Progressive planning power grid evaluation method and system based on combination evaluation theory

Abstract

The invention discloses a progressive planning power grid evaluation method and system based on a combination evaluation theory, and relates to the technical field of power grid evaluation and optimization, and the method comprises the steps: carrying out the weight correction of an obtained index through employing a combination weighting method; obtaining an evaluation value based on a similarity theory evaluation method; carrying out standardization processing on the indexes by utilizing dimensionless processing; and performing evaluation based on nonparametric regression. According to the method, objectivity and fairness of an evaluation result are ensured through a subjective and objective combination weighting method. Through a deep learning model, especially an LSTM network and an attention mechanism, the evaluation efficiency and accuracy are improved. The systematization method disclosed by the invention has a wide application prospect. Through the design of automatically optimizing evaluation parameters, adaptive adjustment can be performed according to historical data and real-time data, so that the evaluation result is closer to reality, and the evaluation practicability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid evaluation and optimization, and in particular to a progressive planning power grid evaluation method and system based on combined evaluation theory. Background Art

[0002] With the rapid development of the power industry, the demand for the construction of distribution networks is increasing day by day. At the same time, the construction of distribution networks presents the characteristics of wide coverage, large scale, and many related factors involved. These factors have made the planning, transformation and construction of distribution networks a huge and complex system project. The distribution network is located at the end of the power system and is directly connected to the user end, reflecting the operating capacity of the entire power system. With the gradual increase in the scale of the distribution network, the planning and design of the distribution network has gradually become an issue of increasing concern in the power industry. In order to achieve the economic or safe optimization of the distribution network construction, it is necessary to conduct a comprehensive and accurate evaluation of the operating level of the distribution network. Therefore, it is necessary to establish a comprehensive and accurate evaluation index system to objectively measure the effect and achievement of the planning. The weight allocation of evaluation indicators is a key issue in the evaluation process. Different indicators have different importance in distribution network planning, and traditional weighting methods are often based on subjective judgment or expert opinions, which are easily affected by subjective bias and uncertainty, resulting in deviations in the evaluation results. For this reason, the study of objective weighting methods and subjective and objective mixed interval affine weighting methods can consider the relationship and weight allocation between indicators under uncertainty conditions, which can improve the accuracy and reliability of the evaluation results. Summary of the invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by the present invention is: how to evaluate the performance and status of the power grid more accurately and efficiently.

[0005] To solve the above technical problems, the present invention provides the following technical solution: a progressive planning power grid evaluation method based on combined evaluation theory, which comprises the following steps:

[0006] Collect power grid system data, and modify the weight of the collected power grid system data based on the combined weighting method; obtain the evaluation value based on the similarity theory evaluation method; standardize the indicators using dimensionless processing; and evaluate based on non-parametric regression. As a preferred solution of the progressive planning power grid evaluation method based on the combined evaluation theory described in the present invention, the power grid system data includes power grid operation status data, power grid equipment operation data, power grid load data, power grid fault data, and power grid environment data.

[0007] The combined weighting method includes a subjective weighting method and an objective weighting method, and the weighting method is selected according to the performance analysis of the power grid system.

[0008] The data indicators of the power grid system are evaluated according to the subjective weighting method to generate a judgment matrix, which is normalized and checked, and the system weight of the matrix is ​​obtained through iterative calculation.

[0009] The data indicators of the power grid system are weighted by the objective weighting method to generate the original data matrix, and the weight values ​​are dimensionless and normalized to obtain the comprehensive weight of the power grid system.

[0010] The generating of the judgment matrix includes comparing the evaluation factors and filling the comparison result into the judgment matrix.

[0011] The normalized check is expressed as,

[0012]

[0013] If CR is less than 0.1, it means that the normalization check has passed.

[0014] Among them, CI is represented as the consistency index, λ mad represents the maximum eigenvalue of the judgment matrix, CR represents the consistency ratio, RI represents the random consistency index, and n represents the number of evaluation factors.

[0015] As a preferred scheme of the progressive planning power grid evaluation method based on combined evaluation theory described in the present invention, wherein: the comprehensive weight of the power grid system is obtained, including calculating the weight of each data indicator according to the objective weighting method, and verifying it according to the Kendall concordance coefficient test method.

[0016] When d(W (1) , W (2) ) is greater than the test threshold, it means that the test is passed. If the test is passed, use Calculate the comprehensive weight of the power grid system.

[0017] The Kendall concordance coefficient verification method is expressed as:

[0018]

[0019] Among them, d(W (1) , W (2) ) represents the weight vector W (1) and W (2) The Euclidean distance between j (1) , W j (2) ) 2 represents the square of the difference between the weight values ​​of the two weight vectors at the jth position, represents the calculation of all weight differences, W represents the comprehensive weight of the power grid system, and λ krepresents the weight of the kth evaluation criterion, and q represents the total number of evaluation criteria.

[0020] As a preferred scheme of the progressive planning power grid evaluation method based on combined evaluation theory described in the present invention, the evaluation method of similarity theory includes constructing a new model for system level evaluation based on the Shepard similarity interpolation method of accelerated genetic algorithm and an ideal interval method based on genetic algorithm.

[0021] The new model for constructing system grade evaluation includes randomly generating evaluation grade sample series x(i, j) and y(i) according to the system evaluation standard table, wherein i=1-n, j=1-m.

[0022] According to the sample parameter b, the optimal estimate is performed. Sample i is taken from the sample series, and Shepard interpolation is performed on the other n-1 samples to obtain the interpolation value corresponding to the evaluation level y(i), which is recorded as y c (i).

[0023] The accelerated genetic algorithm is expressed as,

[0024]

[0025] st1≤b≤5

[0026] in, Indicates that the sum meets, y c (i) represents the i-th observed value, y(i) represents the i-th actual value, and st represents the constraint condition.

[0027] The ideal interval method based on genetic algorithm includes generating a series of evaluation standard samples and performing dimensionless processing, calculating the distance between each standard sample and the ideal interval of the standard grade, calculating the relative membership value of each standard sample to the ideal interval of the standard grade and equivalent comprehensive evaluation.

[0028] As a preferred solution of the progressive planning power grid evaluation method based on combined evaluation theory described in the present invention, wherein: the dimensionless processing is expressed as,

[0029] x(k,j)=x*(k,j) / x max (j)(k=1~n k ,j=1~n j )

[0030] a(i,j)=a*(i,j) / x max (j)(i=1~n i ,j=1~n j )

[0031] b(i,j)=b*(i,j) / x max(j)(i=1~n i ,j=1~n j )

[0032] Among them, x(k, j), a(i, j), b(i, j) represent two-dimensional variables, x * (k, j), a * (i, j), b * (i, j) represent the original values ​​of x(k, j), a(i, j), and b(i, j), respectively. max (j) represents the maximum value of the jth evaluation index in the evaluation standard sample series, which can be expressed as,

[0033]

[0034] The distance between each standard sample and the ideal range of the standard grade is expressed as:

[0035]

[0036]

[0037] Among them, D(k, i) represents the total distance between the kth observation and the i-th reference value, w(j) represents the weight of the j-th indicator, d(k, i, j) represents the distance between the k-th observation and the i-th reference value on the j-th feature, a(i, j) and b(i, j) represent the lower and upper bounds of the i-th reference value on the j-th feature, and x(k, j) represents the value of the k-th observation on the j-th feature.

[0038] As a preferred solution of the progressive planning power grid evaluation method based on combined evaluation theory described in the present invention, wherein: the evaluation based on non-parametric regression includes obtaining a one-dimensional projection value z(i) of the evaluation value, expressed as,

[0039]

[0040] The projection index function is obtained through the one-dimensional projection value, which is expressed as:

[0041] Q(a)=S z |R xy |

[0042] Among them, || represents the absolute value, S z represents the standard deviation of the projection value z(i);

[0043] Estimate the optimal projection direction by solving the projection index function maximization problem

[0044] maxQ(a)=S z |R xy |

[0045]

[0046] Optimization is performed using an accelerated genetic algorithm.

[0047] As a preferred solution of the progressive planning power grid evaluation method based on combined evaluation theory described in the present invention, wherein: the evaluation based on non-parametric regression also includes establishing a corresponding Nadaraya-Watson non-parametric model, expressed as:

[0048]

[0049] Using standard evaluation objects * (i)|i=1~n} series and accelerated genetic algorithm are optimized by solving the following optimization problem, expressed as,

[0050]

[0051] Another object of the present invention is to provide a progressive planning power grid evaluation system based on combined evaluation theory, which can systematically collect, process and analyze power grid data, and combine LSTM network and attention mechanism to build and train deep learning models, thereby solving the problems of low efficiency and low accuracy of existing evaluation methods when processing large amounts of complex power grid data.

[0052] To solve the above technical problems, the present invention provides the following technical solutions: a progressive planning power grid evaluation system based on combined evaluation theory, comprising: a data processing module, a feature engineering module, a deep learning model training module, and a regularization and optimization module.

[0053] The data processing module is responsible for collecting historical data and real-time data from the power grid system.

[0054] The feature engineering module is responsible for selecting and constructing the features required for evaluation.

[0055] The deep learning model training module is responsible for building and training a model combining an LSTM network and an attention mechanism.

[0056] The regularization and optimization module is responsible for preventing the model from overfitting.

[0057] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the progressive planning power grid evaluation method based on the combined evaluation theory as described above are implemented.

[0058] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the progressive planning power grid evaluation method based on the combined evaluation theory as described above are implemented.

[0059] Beneficial effects of the present invention: The present invention ensures the objectivity and fairness of the evaluation results through the subjective and objective combined weighting method, making the evaluation more comprehensive. Through the deep learning model, especially the LSTM network and the attention mechanism, the present invention can process large amounts of complex power grid data, improving the efficiency and accuracy of the evaluation. The systematic method of the present invention is not only suitable for power grid evaluation, but can also be extended to evaluation and analysis in other fields, and has broad application prospects. Through the design of automatic optimization of evaluation parameters, the present invention can perform adaptive adjustments based on historical data and real-time data, so that the evaluation results are closer to reality, improving the practicability of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0061] Figure 1 An overall flow chart of the progressive planning power grid evaluation method based on combined evaluation theory provided for the first embodiment of the present invention.

[0062] Figure 2 The overall framework diagram of the progressive planning power grid evaluation system based on the combined evaluation theory provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0064] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0065] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0066] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0067] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0068] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0069] Example 1

[0070] Reference Figure 1 , which is an embodiment of the present invention, provides a progressive planning power grid evaluation method based on combined evaluation theory, characterized in that:

[0071] S1: Collect power grid system data and modify the weight of the collected power grid system data based on the combined weighting method.

[0072] The power grid system data includes power grid operation status data, power grid equipment operation data, power grid load data, power grid fault data and power grid environment data.

[0073] The combined weighting method includes a subjective weighting method and an objective weighting method, and the weighting method is selected according to the performance analysis of the power grid system.

[0074] The data indicators of the power grid system are evaluated according to the subjective weighting method to generate a judgment matrix, which is normalized and checked, and the system weight of the matrix is ​​obtained through iterative calculation.

[0075] The data indicators of the power grid system are weighted by the objective weighting method to generate the original data matrix, and the weight values ​​are dimensionless and normalized to obtain the comprehensive weight of the power grid system.

[0076] The generating of the judgment matrix includes comparing the evaluation factors and filling the comparison result into the judgment matrix.

[0077] The normalized check is expressed as,

[0078]

[0079] If CR is less than 0.1, it means that the normalization check has passed.

[0080] Among them, CI is represented as the consistency index, λ mad represents the maximum eigenvalue of the judgment matrix, CR represents the consistency ratio, RI represents the random consistency index, and n represents the number of evaluation factors.

[0081] Furthermore, obtaining the comprehensive weight of the power grid system includes calculating the weight of each data indicator according to the objective weighting method and verifying it according to the Kendall concordance coefficient test method.

[0082] When d(W (1) , W (2) ) is greater than the test threshold, it means that the test is passed. If the test is passed, use Calculate the comprehensive weight of the power grid system.

[0083] The Kendall concordance coefficient verification method is expressed as:

[0084]

[0085] Among them, d(W (1) , W (2) ) represents the weight vector W (1) and W (2) The Euclidean distance between j (1) , W j (2) ) 2 represents the square of the difference between the weight values ​​of the two weight vectors at the jth position, represents the calculation of all weight differences, W represents the comprehensive weight of the power grid system, and λ k represents the weight of the kth evaluation criterion, and q represents the total number of evaluation criteria.

[0086] S2: The evaluation value is obtained based on the similarity theory evaluation method.

[0087] Furthermore, the evaluation methods of similarity theory include constructing a new model for system grade evaluation based on the Shepard similarity interpolation method of accelerated genetic algorithm and the ideal interval method based on genetic algorithm.

[0088] Constructing a new model for system grade evaluation includes randomly generating evaluation grade sample series x(i, j) and y(i) according to the system evaluation standard table, where i=1~n, j=1~m.

[0089] According to the sample parameter b, the optimal estimate is performed. Sample i is taken from the sample series, and Shepard interpolation is performed on the other n-1 samples to obtain the interpolation value corresponding to the evaluation level y(i), which is recorded as y c (i).

[0090] The accelerated genetic algorithm is expressed as,

[0091]

[0092] st1≤b≤5

[0093] in, Indicates that the sum meets, y c (i) represents the i-th observed value, y(i) represents the i-th actual value, and st represents the constraint condition.

[0094] The ideal interval method based on genetic algorithm includes generating a series of evaluation standard samples and performing dimensionless processing, calculating the distance between each standard sample and the ideal interval of the standard grade, calculating the relative membership value of each standard sample to the ideal interval of the standard grade and equivalent comprehensive evaluation.

[0095] S3: Standardize the indicators using dimensionless processing.

[0096] Further dimensionless processing is expressed as,

[0097] x(k,j)=x*(k,j) / x max (j)(k=1~n k ,j=1~n j )

[0098] a(i,j)=a*(i,j) / x max (j)(i=1~n i ,j=1~n j )

[0099] b(i,j)=b*(i,j) / x max (j)(i=1~n i,j=1~n j )

[0100] Among them, x(k, j), a(i, j), b(i, j) represent two-dimensional variables, x * (k, j), a * (i, j), b * (i, j) represent the original values ​​of x(k, j), a(i, j), and b(i, j), respectively. max (j) represents the maximum value of the jth evaluation index in the evaluation standard sample series, which can be expressed as,

[0101]

[0102] The distance between each standard sample and the ideal interval of the standard grade is calculated as:

[0103]

[0104]

[0105] Among them, D(k, i) represents the total distance between the kth observation and the i-th reference value, w(j) represents the weight of the j-th indicator, d(k, i, j) represents the distance between the k-th observation and the i-th reference value on the j-th feature, a(i, j) and b(i, j) represent the lower and upper bounds of the i-th reference value on the j-th feature, and x(k, j) represents the value of the k-th observation on the j-th feature.

[0106] S4: Evaluation based on nonparametric regression.

[0107] The evaluation based on nonparametric regression includes obtaining the one-dimensional projection value z(i) of the evaluation value, expressed as,

[0108]

[0109] The projection index function is obtained through the one-dimensional projection value, which is expressed as:

[0110] Q(a)=S z |R xy |

[0111] Among them, || represents the absolute value, S z represents the standard deviation of the projection value z(i);

[0112] Estimate the optimal projection direction by solving the projection index function maximization problem

[0113] maxQ(a)=S z |R xy |

[0114]

[0115] Optimization is performed using an accelerated genetic algorithm.

[0116] The evaluation based on nonparametric regression also includes establishing the corresponding Nadaraya-Watson nonparametric model, expressed as,

[0117]

[0118] Using standard evaluation objects * (i)|i=1~n} series and accelerated genetic algorithm are optimized by solving the following optimization problem, expressed as,

[0119]

[0120] Example 2

[0121] Reference Figure 2 , which is an embodiment of the present invention, provides a system of a progressive planning power grid evaluation method based on combined evaluation theory. The progressive planning power grid evaluation system based on combined evaluation theory includes a data processing module, a feature engineering module, a deep learning model training module, and a regularization and optimization module.

[0122] The data processing module is responsible for collecting historical data and real-time data from the power grid system; the feature engineering module is responsible for selecting and constructing the features required for evaluation; the deep learning model training module is responsible for building and training the LSTM network combined with the attention mechanism model; the regularization and optimization module is responsible for preventing the model from overfitting.

[0123] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0124] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0125] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0126] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0127] Example 3

[0128] In this embodiment, in order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0129] Data source: The historical and real-time data of the power grid in a certain area were selected, including key indicators such as current, voltage, power, frequency, and harmonic content.

[0130] Evaluation objectives: stability, efficiency, reliability, response speed, fault tolerance and economy of the power grid.

[0131] Evaluation method: On the one hand, the method of the present invention is used, and on the other hand, the traditional power grid evaluation method is used

[0132] This embodiment experiments the existing traditional method and the method of this embodiment, as shown in Table 1.

[0133] Table 1 Experimental effect comparison chart

[0134] Evaluation indicators Traditional scoring method Our invention method score stability 75 92 efficiency 80 95 reliability 78 94 Response speed 70 90 Fault Tolerance 72 91 Economical 77 93

[0135] It can be seen from the above table that the evaluation method of the present invention is higher than the traditional method in all evaluation indicators, which shows that the method of the present invention is more accurate and comprehensive and can better reflect the actual performance of the power grid.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A progressive planning power grid evaluation method based on combined evaluation theory, characterized in that: include: Collect power grid system data and modify the weight of the collected power grid system data based on the combined weighting method; The evaluation value is obtained based on the similarity theory evaluation method; The indicators are standardized by using dimensionless processing; Evaluation was based on nonparametric regression.

2. The method for evaluating a progressively planned power grid based on combined evaluation theory according to claim 1, characterized in that: The power grid system data includes power grid operation status data, power grid equipment operation data, power grid load data, power grid fault data and power grid environment data; The combined weighting method includes a subjective weighting method and an objective weighting method, and the weighting method is selected according to the performance analysis of the power grid system; The data indicators of the power grid system are evaluated according to the subjective weighting method, a judgment matrix is ​​generated, the judgment matrix is ​​normalized and checked, and the system weight of the matrix is ​​obtained through iterative calculation; The data indicators of the power grid system are weighted by the objective weighting method to generate the original data matrix, and the weight values ​​are dimensionless and normalized to obtain the comprehensive weight of the power grid system. Generating the judgment matrix includes comparing the evaluation factors and filling the comparison results into the judgment matrix; The normalized check is expressed as, If CR is less than 0.1, it means that the normalization verification has passed; Among them, CI is the consistency index, λ mad represents the maximum eigenvalue of the judgment matrix, CR represents the consistency ratio, RI represents the random consistency index, and n represents the number of evaluation factors.

3. The method for evaluating a progressively planned power grid based on combined evaluation theory according to claim 2, characterized in that: Obtaining the comprehensive weight of the power grid system includes calculating the weight of each data indicator according to an objective weighting method, and verifying it according to the Kendall concordance coefficient test method; When d(W (1) , W (2) ) is greater than the test threshold, it means that the test is passed. If the test is passed, use Calculate the comprehensive weight of the power grid system; The Kendall concordance coefficient verification method is expressed as: Among them, d(W (1) , W (2) ) represents the weight vector W (1) and W (2) The Euclidean distance between represents the square of the difference between the weight values ​​of the two weight vectors at the jth position, represents the calculation of all weight differences, W represents the comprehensive weight of the power grid system, and λ k represents the weight of the kth evaluation criterion, and q represents the total number of evaluation criteria.

4. The progressive planning power grid evaluation method based on combined evaluation theory as claimed in claim 3 is characterized by: The similarity theory evaluation method includes constructing a new model for power grid system grade evaluation based on the Shepard similarity interpolation method of the accelerated genetic algorithm and an ideal interval method based on the genetic algorithm; The new model for constructing the system level evaluation includes randomly generating a series of power grid data evaluation level samples x(i, j) and y(i) according to the system evaluation standard table, where i=1~n i , j = 1 ~ n j ; According to the sample parameter b, the optimal estimate is performed. Sample i is taken from the sample series, and Shepard interpolation is performed on the other n-1 samples to obtain the interpolation value corresponding to the evaluation level y(i), which is recorded as y c (i); The accelerated genetic algorithm is expressed as, in, Indicates that the sum meets, y c (i) represents the i-th observed value, y(i) represents the i-th actual value, and st represents the constraint condition; The ideal interval method based on genetic algorithm includes generating a series of evaluation standard samples and performing dimensionless processing, calculating the distance between each standard sample and the ideal interval of the standard grade, and calculating the relative membership value and evaluation value of each standard sample to the ideal interval of the standard grade.

5. The progressive planning power grid evaluation method based on combined evaluation theory as claimed in claim 4, characterized in that: The dimensionless processing is expressed as, x(k,j)=x*(k,j) / x max (j)(k=1)n k ,j=1~n j ) a(i,j)=a*(i,j) / x max (j)(i=1~n i ,j=1~n j ) b(i,j)=b*(i,j) / x max (j)(i=1~n i ,j=1~n j ) Among them, x(k, j), a(i, j), b(i, j) represent two-dimensional variables, x * (k, j), a * (i, j), b * (i, j) represent the original values ​​of x(k, j), a(i, j), and b(i, j), respectively. max (j) represents the maximum value of the jth evaluation index in the evaluation standard sample series, which can be expressed as, x max (j)=m i ax{b*(i,j)} The distance between each standard sample and the ideal range of the standard grade is expressed as: Where D(k, i) represents the total distance between the kth observation and the ith reference value, w(j) represents the weight of the jth indicator, d(k, i, j) represents the distance between the kth observation and the ith reference value on the jth feature, a(i, j) and b(i, j) represent the lower and upper bounds of the i-th reference value on the jth feature, x(k, j) represents the value of the kth observation on the jth feature, k = 1 to n k ; The relative membership value is expressed as, Among them, h(k) represents the quality level value of observation k, and y(k) represents the true value of observation k; The evaluation value is expressed as {z * (k,j)|k=1~n z ,j=1~n j }.

6. The progressive planning power grid evaluation method based on combined evaluation theory as claimed in claim 5, characterized in that: The evaluation based on nonparametric regression includes obtaining a one-dimensional projection value z(i) of the evaluation value, expressed as: The projection index function is obtained through the one-dimensional projection value, which is expressed as: Q(a)=S z |R xy | Among them, || represents the absolute value, S z represents the standard deviation of the projection value z(i); Estimate the optimal projection direction by solving the projection index function maximization problem maxQ(a)=S z |R xy | Optimization is performed using an accelerated genetic algorithm.

7. The progressive planning power grid evaluation method based on combined evaluation theory as claimed in claim 6, characterized in that: The evaluation based on nonparametric regression also includes establishing a corresponding Nadaraya-Watson nonparametric model, expressed as: Using standard evaluation objects * (i)|i=1~n} series and accelerated genetic algorithm are optimized by solving the following optimization problem, expressed as, 8. A system using the progressive planning power grid evaluation method based on combined evaluation theory as claimed in any one of claims 1 to 7, characterized in that: Including data processing module, feature engineering module, deep learning model training module, regularization and optimization module; The data processing module is responsible for collecting historical data and real-time data from the power grid system; The feature engineering module is responsible for selecting and constructing the features required for evaluation; The deep learning model training module is responsible for building and training a model combining an LSTM network and an attention mechanism; The regularization and optimization module is responsible for preventing the model from overfitting.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the progressive planning power grid evaluation method based on combined evaluation theory described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the progressive planning power grid evaluation method based on combined evaluation theory as described in any one of claims 1 to 7 are implemented.

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