CN117764769A - Power distribution network operation and inspection risk evaluation method, device, equipment and medium - Google Patents
Power distribution network operation and inspection risk evaluation method, device, equipment and medium Download PDFInfo
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Abstract
The invention belongs to the technical field of operation, maintenance and overhaul of a power distribution network, and particularly discloses a power distribution network operation and inspection risk evaluation method, device, equipment and medium. Acquiring operation and detection historical data of the power distribution network, and constructing a plurality of risk indexes according to the operation and detection historical data of the power distribution network; screening the multiple risk indexes to obtain multiple key risk indexes; calculating subjective weights of the key risk indexes by adopting an analytic hierarchy process; calculating objective weights of the key risk indexes by adopting an entropy weight method; calculating the comprehensive weight of each key risk index by adopting a linear weighting method according to the subjective weight and the objective weight of each key risk index; and generating a risk distribution cloud image by adopting a cloud model according to the comprehensive weight of each key risk index. According to the invention, the risk index weight is comprehensively weighted by combining the analytic hierarchy process and the entropy weight process, so that subjective influence is reduced, file risk can be accurately evaluated, and safety accidents are avoided.
Description
Technical Field
The invention relates to the technical field of operation, maintenance and overhaul of a power distribution network, in particular to a power distribution network operation and inspection risk evaluation method, device, equipment and medium.
Background
The power distribution network bears the important task of connecting the power grid and directly supplying power to users, and continuous and good operation of power distribution equipment is related to continuous and reliable power supply of vast power users and safe and reliable operation of the power grid. With the development of social economy, the scale of distribution network equipment is in a rapid growth trend, meanwhile, the requirements of users on the power supply reliability of the distribution network are continuously improved, higher and higher requirements are put forward on the operation and maintenance work of the distribution network, and the contradiction between the operation and maintenance requirements of a large amount of distribution network and limited operation and maintenance resources is increasingly prominent. For the end of energy resource shortage province and energy transmission, the pressure exists for a long time in energy supply, and with the commissioning of a 1 000kV extra-high voltage circuit, the main grid frame of the power grid is further perfected, the power exchange capacity between provinces is remarkably improved, the safe operation of the power grid system is ensured, and the power supply capacity of 200-300 kW is increased for the region.
The ultra/extra-high voltage transmission line in the power distribution network has remarkable effect in large-scale cross-region configuration of energy, generally spans the region of hundreds of kilometers in short and thousands of kilometers in long, and needs different landforms such as a way plateau, a forest, a hillside and the like to be subjected to the test of wind, frost, rain, snow and thunderstorm mountain fire for a long time. In order to ensure safe and reliable operation of a power transmission line of a power distribution network, a large number of electric workers work on an operation and maintenance line of the power distribution network, and line operation safety is concerned at any time. With the intensive operation of the ultra/extra high voltage transmission lines in the current stage of each region, the security risks of the power grid, equipment and personnel for operation maintenance and overhaul of the transmission lines of the power distribution network rise year by year, and the risks in the operation maintenance and overhaul process of the power distribution network in the region are required to be evaluated, so that effective risk management and control measures are provided.
Disclosure of Invention
The invention aims to provide a power distribution network operation and inspection risk evaluation method, device, equipment and medium, which are used for solving the technical problems that the existing power distribution network is high in maintenance safety risk, and the risk evaluation is difficult to carry out in the operation and maintenance process, so that safety accidents occur.
In order to achieve the above purpose, the invention adopts the following technical scheme:
in a first aspect, the invention provides a power distribution network operation detection risk evaluation method, which comprises the following steps:
acquiring operation and detection historical data of the power distribution network, and constructing a plurality of risk indexes according to the operation and detection historical data of the power distribution network;
screening the multiple risk indexes to obtain multiple key risk indexes;
calculating subjective weights of the key risk indexes by adopting an analytic hierarchy process;
calculating objective weights of the key risk indexes by adopting an entropy weight method;
calculating the comprehensive weight of each key risk index by adopting a linear weighting method according to the subjective weight and the objective weight of each key risk index;
and generating a risk distribution cloud image by adopting a cloud model according to the comprehensive weight of each key risk index.
The invention further improves that: the plurality of risk indexes comprise management level risk, human resource risk, system reward and punishment risk, coordination communication risk, design bid and bid risk, material fatigue risk, construction process risk, material acceptance risk, balance policy environment risk, natural environment risk, legal environment risk and fund audit risk.
The invention further improves that: in the step of screening the plurality of risk indexes to obtain the plurality of key risk indexes, the method specifically comprises the following steps:
judging whether each risk index is critical or not by an expert to obtain a judging result;
calculating the hesitation degree of each risk index according to the judgment result;
calculating to obtain a risk factor ranking value of each risk index according to the hesitation degree and the judgment result of each risk index;
and comparing each risk factor sorting value with a first preset value, and marking the risk index with the risk factor sorting value being greater than or equal to the first preset value as a key risk index.
The invention further improves that: the step of calculating subjective weights of the key risk indexes by adopting the analytic hierarchy process specifically comprises the following steps:
obtaining scoring results of the expert on the importance degree among the key risk indexes, and constructing a judgment matrix according to the scoring results;
and calculating according to the judgment matrix by adopting a summation method to obtain subjective weight.
The invention further improves that: the step of calculating the objective weight of each key risk index by adopting the entropy weight method specifically comprises the following steps:
obtaining expert scoring of each key risk index;
calculating the specific gravity of the risk evaluation value of each key risk index according to the expert scoring result;
calculating the scoring information entropy of each key risk index according to the risk evaluation value proportion of each key risk index;
and calculating objective weights of the key risk indexes according to the scoring information entropy of the key risk indexes.
The invention further improves that: the step of calculating the comprehensive weight of each key risk index by adopting a linear weighting method according to the subjective weight and the objective weight of each key risk index specifically comprises the following steps:
wherein omega Ai Representing subjective weight, ω Bi Represents objective weight, ω Ci Representing the comprehensive weight, gamma beingThe weighting factor of subjective weight, σ is the weighting factor of objective weight, d (ω Ai ,ω Bi ) Representing the difference function of the subjective and objective weight values.
The invention further improves that: the step of generating the risk distribution cloud image by adopting the cloud model according to the comprehensive weight of each key risk index specifically comprises the following steps:
constructing a standard cloud model according to the comprehensive weight of each key risk index;
calculating and evaluating the cloud normal distribution number according to a standard cloud model;
calculating a standard cloud normal distribution number according to a standard cloud model;
calculating membership according to the evaluation cloud normal distribution number and the standard cloud normal distribution number;
calculating the similarity between the evaluation cloud and the standard cloud according to the membership degree;
and generating a risk distribution cloud image according to the similarity between the evaluation cloud and the standard cloud.
In a second aspect, the present invention provides a power distribution network operation detection risk evaluation device, including:
the index construction module is used for: the method comprises the steps of acquiring operation and detection historical data of a power distribution network, and constructing a plurality of risk indexes according to the operation and detection historical data of the power distribution network;
and a screening module: the method comprises the steps of screening a plurality of risk indexes to obtain a plurality of key risk indexes;
subjective weight calculation module: calculating subjective weights of the key risk indexes by adopting an analytic hierarchy process;
an objective weight calculation module: calculating objective weights of the key risk indexes by adopting an entropy weight method;
and the comprehensive weight calculation module is used for: the comprehensive weight of each key risk index is calculated by adopting a linear weighting method according to the subjective weight and the objective weight of each key risk index;
and an evaluation module: and the cloud model is used for generating a risk distribution cloud image according to the comprehensive weight of each key risk index.
In a third aspect, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the above-mentioned method for evaluating risk of operation and inspection of a power distribution network when executing the computer program.
In a fourth aspect, the present invention provides a computer readable storage medium, where a computer program is stored, where the computer program when executed by a processor implements a power distribution network operation detection risk evaluation method as described above.
Compared with the prior art, the invention at least comprises the following beneficial effects:
the invention fully considers the full life cycle risk control management of the power distribution network operation and inspection related to the power grid, equipment and personnel, weights the combination weight of the risk factors by means of the characteristics of highlighting local differences by an Analytic Hierarchy Process (AHP) and an entropy weight method, reduces subjective influence, enables the combination weight to be more universal, and improves the key risk factor identification capability of the power distribution network by using the dual advantages of visual and visual risk distribution cloud image and quantitative evaluation of the risk severity of the cloud model risk evaluation method, thereby reducing the operation and maintenance risk probability of the power distribution network.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
In the drawings:
FIG. 1 is a flow chart of a power distribution network operation and detection risk evaluation method of the invention;
FIG. 2 is a schematic diagram of risk indicators in a method for evaluating the risk of operation and detection of a power distribution network according to the present invention;
fig. 3 is a block diagram of a power distribution network operation risk evaluation device according to the present invention.
Detailed Description
The invention will be described in detail below with reference to the drawings in connection with embodiments. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
The following detailed description is exemplary and is intended to provide further details of the invention. Unless defined otherwise, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the invention.
Example 1
A power distribution network operation and inspection risk evaluation method, as shown in figure 1, comprises the following steps:
s1, acquiring operation and detection historical data of a power distribution network, and constructing a plurality of risk indexes according to the operation and detection historical data of the power distribution network;
specifically, the several risk indexes are shown in fig. 2, including management level risk, human resource risk, system punishment risk, coordination communication risk, design bid risk, material fatigue risk, construction process risk, material acceptance risk, balance policy environment risk, natural environment risk, legal environment risk and fund audit risk.
The management level risk, the human resource risk, the system punishment risk and the coordination communication risk belong to management risks;
designing bid risk, material fatigue risk, construction process risk and material acceptance risk to belong to equipment risk;
the environmental risk of balance policy, natural environment risk, legal environment risk and fund audit risk belong to the environmental risk.
S2, screening the multiple risk indexes to obtain multiple key risk indexes;
specifically, in the step of screening a plurality of risk indexes to obtain a plurality of key risk indexes, the method includes:
s21, judging whether each risk index is critical or not by an expert to obtain a judging result;
specifically, the judgment result is t A (x i ) Representing the ratio of the number of experts in favor of the ith risk index to the total number of the experts, f A (x i ) Representing the ratio of the number of experts to the total number of the experts, which is opposite to the i-th risk index to be included into the key risk index;
s22, calculating the hesitation degree of each risk index according to the judging result;
specifically, the calculation formula of the hesitation is as follows:
π A (x i )＝1-t A (x i )-f A (x i )；
in the formula, pi A (x i ) The number of people who throw the ticket is expressed as a hesitation function.
S23, calculating to obtain a risk factor ranking value of each risk index according to the hesitation degree and the judgment result of each risk index;
specifically, the risk factor ranking value is calculated as follows:
S A (x i )＝(t A (x i )-f A (x i ))(1+π A (x i ))；
wherein S is A (x i ) The values are ordered for risk factors.
S24, comparing each risk factor sorting value with a first preset value, and marking the risk index with the risk factor sorting value larger than or equal to the first preset value as a key risk index;
specifically, a is a first preset value, and S is selected A (x i ) And (3) taking ≡ a as a screening standard, wherein the number of votes in favor of subtracting the objection is more than a times of the total number of the samples, and the risk factor is considered as a key risk factor. a is set according to the actual situation, and takes a value between 0 and 1.
S3, calculating subjective weights of the key risk indexes by using an analytic hierarchy process;
specifically, the step of calculating subjective weights of the key risk indexes by using an analytic hierarchy process includes:
s31, obtaining a scoring result of the expert on the importance degree of each key risk index, and constructing a judgment matrix according to the scoring result;
specifically, the judgment matrix is as follows:
wherein a is ef And (5) representing the average value of scoring results obtained by comparing the e factor with the f factor.
S32, calculating to obtain subjective weight by adopting a summation method according to the judgment matrix;
wherein omega Ai Subjective weight representing a key risk indicator i; c represents a normalized vector; b f A sum of column data representing vector a; b (B) f Representation b f Sum vector; c ef Representation a ef Normalized data values.
S4, calculating objective weights of the key risk indexes by adopting an entropy weight method;
specifically, the step of calculating the objective weight of each key risk index by adopting the entropy weight method comprises the following steps:
s41, obtaining expert scoring of each key risk index;
expert scoring risk levels for key risk indicators;
specifically, a risk factor scoring table is formed by scoring and summarizing of experts and is used for recording scores;
s42, calculating the specific gravity of the risk evaluation value of each key risk index according to the expert scoring result;
wherein r is ij Represents the specific gravity of the risk evaluation value, Q ij Expert scoring representing each key risk indicator.
S43, calculating the score information entropy of each key risk index according to the risk evaluation value proportion of each key risk index;
wherein m represents measurementData set number, H ij And (5) scoring information entropy representing each key risk index.
S44, calculating objective weights of the key risk indexes according to the scoring information entropy of the key risk indexes;
wherein omega is Bi Objective weights of the key risk indexes.
S5, calculating the comprehensive weight of each key risk index by adopting a linear weighting method according to the subjective weight and the objective weight of each key risk index;
specifically, the following formula is adopted in the process of calculating the comprehensive weight of each key risk index:
wherein omega Ci Represents the comprehensive weight, gamma is the weighting coefficient of the subjective weight, sigma is the weighting coefficient of the objective weight, d (omega Ai ,ω Bi ) Representing the difference function of the subjective and objective weight values.
Considering that the risk identification object is a special electric power industry, the subjective weight is important to show personal and equipment safety benefits, the objective weight is more important to show social and economic benefits, and the importance of the subjective weight is greater than that of the objective weight, so that the assignment weighting coefficient gamma is set to be more than or equal to sigma.
And S6, generating a risk distribution cloud image by adopting a cloud model according to the comprehensive weight of each key risk index.
Specifically, the step of generating a risk distribution cloud image by adopting a cloud model according to the comprehensive weight of each key risk index includes:
s61, constructing a standard cloud model according to the comprehensive weight of each key risk index;
standard cloud parameters (E) xi ,E ni ,H ei ). Standard cloud model parameters were calculated as follows:
wherein E is xi Representing cloud model expectations, B imin Minimum value of risk level score interval representing key risk factors, B imax Maximum value of risk level score interval representing key risk factors, E ni Representing entropy, H ei The super entropy is represented, b is a constant, and the value is generally 0.15 according to specific requirements.
Specifically, parameters in the standard cloud model are calculated as follows:
wherein,representing a sample mean; />Representing the sample variance; e (E) xij 、E nij 、H eij Cloud parameters of the secondary risk factors respectively.
And calculating cloud parameters of the primary risk factors through cloud parameters of the secondary risk factors. The specific formula is as follows:
wherein E is xj 、E nj 、H ej Cloud parameters omega respectively being first-class risk factors Ci Is the comprehensive weight of the key risk index i, omega i Weights are combined for the first-level risk factors.
S62, calculating and evaluating the normal distribution number of the cloud according to a standard cloud model;
wherein E is nk Represented by E n Is as desired,A random normal distribution number that is a variance; x is x k Represented by E x Is hoped for,/->And evaluating the cloud normal distribution number which is the random normal distribution number of the variance.
S63, calculating a standard cloud normal distribution number according to a standard cloud model;
wherein E 'is' nk Represented by E nq Is as desired,The random normal distribution number of the variance is the normal distribution number of the standard cloud.
S64, calculating membership according to the evaluation cloud normal distribution number and the standard cloud normal distribution number;
wherein mu xq Representing the membership degree of the evaluation cloud normal distribution number in the standard cloud normal distribution number, E xq Representing expected values of different risk interval standard clouds.
S65, calculating similarity between the evaluation cloud and the standard cloud according to the membership degree;
wherein T is the number of times the normalized calculation program circularly calculates to meet the evaluation accuracy.
S65, generating a risk distribution cloud image according to similarity between the evaluation cloud and the standard cloud.
In order to better evaluate the risk control effect, the cloud model parameter adjustment is performed on each risk factor after the control decision is applied, and the adjustment rule is as follows: after a series of effective control measures are adopted for the internal important risk factors, the expected value of each risk factor cloud model parameter is reduced by y; and after the external important risk factors take effective control measures, the expected value of each risk factor cloud model parameter is reduced by d. Wherein y and d are constants, and reasonable values are obtained according to specific calculation results.
Example 2
A power distribution network operation and inspection risk evaluation device, as shown in fig. 3, includes:
the index construction module is used for: the method comprises the steps of acquiring operation and detection historical data of a power distribution network, and constructing a plurality of risk indexes according to the operation and detection historical data of the power distribution network;
specifically, the several risk indexes are shown in fig. 2, including management level risk, human resource risk, system punishment risk, coordination communication risk, design bid risk, material fatigue risk, construction process risk, material acceptance risk, balance policy environment risk, natural environment risk, legal environment risk and fund audit risk.
The management level risk, the human resource risk, the system punishment risk and the coordination communication risk belong to management risks;
designing bid risk, material fatigue risk, construction process risk and material acceptance risk to belong to equipment risk;
the environmental risk of balance policy, natural environment risk, legal environment risk and fund audit risk belong to the environmental risk.
And a screening module: the method comprises the steps of screening a plurality of risk indexes to obtain a plurality of key risk indexes;
judging whether each risk index is critical or not by each expert to obtain a judging result;
specifically, the judgment result is t A (x i ) Expert number representing approval of ith risk indicator to incorporate key risk indicatorRatio of total number of people, f A (x i ) Representing the ratio of the number of experts to the total number of the experts, which is opposite to the i-th risk index to be included into the key risk index;
calculating the hesitation degree of each risk index according to the judgment result;
specifically, the calculation formula of the hesitation is as follows:
π A (x i )＝1-t A (x i )-f A (x i )；
in the formula, pi A (x i ) The number of people who throw the ticket is expressed as a hesitation function.
Calculating to obtain a risk factor ranking value of each risk index according to the hesitation degree and the judgment result of each risk index;
specifically, the risk factor ranking value is calculated as follows:
S A (x i )＝(t A (x i )-f A (x i ))(1+π A (x i ))；
wherein S is A (x i ) The values are ordered for risk factors.
Comparing each risk factor sorting value with a first preset value, and marking the risk index with the risk factor sorting value being greater than or equal to the first preset value as a key risk index;
specifically, a is a first preset value, and S is selected A (x i ) And (3) taking ≡ a as a screening standard, wherein the number of votes in favor of subtracting the objection is more than a times of the total number of the samples, and the risk factor is considered as a key risk factor. a is set according to the actual situation, and takes a value between 0 and 1.
Subjective weight calculation module: calculating subjective weights of the key risk indexes by adopting an analytic hierarchy process;
specifically, the step of calculating subjective weights of the key risk indexes by using an analytic hierarchy process includes:
obtaining scoring results of the expert on the importance degree among the key risk indexes, and constructing a judgment matrix according to the scoring results;
specifically, the judgment matrix is as follows:
wherein a is ef And (5) representing the average value of scoring results obtained by comparing the e factor with the f factor.
Calculating to obtain subjective weight by adopting a summation method according to the judgment matrix;
wherein omega Ai Subjective weight representing a key risk indicator i; c represents a normalized vector; b f A sum of column data representing vector a; b (B) f Representation b f Sum vector; c ef Representation a ef Normalized data values.
An objective weight calculation module: calculating objective weights of the key risk indexes by adopting an entropy weight method;
specifically, the step of calculating the objective weight of each key risk index by adopting the entropy weight method comprises the following steps:
obtaining expert scoring of each key risk index;
expert scoring risk levels for key risk indicators;
specifically, a risk factor scoring table is formed by scoring and summarizing of experts and is used for recording scores;
calculating the specific gravity of the risk evaluation value of each key risk index according to the expert scoring result;
wherein r is ij Represents the specific gravity of the risk evaluation value, Q ij Expert scoring representing each key risk indicator.
Calculating the scoring information entropy of each key risk index according to the risk evaluation value proportion of each key risk index;
wherein m represents the number of data sets measured, H ij And (5) scoring information entropy representing each key risk index.
Calculating objective weights of the key risk indexes according to the scoring information entropy of the key risk indexes;
wherein omega is Bi Objective weights of the key risk indexes.
And the comprehensive weight calculation module is used for: the comprehensive weight of each key risk index is calculated by adopting a linear weighting method according to the subjective weight and the objective weight of each key risk index;
specifically, the following formula is adopted in the process of calculating the comprehensive weight of each key risk index:
wherein omega Ci Represents the comprehensive weight, gamma is the weighting coefficient of the subjective weight, sigma is the weighting coefficient of the objective weight, d (omega Ai ,ω Bi ) Representing the difference function of the subjective and objective weight values.
Considering that the risk identification object is a special electric power industry, the subjective weight is important to show personal and equipment safety benefits, the objective weight is more important to show social and economic benefits, and the importance of the subjective weight is greater than that of the objective weight, so that the assignment weighting coefficient gamma is set to be more than or equal to sigma.
And an evaluation module: and the cloud model is used for generating a risk distribution cloud image according to the comprehensive weight of each key risk index.
Specifically, the step of generating a risk distribution cloud image by adopting a cloud model according to the comprehensive weight of each key risk index includes:
constructing a standard cloud model according to the comprehensive weight of each key risk index;
standard cloud parameters (E) xi ,E ni ,H ei ). Standard cloud model parameters were calculated as follows:
wherein E is xi Representing cloud model expectations, B imin Minimum value of risk level score interval representing key risk factors, B imax Maximum value of risk level score interval representing key risk factors, E ni Representing entropy, H ei The super entropy is represented, b is a constant, and the value is generally 0.15 according to specific requirements.
Specifically, parameters in the standard cloud model are calculated as follows:
wherein,representing a sample mean; />Representing the sample variance; e (E) xij 、E nij 、H eij Cloud parameters of the secondary risk factors respectively.
And calculating cloud parameters of the primary risk factors through cloud parameters of the secondary risk factors. The specific formula is as follows:
wherein E is xj 、E nj 、H ej Cloud parameters omega respectively being first-class risk factors Ci Is the comprehensive weight of the key risk index i, omega i Weights are combined for the first-level risk factors.
Calculating and evaluating the cloud normal distribution number according to a standard cloud model;
wherein E is nk Represented by E n Is as desired,A random normal distribution number that is a variance; x is x k Represented by E x Is hoped for,/->And evaluating the cloud normal distribution number which is the random normal distribution number of the variance.
Calculating a standard cloud normal distribution number according to a standard cloud model;
wherein E 'is' nk Represented by E nq Is as desired,The random normal distribution number of the variance is the normal distribution number of the standard cloud.
Calculating membership according to the evaluation cloud normal distribution number and the standard cloud normal distribution number;
wherein mu xq Representing the membership degree of the evaluation cloud normal distribution number in the standard cloud normal distribution number, E xq Representing expected values of different risk interval standard clouds.
Calculating the similarity between the evaluation cloud and the standard cloud according to the membership degree;
wherein T is the number of times the normalized calculation program circularly calculates to meet the evaluation accuracy.
S65, generating a risk distribution cloud image according to similarity between the evaluation cloud and the standard cloud.
In order to better evaluate the risk control effect, the cloud model parameter adjustment is performed on each risk factor after the control decision is applied, and the adjustment rule is as follows: after a series of effective control measures are adopted for the internal important risk factors, the expected value of each risk factor cloud model parameter is reduced by y; and after the external important risk factors take effective control measures, the expected value of each risk factor cloud model parameter is reduced by d. Wherein y and d are constants, and reasonable values are obtained according to specific calculation results.
Example 3
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing a method for evaluating the risk of operation and inspection of a power distribution network when executing the computer program, comprising the steps of:
acquiring operation and detection historical data of the power distribution network, and constructing a plurality of risk indexes according to the operation and detection historical data of the power distribution network;
screening the multiple risk indexes to obtain multiple key risk indexes;
calculating subjective weights of the key risk indexes by adopting an analytic hierarchy process;
calculating objective weights of the key risk indexes by adopting an entropy weight method;
calculating the comprehensive weight of each key risk index by adopting a linear weighting method according to the subjective weight and the objective weight of each key risk index;
and generating a risk distribution cloud image by adopting a cloud model according to the comprehensive weight of each key risk index.
Example 4
A computer readable storage medium storing a computer program which when executed by a processor implements a method for evaluating risk of operation and inspection of a power distribution network, comprising the steps of:
acquiring operation and detection historical data of the power distribution network, and constructing a plurality of risk indexes according to the operation and detection historical data of the power distribution network;
screening the multiple risk indexes to obtain multiple key risk indexes;
calculating subjective weights of the key risk indexes by adopting an analytic hierarchy process;
calculating objective weights of the key risk indexes by adopting an entropy weight method;
calculating the comprehensive weight of each key risk index by adopting a linear weighting method according to the subjective weight and the objective weight of each key risk index;
and generating a risk distribution cloud image by adopting a cloud model according to the comprehensive weight of each key risk index.
It will be appreciated by those skilled in the art that the present invention can be carried out in other embodiments without departing from the spirit or essential characteristics thereof. Accordingly, the above disclosed embodiments are illustrative in all respects, and not exclusive. All changes that come within the scope of the invention or equivalents thereto are intended to be embraced therein.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. The power distribution network operation and detection risk evaluation method is characterized by comprising the following steps of:
acquiring operation and detection historical data of the power distribution network, and constructing a plurality of risk indexes according to the operation and detection historical data of the power distribution network;
screening the multiple risk indexes to obtain multiple key risk indexes;
calculating subjective weights of the key risk indexes by adopting an analytic hierarchy process;
calculating objective weights of the key risk indexes by adopting an entropy weight method;
calculating the comprehensive weight of each key risk index by adopting a linear weighting method according to the subjective weight and the objective weight of each key risk index;
and generating a risk distribution cloud image by adopting a cloud model according to the comprehensive weight of each key risk index.
2. The method for evaluating the risk of operation and inspection of the power distribution network according to claim 1, wherein the plurality of risk indexes comprise management level risk, human resource risk, system reward and punishment risk, coordination communication risk, design bid risk, material fatigue risk, construction process risk, material acceptance risk, balance policy environment risk, natural environment risk, legal environment risk and fund audit risk.
3. The method for evaluating the risk of operation and detection of the power distribution network according to claim 1, wherein the step of screening the plurality of risk indexes to obtain the plurality of key risk indexes specifically comprises:
judging whether each risk index is critical or not by an expert to obtain a judging result;
calculating the hesitation degree of each risk index according to the judgment result;
calculating to obtain a risk factor ranking value of each risk index according to the hesitation degree and the judgment result of each risk index;
and comparing each risk factor sorting value with a first preset value, and marking the risk index with the risk factor sorting value being greater than or equal to the first preset value as a key risk index.
4. The method for evaluating the risk of operation and inspection of a power distribution network according to claim 1, wherein the step of calculating the subjective weight of each key risk index by using a hierarchical analysis method specifically comprises the following steps:
obtaining scoring results of the expert on the importance degree among the key risk indexes, and constructing a judgment matrix according to the scoring results;
and calculating according to the judgment matrix by adopting a summation method to obtain subjective weight.
5. The method for evaluating the risk of operation and detection of the power distribution network according to claim 1, wherein the step of calculating the objective weight of each key risk index by adopting the entropy weight method specifically comprises the following steps:
obtaining expert scoring of each key risk index;
calculating the specific gravity of the risk evaluation value of each key risk index according to the expert scoring result;
calculating the scoring information entropy of each key risk index according to the risk evaluation value proportion of each key risk index;
and calculating objective weights of the key risk indexes according to the scoring information entropy of the key risk indexes.
6. The method for evaluating the risk of operation and inspection of a power distribution network according to claim 1, wherein the step of calculating the comprehensive weight of each key risk index by using a linear weighting method according to the subjective weight and the objective weight of each key risk index specifically comprises the following steps:
wherein omega Ai Representing subjective weight, ω Bi Represents objective weight, ω Ci Represents the comprehensive weight, gamma is the weighting coefficient of the subjective weight, sigma is the weighting coefficient of the objective weight, d (omega Ai ,ω Bi ) Representing the difference function of the subjective and objective weight values.
7. The method for evaluating the risk of operation and detection of the power distribution network according to claim 1, wherein in the step of generating a risk distribution cloud chart by adopting a cloud model according to the comprehensive weight of each key risk index, the method specifically comprises the following steps:
constructing a standard cloud model according to the comprehensive weight of each key risk index;
calculating and evaluating the cloud normal distribution number according to a standard cloud model;
calculating a standard cloud normal distribution number according to a standard cloud model;
calculating membership according to the evaluation cloud normal distribution number and the standard cloud normal distribution number;
calculating the similarity between the evaluation cloud and the standard cloud according to the membership degree;
and generating a risk distribution cloud image according to the similarity between the evaluation cloud and the standard cloud.
8. The utility model provides a distribution network fortune examines risk evaluation device which characterized in that includes:
the index construction module is used for: the method comprises the steps of acquiring operation and detection historical data of a power distribution network, and constructing a plurality of risk indexes according to the operation and detection historical data of the power distribution network;
and a screening module: the method comprises the steps of screening a plurality of risk indexes to obtain a plurality of key risk indexes;
subjective weight calculation module: calculating subjective weights of the key risk indexes by adopting an analytic hierarchy process;
an objective weight calculation module: calculating objective weights of the key risk indexes by adopting an entropy weight method;
and the comprehensive weight calculation module is used for: the comprehensive weight of each key risk index is calculated by adopting a linear weighting method according to the subjective weight and the objective weight of each key risk index;
and an evaluation module: and the cloud model is used for generating a risk distribution cloud image according to the comprehensive weight of each key risk index.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements a method for evaluating the risk of operation of a distribution network according to any one of claims 1-7 when executing the computer program.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements a method for evaluating risk of operation and inspection of a power distribution network according to any one of claims 1 to 7.
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| CN202311791188.1A CN117764769A (en) | 2023-12-23 | 2023-12-23 | Power distribution network operation and inspection risk evaluation method, device, equipment and medium |
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