CN113361729A - Risk assessment method, device, terminal and storage medium based on maintenance plan - Google Patents

Risk assessment method, device, terminal and storage medium based on maintenance plan Download PDF

Info

Publication number
CN113361729A
CN113361729A CN202110715667.XA CN202110715667A CN113361729A CN 113361729 A CN113361729 A CN 113361729A CN 202110715667 A CN202110715667 A CN 202110715667A CN 113361729 A CN113361729 A CN 113361729A
Authority
CN
China
Prior art keywords
plan
maintenance
risk
maintenance plan
real
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110715667.XA
Other languages
Chinese (zh)
Other versions
CN113361729B (en
Inventor
潘远
王乃啸
何祥针
刘佳乐
孟子杰
杨民京
郭俊宏
陈奎烨
傅伟豪
蔡新雷
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong Power Grid Co Ltd
Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
Original Assignee
Guangdong Power Grid Co Ltd
Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong Power Grid Co Ltd, Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd filed Critical Guangdong Power Grid Co Ltd
Priority to CN202110715667.XA priority Critical patent/CN113361729B/en
Publication of CN113361729A publication Critical patent/CN113361729A/en
Application granted granted Critical
Publication of CN113361729B publication Critical patent/CN113361729B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • Theoretical Computer Science (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Development Economics (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Educational Administration (AREA)
  • Game Theory and Decision Science (AREA)
  • Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a risk assessment method, a device, a terminal and a storage medium based on a maintenance plan, wherein the method comprises the following steps: acquiring a historical maintenance plan, and extracting maintenance implementation risk influence factors from the historical maintenance plan to obtain the maintenance implementation risk influence factors of the historical maintenance plan; constructing an implementation risk evaluation training database of the maintenance plan according to the maintenance implementation risk influence factors of the historical maintenance plan; inputting data in a training database for risk assessment into a preset discriminant analysis model for training to obtain a trained discriminant analysis model; acquiring a real-time maintenance plan, extracting influence factors of real-time maintenance implementation risks from the real-time maintenance plan, and acquiring maintenance implementation risk influence factors of the real-time maintenance plan; and inputting the overhaul implementation risk influence factors of the real-time overhaul plan into the trained discriminant analysis model to obtain a risk evaluation result of the real-time overhaul plan. The invention can effectively and quantitatively evaluate the maintenance plan implementation risk.

Description

Risk assessment method, device, terminal and storage medium based on maintenance plan
Technical Field
The invention relates to the technical field of power dispatching, in particular to a risk assessment method, device, terminal and storage medium based on a maintenance plan.
Background
Regular maintenance of power equipment including transmission, transformation, distribution and use of a power system is critical to maintaining safe and stable operation of the power system, and a corresponding maintenance plan is usually formulated according to the operation condition of the equipment and the requirements of regular detection tests. On one hand, the arrangement and implementation of the maintenance plan are closely related to the economic benefits of the power grid company, and on the other hand, the reliability of the operation of the power system is also deeply influenced. Therefore, the reasonable evaluation of the operation risk of the maintenance plan is beneficial to improving the service cycle of the power equipment and also ensures the safe and stable operation of the power system.
At present, in the maintenance plan implementation stage, the risk assessment of the maintenance plan is mainly performed by operators, but because the risk assessment of the operators depends on personal experience and subjective judgment, the effective quantitative assessment of the maintenance plan implementation risk is difficult to perform.
Disclosure of Invention
The purpose of the invention is: the risk assessment method, the risk assessment device, the risk assessment terminal and the storage medium based on the maintenance plan are provided, and the risk of the maintenance plan can be effectively and quantitatively assessed.
In order to achieve the above object, the present invention provides a risk assessment method based on a maintenance plan, including:
acquiring a historical maintenance plan, and extracting maintenance implementation risk influence factors from the historical maintenance plan to obtain the maintenance implementation risk influence factors of the historical maintenance plan;
constructing an implementation risk evaluation training database of the maintenance plan according to the maintenance implementation risk influence factors of the historical maintenance plan;
inputting the data in the training database for implementing risk assessment into a preset discriminant analysis model for training to obtain a trained discriminant analysis model;
acquiring a real-time maintenance plan, extracting influence factors of real-time maintenance implementation risks from the real-time maintenance plan, and acquiring maintenance implementation risk influence factors of the real-time maintenance plan;
and inputting the overhaul implementation risk influence factors of the real-time overhaul plan into the trained discriminant analysis model to obtain a risk evaluation result of the real-time overhaul plan.
Further, the influencing factors include: different voltage grades, equipment power failure, equipment power restoration, single line, multi-line, single bus, multi-bus, mother reversing operation, main transformer, main dispatching coordination operation, main dispatching commission operation, secondary equipment change, requirements of power transmission and transformation on the unit, secondary work and bus mode adjustment.
Further, the preset discriminant analysis model adopts the following calculation formula:
Figure BDA0003131802460000021
Figure BDA0003131802460000022
Figure BDA0003131802460000023
Figure BDA0003131802460000024
Figure BDA0003131802460000025
wherein d is2(x,Gi) Denotes the Mahalanobis distance, μiRepresents a mathematical expectation, ΣiRepresenting a covariance matrix, x representing an input quantity,
Figure BDA0003131802460000026
in order to mathematically expect an estimate of the position,
Figure BDA0003131802460000027
representing sample data, nkRepresents the sample volume, SkThe variance is estimated by the variance estimation method,
Figure BDA0003131802460000028
representing the joint unbiased estimation, g representing the number of classes,
Figure BDA0003131802460000029
in order to be a function of the discriminant,
Figure BDA00031318024600000210
and
Figure BDA00031318024600000211
representing a mathematical expectation estimate.
Further, the preset discriminant analysis model further includes a misjudgment rate calculation, and the following calculation formula is adopted:
Figure BDA00031318024600000212
wherein the content of the first and second substances,
Figure BDA0003131802460000031
the rate of the erroneous judgment is represented,
Figure BDA0003131802460000032
indicates the number of misjudgments, n1+n2+n3Representing the total number of variables participating in the misinterpretation analysis.
The invention also provides a risk assessment device based on the overhaul plan, which comprises: a first acquisition module, a construction module, a training module, a second acquisition module, and an evaluation module, wherein,
the first acquisition module is used for acquiring a historical maintenance plan, extracting maintenance implementation risk influence factors from the historical maintenance plan and acquiring the maintenance implementation risk influence factors of the historical maintenance plan;
the construction module is used for constructing an implementation risk evaluation training database of the maintenance plan according to the maintenance implementation risk influence factors of the historical maintenance plan;
the training module is used for inputting the data in the risk assessment training database into a preset discriminant analysis model for training to obtain a trained discriminant analysis model;
the second acquisition module is used for acquiring the real-time maintenance plan, extracting the influence factors of the real-time maintenance implementation risk from the real-time maintenance plan and acquiring the maintenance implementation risk influence factors of the real-time maintenance plan;
and the evaluation module is used for inputting the overhaul implementation risk influence factors of the real-time overhaul plan into the trained discriminant analysis model to obtain a risk evaluation result of the real-time overhaul plan.
Further, the influencing factors include: different voltage grades, equipment power failure, equipment power restoration, single line, multi-line, single bus, multi-bus, mother reversing operation, main transformer, main dispatching coordination operation, main dispatching commission operation, secondary equipment change, requirements of power transmission and transformation on the unit, secondary work and bus mode adjustment.
Further, the preset discriminant analysis model adopts the following calculation formula:
Figure BDA0003131802460000033
Figure BDA0003131802460000034
Figure BDA0003131802460000041
Figure BDA0003131802460000042
Figure BDA0003131802460000043
wherein d is2(x,Gi) Denotes the Mahalanobis distance, μiRepresents a mathematical expectation, ΣiRepresenting a covariance matrix, x representing an input quantity,
Figure BDA0003131802460000044
in order to mathematically expect an estimate of the position,
Figure BDA0003131802460000045
representing sample data, nkRepresents the sample volume, SkThe variance is estimated by the variance estimation method,
Figure BDA0003131802460000046
representing the joint unbiased estimation, g representing the number of classes,
Figure BDA0003131802460000047
in order to be a function of the discriminant,
Figure BDA0003131802460000048
and
Figure BDA0003131802460000049
representing a mathematical expectation estimate.
Further, the preset discriminant analysis model further includes a misjudgment rate calculation, and the following calculation formula is adopted:
Figure BDA00031318024600000410
wherein the content of the first and second substances,
Figure BDA00031318024600000411
the rate of the erroneous judgment is represented,
Figure BDA00031318024600000412
indicates the number of misjudgments, n1+n2+n3Representing the total number of variables participating in the misinterpretation analysis.
The present invention also provides a computer terminal device, comprising: one or more processors; a memory coupled to the processor for storing one or more programs; when executed by the one or more processors, cause the one or more processors to implement a service plan based risk assessment method as in any one of the above.
The invention also provides a computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method for identifying a hanging foreign object on a power transmission line according to any one of the above.
Compared with the prior art, the risk assessment method and device based on the maintenance plan, the terminal equipment and the computer readable storage medium have the advantages that:
according to the method, risk influence factors of the historical maintenance plan are extracted, the risk influence factors are used for training the judgment model, the real-time maintenance plan is judged and evaluated through the trained judgment model, an evaluation result of implementation risk is obtained, and the high-risk maintenance plan is subjected to alarm marking according to the evaluation result of implementation risk. The invention can effectively and quantitatively evaluate the maintenance plan implementation risk.
Drawings
FIG. 1 is a schematic flow chart of a risk assessment method based on a maintenance plan according to the present invention;
fig. 2 is a schematic structural diagram of a risk assessment device based on a maintenance plan according to 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.
It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
As shown in fig. 1, the risk assessment method based on a service plan provided by the present invention at least includes the following steps:
s1, acquiring a historical overhaul plan, extracting overhaul implementation risk influence factors from the historical overhaul plan, and acquiring overhaul implementation risk influence factors of the historical overhaul plan;
specifically, the influence factors influencing the maintenance plan to implement risk assessment in the maintenance list are extracted, a sample data set containing n influence factors is established as an assessment independent variable, the influence factor variable of the data set is a variable from 0 to 1, and the operation plan has two variable results including the influence factor (set to 1) and not including the influence factor (set to 0) for each influence factor.
S2, constructing an implementation risk evaluation training database of the maintenance plan according to the maintenance implementation risk influence factors of the historical maintenance plan;
specifically, a plurality of maintenance plan implementation risk assessment training databases are established, wherein each maintenance plan data comprises n assessment influence factor data sets as training independent variables, and 1 implementation risk assessment result is used as a dependent variable. The risk evaluation result is divided into three classification intervals of high risk (not less than 3), medium risk (not less than 2 and less than 3) and low risk (less than 2)
S3, inputting the data in the training database for risk assessment into a preset discriminant analysis model for training to obtain a trained discriminant analysis model;
specifically, the preset discriminant analysis model specifically comprises the following modeling processes:
1. the classification types of the modeling process are divided into 3 types, namely three classification intervals of high risk (not less than 3), medium risk (not less than 2 and less than 3) and low risk (less than 2), each type has p indexes, and a p-dimensional vector G is used as (G)1,G2,…,Gp)T. And establishing a maintenance plan implementation risk evaluation model taking the discriminant analysis model as an evaluation method. The training model inputs n evaluation influence factor data sets in the independent variable of 2, and inputs the dependent variable as the result of risk evaluation.
2. There are 3 p-dimensional total G ═ G (G)1,G2,…,Gp)TMathematical expectation (mean vector) is μ1，μ2，μ3The covariance matrix is ∑1,∑2,∑3. The squared mahalanobis distance of the input amount x to each population is calculated as shown in equation (1):
Figure BDA0003131802460000071
wherein G ═ G (G)1,G2,…,Gp)TIs p-dimensional overall, mu1，μ2，μ3For mathematical expectation (mean vector), Σ1,∑2,Σ3Is a covariance matrix.
Comparing the 3 distances, judging that x belongs to the population with the shortest distance (if the shortest distance is not unique, judging that x belongs to any population).
3. When mu is1，μ2，μ3Sum-sigma1,Σ2,Σ3When unknown, the training samples can be used for estimation. Is provided with
Figure BDA0003131802460000072
Is from GkSample of (2), sample capacity nk(
Figure BDA0003131802460000073
P-dimensional vector) as shown in equations (2) and (3), the mathematical expectation estimate (mean vector) is shown in equation (2), and the variance estimate is shown in equation (3):
Figure BDA0003131802460000074
Figure BDA0003131802460000075
wherein the content of the first and second substances,
Figure BDA0003131802460000076
is from GkG represents the number of classifications, the number of discriminant analysis classifications of the text sample is 3, and the sample capacity is nk(
Figure BDA0003131802460000077
Is a p-dimensional vector),
Figure BDA0003131802460000078
for mathematical expectation estimation, SkAnd (4) estimating the variance.
Then
Figure BDA0003131802460000079
Can be used as mukIs estimated, and1,∑2,Σ3the joint unbiased estimate of (c) is, as shown in equation (4):
Figure BDA00031318024600000710
wherein
Figure BDA00031318024600000711
nkRepresents the sample volume, SkThe variance estimate is represented as an estimate of the variance,
Figure BDA00031318024600000712
represents sigma1,∑2,∑3Joint unbiased estimation of (1).
4. Thus, the discriminant function Wij(x) Is estimated as shown in equation (5):
Figure BDA0003131802460000081
wherein the content of the first and second substances,
Figure BDA0003131802460000082
and
Figure BDA0003131802460000083
represents a mathematical expectation estimate, Wij(x) For the discriminant function, x is the input quantity.
The discriminant function obtained by training is generally represented by the formula (6):
Figure BDA0003131802460000084
wherein, anDenotes the discrimination coefficient, x, of each evaluation influence factornEach evaluation impact factor value, i.e., input quantity, is represented.
5. Cross validation method misjudgment rate judgment
Adopting an interactive verification method according to the following steps of 7: 3, dividing the training set and the test set according to the proportion, and introducing 30% of test data into the training set and the test set
Figure BDA0003131802460000085
And calculating the result in the function, judging the result with a correct value, and confirming whether the prediction result is correct or not. The misjudgment rate is calculated as shown in formula (7):
Figure BDA0003131802460000086
wherein the content of the first and second substances,
Figure BDA0003131802460000087
the rate of the erroneous judgment is represented,
Figure BDA0003131802460000088
indicates the number of misjudgments, n1+n2+n3Representing the total number of variables participating in the misinterpretation analysis.
The discriminant function obtained by training is generally in the form of:
Wij(x)＝a1x1+a2x2+……+anxn (8)
where an represents each evaluation impact factor discrimination coefficient and xn represents each evaluation impact factor value.
S4, acquiring a real-time maintenance plan, extracting influence factors of real-time maintenance implementation risks from the real-time maintenance plan, and acquiring maintenance implementation risk influence factors of the real-time maintenance plan;
specifically, a real-time maintenance plan is obtained, and an influence factor of a real-time maintenance implementation risk is extracted from the real-time maintenance plan, so that a maintenance implementation risk influence factor of the real-time maintenance plan is obtained;
and S5, inputting the maintenance implementation risk influence factors of the real-time maintenance plan into the trained discriminant analysis model to obtain the risk evaluation result of the real-time maintenance plan.
Specifically, a maintenance plan influence factor data set of the risk to be evaluated is extracted, data of the data set is input into the discriminant function, a risk evaluation value is obtained through calculation, and a risk evaluation implementation classification result is confirmed according to the risk evaluation value. And performing alarm marking on the high-risk overhaul plan according to the implemented risk evaluation result.
In one embodiment of the present invention, the influencing factors include: different voltage grades, equipment power failure, equipment power restoration, single line, multi-line, single bus, multi-bus, mother reversing operation, main transformer, main dispatching coordination operation, main dispatching commission operation, secondary equipment change, requirements of power transmission and transformation on the unit, secondary work and bus mode adjustment.
In an embodiment of the present invention, the preset discriminant analysis model adopts the following calculation formula:
Figure BDA0003131802460000091
Figure BDA0003131802460000092
Figure BDA0003131802460000093
Figure BDA0003131802460000094
Figure BDA0003131802460000095
wherein d is2(x,Gi) Denotes the Mahalanobis distance, μiRepresents a mathematical expectation, ΣiRepresenting a covariance matrix, x representing an input quantity,
Figure BDA0003131802460000101
in order to mathematically expect an estimate of the position,
Figure BDA0003131802460000102
representing sample data, nkRepresents the sample volume, SkThe variance is estimated by the variance estimation method,
Figure BDA0003131802460000103
representing the joint unbiased estimation, g representing the number of classes,
Figure BDA0003131802460000104
in order to be a function of the discriminant,
Figure BDA0003131802460000105
and
Figure BDA0003131802460000106
representing a mathematical expectation estimate.
In an embodiment of the present invention, the preset discriminant analysis model further includes a false-positive rate calculation, which uses the following calculation formula:
Figure BDA0003131802460000107
wherein the content of the first and second substances,
Figure BDA0003131802460000108
the rate of the erroneous judgment is represented,
Figure BDA0003131802460000109
indicates the number of misjudgments, n1+n2+n3Representing the total number of variables participating in the misinterpretation analysis.
Compared with the prior art, the risk assessment method based on the maintenance plan has the beneficial effects that:
according to the method, risk influence factors of the historical maintenance plan are extracted, the risk influence factors are used for training the judgment model, the real-time maintenance plan is judged and evaluated through the trained judgment model, an evaluation result of implementation risk is obtained, and the high-risk maintenance plan is subjected to alarm marking according to the evaluation result of implementation risk. The invention can effectively and quantitatively evaluate the maintenance plan implementation risk.
As shown in fig. 2, the present invention also provides a risk assessment apparatus 200 based on a service plan, comprising: a first acquisition module 201, a construction module 202, a training module 203, a second acquisition module 204, and an evaluation module 205, wherein,
the first obtaining module 201 is configured to obtain a historical maintenance plan, and extract a maintenance implementation risk influence factor from the historical maintenance plan to obtain a maintenance implementation risk influence factor of the historical maintenance plan;
the construction module 202 is used for constructing an implementation risk evaluation training database of the maintenance plan according to the maintenance implementation risk influence factors of the historical maintenance plan;
the training module 203 inputs the data in the training database for performing risk assessment into a preset discriminant analysis model for training to obtain a trained discriminant analysis model;
the second obtaining module 204 is configured to obtain a real-time maintenance plan, and extract an influence factor of a real-time maintenance implementation risk from the real-time maintenance plan to obtain a maintenance implementation risk influence factor of the real-time maintenance plan;
the evaluation module 205 is configured to input the overhaul implementation risk influence factor of the real-time overhaul plan into the trained discriminant analysis model, so as to obtain a risk evaluation result of the real-time overhaul plan.
In one embodiment of the present invention, the influencing factors include: different voltage grades, equipment power failure, equipment power restoration, single line, multi-line, single bus, multi-bus, mother reversing operation, main transformer, main dispatching coordination operation, main dispatching commission operation, secondary equipment change, requirements of power transmission and transformation on the unit, secondary work and bus mode adjustment.
In an embodiment of the present invention, the preset discriminant analysis model adopts the following calculation formula:
Figure BDA0003131802460000111
Figure BDA0003131802460000112
Figure BDA0003131802460000113
Figure BDA0003131802460000114
Figure BDA0003131802460000115
wherein d is2(x,Gi) Denotes the Mahalanobis distance, μiRepresents a mathematical expectation, ΣiRepresenting a covariance matrix, x representing an input quantity,
Figure BDA0003131802460000116
in order to mathematically expect an estimate of the position,
Figure BDA0003131802460000117
representing sample data, nkRepresents the sample volume, SkThe variance is estimated by the variance estimation method,
Figure BDA0003131802460000118
representing the joint unbiased estimation, g representing the number of classes,
Figure BDA0003131802460000119
in order to be a function of the discriminant,
Figure BDA00031318024600001110
and
Figure BDA00031318024600001111
representing a mathematical expectation estimate.
In an embodiment of the present invention, the preset discriminant analysis model further includes a false-positive rate calculation, which uses the following calculation formula:
Figure BDA0003131802460000121
wherein the content of the first and second substances,
Figure BDA0003131802460000122
the rate of the erroneous judgment is represented,
Figure BDA0003131802460000123
indicates the number of misjudgments, n1+n2+n3Representing the total number of variables participating in the misinterpretation analysis.
The present invention also provides a computer terminal device, comprising: one or more processors; a memory coupled to the processor for storing one or more programs; when executed by the one or more processors, cause the one or more processors to implement a service plan based risk assessment method as in any one of the above.
It should be noted that the processor may be a Central Processing Unit (CPU), other general-purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an application-specific programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., the general-purpose processor may be a microprocessor, or the processor may be any conventional processor, the processor is a control center of the terminal device, and various interfaces and lines are used to connect various parts of the terminal device.
The memory mainly includes a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like, and the data storage area may store related data and the like. In addition, the memory may be a high speed random access memory, may also be a non-volatile memory, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash card (FlashCard), and the like, or may also be other volatile solid state memory devices.
It should be noted that the terminal device may include, but is not limited to, a processor and a memory, and those skilled in the art will understand that the terminal device is only an example and does not constitute a limitation of the terminal device, and may include more or less components, or combine some components, or different components.
The invention also provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a service plan-based risk assessment method according to any one of the preceding claims.
It should be noted that the computer program may be divided into one or more modules/units (e.g., computer program), and the one or more modules/units are stored in the memory and executed by the processor to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the terminal device.
The above-mentioned embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, and it should be understood that the above-mentioned embodiments are only examples of the present invention and are not intended to limit the scope of the present invention. It should be understood that any modifications, equivalents, improvements and the like, which come within the spirit and principle of the invention, may occur to those skilled in the art and are intended to be included within the scope of the invention. The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and substitutions can be made without departing from the technical principle of the present invention, and these modifications and substitutions should also be regarded as the protection scope of the present invention.

Claims (10)

1. A risk assessment method based on a maintenance plan is characterized by comprising the following steps:
acquiring a historical maintenance plan, and extracting maintenance implementation risk influence factors from the historical maintenance plan to obtain the maintenance implementation risk influence factors of the historical maintenance plan;
constructing an implementation risk evaluation training database of the maintenance plan according to the maintenance implementation risk influence factors of the historical maintenance plan;
inputting the data in the training database for implementing risk assessment into a preset discriminant analysis model for training to obtain a trained discriminant analysis model;
acquiring a real-time maintenance plan, extracting influence factors of real-time maintenance implementation risks from the real-time maintenance plan, and acquiring maintenance implementation risk influence factors of the real-time maintenance plan;
and inputting the overhaul implementation risk influence factors of the real-time overhaul plan into the trained discriminant analysis model to obtain a risk evaluation result of the real-time overhaul plan.
2. The service plan based risk assessment method according to claim 1, wherein said influencing factors comprise: different voltage grades, equipment power failure, equipment power restoration, single line, multi-line, single bus, multi-bus, mother reversing operation, main transformer, main dispatching coordination operation, main dispatching commission operation, secondary equipment change, requirements of power transmission and transformation on the unit, secondary work and bus mode adjustment.
3. The overhaul plan-based risk assessment method according to claim 1, wherein the preset discriminant analysis model adopts the following calculation formula:
Figure FDA0003131802450000011
Figure FDA0003131802450000012
Figure FDA0003131802450000021
Figure FDA0003131802450000022
Figure FDA0003131802450000023
wherein d is2(x,Gi) Denotes the Mahalanobis distance, μiRepresents a mathematical expectation, ΣiRepresenting a covariance matrix, x representing an input quantity,
Figure FDA0003131802450000024
estimate for mathematical expectationThe counting is carried out by the following steps of,
Figure FDA0003131802450000025
representing sample data, nkRepresents the sample volume, SkThe variance is estimated by the variance estimation method,
Figure FDA0003131802450000026
representing the joint unbiased estimation, g representing the number of classes,
Figure FDA0003131802450000027
in order to be a function of the discriminant,
Figure FDA0003131802450000028
and
Figure FDA0003131802450000029
representing a mathematical expectation estimate.
4. The overhaul plan-based risk assessment method according to claim 3, wherein the preset discriminant analysis model further comprises a misjudgment rate calculation using the following calculation formula:
Figure FDA00031318024500000210
wherein the content of the first and second substances,
Figure FDA00031318024500000211
the rate of the erroneous judgment is represented,
Figure FDA00031318024500000212
indicates the number of misjudgments, n1+n2+n3Representing the total number of variables participating in the misinterpretation analysis.
5. A maintenance plan-based risk assessment device, comprising: a first acquisition module, a construction module, a training module, a second acquisition module, and an evaluation module, wherein,
the first acquisition module is used for acquiring a historical maintenance plan, extracting maintenance implementation risk influence factors from the historical maintenance plan and acquiring the maintenance implementation risk influence factors of the historical maintenance plan;
the construction module is used for constructing an implementation risk evaluation training database of the maintenance plan according to the maintenance implementation risk influence factors of the historical maintenance plan;
the training module is used for inputting the data in the risk assessment training database into a preset discriminant analysis model for training to obtain a trained discriminant analysis model;
the second acquisition module is used for acquiring the real-time maintenance plan, extracting the influence factors of the real-time maintenance implementation risk from the real-time maintenance plan and acquiring the maintenance implementation risk influence factors of the real-time maintenance plan;
and the evaluation module is used for inputting the overhaul implementation risk influence factors of the real-time overhaul plan into the trained discriminant analysis model to obtain a risk evaluation result of the real-time overhaul plan.
6. The service plan based risk assessment device according to claim 5, wherein said influencing factors comprise: different voltage grades, equipment power failure, equipment power restoration, single line, multi-line, single bus, multi-bus, mother reversing operation, main transformer, main dispatching coordination operation, main dispatching commission operation, secondary equipment change, requirements of power transmission and transformation on the unit, secondary work and bus mode adjustment.
7. The overhaul plan-based risk assessment device according to claim 5, wherein the preset discriminant analysis model adopts the following calculation formula:
Figure FDA0003131802450000031
Figure FDA0003131802450000032
Figure FDA0003131802450000033
Figure FDA0003131802450000034
Figure FDA0003131802450000035
wherein d is2(x,Gi) Denotes the Mahalanobis distance, μiRepresents a mathematical expectation, ΣiRepresenting a covariance matrix, x representing an input quantity,
Figure FDA0003131802450000041
in order to mathematically expect an estimate of the position,
Figure FDA0003131802450000042
representing sample data, nkRepresents the sample volume, SkThe variance is estimated by the variance estimation method,
Figure FDA0003131802450000043
representing the joint unbiased estimation, g representing the number of classes,
Figure FDA0003131802450000044
in order to be a function of the discriminant,
Figure FDA0003131802450000045
and
Figure FDA0003131802450000046
representing a mathematical expectation estimate.
8. The overhaul plan-based risk assessment device according to claim 5, wherein the preset discriminant analysis model further comprises a misjudgment rate calculation using the following calculation formula:
Figure FDA0003131802450000047
wherein the content of the first and second substances,
Figure FDA0003131802450000048
the rate of the erroneous judgment is represented,
Figure FDA0003131802450000049
indicates the number of misjudgments, n1+n2+n3Representing the total number of variables participating in the misinterpretation analysis.
9. A computer terminal device, comprising: one or more processors; a memory coupled to the processor for storing one or more programs; when executed by the one or more processors, cause the one or more processors to implement the service plan based risk assessment method of any one of claims 1 to 4.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a service plan-based risk assessment method according to any one of claims 1 to 4.
CN202110715667.XA 2021-06-24 2021-06-24 Risk assessment method, device, terminal and storage medium based on maintenance plan Active CN113361729B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110715667.XA CN113361729B (en) 2021-06-24 2021-06-24 Risk assessment method, device, terminal and storage medium based on maintenance plan

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110715667.XA CN113361729B (en) 2021-06-24 2021-06-24 Risk assessment method, device, terminal and storage medium based on maintenance plan

Publications (2)

Publication Number Publication Date
CN113361729A true CN113361729A (en) 2021-09-07
CN113361729B CN113361729B (en) 2022-09-02

Family

ID=77536681

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110715667.XA Active CN113361729B (en) 2021-06-24 2021-06-24 Risk assessment method, device, terminal and storage medium based on maintenance plan

Country Status (1)

Country Link
CN (1) CN113361729B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115577996A (en) * 2022-12-12 2023-01-06 广东电网有限责任公司中山供电局 Risk assessment method, system, equipment and medium for power grid power failure plan

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009238190A (en) * 2008-03-28 2009-10-15 Hitachi Plant Technologies Ltd Maintenance plan support system
CN104392391A (en) * 2014-11-14 2015-03-04 国家电网公司 Power grid running safety risk quantification method
CN106384210A (en) * 2016-10-28 2017-02-08 贵州电网有限责任公司贵阳供电局 Power transmission and transformation equipment maintenance priority ordering method based on maintenance risk premium
CN109559043A (en) * 2018-11-30 2019-04-02 天津大学 A kind of power distribution system equipment Decision-making of Condition-based Maintenance method based on risk assessment
CN109858823A (en) * 2019-02-15 2019-06-07 广东电网有限责任公司 Main distribution, which has a power failure, plans selection method and device
CN110321520A (en) * 2019-03-22 2019-10-11 广东电网有限责任公司 A kind of transformer state evaluation method based on Weighted distance diagnostic method
CN111386540A (en) * 2017-11-29 2020-07-07 三菱电机株式会社 Maintenance planning system and maintenance planning method
CN111639882A (en) * 2020-06-15 2020-09-08 江苏电力信息技术有限公司 Deep learning-based power utilization risk judgment method
CN111695755A (en) * 2020-04-21 2020-09-22 国网浙江江山市供电有限公司 Power distribution network maintenance plan optimization method
CN111798005A (en) * 2020-06-02 2020-10-20 中核核电运行管理有限公司 Development method of nuclear power plant overhaul nuclear safety risk management tool

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009238190A (en) * 2008-03-28 2009-10-15 Hitachi Plant Technologies Ltd Maintenance plan support system
CN104392391A (en) * 2014-11-14 2015-03-04 国家电网公司 Power grid running safety risk quantification method
CN106384210A (en) * 2016-10-28 2017-02-08 贵州电网有限责任公司贵阳供电局 Power transmission and transformation equipment maintenance priority ordering method based on maintenance risk premium
CN111386540A (en) * 2017-11-29 2020-07-07 三菱电机株式会社 Maintenance planning system and maintenance planning method
CN109559043A (en) * 2018-11-30 2019-04-02 天津大学 A kind of power distribution system equipment Decision-making of Condition-based Maintenance method based on risk assessment
CN109858823A (en) * 2019-02-15 2019-06-07 广东电网有限责任公司 Main distribution, which has a power failure, plans selection method and device
CN110321520A (en) * 2019-03-22 2019-10-11 广东电网有限责任公司 A kind of transformer state evaluation method based on Weighted distance diagnostic method
CN111695755A (en) * 2020-04-21 2020-09-22 国网浙江江山市供电有限公司 Power distribution network maintenance plan optimization method
CN111798005A (en) * 2020-06-02 2020-10-20 中核核电运行管理有限公司 Development method of nuclear power plant overhaul nuclear safety risk management tool
CN111639882A (en) * 2020-06-15 2020-09-08 江苏电力信息技术有限公司 Deep learning-based power utilization risk judgment method

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
DONG HUANG 等: "A new local PCA-SOM algorithm", 《NEUROCOMPUTING》 *
JIANGYUAN MEI 等: "Learning a Mahalanobis Distance-Based Dynamic Time Warping Measure for Multivariate Time Series Classification", 《 IEEE TRANSACTIONS ON CYBERNETICS》 *
XAVIER PENNEC: "Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements", 《MATH IMAGING》 *
蔡新雷 等: "基于人工智能的电网调度操作智能防误系统建设及实践", 《电力大数据》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115577996A (en) * 2022-12-12 2023-01-06 广东电网有限责任公司中山供电局 Risk assessment method, system, equipment and medium for power grid power failure plan

Also Published As

Publication number Publication date
CN113361729B (en) 2022-09-02

Similar Documents

Publication Publication Date Title
CN113792825B (en) Fault classification model training method and device for electricity information acquisition equipment
CN108052628B (en) Turnout starting current detection method, system and terminal equipment
CN105354198A (en) Data processing method and apparatus
CN108008244A (en) A kind of small current grounding fault progressive classifying identification method at many levels
CN108052974B (en) Fault diagnosis method, system, equipment and storage medium
CN111786385B (en) Power grid operation and maintenance scheme planning method, system and equipment
CN113361729B (en) Risk assessment method, device, terminal and storage medium based on maintenance plan
CN109685670A (en) Social security violation detection method, device, equipment and computer readable storage medium
CN111796233A (en) Method for evaluating secondary errors of multiple voltage transformers in double-bus connection mode
CN106780222B (en) Method and device for acquiring simulated comprehensive scores
CN114139931A (en) Enterprise data evaluation method and device, computer equipment and storage medium
CN111090833A (en) Data processing method, system and related equipment
CN112070180B (en) Power grid equipment state judging method and device based on information physical bilateral data
CN110991741B (en) Section constraint probability early warning method and system based on deep learning
McFarland III et al. Exact misclassification probabilities for plug-in normal quadratic discriminant functions: Ii. the heterogeneous case
CN116662186A (en) Log playback assertion method and device based on logistic regression and electronic equipment
CN113361730B (en) Risk early warning method, device, equipment and medium for maintenance plan
CN114925895A (en) Maintenance equipment prediction method, terminal and storage medium
CN113506190A (en) Abnormal electricity consumption behavior identification method, device, equipment and storage medium
CN112465380A (en) Method, device, equipment and medium for enterprise behavior analysis based on hazardous waste data
CN113298642A (en) Order detection method and device, electronic equipment and storage medium
CN109726401B (en) Patent combination generation method and system
CN112763678A (en) PCA-based sewage treatment process monitoring method and system
CN112346995A (en) Construction method and device of test risk estimation model based on banking industry
CN110837459A (en) Big data-based operation performance analysis method and system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant