameter level The number of parameters; From the above, we can see that for ,when , Represents the parameter level judgment matrix reflecting the safety index; when , Represents the parameter level judgment matrix reflecting the smoothness index; when , Represents the parameter level judgment matrix reflecting the current receiving performance index; when , Represents a parameter level judgment matrix reflecting electrical performance indicators;

[0095] After the judgment matrix passes the consistency test, the eigenvector of the judgment matrix is ​​calculated and normalized to obtain the parameter level reflection index. No. Parameter AHP weights ,in ;

[0096] Specifically, performing a consistency check on the judgment matrix to determine whether there is a contradiction within the matrix is ​​well known to those skilled in the art. The order of the judgment matrix is ​​less than or equal to 7, and the calculation formula for the consistency check is:

[0097] ;

[0098] ;

[0099] Among them, the order of the judgment matrix here is n That is, the parameter level reflects the index The number of parameters; is the judgment matrix The maximum eigenvalue of CI is the consistency indicator, CR is the random consistency ratio, RI is the average random consistency index, and the average random consistency index of each order judgment matrix is:

[0100]

[0101] when CR <0.1, the judgment matrix passes the consistency test; when CR≥0.1, the judgment matrix needs to be adjusted in , , optimize the consistency of the judgment matrix until it satisfies CR <0.1;

[0102] For the judgment matrix that passes the consistency test , solve its characteristic equation , and get the maximum eigenvalue The corresponding eigenvector ; For the feature vector To standardize the process, , get the parameter level reflection index No. Parameter weights of the AHP ,in, for No. elements, , ;

[0103] Step (3.2): Use the entropy weight method to calculate the parameter level reflection index The objective weights of each parameter are constructed and the evaluation matrix is ​​normalized to obtain ;

[0104] in, Represents the reflection index after normalization The parameter level evaluation matrix of Indicates the Catenary section reflection index No. The normalized measurement values ​​of the parameters, , , m is the number of catenary sections; from the above, we can see that for ,when , hour, Represents the parameter level evaluation matrix reflecting the safety index; when , hour, Represents the parameter level evaluation matrix reflecting the smoothness index; when , hour, Represents the parameter level evaluation matrix reflecting the current receiving performance index; when , hour, It represents the parameter level evaluation matrix reflecting the electrical performance index;

[0105] Calculate the parameter level reflection index No. Information entropy of parameters :

[0106] , ;

[0107] in, Indicates the Catenary section reflection index No. The probability of a parameter appearing;

[0108] Calculate the parameter level reflection index No. Entropy weight method weight of parameters :

[0109] ;

[0110] Step (3.3): Calculate the parameter level reflection index using the combined weight method No. Static composite weights of parameters .

[0111] The method for calculating the real-time degradation rate of the dynamic and static parameters of the contact network in step (4) is:

[0112] ;

[0113] in, Indicates that the parameter level reflects the index No. Parameters in The real-time degradation rate at the moment, express Time parameter level reflection indicators No. The measured values ​​of the parameters should be noted that the measured values ​​of these parameters are obtained by dynamic or static measurement of the contact network detection equipment. Specifically: the pull-out value parameter X 11 , guide height parameter X 12 It is directly measured by a contact network geometric parameter detection device (such as a laser measuring instrument or an image recognition system); the hard point parameter X 21 , Span height parameter X 22 It is calculated based on the pantograph acceleration sensor or the contact line height continuous scanning data; the contact force parameter X 31 , offline rate parameter X 32 The grid voltage parameter X is collected in real time by the force sensor and arc detection device carried by the pantograph. 41 , insulation resistance parameter X42 It is directly measured using on-board voltage transformers and insulation monitoring equipment; , n Indicates that the parameter level reflects the index The number of parameters, Indicates the data collection time interval.

[0114] In step (4), a dynamic response mechanism is designed to determine the next response time:

[0115] ;

[0116] in, Reflects indicators for the parameter level No. The first parameter i +1 response time, i is a positive integer; inf is the lower bound, is a set of integers; Indicates the data collection time, Reflects indicators for the parameter level The parameters of i Response time, Reflects indicators for the parameter level No. The response error of the parameters, Reflects indicators for the parameter level No. Parameters in The real-time degradation rate at the moment, Reflects indicators for the parameter level No. The parameter in i The real-time degradation rate of each response moment, is the response mechanism parameter; n Indicates that the parameter level reflects the index The number of parameters; is the preset threshold parameter; is an internal dynamic variable, and the update rule is:

[0117] ;

[0118] in, is the step size parameter, satisfying , ; is the initial value of the internal dynamic variable, is a constant;

[0119] Introducing indicator factors Indicates whether the response condition is met:

[0120] .

[0121] If the response condition is met in step (4), the real-time degradation rate of the dynamic and static parameters of the contact network is calculated. Based on the real-time degradation rate and the expected degradation rate, a multivariable adaptive method is used to dynamically optimize the static composite weights of the parameters of the parameter level in step (3). The method for obtaining the dynamic composite weight optimization result is as follows:

[0122] ;

[0123] in, Indicates the data collection time, express Time parameter level reflection indicators The weight vector of each parameter, express Time parameter level reflection indicators No. n The weight vector of the parameters, Indicates the Response time parameter level reflection index The weight vector of each parameter, Indicates the Response time parameter level reflection index No. n The weight vector of the parameters; n Indicates that the parameter level reflects the index The number of parameters; 、 are the first adaptive gain matrix and the second adaptive gain matrix respectively, 、 Indicates that the parameter level reflects the index No. The first parameter N The first adaptive gain, N Order second adaptive gain; Represented by vector , , , common N A vector of vectors, Indicated by , , , common n A vector of elements, Indicates that the parameter level reflects the index No. The expected degradation rate and real-time degradation rate of the parameters are The degradation rate difference at each moment, express The first-order backward difference of Indicates that the parameter level reflects the index No. Parameters in The expected degradation rate at time t, Indicates that the parameter level reflects the index No. Parameters in The real-time degradation rate at the moment, , N is the adaptive method order, N is a positive integer; Represented by vector , , , common N A vector of vectors, Indicated by , , , common n A vector consisting of elements, express The first-order backward difference of Indicates that the parameter level reflects the index No. Parameters in Environmental factors at all times;

[0124] Parameter level reflection indicators No. The dynamic composite weight optimization results of the parameters are:

[0125] ;

[0126] in, Indicates that the parameter level reflects the index No. Dynamic composite weight optimization results of parameters; express No. elements, , Reflect indicators at the parameter level No. The static composite weight of the parameters.

[0127] In addition, the cost function based on the second-order degradation rate differential is constructed in step (6). In combination with the dynamic response mechanism, if the response condition is met, the cost function is solved using the function extreme value method. The method for optimizing and updating the first adaptive gain matrix and the second adaptive gain matrix in step (4) is:

[0128] Step (6.1): Construct a cost function based on the second-order degradation rate differential :

[0129] ;

[0130] in, Indicates that the parameter level reflects the index The parameters are The expected degradation rate vector at time , Indicates that the parameter level reflects the index No. Parameters in The expected degradation rate at time t; Indicates that the parameter level reflects the index The parameters are The real-time degradation rate vector at time , Indicates that the parameter level reflects the index No. Parameters in Real-time degradation rate at the moment; Indicates that the parameter level reflects the index The parameters are The degradation rate error at time The difference in degradation rate error at time , is a constant, is a constant, represents the two-norm;

[0131] Step (6.2): ​​Combined with the dynamic response mechanism, if the response conditions are met, the cost function in step (6.1) is solved using the function extreme value method to optimize and update the first adaptive gain matrix. , the second adaptive gain matrix :

[0132] ;

[0133] ;

[0134] in, and Respectively , In the The adaptive gain matrix of the response time; indicator factors representing response conditions; 、 , respectively the first and second step length factors; is the pseudo partial derivative matrix;

[0135] Specifically, The calculation formula is as follows:

[0136] ;

[0137] The calculation formula is as follows:

[0138] ;

[0139] Among them, the pseudo partial derivative matrix The calculation method is:

[0140] ;

[0141] in, for t The pseudo partial derivative matrix at time -1, Indicates that the parameter level reflects the index The parameters are The real-time degradation rate vector at time , The calculation formula is , , is a constant, is a constant. Example 2

[0142] Figure 3 This is a schematic diagram of the module connections of the contact network status assessment system based on responsive multivariable weighted optimization provided in this embodiment. This embodiment provides a contact network status assessment system based on responsive multivariable weighted optimization, including:

[0143] Catenary status evaluation system establishment module: used to establish a catenary status evaluation system, the evaluation system includes an indicator level and a parameter level, the indicator level includes a safety index X1, a smoothness index X2, a current-collecting performance index X3, and an electrical performance index X4, the parameter level includes several parameters reflecting each indicator level; specifically, the parameter level includes a pull-out value parameter X1 reflecting the safety index X1, 11 , guide height parameter X 12 , reflecting the hard point parameter X2 of the smoothness index 21 , Span height parameter X 22 , reflecting the contact force parameter X3 of the current-carrying performance index 31 , offline rate parameter X 32, reflecting the grid voltage parameter X4 of the electrical performance index X 41 , insulation resistance parameter X 42 ;

[0144] Data processing module: used for collecting dynamic and static parameters of the contact network and processing the dynamic and static parameters of the contact network;

[0145] Initial weight calculation module: used to calculate the subjective and objective weights of each parameter in the parameter level by using the hierarchical analysis method and the entropy weight method respectively, and to calculate the static composite weight of each parameter in the parameter level by using the combined weight method;

[0146] Responsive multivariable weight optimization module: used to collect historical degradation data of the dynamic and static parameters of the contact network, use time series neural network to establish multivariable degradation rate prediction models for each indicator of the indicator level, and obtain the expected degradation rate of each parameter through the multivariable degradation rate prediction model; design a dynamic response mechanism, if the response conditions are met, calculate the real-time degradation rate of the dynamic and static parameters of the contact network, based on the real-time degradation rate and the expected degradation rate, use a multivariable adaptive method to dynamically optimize the static composite weights of each parameter of the parameter level to obtain a dynamic composite weight optimization result, the multivariable adaptive method includes a first adaptive gain matrix and a second adaptive gain matrix; if the response conditions are not met, maintain the dynamic composite weight optimization result of the previous response moment;

[0147] Responsive gain matrix update module: used to construct a cost function based on the second-order degradation rate differential, combined with the dynamic response mechanism. If the response condition is met, the cost function is solved using the function extreme value method to optimize and update the first adaptive gain matrix and the second adaptive gain matrix in the responsive multivariable weight optimization module; if the response condition is not met, the adaptive gain matrix at the previous response moment is maintained;

[0148] Among them, the adaptive gain matrix is ​​optimized and updated 、 The method is:

[0149] ;

[0150] ;

[0151] in, and Respectively , In the The adaptive gain matrix of the response time; indicator factors representing response conditions; 、 , respectively the first and second step length factors; is a constant, is a constant, is the pseudo partial derivative matrix; Indicates that the parameter level reflects the index The parameters are The expected degradation rate vector at time t; Indicates that the parameter level reflects the index The parameters are The real-time degradation rate vector at time t; Indicates that the parameter level reflects the index The parameters are The degradation rate error at time The difference in degradation rate error at the moment; ; ; Indicates that the parameter level reflects the index The parameters are The degradation rate error at time The difference in degradation rate error at the moment; Indicates that the parameter level reflects the index The parameters are Environmental factors at all times and The difference in environmental factors at each moment; , N is the adaptive method order, N is a positive integer; express t Time parameter level reflection indicators The weight vector increments of each parameter;

[0152] Comprehensive status assessment module: used to assess the status of the contact network at the data collection point based on the dynamic composite weight optimization results.

[0153] Furthermore, the present invention adopts the following technical solutions:

[0154] A non-transitory computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned contact network state assessment method based on responsive multivariable weighted optimization.

[0155] Furthermore, the present invention adopts the following technical solutions:

[0156] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned contact network status assessment method based on responsive multivariable weighted optimization is implemented.

[0157] From the above description of the embodiments, it will be clear to those skilled in the art that the facilities of the present invention can be implemented using software plus the necessary general-purpose hardware platforms. Embodiments of the present invention can be implemented using existing processors, or by dedicated processors used in appropriate systems for this or other purposes, or by hardwired systems. Embodiments of the present invention also include non-transitory computer-readable storage media, including machine-readable media for carrying or having stored thereon machine-executable instructions or data structures. Such machine-readable media can be any available medium that can be accessed by a general-purpose or special-purpose computer or other machine with a processor. For example, such machine-readable media can include RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store the required program code in the form of machine-executable instructions or data structures and that can be accessed by a general-purpose or special-purpose computer or other machine with a processor. When information is transmitted or provided to a machine via a network or other communications connection (hardwired, wireless, or a combination of hardwired and wireless), such connection is also considered a machine-readable medium.

[0158] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A method for evaluating the state of a contact network based on responsive multivariable weighted optimization, characterized in that: The method comprises the following steps: Step (1): Establish a contact network status evaluation system, which includes an indicator level and a parameter level. The indicator level includes a safety indicator X1, a smoothness indicator X2, a current-collecting performance indicator X3, and an electrical performance indicator X4. The parameter level includes several parameters reflecting each indicator level. Step (2): collecting dynamic and static parameters of the contact network and processing the dynamic and static parameters of the contact network; Step (3): Obtain the subjective and objective weights of each parameter at the parameter level, and calculate the static composite weight of each parameter at the parameter level; Step (4): collecting historical degradation data of the dynamic and static parameters of the contact network, using a time series neural network to establish a multivariate degradation rate prediction model for each indicator of the indicator level, and obtaining the expected degradation rate of each parameter through the multivariate degradation rate prediction model; designing a dynamic response mechanism, if the response condition is met, calculating the real-time degradation rate of the dynamic and static parameters of the contact network, based on the real-time degradation rate and the expected degradation rate, using a multivariate adaptive method to dynamically optimize the static composite weights of each parameter of the parameter level in step (3), and obtaining a dynamic composite weight optimization result, the multivariate adaptive method includes a first adaptive gain matrix and a second adaptive gain matrix; if the response condition is not met, maintaining the dynamic composite weight optimization result of the previous response moment; Wherein, if the response condition is met, the real-time degradation rate of the dynamic and static parameters of the contact network is calculated, and based on the real-time degradation rate and the expected degradation rate, the static composite weights of the parameters of the parameter level in step (3) are dynamically optimized using a multivariable adaptive method. The method for obtaining the dynamic composite weight optimization result is: ； in, Indicates the data collection time, express Time parameter level reflection indicators The weight vector of each parameter, express Time parameter level reflection indicators No. n The weight vector of the parameters, Indicates the Response time parameter level reflection index The weight vector of each parameter, Indicates the Response time parameter level reflection index No. n The weight vector of the parameters; n Indicates that the parameter level reflects the index The number of parameters; 、 are the first adaptive gain matrix and the second adaptive gain matrix respectively, 、 Indicates that the parameter level reflects the index No. The first parameter N The first adaptive gain, N Order second adaptive gain; Represented by vector , , , common N A vector of vectors, Indicated by , , , common n A vector consisting of elements, Indicates that the parameter level reflects the index No. The expected degradation rate and real-time degradation rate of the parameters are The degradation rate difference at each moment, express The first-order backward difference of Indicates that the parameter level reflects the index No. Parameters in The expected degradation rate at time t, Indicates that the parameter level reflects the index No. Parameters in The real-time degradation rate at the moment, , N is the adaptive method order, N is a positive integer; Represented by vector , , , common N A vector of vectors, Indicated by , , , common n A vector of elements, express The first-order backward difference of Indicates that the parameter level reflects the index No. Parameters in Environmental factors at all times; Parameter level reflection indicators No. The dynamic composite weight optimization results of the parameters are: ； in, Indicates that the parameter level reflects the index No. Dynamic composite weight optimization results of parameters; express No. elements, , Reflect indicators at the parameter level No. The static composite weight of the parameters; Introducing indicator factors Indicates whether the response condition is met: ； Step (5): Based on the dynamic composite weight optimization results, the state of the contact network at the data collection point is evaluated; Step (6): constructing a cost function based on the second-order degradation rate differential, combined with the dynamic response mechanism, if the response condition is met, then using the function extreme value method to solve the cost function, and optimizing and updating the first adaptive gain matrix and the second adaptive gain matrix in step (4); if the response condition is not met, then maintaining the adaptive gain matrix at the previous response moment; Among them, the method of constructing the cost function based on the second-order degradation rate differential in step (6) is: Constructing a cost function based on the second-order degradation rate differential : ； in, Indicates that the parameter level reflects the index The parameters are The expected degradation rate vector at time , Indicates that the parameter level reflects the index No. Parameters in The expected degradation rate at time t; Indicates that the parameter level reflects the index The parameters are The real-time degradation rate vector at time , Indicates that the parameter level reflects the index No. Parameters in Real-time degradation rate at the moment; Indicates that the parameter level reflects the index The parameters are The degradation rate error at time The difference in degradation rate error at time , is a constant, is a constant, represents the two-norm; Optimize and update the first adaptive gain matrix , the second adaptive gain matrix The method is: ； ； in, and Respectively , In the The adaptive gain matrix of the response time; indicator factors representing response conditions; 、 , respectively the first and second step length factors; is a constant, is a constant, is the pseudo partial derivative matrix; Indicates that the parameter level reflects the index The parameters are The expected degradation rate vector at time t; Indicates that the parameter level reflects the index The parameters are The real-time degradation rate vector at time t; Indicates that the parameter level reflects the index The parameters are The degradation rate error at time The difference in degradation rate error at the moment; ; ; Indicates that the parameter level reflects the index The parameters are The degradation rate error at time The difference in degradation rate error at the moment; Indicates that the parameter level reflects the index The parameters are Environmental factors at all times and The difference in environmental factors at each moment; , N is the order of the adaptive method; express t Time parameter level reflection indicators The weight vector increments of each parameter; Repeat the above steps (4) to (6) to obtain the contact network status evaluation results at the data collection points at different times.

2. The method for evaluating the state of a contact network based on responsive multivariable weighted optimization according to claim 1, characterized in that: In step (3), the subjective and objective weights of each parameter at the parameter level are obtained, and the static composite weights of each parameter at the parameter level are calculated, including the following steps: Step (3.1): Use the hierarchical analysis method to calculate the parameter level reflection index The subjective weight of each parameter is used to quantify the relative importance of the parameters through expert scoring, and a judgment matrix is ​​constructed. ; in, Indicates reflection indicators The parameter level judgment matrix, Indicates that the parameter level reflects the index No. Parameters and The relative importance of the parameters, , n Reflects indicators for the parameter level The number of parameters; After the judgment matrix passes the consistency test, the eigenvector of the judgment matrix is ​​calculated and normalized to obtain the parameter level reflection index. No. Parameter AHP weights ; Step (3.2): Use the entropy weight method to calculate the parameter level reflection index The objective weights of each parameter are constructed and the evaluation matrix is ​​normalized to obtain ; in, Represents the reflection index after normalization The parameter level evaluation matrix of Indicates the Catenary section reflection index No. The normalized measurement values ​​of the parameters, , m is the number of catenary sections; Calculate the parameter level reflection index No. Information entropy of parameters : , ； in, Indicates the Catenary section reflection index No. The probability of a parameter appearing; Calculate the parameter level reflection index No. Entropy weight method weight of parameters : ； Step (3.3): Calculate the parameter level reflection index using the combined weight method No. Static composite weights of parameters : 。 3. The method for evaluating the state of a contact network based on responsive multivariable weighted optimization according to claim 1, characterized in that: The method for calculating the real-time degradation rate of the dynamic and static parameters of the contact network in step (4) is: ； in, Indicates that the parameter level reflects the index No. Parameters in The real-time degradation rate at the moment, express Time parameter level reflection indicators No. The measured values ​​of the parameters, , n Indicates that the parameter level reflects the index The number of parameters, Indicates the data collection time interval.

4. The method for evaluating the state of a contact network based on responsive multivariable weighted optimization according to claim 1, characterized in that: In step (4), a dynamic response mechanism is designed to determine the next response time: ； in, Reflects indicators for the parameter level No. The first parameter i +1 response time, i is a positive integer; inf is the lower bound, is a set of integers; Indicates the data collection time, Reflects indicators for the parameter level No. The first parameter i Response time, Reflects indicators for the parameter level No. The response error of the parameters, Reflects indicators for the parameter level No. Parameters in The real-time degradation rate at the moment, Reflects indicators for the parameter level No. The parameter in i The real-time degradation rate of each response moment, is the response mechanism parameter; n Indicates that the parameter level reflects the index The number of parameters; is the preset threshold parameter; is an internal dynamic variable, and the update rule is: ； in, is the step size parameter, satisfying , ; is the initial value of the internal dynamic variable, is a constant.

5. The method for evaluating the state of a contact network based on responsive multivariable weighted optimization according to claim 1, characterized in that: Pseudo partial derivative matrix The calculation method is: ； in, for t The pseudo partial derivative matrix at time -1, Indicates that the parameter level reflects the index The parameters are The real-time degradation rate vector at time , , The calculation formula is , is a constant, is a constant.

6. The contact network status assessment system based on responsive multivariable weighted optimization is characterized by: include: Catenary status assessment system establishment module: used to establish a catenary status assessment system, the assessment system includes an indicator level and a parameter level, the indicator level includes a safety indicator X1, a smoothness indicator X2, a current-collecting performance indicator X3, and an electrical performance indicator X4, and the parameter level includes several parameters reflecting each indicator level; Data processing module: used for collecting dynamic and static parameters of the contact network and processing the dynamic and static parameters of the contact network; Initial weight calculation module: used to obtain the subjective and objective weights of each parameter in the parameter level, and calculate the static composite weights of each parameter in the parameter level; Responsive multivariable weight optimization module: used to collect historical degradation data of the dynamic and static parameters of the contact network, use time series neural network to establish multivariable degradation rate prediction models for each indicator of the indicator level, and obtain the expected degradation rate of each parameter through the multivariable degradation rate prediction model; design a dynamic response mechanism, if the response conditions are met, calculate the real-time degradation rate of the dynamic and static parameters of the contact network, based on the real-time degradation rate and the expected degradation rate, use a multivariable adaptive method to dynamically optimize the static composite weights of each parameter of the parameter level to obtain a dynamic composite weight optimization result, the multivariable adaptive method includes a first adaptive gain matrix and a second adaptive gain matrix; If the response condition is not met, the dynamic composite weight optimization result of the previous response moment will be maintained; If the response condition is met, the real-time degradation rate of the dynamic and static parameters of the contact network is calculated. Based on the real-time degradation rate and the expected degradation rate, a multivariable adaptive method is used to dynamically optimize the static composite weights of the parameters at the parameter level. The method for obtaining the dynamic composite weight optimization result is: ； in, Indicates the data collection time, express Time parameter level reflection indicators The weight vector of each parameter, express Time parameter level reflection indicators No. n The weight vector of the parameters, Indicates the Response time parameter level reflection index The weight vector of each parameter, Indicates the Response time parameter level reflection index No. n The weight vector of the parameters; n Indicates that the parameter level reflects the index The number of parameters; 、 are the first adaptive gain matrix and the second adaptive gain matrix respectively, 、 Indicates that the parameter level reflects the index No. The first parameter N The first adaptive gain, N Order second adaptive gain; Represented by vector , , , common N A vector of vectors, Indicated by , , , common n A vector of elements, Indicates that the parameter level reflects the index No. The expected degradation rate and real-time degradation rate of the parameters are The degradation rate difference at each moment, express The first-order backward difference of Indicates that the parameter level reflects the index No. Parameters in The expected degradation rate at time t, Indicates that the parameter level reflects the index No. Parameters in The real-time degradation rate at the moment, , N is the adaptive method order, N is a positive integer; Represented by vector , , , common N A vector of vectors, Indicated by , , , common n A vector consisting of elements, express The first-order backward difference of Indicates that the parameter level reflects the index No. Parameters in Environmental factors at all times; Parameter level reflection indicators No. The dynamic composite weight optimization results of the parameters are: ； in, Indicates that the parameter level reflects the index No. Dynamic composite weight optimization results of parameters; express No. elements, , Reflect indicators at the parameter level No. The static composite weight of the parameters; Introducing indicator factors Indicates whether the response condition is met: ； Comprehensive status assessment module: used to assess the status of the contact network at the data collection point based on the dynamic composite weight optimization results; Responsive gain matrix update module: used to construct a cost function based on the second-order degradation rate differential, combined with the dynamic response mechanism. If the response condition is met, the cost function is solved using the function extreme value method to optimize and update the first adaptive gain matrix and the second adaptive gain matrix in the responsive multivariable weight optimization module; if the response condition is not met, the adaptive gain matrix at the previous response moment is maintained; Among them, the method of constructing the cost function based on the second-order degradation rate differential is: Constructing a cost function based on the second-order degradation rate differential : ； in, Indicates that the parameter level reflects the index The parameters are The expected degradation rate vector at time , Indicates that the parameter level reflects the index No. Parameters in The expected degradation rate at time t; Indicates that the parameter level reflects the index The parameters are The real-time degradation rate vector at time , Indicates that the parameter level reflects the index No. Parameters in Real-time degradation rate at the moment; Indicates that the parameter level reflects the index The parameters are The degradation rate error at time The difference in degradation rate error at time , is a constant, is a constant, represents the two-norm; Optimize and update the first adaptive gain matrix , the second adaptive gain matrix The method is: ； ； in, and Respectively , In the The adaptive gain matrix of the response time; indicator factors representing response conditions; 、 , respectively the first and second step length factors; is a constant, is a constant, is the pseudo partial derivative matrix; Indicates that the parameter level reflects the index The parameters are The expected degradation rate vector at time t; Indicates that the parameter level reflects the index The parameters are The real-time degradation rate vector at time t; Indicates that the parameter level reflects the index The parameters are The degradation rate error at time The difference in degradation rate error at the moment; ; ; Indicates that the parameter level reflects the index The parameters are The degradation rate error at time The difference in degradation rate error at the moment; Indicates that the parameter level reflects the index The parameters are Environmental factors at all times and The difference in environmental factors at each moment; , N is the order of the adaptive method; express t Time parameter level reflection indicators The weight vector increments of each parameter .

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating the state of an overhead line based on responsive multivariable weighted optimization as described in any one of claims 1 to 5 is implemented.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the contact network status assessment method based on responsive multivariable weighted optimization as described in any one of claims 1 to 5 is implemented.

Images