CN110796377B - Power grid service system monitoring method supporting fuzzy theory - Google Patents

Abstract

The invention discloses a power grid service system monitoring method supporting a fuzzy theory, which comprises the steps of constructing basic probability data of a corresponding subset of events by considering the urgency degree of the events, and establishing another group of basic probability data by considering the dependency relationship and the deadline time among the events; and combining basic probability data obtained by multiple attributes, fusing the multiple attributes by adopting D-S to enable the multiple attributes to become the characteristics of the multiple attributes, deducing the membership degree and the non-membership degree of the fuzzy ending moment through the relation between the D-S and the IFS, further reversely deducing the membership degree and the non-membership degree of the fuzzy starting moment according to the operating time reasoning characteristic, wherein the starting time represents the moment most relevant to the scheduling sequence, so that scoring is further performed through the obtained membership degree and the non-membership degree of the fuzzy starting moment, and the events are sequentially loaded into the time scheduling sequence according to the fuzzy starting moment time sequence and the score, so that the scheduling of the parallel risk events for responding to the time sequence is realized.

Description

Power grid service system monitoring method supporting fuzzy theory

Technical Field

The invention relates to a power grid service system monitoring method supporting a fuzzy theory, and belongs to the field of power grid system monitoring.

Background

The power grid is a main constituent part of a power system and is one of important infrastructures for urban modern construction, and the advantages and disadvantages of the modern power grid not only directly influence the safe operation of a power department, but also relate to the development of national economy and the life quality of people. The comprehensive evaluation criteria for grid services are not absolute, but it is important to reduce the losses in the grid supply and to restore operation as soon as possible. The method and the device can accurately determine the coping time sequence of the parallel risk events occurring when the power grid system is abnormally operated, so that the power grid system can be recovered to normally operate as soon as possible, the energy consumption in the work of detecting the power grid system is reduced, and the adverse effects and the loss caused by the abnormal operation of the power grid system are reduced.

Disclosure of Invention

The invention provides a power grid service system monitoring method supporting a fuzzy theory, which is used for coping when a power grid system is abnormally operated and scientifically arranging scheduling of coping time sequences of parallel risk events.

The technical scheme of the invention is as follows: a power grid service system monitoring method supporting a fuzzy theory comprises the following steps:

step1, a plurality of events which have influences on the normal rotation of the power grid system and occur at the same time when the power grid service system is monitored are called as time uncertain events; obtaining any subset ET of the general urgent time uncertainty event UTE by adopting an average distribution method_iMET of₁(ET_i) (ii) a Obtaining any subset ET of urgent time uncertainty event UTE and untightened time uncertainty event UTE by adopting normal distribution method_iMET of₁(ET_i)；

Step2, according to ET_iAnd (3) respectively calculating the basic probability distribution number of each subset according to different numbers of the subset elements:

if the number of elements in the subset is 1, the number of elementary probability distributions for the subset is represented as: pt₁,…,Pt_j,…,Pt_sigm(ii) a All subsets with the number of elements in the subset 1 in the time uncertainty event UTE are assigned a constraint probability P_UTE1(ii) a Satisfies Pt₁+…+Pt_j+…+Pt_sigm＝P_UTE1According to P_dep、P_dlPush out P_UTE1：

According to the obtained P_UTE1Finding Pt_j：

If the number of elements in the subset is m, the subset with the number of elements being m has C (sigm, m), and the basic probability distribution number for constructing the subset with the number of elements being m is expressed as: p_(m,1),…,P_(m,n),…,P_{(m,C(sigm,m))}(ii) a All subsets with m elements in the subset of time uncertainty event UTE are assigned a constraint probability P_UTEmAccording to P_UTE1Calculating P_UTEm：

According to P_UTEmCalculating the number of basic probability distribution P with the n-th subset element number m_(m,n)：

The obtained Pt is subjected to heat treatment_j、P_(m,n)MET as corresponding respective subsets₂(ET_i)；

Wherein the fuzzy end time t of the time uncertainty event UTE is determined_jAs sample space DE, ET_iRepresenting the ith subset of the sample space DE, and sigm representing the total number of elements of the sample space DE of the time uncertainty event UTE; t is t_jRepresents the jth blurring end time and also represents the jth element of the sample space DE; p_dep、P_dlIs a constant number 0<P_dep、P_dl<1；j∈[1,sigm]C (sigm, m) represents a permutation combination;

step3, MET obtained from Step1₁(ET_i) MET with results obtained at Step2₂(ET_i) Obtaining MET (E) according to the basic probability orthogonality of the D-S evidence theoryT_i)；

Step4, according to MET (ET)_j) To ET_jDegree of membership mu (ET)_j) Non-membership gamma (ET)_j) The calculation of (2):

means all belonging to ET_jSubset ET of_jMET (ET) of `_j') a cumulative sum;

represents MET (ET)_j'') of a plurality of different combinations, wherein ET_j'' indicates all subsets in the event of uncertainty and the currently sought subset ET_jSubsets whose intersections are not null;

wherein, ET_jRefers to a subset with 1 element number;

step5, calculating the membership degree mu 1 (ET) of the matching fuzzy starting time according to the result obtained in the Step4_k' ' ') and non-membership degree gamma 1(ET_k''' )：

Wherein the fuzzy start time represents a difference between the fuzzy end time and the event execution time; ET_k'' denotes ET_jA subset formed by fuzzy start time corresponding to the middle element;

the hesitation degree of the blurring start timing is:

π(ET_k''' )＝1-(μ1(ET_k''' )+γ1(ET_k''' ))

mixing mu 1 (ET)_k''' )、γ1(ET_k''' )、π(ET_k' ' ') into the scoring function:

step6, loading the events into the time scheduling sequence in turn according to the fuzzy start time sequence and the score.

For the general urgent time uncertainty event UTE, any subset ET of the general urgent time uncertainty event UTE is obtained by adopting an average distribution method_iMET of₁(ET_i)：

For a subset of one element number, the basic probability number calculation formula is as follows:

for a subset with a plurality of element numbers, the basic probability number calculation formula is as follows:

wherein, ω is₁、ω₂Representing the weight, ω₁+ω₂＝1；ET_jRefers to a subset of 1 element, ET_pRefers to a subset of a plurality of elements.

The method for normally distributing the urgent time uncertainty event UTE and the untightened time uncertainty event UTE is adopted to obtain any subset ET of the urgent time uncertainty event UTE and the untightened time uncertainty event UTE_iMET of₁(ET_i)：

For a subset of one element number, the basic probability number calculation formula is as follows:

subjecting the obtained MET to a thermal treatment₁(t_j) T as a corresponding subset element_jSubset ET of_jMET of₂(ET_j)；

For a subset with a plurality of element numbers, the basic probability number calculation formula is as follows:

wherein σ represents a standard deviation of a normal distribution function and satisfies 2 σ sigm, ω₃、ω₄Representing the weight, ω₃+ω ₄1 is ═ 1; mu get t₁Means for expressing normal distribution function under non-urgent condition, mu is t_sigmExpressing the mean of normal distribution functions in a pressing state; ET_pRefers to a subset of a plurality of elements.

In Step2, for the case that the number of the subset elements is C (signm, m), the ordering of the subsets is 1,2,. n,. C (signm, m); the sequencing mode is as follows:

the subsets are sorted in an ascending order according to the size of the first element of the subsets, and then in an ascending order on the basis of the last sorting according to the sizes of the second element to the C (sigm, m).

The above-mentioned

p_riIndicating that the current time uncertainty event UTE depends on the number of other time uncertainty events UTE.

The above-mentioned

dl represents the difference between the deadline of the current time uncertainty event UTE and the minimum deadline among all time uncertainty events.

The Step6 is specifically as follows:

if only a single event which is not loaded with the time scheduling sequence exists at the same time, loading the event into the time scheduling sequence;

if there are multiple events not loaded in the time schedule sequence at the same time, the high scoring events are loaded in the time schedule sequence.

The invention has the beneficial effects that: step1 of the invention considers the urgency degree of the events to construct basic probability data of the corresponding subset of the events, and step2 considers the dependency relationship and the deadline time among the events to establish another group of basic probability data; combining basic probability data obtained by multiple attributes, fusing the multiple attributes by using D-S to enable the multiple attributes to become the characteristics of the multiple attributes, deducing the membership and the non-membership of a fuzzy ending moment through the relation between the D-S and the IFS, further reversely deducing the membership and the non-membership of the fuzzy starting moment according to the reasoning characteristic of working time, wherein the starting time represents the moment most relevant to a scheduling sequence, so that the obtained membership and the non-membership of the fuzzy starting moment are used for scoring, and events are sequentially loaded into the time scheduling sequence according to the time sequence and the score of the fuzzy starting moment, so that the scheduling of parallel risk event handling time sequence is realized, the working efficiency of a power grid, the running stability of the power system are enhanced, the energy loss and the manpower resource waste in detection are reduced, and the speed of abnormal work restoration of the power grid system is accelerated through the scheduling, the adverse effects of business service stop and the like and adverse consequences of incapability of timely recovering normal operation due to unscientific coping of power grid system risk events are reduced.

Drawings

FIG. 1 is a flow chart of the present invention;

FIG. 2 is a time uncertainty event constraint topology diagram;

FIG. 3 is a time interval diagram of a time uncertainty event;

fig. 4 is a time indeterminate event scheduling sequence diagram.

Detailed Description

Example 1: as shown in fig. 1 to 4, a method for monitoring a power grid service system supporting a fuzzy theory includes the following steps:

step1, normally rotating a plurality of pairs of power grid systems at the same time when the power grid service systems are monitoredThe influential events are called time indeterminate events; obtaining any subset ET of the general urgent time uncertainty event UTE by adopting an average distribution method_iMET of₁(ET_i) (ii) a Obtaining any subset ET of urgent time uncertainty event UTE and untightened time uncertainty event UTE by adopting normal distribution method_iMET of₁(ET_i)；

Step2, according to ET_iAnd (3) respectively calculating the basic probability distribution number of each subset according to different numbers of the subset elements:

if the number of elements in the subset is 1, the number of elementary probability distributions for the subset is represented as: pt₁,…,Pt_j,…,Pt_sigm(ii) a All subsets with the number of elements in the subset 1 in the time uncertainty event UTE are assigned a constraint probability P_UTE1(ii) a Satisfies Pt₁+…+Pt_j+…+Pt_sigm＝P_UTE1According to P_dep、P_dlPush out P_UTE1：

According to the obtained P_UTE1Finding Pt_j：

If the number of elements in the subset is m, the subset with the number of elements being m has C (sigm, m), and the basic probability distribution number for constructing the subset with the number of elements being m is expressed as: p_(m,1),…,P_(m,n),…,P_{(m,C(sigm,m))}(ii) a All subsets with m elements in the subset of time uncertainty event UTE are assigned a constraint probability P_UTEmAccording to P_UTE1Calculating P_UTEm：

According to P_UTEmCalculating the number of basic probability distribution P with the n-th subset element number m_(m,n)：

The obtained Pt is subjected to heat treatment_j、P_(m,n)MET as corresponding respective subsets₂(ET_i)；

Wherein the fuzzy end time t of the time uncertainty event UTE is determined_jAs sample space DE, ET_iRepresenting the ith subset of the sample space DE, and sigm representing the total number of elements of the sample space DE of the time uncertainty event UTE; t is t_jRepresents the jth blurring end time and also represents the jth element of the sample space DE; p_dep、P_dlIs a constant number 0<P_dep、P_dl<1；j∈[1,sigm]C (sigm, m) represents a permutation combination;

step3, MET obtained from Step1₁(ET_i) MET with results obtained at Step2₂(ET_i) Obtaining MET (ET) according to the basic probability orthogonality of the D-S evidence theory_i)；

Step4, according to MET (ET)_j) To ET_jDegree of membership mu (ET)_j) Non-membership gamma (ET)_j) The calculation of (2):

means all belonging to ET_jSubset ET of_jMET (ET) of `_j') a cumulative sum;

represents MET (ET)_j'') of a plurality of different combinations, wherein ET_j'' indicates all subsets in the event of uncertainty and the currently sought subset ET_jSubsets whose intersections are not null;

wherein, ET_jRefers to a subset with 1 element number;

step5, calculating the membership degree mu 1 (ET) of the matching fuzzy starting time according to the result obtained in the Step4_k' ' ') and non-membership degree gamma 1(ET_k''' )：

Wherein the fuzzy start time represents a difference between the fuzzy end time and the event execution time; ET_k'' denotes ET_jA subset formed by fuzzy start time corresponding to the middle element;

the hesitation degree of the blurring start timing is:

π(ET_k''' )＝1-(μ1(ET_k''' )+γ1(ET_k''' ))

mixing mu 1 (ET)_k''' )、γ1(ET_k''' )、π(ET_k' ' ') into the scoring function:

step6, loading the events into the time scheduling sequence in turn according to the fuzzy start time sequence and the score.

Further, the general urgent time uncertainty event UTE can be set, and any subset ET thereof can be obtained by adopting an average distribution method_iMET of₁(ET_i)：

For a subset of one element number, the basic probability number calculation formula is as follows:

for a subset with a plurality of element numbers, the basic probability number calculation formula is as follows:

wherein, ω is₁、ω₂Representing the weight, ω₁+ω₂＝1；ET_jRefers to a subset of 1 element, ET_pRefers to a subset of a plurality of elements.

Further, it may be set that the normal distribution method is adopted for both the urgent time uncertainty event UTE and the urgent time uncertainty event UTE to obtain any subset ET thereof_iMET of₁(ET_i)：

For a subset of one element number, the basic probability number calculation formula is as follows:

subjecting the obtained MET to a thermal treatment₁(t_j) T as a corresponding subset element_jSubset ET of_jMET of₂(ET_j)；

For a subset with a plurality of element numbers, the basic probability number calculation formula is as follows:

wherein σ represents a standard deviation of a normal distribution function and satisfies 2 σ sigm, ω₃、ω₄Representing the weight, ω₃+ω ₄1 is ═ 1; mu get t₁Means for expressing normal distribution function under non-urgent condition, mu is t_sigmTo representThe mean of normal distribution functions in a pressing state; ET_pRefers to a subset of a plurality of elements.

Further, in Step2, for the case that the number of the subset elements is C (sigm, m), the ordering of the subsets may be 1,2,. n.,. C (sigm, m); the sequencing mode is as follows:

the subsets are sorted in an ascending order according to the size of the first element of the subsets, and then in an ascending order on the basis of the last sorting according to the sizes of the second element to the C (sigm, m).

The above-mentioned

p_riIndicating that the current time uncertainty event UTE depends on the number of other time uncertainty events UTE. (As shown in FIG. 2, the number is the number of current time uncertainty events counted by the arrow)

Further, the above may be provided

dl represents the difference between the deadline of the current time uncertainty event UTE and the minimum deadline among all time uncertainty events. The cutoff time represents the last fuzzy end time in the sample space of the current time uncertainty event UTE;

further, the Step6 may be set to specifically be:

if only a single event which is not loaded with the time scheduling sequence exists at the same time, loading the event into the time scheduling sequence;

if there are multiple events not loaded in the time schedule sequence at the same time, the high scoring events are loaded in the time schedule sequence.

Further, the following example is made for the steps in the present application:

supposing that when the abnormal condition of the power grid system occurs, a plurality of risk events are monitored to occur simultaneously, and the risk events are called as time uncertainty events UTE. How to get a response to multiple risk events when these risk events occur simultaneouslyIt is crucial that the sequence is such that an optimal healing effect is achieved. The monitored time uncertainty event UTE in the power grid system comprises the following steps: UTE₁、UTE₂、UTE₃、UTE₄、UTE₅(e.g., the events may be, in order, a system response timeout event, a system access slow event, a system unavailable event, a network failure event, a service down event). A time uncertainty event constraint (UTEC) topological graph is shown in fig. 2 and a time uncertainty event time interval is shown in fig. 3.

The steps in this example are all UTE₅For example, the following steps are carried out:

step1, the time uncertainty event UTE adopts different methods according to the respective urgency: UTE₂The degree of influence on the operation of the power grid does not change with the lapse of time, so that the average distribution is adopted; UTE₁、UTE₃、UTE₄、 UTE₅The influence on the normal operation of the power grid is more and more serious along with the time, so normal distribution is adopted. And the UTE can be better distinguished according to different urgency degrees by adopting different modes for classification, and the UTE is more suitable for actual conditions. The preset information of each event of the UTEF is shown in Table 1 (the data given in the embodiment are all generated at the integral point time, but when the system is actually used for collecting event information, half an hour can be taken as a step length, such as 0.5h and 1h, and for the actual time between 0h and 0.5h, 0.5h is selected as the fuzzy end time, other similar reasons, further, the fuzzy end time interval is designed to be the same event in all the participating events, and the maximum number of the events is 2):

TABLE 1 Preset information for time uncertain event streams

Known UTE₅End of blur time t_jHas a sample space DE of {3,4}, and ET_iAs any subset of DE, i.e. ET₁{3} or ET₂{4} or ET₃The basic probability number is not considered because the empty set can not be calculated, and the following processes are not considered because of the same reasonAnd (4) empty collection. UTE₅The blur end time of (2) is time 3 and time 4, and is obtained by an average distribution according to the degree of urgency: MET₁{3}＝0.3333、MET₁{4}＝0.3333、 MET₁{3,4}＝0.3334。

Step2, let UTE₅The fuzzy end interval of (2) has 2 primitive elements: t is t₁,t₂And t is₁＝3,t₂Further, let Pt be 4₁+Pt₂＝P_UTE1The resulting probabilities are shown in table 2 below:

TABLE 2

When in UTE₅Get t₁When the value is 3, P is added_dep、P_dlSubstituting the value of (b) into the formula:

and according to the obtained P_UTE1Determining Pt as 0.6500₁The value of (c):

by analogy, the basic probability distribution number Pt of all elementary elements can be obtained_jWhen t is₂When equal to 4, P₄0.2167, i.e. MET₂(ET₁)＝MET₂{3}＝0.4333，MET₂(ET₂)＝MET₂{4} ═ 0.2167. After the formula is improved, the event can be scheduled as early as possible in the scheduling process, and the event scheduling efficiency is improved.

For UTE₅When the number of elements in the subset is m is 2, that is, {3,4}, there is 1 in the subset having the number of elements m, that is, C (sigm, m) ═ C (2, 2).

According to P_UTE1Calculating P_UTEm：

According to P_UTEmCalculating the number of basic probability distribution P with the n-th subset element number m_(m,n)：

Step3, in UTE₅When ET₁When the measured value is {3}, MET obtained by Step1 is obtained according to the orthogonality of basic probabilities of D-S evidence theory₁{3} MET obtained with Step2₂{3} orthogonal combination result MET {3 }.

When ET is a non-empty set, the following orthogonal formula is calculated:

in this example, substituting each datum into the formula yields:

the same can be obtained:

MET (ET) result orthogonally found from fundamental probability of D-S evidence theory_i) The characteristics of the two calculation modes are integrated, the accuracy of the obtained result is improved, the result is more comprehensive, and the multi-attribute characteristic is expressed by using the result of the single attribute.

In other UTEs, repeating the above process can obtain MET calculated in two ways from Step1-Step3 in all UTEs₁(ET_i)、MET₂(ET_i) And finally orthogonally combined MET (ET)_i) As a result, the results are asTable 3 below shows:

TABLE 3

Step4, according to MET (ET)_j) To ET_jDegree of membership mu (ET)_j) Non-membership gamma (ET)_j) The calculation of (2):

in UTE₅In when taking ET₁When not equal to 3, MET (ET)₁) MET {3} ═ 0.5177, substituting the following equation:

because of ET₁Is only 3 regardless of the empty set, so ET₁' {3}, the cumulative sum of the elementary probability numbers is MET {3} itself:

all subsets ET₁＝{3}、ET₂＝{4}、ET₃In {3,4}, ET₁＝{3}、ET₃The intersection of {3,4} and {3} is not null, and the condition is satisfied. Since only one {4} of the total subset satisfies the condition, the cumulative sum of the elementary probability numbers is MET {4 }:

by analogy, since the results from the D-S evidence theory do not have the advantage of time derivation, Step4 prepares for the next application of the negative-going time inference theory of IFS, the results are shown in table 4 below:

TABLE 4

Step5, in UTE₅In when getting t_jWhen the time is 3, the corresponding module is obtained according to the negative time reasoning theory of IFSPaste start time t_kThe intuitive fuzzy set membership of the fuzzy set 2 on the sample space DS is:

in the same way, find t_jWhen 4, the corresponding blurring start time t_kThe intuitive fuzzy set membership of the fuzzy set of 3 on the sample space DS is:

solving UTE through an intuitionistic fuzzy set negative time reasoning theory₅When t is in_jT corresponding to 3_iDegree of membership μ 1 (ET) at the start of blurring of 2_k' ' ') and non-membership degree gamma 1(ET_k' ' ') and further determining a blurring start time t_iHesitation degree pi (ET) of 2_k''' )：

π(ET_k”')＝1-(μ1(ET_k''' )+γ1(ET_k''' ))＝1-(0.7589+0.1667)＝0.0744

And finally, utilizing a scoring function formula:

by analogy, all the membership degree, the non-membership degree and the hesitation degree of the fuzzy starting time and the scores of all the fuzzy starting times are obtained, the characteristics and the calculation process of fusing the various attributes in the front can be embodied by using the intuitive digital scores, the scheduling sequence of the UTE is clearly embodied, and the obtained result is shown in the following table 5:

TABLE 5

Score each UTE high in time series and verticallyThe low-level, time scheduling queues are loaded in sequence, and the finally obtained scheduling sequence is shown in fig. 4, and a brief analysis shows that: UTE at time 2₅Loading a time scheduling queue; at time 3 due to UTE₅Has already been performed, UTE₄Loading a time scheduling queue; UTE is compared at time 4₃And UTE₂Fuzzy start time score, UTE since 0.8784 > 0.6814₃Loading a time scheduling queue; UTE is compared at time 5₂And UTE₁Fuzzy start time score, UTE since 0.6459 < 0.6650₁Loading a time scheduling queue; at time 6, due to UTE₁、UTE₃、UTE₄、UTE₅Has all been loaded into the time scheduling queue, so UTE will be₂The time scheduling queue is loaded.

In summary, the final response sequence is: UTE5, UTE4, UTE3, UTE1, UTE 2. When abnormal power supply condition occurs in a certain area, the system carries out maintenance treatment on the conditions of service downtime, network fault, system unavailability, system response timeout, system access slow and the like in sequence.

The working principle of the invention is as follows: according to the method, when the power grid system is abnormal in operation, the risk event response time sequence generated in the power grid system is scheduled according to the fuzzy theory. First, MET is obtained by two different ways₁(ET_i)、 MET₂(ET_i) For fusing multi-attribute features, MET is mapped according to D-S evidence theory₁(ET_i)、MET₂(ET_i) Orthogonal combination is carried out to obtain MET (ET) fusing the information obtained in the calculation process of the two ways_i) Substituting the result into a formula to calculate the IFS membership and the non-membership of each UTE fuzzy end moment, providing for utilizing the advantages in the aspect of time derivation of an intuitive fuzzy set, deducing the corresponding membership and the non-membership of the fuzzy start moment and the hesitation degree of the fuzzy start moment from the IFS membership and the non-membership of each UTE fuzzy end moment and the hesitation degree of the fuzzy start moment through an intuitive fuzzy set negative time reasoning theory, and finally obtaining the probability score of each UTE fuzzy start moment according to a scoring function formula, thereby sequentially loading the UTEs into a time scheduling queue and establishing an accurate and effective risk event coping partAnd (4) processing corresponding time sequence.

While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.

Claims (7)

1. A power grid service system monitoring method supporting a fuzzy theory is characterized in that: the method comprises the following steps:

step1, a plurality of events which have influences on the normal rotation of the power grid system and occur at the same time when the power grid service system is monitored are called as time uncertain events; obtaining any subset ET of the general urgent time uncertainty event UTE by adopting an average distribution method_iMET of₁(ET_i) (ii) a Obtaining any subset ET of urgent time uncertainty event UTE and untightened time uncertainty event UTE by adopting normal distribution method_iMET of₁(ET_i)；

Step2, according to ET_iAnd (3) respectively calculating the basic probability distribution number of each subset according to different numbers of the subset elements:

if the number of elements in the subset is 1, the number of elementary probability distributions for the subset is represented as: pt₁,…,Pt_j,…,Pt_sigm(ii) a All subsets with the number of elements in the subset 1 in the time uncertainty event UTE are assigned a constraint probability P_UTE1(ii) a Satisfies Pt₁+…+Pt_j+…+Pt_sigm＝P_UTE1According to P_dep、P_dlPush out P_UTE1：

According to the obtained P_UTE1Finding Pt_j：

If the number of elements in the subset is m, the subset with the number of elements being m has C (sigm, m), and the basic probability distribution number for constructing the subset with the number of elements being m is expressed as: p_(m,1),…,P_(m,n),…,P_{(m,C(sigm,m))}(ii) a All subsets with m elements in the subset of time uncertainty event UTE are assigned a constraint probability P_UTEmAccording to P_UTE1Calculating P_UTEm：

According to P_UTEmCalculating the number of basic probability distribution P with the n-th subset element number m_(m,n)：

The obtained Pt is subjected to heat treatment_j、P_(m,n)MET as corresponding respective subsets₂(ET_i)；

Wherein the fuzzy end time t of the time uncertainty event UTE is determined_jAs sample space DE, ET_iRepresenting the ith subset of the sample space DE, and sigm representing the total number of elements of the sample space DE of the time uncertainty event UTE; t is t_jRepresents the jth blurring end time and also represents the jth element of the sample space DE; p_dep、P_dlIs a constant number 0<P_dep、P_dl<1；j∈[1,sigm]C (sigm, m) represents a permutation combination;

step3, MET obtained from Step1₁(ET_i) MET with results obtained at Step2₂(ET_i) Obtaining MET (ET) according to the basic probability orthogonality of the D-S evidence theory_i)；

Step4, according to MET (ET)_j) To ET_jDegree of membership mu (ET)_j) Non-membership gamma (ET)_j) Is/are as followsAnd (3) calculating:

means all belonging to ET_jSubset ET of_jMET (ET) of `_j') a cumulative sum;

represents MET (ET)_j'') of a plurality of different combinations, wherein ET_j'' indicates all subsets in the event of uncertainty and the currently sought subset ET_jSubsets whose intersections are not null;

wherein, ET_jRefers to a subset with 1 element number;

step5, calculating the membership degree mu 1 (ET) of the matching fuzzy starting time according to the result obtained in the Step4_k' ' ') and non-membership degree gamma 1(ET_k''' )：

Wherein the fuzzy start time represents a difference between the fuzzy end time and the event execution time; ET_k'' denotes ET_jA subset formed by fuzzy start time corresponding to the middle element;

the hesitation degree of the blurring start timing is:

π(ET_k''' )＝1-(μ1(ET_k''' )+γ1(ET_k''' ))

mixing mu 1 (ET)_k- ''' )、γ1(ET_k''' )、π(ET_k' ' ') into the scoring function:

step6, loading the events into the time scheduling sequence in turn according to the fuzzy start time sequence and the score.

2. The method for monitoring the power grid service system supporting the fuzzy theory according to claim 1, wherein: for the general urgent time uncertainty event UTE, any subset ET of the general urgent time uncertainty event UTE is obtained by adopting an average distribution method_iMET of₁(ET_i)：

For a subset of one element number, the basic probability number calculation formula is as follows:

for a subset with a plurality of element numbers, the basic probability number calculation formula is as follows:

wherein, ω is₁、ω₂Representing the weight, ω₁+ω₂＝1；ET_jRefers to a subset of 1 element, ET_pRefers to a subset of a plurality of elements.

3. The method for monitoring the power grid service system supporting the fuzzy theory according to claim 1, wherein: the method for normally distributing the urgent time uncertainty event UTE and the untightened time uncertainty event UTE is adopted to obtain any subset ET of the urgent time uncertainty event UTE and the untightened time uncertainty event UTE_iMET of₁(ET_i)：

For a subset of one element number, the basic probability number calculation formula is as follows:

subjecting the obtained MET to a thermal treatment₁(t_j) T as a corresponding subset element_jSubset ET of_jMET of₂(ET_j)；

For a subset with a plurality of element numbers, the basic probability number calculation formula is as follows:

wherein σ represents a standard deviation of a normal distribution function and satisfies 2 σ sigm, ω₃、ω₄Representing the weight, ω₃+ω₄1 is ═ 1; mu get t₁Means for expressing normal distribution function under non-urgent condition, mu is t_sigmExpressing the mean of normal distribution functions in a pressing state; ET_pRefers to a subset of a plurality of elements.

4. The method for monitoring the power grid service system supporting the fuzzy theory according to claim 1, wherein: in Step2, for the case that the number of the subset elements is C (signm, m), the ordering of the subsets is 1,2,. n,. C (signm, m); the sequencing mode is as follows:

the subsets are sorted in an ascending order according to the size of the first element of the subsets, and then in an ascending order on the basis of the last sorting according to the sizes of the second element to the C (sigm, m).

5. The method for monitoring the power grid service system supporting the fuzzy theory according to claim 1, wherein: the above-mentioned

p_riIndicating that the current time uncertainty event UTE depends on the number of other time uncertainty events UTE.

6. The method for monitoring the power grid service system supporting the fuzzy theory according to claim 1, wherein: the above-mentioned

dl represents the difference between the deadline of the current time uncertainty event UTE and the minimum deadline among all time uncertainty events.

7. The method for monitoring the power grid service system supporting the fuzzy theory according to claim 1, wherein: the Step6 is specifically as follows:

if only a single event which is not loaded with the time scheduling sequence exists at the same time, loading the event into the time scheduling sequence;

if there are multiple events not loaded in the time schedule sequence at the same time, the high scoring events are loaded in the time schedule sequence.

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