CN112288593A - Evaluation data processing method, device and medium for power grid emergency system - Google Patents
Evaluation data processing method, device and medium for power grid emergency system Download PDFInfo
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Abstract
The invention discloses an evaluation data processing method, device and medium for a power grid emergency system, which are used for acquiring evaluation data of power grid emergency drilling; the evaluation data comprises evaluation grades given by a plurality of evaluation experts to each evaluation index of the power grid emergency drilling; determining an index weight value of each evaluation index, and calculating a basic probability distribution function of each evaluation index according to the index weight value of each evaluation index and the basic support degree of each evaluation index under each evaluation level; and performing data fusion on the basic probability distribution function of the evaluation index based on a synthetic rule of a D-S evidence theory to obtain the basic probability of each evaluation grade, and obtaining the evaluation result of the power grid emergency drilling based on a maximum membership rule. By implementing the method, the evaluation data is fused by adopting a D-S evidence theory to obtain the most appropriate evaluation result, so that the accuracy of the evaluation result can be effectively improved, and the emergency drilling condition can be objectively and comprehensively reflected.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to an evaluation data processing method, device and medium for a power grid emergency system.
Background
The power grid is one of the strong pillars for maintaining the life of the people, and the occurrence of the fault of the power grid can greatly influence the normal operation of the society and the normal life of the people. In recent years, countries have begun to reinforce the construction of emergency systems. Emergency drilling and training are important contents of emergency management work, when an emergency event occurs, power supply capacity is quickly recovered, normal production and life of residents are guaranteed, and the emergency drilling and training system is an important responsibility of power supply enterprises and power emergency repair departments. The emergency drilling and training can improve the proficiency and the reaction capability of emergency commanders and rescuers at all levels corresponding to emergency duties and processes, and promote the cooperation among all departments, so that emergency teams can better face real accident scenes, the emergency capability is improved, and casualties and property loss caused by emergency events are effectively reduced.
In order to find out the defects of the power grid emergency system in time and bring convenience to timely proposing and taking perfect corrective measures, the evaluation of emergency drilling needs to be carried out. And scoring each drilling evaluation index by a plurality of evaluation experts according to the emergency drilling condition. However, in the process of implementing the invention, the inventor finds that the prior art has at least the following problems: when multiple evaluation experts evaluate the drilling, multiple possible evaluation results may appear, and if only the evaluation data of the evaluation experts are subjected to a simple averaging method to obtain a final evaluation result, the accuracy of the evaluation result is greatly influenced, and the emergency drilling situation cannot be objectively and comprehensively reflected.
Disclosure of Invention
The embodiment of the invention aims to provide an evaluation data processing method, device and medium for a power grid emergency system, which are used for fusing evaluation data by adopting a D-S evidence theory so as to obtain the most appropriate evaluation result, effectively improve the accuracy of the evaluation result and objectively and comprehensively reflect the emergency drilling condition.
In order to achieve the above object, an embodiment of the present invention provides an evaluation data processing method for a power grid emergency system, including:
acquiring evaluation data of power grid emergency drilling; the evaluation data comprises evaluation grades given by a plurality of evaluation experts to each evaluation index of the power grid emergency drilling;
determining an index weight value of each evaluation index;
calculating a basic probability distribution function of each evaluation index according to the index weight value of each evaluation index and the basic support degree of each evaluation index under each evaluation level; the basic support degree represents the ratio of the obtained quantity of each evaluation grade corresponding to the evaluation index to the quantity of the evaluation experts;
performing data fusion on the basic probability distribution function of the evaluation index based on a synthetic rule of a D-S evidence theory to obtain the basic probability of each evaluation grade;
and obtaining an evaluation result of the power grid emergency drilling according to the basic probability of each evaluation grade based on the maximum membership principle.
As an improvement of the above scheme, the calculating a basic probability distribution function of each evaluation index according to the index weight value of each evaluation index and the basic support degree of each evaluation index at each evaluation level specifically includes:
dividing key indexes and non-key indexes according to the index weight value of each evaluation index; wherein the index weight value of the key index is the largest;
calculating a basic probability distribution function of the key indexes according to the basic support degree of the key indexes under each evaluation level:
mi(As)＝βm′i(As)(s＝1,2,...,S)；
calculating a basic probability distribution function of each non-key index according to the basic support degree of each non-key index under each evaluation level and the index weight value of the evaluation index:
wherein m isi(As) A basic probability distribution function for the evaluation index; m'i(As) Basic support degree of each evaluation index under each evaluation grade;
theta represents a preset evaluation level set; beta is a preset discount rate; omegaiIs an index weighted value; omegai,maxThe maximum index weight value; i is an element of [1, N ∈](ii) a N is the number of evaluation indexes, and S is the number of evaluation grades.
As an improvement of the above scheme, the data fusion is performed on the basic probability distribution function of the evaluation index based on the synthesis rule of the D-S evidence theory to obtain the basic probability of each evaluation level, and the method specifically includes:
and obtaining the basic probability of each evaluation grade according to the basic probability distribution function of the evaluation index and the following calculation formula based on the synthesis rule of the D-S evidence theory:
wherein m (A)s) Is the basic probability for each rating level.
As an improvement of the above scheme, the determining the index weight value of each evaluation index specifically includes:
determining an index weight value of each evaluation index based on an analytic hierarchy process;
the determining of the index weight value of each evaluation index based on the analytic hierarchy process specifically comprises the following steps:
traversing all the evaluation indexes according to a preset judgment matrix assignment rule, comparing the importance degrees of every two evaluation indexes, and assigning an importance scale to every two evaluation indexes according to a comparison result;
establishing a judgment matrix of an evaluation index according to the importance scale;
and acquiring a characteristic vector of a judgment matrix of the evaluation indexes to obtain an index weight value of each evaluation index.
As an improvement of the above solution, after the establishing a judgment matrix of an evaluation index according to the importance scale, the method further includes:
carrying out consistency check on the judgment matrix of the evaluation index to judge whether the judgment matrix of the evaluation index meets a preset consistency requirement or not;
and when the judgment matrix of the evaluation indexes does not meet the preset consistency requirement, comparing the importance degrees of every two evaluation indexes again, and assigning an importance scale to every two evaluation indexes according to the comparison result.
As an improvement of the above scheme, the evaluation data further includes evaluation scores given by a plurality of review experts for each evaluation index of the power grid emergency drilling;
the determining of the index weight value of each evaluation index specifically includes:
determining an index weight value of each evaluation index based on an entropy weight method;
the determining of the index weight value of each evaluation index based on the entropy weight method specifically includes:
and calculating the ratio of the evaluation scores of each evaluation index according to the evaluation scores given by each evaluation index based on each review expert:
calculating the entropy value of each evaluation index according to the evaluation score ratio of each evaluation index:
according to the entropy value of each evaluation index, calculating the index weight value of each evaluation index:
wherein eta isijFor the evaluation score ratio of each evaluation index, aijThe evaluation index is based on the evaluation score given by each evaluation expert; hiEntropy, ω ″, for each evaluation indexiAn index weight value for each evaluation index; i is an element of [1, N ∈](ii) a N is the number of evaluation indexes, j belongs to [1, M ∈](ii) a M is the number of review experts.
As an improvement of the above scheme, the determining the index weight value of each evaluation index specifically includes:
determining an index weight value of each evaluation index based on an analytic hierarchy process as a first index weight value;
determining an index weight value of each evaluation index based on an entropy weight method to serve as a second index weight value;
and determining the index weight value of each evaluation index by combining the first index weight value, the second index weight value and a preset weight factor:
ωi＝αω′i+(1-α)ω″i；
wherein, ω isiAn index weight value for each evaluation index; omega'iA first index weight value for each evaluation index; omega ″)iA second index weight value for each evaluation index; alpha is a preset weight factor, and alpha is more than or equal to 0 and less than or equal to 1.
The embodiment of the invention also provides an evaluation data processing device of a power grid emergency system, which comprises:
the evaluation data acquisition module is used for acquiring evaluation data of power grid emergency drilling; the evaluation data comprises evaluation grades given by a plurality of evaluation experts to each evaluation index of the power grid emergency drilling;
the index weight value determining module is used for determining the index weight value of each evaluation index;
the probability distribution function calculation module is used for calculating a basic probability distribution function of each evaluation index according to the index weight value of each evaluation index and the basic support degree of each evaluation index under each evaluation level; the basic support degree represents the ratio of the obtained quantity of each evaluation grade corresponding to the evaluation index to the quantity of the evaluation experts;
the basic probability calculation module is used for carrying out data fusion on the basic probability distribution function of the evaluation index based on the synthetic rule of the D-S evidence theory to obtain the basic probability of each evaluation grade;
and the evaluation result obtaining module is used for obtaining the evaluation result of the power grid emergency drilling according to the basic probability of each evaluation grade based on the maximum membership degree principle.
The embodiment of the present invention further provides an evaluation data processing apparatus for a power grid emergency system, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor, when executing the computer program, implements the evaluation data processing method for the power grid emergency system according to any one of the above claims.
The embodiment of the invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, the device where the computer-readable storage medium is located is controlled to execute the evaluation data processing method of the power grid emergency system.
Compared with the prior art, the evaluation data processing method, the evaluation data processing device and the evaluation data processing medium for the power grid emergency system are used for obtaining the evaluation data of power grid emergency drilling; the evaluation data comprises evaluation grades given by a plurality of evaluation experts to each evaluation index of the power grid emergency drilling; determining an index weight value of each evaluation index, and calculating a basic probability distribution function of each evaluation index according to the index weight value of each evaluation index and the basic support degree of each evaluation index under each evaluation level; and performing data fusion on the basic probability distribution function of the evaluation index based on a synthetic rule of a D-S evidence theory to obtain the basic probability of each evaluation grade, and obtaining the evaluation result of the power grid emergency drilling based on a maximum membership rule. In the embodiment of the invention, the problem that the uncertainty of the evaluation result is large due to various possible evaluation results in the evaluation process of the power grid emergency drilling is considered, and the evaluation data of the power grid emergency drilling is fused by adopting a D-S evidence theory to obtain the most appropriate evaluation result, so that the evaluation result of the power grid emergency drilling is close to the actual situation as much as possible. Therefore, the accuracy of the evaluation result is effectively improved, and the emergency drilling situation is objectively and comprehensively reflected.
Drawings
Fig. 1 is a schematic step flow diagram of a method for processing evaluation data of a power grid emergency system according to an embodiment of the present invention;
fig. 2 is a schematic flow chart illustrating a step of determining an index weight value based on an analytic hierarchy process in the evaluation data processing method for a power grid emergency system according to the second embodiment of the present invention;
fig. 3 is a schematic flow chart illustrating a step of determining an index weight value based on an entropy weight method in a data evaluation processing method for a power grid emergency system according to a third embodiment of the present invention;
fig. 4 is a schematic flow chart illustrating steps of preferentially determining an index weight value in the evaluation data processing method of the grid emergency system according to the fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an evaluation data processing device of a power grid emergency system according to a fifth embodiment of the present invention;
fig. 6 is a schematic structural diagram of an evaluation data processing device of a power grid emergency system according to a sixth embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Referring to fig. 1, a schematic step flow diagram of a method for processing evaluation data of a power grid emergency system according to an embodiment of the present invention is shown. The evaluation data processing method for the power grid emergency system provided by the embodiment of the invention is implemented through steps S11 to S15:
s11, obtaining evaluation data of power grid emergency drilling; the evaluation data comprise evaluation grades given by a plurality of evaluation experts to each evaluation index of the power grid emergency drilling.
Specifically, a power grid emergency drilling evaluation index system is established in advance, and various evaluation indexes and corresponding evaluation standards of the power grid emergency drilling are determined and used for evaluating the power grid emergency drilling condition. And after each emergency drilling of the power grid, inviting a plurality of evaluation experts to evaluate each evaluation index according to the emergency drilling condition, thereby obtaining and storing the evaluation data.
For example, in the embodiment of the present invention, five evaluation indexes are preset, which are the completeness of the drilling plan, the implementation condition of the drilling guarantee resource, the performance condition of the drilling implementation process, the completion condition of the drilling implementation process, and the survey summarizing capability after the drilling is finished, respectively; meanwhile, for the evaluation index, determining the corresponding evaluation criterion includes evaluating a rating set Θ ═ { a ═ a1(Excellent), A2(good), A3(in), A4(difference) } four, and the evaluation score corresponding to each evaluation grade is I { (100-85), (84-75), (74-60), (59-0) } in this order.
It can be understood that the above mentioned scenes and values are only used as examples, and in practical applications, each evaluation index and corresponding evaluation criterion of the power grid emergency drilling may be set according to actual conditions or a priori knowledge, and are not limited to the above mentioned embodiments, and do not affect the beneficial effects obtained by the present invention.
And S12, determining the index weight value of each evaluation index.
Specifically, according to the established power grid emergency drilling evaluation indexes, a reasonable weight division model is established to determine the weight values omega of all the evaluation indexesi。
And S13, calculating a basic probability distribution function of each evaluation index according to the index weight value of each evaluation index and the basic support degree of each evaluation index under each evaluation level.
In the embodiment of the present invention, the basic support degree of each evaluation index under each evaluation level represents a ratio of the obtained number of each evaluation level corresponding to the evaluation index to the number of review experts, and an expression formula thereof is specifically:
wherein m'i(As) Is the basic support degree; n isi(As) Evaluation for evaluation index at the evaluation levelThe value grade obtains a quantity, i.e. indicates ni(As) The evaluation grade of the evaluation index i by the evaluation expert belongs to AsThe evaluation levels, S1, 2, N are the number of evaluation levels, and S is the number of evaluation levels. For example, in the embodiment of the present invention, if the number of the evaluation indexes is 5 and the number of the evaluation levels is 4, each evaluation index i has 4 basic support degrees corresponding to the evaluation levels.
Further, step S3 is performed by steps S31 to S33:
s131, dividing key indexes and non-key indexes according to the index weight value of each evaluation index; and the index weight value of the key index is the largest.
According to the index weight value omega of each evaluation indexiDetermining the maximum value and the maximum index weight value omegai,max＝max(ω1,...,ωN) The evaluation index whose index weight has the maximum value is referred to as a key index, and the remaining evaluation indexes are referred to as non-key indexes.
S132, calculating a basic probability distribution function of the key indexes according to the basic support degree of the key indexes under each evaluation level:
mi(As)＝βm′i(As)(s＝1,2,...,S)；
s133, calculating a basic probability distribution function of each non-key index according to the basic support degree of each non-key index under each evaluation level and the index weight value of the evaluation index:
simultaneously, the method comprises the following steps:
wherein m isi(As) A basic probability distribution function for the evaluation index; m ″)i(As) Basic support degree of each evaluation index under each evaluation grade; m isi(Θ) represents the unassigned probability;
Θ is a set representing a preset evaluation level.
Beta is a preset discount rate, which can be set according to actual conditions. Preferably, β ═ 0.9. i belongs to [1, N ], i is an integer, and N is the number of evaluation indexes; s belongs to [1, S ], S is an integer, and S is the number of evaluation grades.
And S14, performing data fusion on the basic probability distribution function of the evaluation index based on the synthetic rule of the D-S evidence theory to obtain the basic probability of each evaluation grade.
Specifically, the basic probability m (A) of each evaluation level is obtained by the following calculation formula according to the basic probability distribution function of the evaluation index based on the synthesis rule of the D-S evidence theorys)：
Wherein,
and S15, obtaining an evaluation result of the power grid emergency drilling according to the basic probability of each evaluation grade based on the maximum membership principle.
Specifically, according to the principle of maximum membership degree, m (A) is takens) Finding the evaluation grade A represented by the maximum value of (1)sAnd the evaluation grade is the final evaluation result of the power grid emergency drilling.
For example, taking the number of evaluation indexes as 2 and the number of evaluation levels as 3 as an example, based on a synthesis rule of a D-S evidence theory, data fusion is performed on the calculated basic probability distribution functions of all the evaluation indexes, so as to obtain the basic probability of each evaluation level.
K＝m1(A1)m2(A1)+m1(A1)m2(Θ)+m1(Θ)m2(A1)+
m1(A2)m2(A2)+m1(A2)m2(Θ)+m1(Θ)m2(A2)+
m1(A3)m2(A3)+m1(A3)m2(Θ)+m1(Θ)m2(A3)+m1(Θ)m2(Θ)；
Then there are:
further, according to the principle of maximum membership degree, m (A) is takens) Assuming that the calculation result is m (A)2) Maximum, then m (A)2) Corresponding rating A2And the evaluation grade is the final evaluation result of the power grid emergency drilling.
The embodiment of the invention provides an evaluation data processing method of a power grid emergency system, which comprises the steps of obtaining evaluation data of power grid emergency drilling; the evaluation data comprises evaluation grades given by a plurality of evaluation experts to each evaluation index of the power grid emergency drilling; determining an index weight value of each evaluation index, and calculating a basic probability distribution function of each evaluation index according to the index weight value of each evaluation index and the basic support degree of each evaluation index under each evaluation level; and performing data fusion on the basic probability distribution function of the evaluation index based on a synthetic rule of a D-S evidence theory to obtain the basic probability of each evaluation grade, and obtaining the evaluation result of the power grid emergency drilling based on a maximum membership rule. In the embodiment of the invention, the problem that the uncertainty of the evaluation result is large due to various possible evaluation results in the evaluation process of the power grid emergency drilling is considered, and the evaluation data of the power grid emergency drilling is fused by adopting a D-S evidence theory to obtain the most appropriate evaluation result, so that the evaluation result of the power grid emergency drilling is close to the actual situation as much as possible. Therefore, the accuracy of the evaluation result is effectively improved, and the emergency drilling situation is objectively and comprehensively reflected.
Example two
Fig. 2 is a schematic flow chart illustrating a step of determining an index weight value based on an analytic hierarchy process in the evaluation data processing method for the grid emergency system according to the second embodiment of the present invention. The second embodiment of the present invention is implemented on the basis of the first embodiment. In the embodiment of the present invention, step S12 specifically includes: and determining the index weight value of each evaluation index based on an analytic hierarchy process.
Specifically, step S12 is performed through steps S21 to S23:
and S21, traversing all the evaluation indexes according to a preset judgment matrix assignment rule, comparing the importance degrees of every two evaluation indexes, and assigning an importance scale to every two evaluation indexes according to the comparison result.
And S22, establishing a judgment matrix of the evaluation index according to the importance scale.
And S23, obtaining the characteristic vector of the judgment matrix of the evaluation index, and obtaining the index weight value of each evaluation index.
In the embodiment of the invention, a judgment matrix assignment rule is preset, the evaluation indexes are compared pairwise according to the preset judgment matrix assignment rule to obtain an importance comparison result, and an importance scale is assigned to each two evaluation indexes according to the comparison result.
In an embodiment, taking the obtained two evaluation indexes as a first index and a second index as an explanation, the preset evaluation matrix assignment rule is shown in table 1:
TABLE 1 decision matrix assignment rule
According to an importance scale obtained by assigning values to every two evaluation indexes, a judgment matrix X of the evaluation indexes can be established:
wherein N is the number of evaluation indexes.
Further, calculating a feature vector of a judgment matrix of the evaluation index to obtain an index weight value of each evaluation index, which is marked as ω'i(i＝1,2,...,N)。
It is understood that, in order to make the comparison result of the importance degrees of the first index and the second index more accurate and comprehensive, the importance scale may be set to be 2,4,6,8, which is used to represent the importance scale with the importance degree between the upper and lower scales. For example, a value of 2 may be assigned when the degree of importance of the first index and the second index is between the same and slightly important. The comparison rule of the importance degree of the first index and the second index may be obtained by judging the importance degree of the evaluation index based on the data in the event library and the opinion of the decision maker.
As a preferred embodiment, to avoid logical errors, after the establishing a judgment matrix of evaluation indexes according to the importance scale, the method further includes:
s24, carrying out consistency check on the judgment matrix of the evaluation index to judge whether the judgment matrix of the evaluation index meets the preset consistency requirement; and when the judgment matrix of the evaluation indexes does not meet the preset consistency requirement, comparing the importance degrees of every two evaluation indexes again, and assigning an importance scale to every two evaluation indexes according to the comparison result.
The step of performing consistency check on the judgment matrix of the evaluation index specifically comprises the following steps:
and (3) calculating a consistency index:
calculating a checking coefficient:
wherein λ ismaxThe maximum eigenvalue in the constructed judgment matrix; n is the index number; RI is a random consistency index, and its values are shown in table 2.
TABLE 2 stochastic consistency index assignment
And taking 0.1 as a consistency requirement threshold value which needs to be met by the consistency index, if CR <0.1, indicating that the constructed judgment matrix meets the preset consistency requirement, and obtaining the index weight value of each evaluation index according to the characteristic vector of the judgment matrix of the evaluation index. Otherwise, comparing the importance degree of the evaluation index and assigning the importance scale again until a judgment matrix meeting the consistency requirement is obtained.
In the embodiment of the invention, in order to reflect the importance of different evaluation indexes, an index weight value of each evaluation index is determined by adopting a subjective weighting method of an analytic hierarchy process, so that the accuracy of an evaluation result of power grid emergency drilling is further improved.
EXAMPLE III
Fig. 3 is a schematic flow chart illustrating a step of determining an index weight value based on an entropy weight method in the evaluation data processing method of the power grid emergency system according to the third embodiment of the present invention. The third embodiment of the present invention is implemented on the basis of the first embodiment. In the embodiment of the present invention, step S12 specifically includes: and determining the index weight value of each evaluation index based on an entropy weight method.
For a certain index, the degree of dispersion of the certain index can be judged by using an entropy value, and the smaller the information entropy value is, the greater the degree of dispersion of the index is, and the greater the influence (i.e. weight) of the index on the comprehensive evaluation is. Therefore, the weight of each index is calculated by using the information entropy tool, and a basis is provided for multi-index comprehensive evaluation.
Specifically, step S12 is performed through steps S31 to S33:
s31, calculating the evaluation score ratio of each evaluation index according to the evaluation score given by each evaluation index based on each review expert:
s32, calculating the entropy value of each evaluation index according to the evaluation score ratio of each evaluation index:
s33, according to the entropy value of each evaluation index, calculating the index weight value of each evaluation index:
wherein, 0 is not less than omega ″)iLess than or equal to 1, and
ηijfor the evaluation score ratio of each evaluation index, aijThe evaluation index is based on the evaluation score given by each evaluation expert; hiFor each evaluation indexEntropy value, ω ″)iAn index weight value for each evaluation index; i is an element of [1, N ∈](ii) a N is the number of evaluation indexes, j belongs to [1, M ∈](ii) a M is the number of review experts.
In the embodiment of the invention, in order to reflect the importance of different evaluation indexes, an entropy weight method, which is an objective weighting method, is adopted to determine the index weight values of all the evaluation indexes, so that the accuracy of the evaluation result of the power grid emergency drilling is further improved.
Example four
Referring to fig. 4, a schematic flow chart of steps for preferably determining the index weight value in the evaluation data processing method of the grid emergency system in the fourth embodiment of the present invention is shown. The fourth embodiment of the present invention is implemented on the basis of the first to third embodiments. In the embodiment of the present invention, step S12 specifically includes:
s41, determining an index weight value of each evaluation index based on an analytic hierarchy process as a first index weight value;
s42, determining an index weight value of each evaluation index based on an entropy weight method to serve as a second index weight value;
s43, determining an index weight value of each evaluation index by combining the first index weight value, the second index weight value, and a preset weight factor:
ωi＝αω′i+(1-α)ω″i；
wherein, ω isiAn index weight value for each evaluation index; omega'iA first index weight value for each evaluation index; omega ″)iA second index weight value for each evaluation index; alpha is a preset weight factor, and alpha is more than or equal to 0 and less than or equal to 1.
In the embodiment of the invention, the first index weight value omega 'of each evaluation index is determined based on analytic hierarchy process'iThe procedure of (1) is similar to the procedure of the second embodiment, and the second index weight value ω ″, of each evaluation index is determined based on the entropy weight methodiThe steps in the method are similar to the flow of the third step in the embodiment, and are not described herein again.
The weighting factor α may be preset, and the larger the value, the more importance is placed on the weighting result. The determination may be made based on the experience of the review expert. When the weighting factor takes 0.5, it means that the two weighting results are equally weighted.
In the embodiment of the invention, in consideration of the one-sided defect of a single index weighting model, the accuracy of the index weight values of each evaluation index can be effectively improved by combining a subjective weighting method with an objective weighting method, namely combining an analytic hierarchy process and an entropy weight method to determine the index weight values of each evaluation index, so that the objectivity and the accuracy of the evaluation result of the emergency drilling of the power grid are further improved.
EXAMPLE five
Fig. 5 is a schematic structural diagram of an evaluation data processing device of a power grid emergency system according to a fifth embodiment of the present invention. The fifth embodiment of the present invention provides an evaluation data processing apparatus 50 for a power grid emergency system, including an evaluation data obtaining module 51, an index weight value determining module 52, a probability distribution function calculating module 53, a basic probability calculating module 54, and an evaluation result obtaining module 55. Wherein,
the evaluation data acquisition module 51 is configured to acquire evaluation data of power grid emergency drilling; the evaluation data comprises evaluation grades given by a plurality of evaluation experts to each evaluation index of the power grid emergency drilling;
the index weight value determining module 52 is configured to determine an index weight value of each evaluation index;
the probability distribution function calculating module 53 is configured to calculate a basic probability distribution function of each evaluation index according to the index weight value of each evaluation index and the basic support degree of each evaluation index at each evaluation level; the basic support degree represents the ratio of the obtained quantity of each evaluation grade corresponding to the evaluation index to the quantity of the evaluation experts;
the basic probability calculation module 54 is configured to perform data fusion on the basic probability distribution function of the evaluation index based on a synthesis rule of a D-S evidence theory to obtain a basic probability of each evaluation level;
and the evaluation result obtaining module 55 is configured to obtain an evaluation result of the power grid emergency drilling according to the basic probability of each evaluation level based on the maximum membership rule.
As a preferred embodiment, the probability distribution function calculating module 53 is specifically configured to:
dividing key indexes and non-key indexes according to the index weight value of each evaluation index; wherein the index weight value of the key index is the largest;
calculating a basic probability distribution function of the key indexes according to the basic support degree of the key indexes under each evaluation level:
mi(As)＝βm′i(As)(s＝1,2,...,S)；
calculating a basic probability distribution function of each non-key index according to the basic support degree of each non-key index under each evaluation level and the index weight value of the evaluation index:
wherein m isi(As) A basic probability distribution function for the evaluation index; m ″)i(As) Basic support degree of each evaluation index under each evaluation grade;
theta represents a preset evaluation level set; beta is a preset discount rate; omegaiIs an index weighted value; omegai,maxThe maximum index weight value; i is an element of [1, N ∈](ii) a N is the number of evaluation indexes, and S is the number of evaluation grades.
As a preferred embodiment, the basic probability calculating module 54 is specifically configured to:
and obtaining the basic probability of each evaluation grade according to the basic probability distribution function of the evaluation index and the following calculation formula based on the synthesis rule of the D-S evidence theory:
wherein m (A)s) Is the basic probability for each rating level.
As a preferred implementation manner, in the first implementation manner, the index weight value determining module 52 is specifically configured to: determining an index weight value of each evaluation index based on an analytic hierarchy process;
the method specifically comprises the following steps:
traversing all the evaluation indexes according to a preset judgment matrix assignment rule, comparing the importance degrees of every two evaluation indexes, and assigning an importance scale to every two evaluation indexes according to a comparison result;
establishing a judgment matrix of an evaluation index according to the importance scale;
and acquiring a characteristic vector of a judgment matrix of the evaluation indexes to obtain an index weight value of each evaluation index.
As a preferred implementation manner, in the second implementation manner, the index weight value determining module 52 is specifically configured to: determining an index weight value of each evaluation index based on an entropy weight method;
the method specifically comprises the following steps:
and calculating the ratio of the evaluation scores of each evaluation index according to the evaluation scores given by each evaluation index based on each review expert:
calculating the entropy value of each evaluation index according to the evaluation score ratio of each evaluation index:
according to the entropy value of each evaluation index, calculating a second index weight value of each evaluation index:
wherein eta isijFor the evaluation score ratio of each evaluation index, aijThe evaluation index is based on the evaluation score given by each evaluation expert; hiEntropy, ω ″, for each evaluation indexiA second index weight value for each evaluation index; i is an element of [1, N ∈](ii) a N is the number of evaluation indexes, j belongs to [1, M ∈](ii) a M is the number of review experts.
As a preferred embodiment, in a third embodiment, the index weight value determining module 52 is specifically configured to:
determining an index weight value of each evaluation index based on an analytic hierarchy process as a first index weight value;
determining an index weight value of each evaluation index based on an entropy weight method to serve as a second index weight value;
and determining the index weight value of each evaluation index by combining the first index weight value, the second index weight value and a preset weight factor:
ωi＝αω′i+(1-α)ω″i；
wherein, ω isiAn index weight value for each evaluation index; omega'iA first index weight value for each evaluation index; omega ″)iA second index weight value for each evaluation index; alpha is a preset weight factor, and alpha is more than or equal to 0 and less than or equal to 1.
It should be noted that, the evaluation data processing apparatus for a power grid emergency system according to the embodiment of the present invention is configured to execute all the process steps of the evaluation data processing method for a power grid emergency system according to the embodiment, and working principles and beneficial effects of the two are in one-to-one correspondence, so that details are not repeated.
EXAMPLE six
Fig. 6 is a schematic structural diagram of an evaluation data processing device of a power grid emergency system according to a sixth embodiment of the present invention. Sixth embodiment of the present invention further provides an evaluation data processing apparatus 60 for a power grid emergency system, including a processor 61, a memory 62, and a computer program stored in the memory and configured to be executed by the processor, where when the processor executes the computer program, the evaluation data processing method for the power grid emergency system according to any one of the first to fifth embodiments is implemented.
The embodiment of the invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, the device where the computer-readable storage medium is located is controlled to execute the evaluation data processing method of the power grid emergency system according to any one of the first to fifth embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A method for processing evaluation data of a power grid emergency system is characterized by comprising the following steps:
acquiring evaluation data of power grid emergency drilling; the evaluation data comprises evaluation grades given by a plurality of evaluation experts to each evaluation index of the power grid emergency drilling;
determining an index weight value of each evaluation index;
calculating a basic probability distribution function of each evaluation index according to the index weight value of each evaluation index and the basic support degree of each evaluation index under each evaluation level; the basic support degree represents the ratio of the obtained quantity of each evaluation grade corresponding to the evaluation index to the quantity of the evaluation experts;
performing data fusion on the basic probability distribution function of the evaluation index based on a synthetic rule of a D-S evidence theory to obtain the basic probability of each evaluation grade;
and obtaining an evaluation result of the power grid emergency drilling according to the basic probability of each evaluation grade based on the maximum membership principle.
2. The method for processing evaluation data of a grid emergency system according to claim 1, wherein the calculating a basic probability distribution function of each evaluation index according to the index weight value of each evaluation index and the basic support degree of each evaluation index at each evaluation level specifically includes:
dividing key indexes and non-key indexes according to the index weight value of each evaluation index; wherein the index weight value of the key index is the largest;
calculating a basic probability distribution function of the key indexes according to the basic support degree of the key indexes under each evaluation level:
mi(As)＝βm′i(As)(s＝1,2,...,S)；
calculating a basic probability distribution function of each non-key index according to the basic support degree of each non-key index under each evaluation level and the index weight value of the evaluation index:
wherein m isi(As) Basic probability distribution function for evaluation index；m′i(As) Basic support degree of each evaluation index under each evaluation grade;
theta represents a preset evaluation level set; beta is a preset discount rate; omegaiIs an index weighted value; omegai,maxThe maximum index weight value; i is an element of [1, N ∈](ii) a N is the number of evaluation indexes, and S is the number of evaluation grades.
3. The evaluation data processing method of the power grid emergency system according to claim 2, wherein the data fusion is performed on the basic probability distribution function of the evaluation index based on the synthesis rule of the D-S evidence theory to obtain the basic probability of each evaluation level, and specifically includes:
and obtaining the basic probability of each evaluation grade according to the basic probability distribution function of the evaluation index and the following calculation formula based on the synthesis rule of the D-S evidence theory:
wherein m (A)s) Is the basic probability for each rating level.
4. The evaluation data processing method of the grid emergency system according to claim 1, wherein the determining of the index weight value of each evaluation index specifically includes:
determining an index weight value of each evaluation index based on an analytic hierarchy process;
the determining of the index weight value of each evaluation index based on the analytic hierarchy process specifically comprises the following steps:
traversing all the evaluation indexes according to a preset judgment matrix assignment rule, comparing the importance degrees of every two evaluation indexes, and assigning an importance scale to every two evaluation indexes according to a comparison result;
establishing a judgment matrix of an evaluation index according to the importance scale;
and acquiring a characteristic vector of a judgment matrix of the evaluation indexes to obtain an index weight value of each evaluation index.
5. The method for processing evaluation data of a grid emergency system according to claim 4, wherein after the establishing of the judgment matrix of the evaluation index according to the importance scale, the method further comprises:
carrying out consistency check on the judgment matrix of the evaluation index to judge whether the judgment matrix of the evaluation index meets a preset consistency requirement or not;
and when the judgment matrix of the evaluation indexes does not meet the preset consistency requirement, comparing the importance degrees of every two evaluation indexes again, and assigning an importance scale to every two evaluation indexes according to the comparison result.
6. The evaluation data processing method of the grid emergency system according to claim 1, wherein the evaluation data further includes evaluation scores given by a plurality of review experts for each evaluation index of the grid emergency drill;
the determining of the index weight value of each evaluation index specifically includes:
determining an index weight value of each evaluation index based on an entropy weight method;
the determining of the index weight value of each evaluation index based on the entropy weight method specifically includes:
and calculating the ratio of the evaluation scores of each evaluation index according to the evaluation scores given by each evaluation index based on each review expert:
calculating the entropy value of each evaluation index according to the evaluation score ratio of each evaluation index:
according to the entropy value of each evaluation index, calculating the index weight value of each evaluation index:
wherein eta isijFor the evaluation score ratio of each evaluation index, aijThe evaluation index is based on the evaluation score given by each evaluation expert; hiEntropy, ω ″, for each evaluation indexiAn index weight value for each evaluation index; i is an element of [1, N ∈](ii) a N is the number of evaluation indexes, j belongs to [1, M ∈](ii) a M is the number of review experts.
7. The evaluation data processing method of the grid emergency system according to claim 1, wherein the determining of the index weight value of each evaluation index specifically includes:
determining an index weight value of each evaluation index based on an analytic hierarchy process as a first index weight value;
determining an index weight value of each evaluation index based on an entropy weight method to serve as a second index weight value;
and determining the index weight value of each evaluation index by combining the first index weight value, the second index weight value and a preset weight factor:
ωi＝αω′i+(1-α)ω″i；
wherein, ω isiAn index weight value for each evaluation index; omega'iA first index weight value for each evaluation index; omega ″)iA second index weight value for each evaluation index;alpha is a preset weight factor, and alpha is more than or equal to 0 and less than or equal to 1.
8. An evaluation data processing device for a power grid emergency system, comprising:
the evaluation data acquisition module is used for acquiring evaluation data of power grid emergency drilling; the evaluation data comprises evaluation grades given by a plurality of evaluation experts to each evaluation index of the power grid emergency drilling;
the index weight value determining module is used for determining the index weight value of each evaluation index;
the probability distribution function calculation module is used for calculating a basic probability distribution function of each evaluation index according to the index weight value of each evaluation index and the basic support degree of each evaluation index under each evaluation level; the basic support degree represents the ratio of the obtained quantity of each evaluation grade corresponding to the evaluation index to the quantity of the evaluation experts;
the basic probability calculation module is used for carrying out data fusion on the basic probability distribution function of the evaluation index based on the synthetic rule of the D-S evidence theory to obtain the basic probability of each evaluation grade;
and the evaluation result obtaining module is used for obtaining the evaluation result of the power grid emergency drilling according to the basic probability of each evaluation grade based on the maximum membership degree principle.
9. An evaluation data processing device of a power grid emergency system, comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the evaluation data processing method of the power grid emergency system according to any one of claims 1 to 7.
10. A computer-readable storage medium, comprising a stored computer program, wherein when the computer program runs, the computer-readable storage medium controls a device to execute the evaluation data processing method of the grid emergency system according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112884318A (en) * | 2021-02-07 | 2021-06-01 | 全球能源互联网研究院有限公司 | Power grid emergency drilling evaluation method and device and storage medium |
| CN113537738A (en) * | 2021-06-30 | 2021-10-22 | 北京航空航天大学 | Multi-cycle and multi-constraint fused aviation emergency rescue efficiency evaluation method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103778575A (en) * | 2014-03-04 | 2014-05-07 | 国网浙江宁波市鄞州区供电公司 | Transformer state evaluation method and system |
| CN107944702A (en) * | 2017-11-23 | 2018-04-20 | 绥化学院 | A kind of network security step analysis appraisal procedure, device and computer-readable recording medium |
| CN108537448A (en) * | 2018-04-12 | 2018-09-14 | 西南交通大学 | High iron catenary health state evaluation method based on Set Pair Analysis and evidence theory |
| CN111667150A (en) * | 2020-05-15 | 2020-09-15 | 南方电网科学研究院有限责任公司 | Power grid emergency operation management method, device, equipment and storage medium |
-
2020
- 2020-10-21 CN CN202011129821.7A patent/CN112288593A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103778575A (en) * | 2014-03-04 | 2014-05-07 | 国网浙江宁波市鄞州区供电公司 | Transformer state evaluation method and system |
| CN107944702A (en) * | 2017-11-23 | 2018-04-20 | 绥化学院 | A kind of network security step analysis appraisal procedure, device and computer-readable recording medium |
| CN108537448A (en) * | 2018-04-12 | 2018-09-14 | 西南交通大学 | High iron catenary health state evaluation method based on Set Pair Analysis and evidence theory |
| CN111667150A (en) * | 2020-05-15 | 2020-09-15 | 南方电网科学研究院有限责任公司 | Power grid emergency operation management method, device, equipment and storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112884318A (en) * | 2021-02-07 | 2021-06-01 | 全球能源互联网研究院有限公司 | Power grid emergency drilling evaluation method and device and storage medium |
| CN113537738A (en) * | 2021-06-30 | 2021-10-22 | 北京航空航天大学 | Multi-cycle and multi-constraint fused aviation emergency rescue efficiency evaluation method |
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