CN113822533A - Real-time event-driven risk assessment quantitative model construction method and system - Google Patents
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Abstract
The invention discloses a risk assessment quantitative model construction method and system based on real-time event driving, and relates to the technical field of risk control.
Description
Technical Field
The invention belongs to the technical field of risk control, and particularly relates to a method and a system for constructing a risk assessment quantification model based on real-time event driving.
Background
The power grid operation hazard mainly refers to potential safety hazards and threats brought by various internal and external unsafe factors such as severe natural weather, unreasonable power grid structure and power distribution and the like to power grid operation and user power supply in the power grid production process.
Risk assessment is mainly developed around basic elements such as assets, threats, vulnerabilities and safety measures, and is converted from qualitative analysis into quantitative assessment on the basis of hazard identification. The risk hazard value is determined according to the levels of power accident events possibly caused, the number of stations possibly losing voltage, the loss load, the power outage area, the importance degree of power outage users and the like, the risk probability value is determined according to the factors such as equipment risk, environmental risk, operation risk and the like, and comprehensive calculation and analysis on the hazard events possibly occurring in the power grid are very important, so that a risk assessment quantification model construction method and a system based on real-time event driving are needed.
Disclosure of Invention
The invention aims to provide a method and a system for constructing a risk assessment quantification model based on real-time event driving, so that the defects of the existing method and system for constructing the risk assessment quantification model based on real-time event driving are overcome.
In order to achieve the purpose, the invention provides a risk assessment quantification model construction method based on real-time event driving, which comprises the following steps:
dynamically triggering risk quantitative evaluation on the equipment by a real-time event;
acquiring risk assessment data of the equipment;
evaluating the state of the equipment according to the risk evaluation data to obtain the current state of the equipment;
counting according to the historical equipment state evaluation result of the equipment to obtain the average fault probability of the equipment in each state;
obtaining the importance of the equipment according to the risk assessment data;
obtaining the loss degree of the equipment fault according to the risk assessment data;
obtaining a fault loss consequence value according to the importance and the loss degree;
normalizing the consequence value and the fault probability to obtain a risk value of the equipment;
and dividing the subsection interval according to the risk value to obtain the control grade and the operation and inspection strategy corresponding to different equipment.
Further, the real-time event is the real-time acquisition of the data of the power transmission and transformation equipment.
Further, the risk assessment data includes: device status, device basic information, and device failure case.
Further, the mean failure probability of the device is calculated according to the following formula:
in the formula (1), P is the mean failure probability of the equipment, f is the equipment state, and t isfTotal time of failure of the equipment in a certain state, TfIs the service life of the device in a certain state.
Further, the importance factor includes: equipment value, load class, and equipment status.
Further, the loss degree factors include: equipment cost loss degree, personal safety loss degree and electric power safety degree.
Further, the consequence value is equal to the degree of importance of the plant multiplied by the degree of loss of the plant.
Further, the calculation formula of the risk value is as follows: and R is LE multiplied by P, wherein R is a risk value, LE is a consequence value, and P is importance.
The risk assessment quantification model construction system based on the real-time event driving applies the risk assessment quantification model construction method based on the real-time event driving, and comprises the following steps:
the real-time event driving module is used for dynamically triggering risk quantitative evaluation on the equipment according to a real-time event and acquiring risk evaluation data of the equipment;
the current state evaluation module is used for carrying out state evaluation on the equipment according to the risk evaluation data to obtain the current state of the equipment;
the equipment mean fault probability module is used for carrying out statistics according to historical equipment state evaluation results of the equipment to obtain equipment mean fault probability in each state;
the importance module is used for obtaining the importance of the equipment according to the risk assessment data;
the loss degree module is used for obtaining the loss degree of the equipment fault according to the risk assessment data;
the consequence value module is used for obtaining the consequence value of the fault loss according to the importance and the loss degree;
the risk value module is used for standardizing the result value and the fault probability to obtain a risk value of the equipment; and
and the management and control level and operation inspection strategy module is used for dividing the subsection interval according to the risk value to obtain management and control levels and operation inspection strategies corresponding to different devices.
Compared with the prior art, the invention has the following beneficial effects:
according to the method and the system for establishing the risk assessment quantification model based on the real-time event drive, the risk quantification assessment of the equipment is triggered through the real-time event dynamic state, the risk assessment data of the equipment is obtained, the importance, the loss degree, the consequence value and the risk value of the equipment are calculated according to the risk assessment data of the equipment, then the section intervals are divided according to the risk value, the control grades and the operation and inspection strategies corresponding to different equipment are obtained, the risk state of the equipment can be monitored in real time through the control grades and the operation and inspection strategies, the monitoring of the power grid is realized through the insistence of the equipment, and powerful data support is provided for the comprehensive calculation and analysis of the possible hazard events of the subsequent power grid.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only one embodiment of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a flow chart of a method for constructing a risk assessment quantification model based on real-time event driving according to the present invention.
Detailed Description
The technical solutions in the present invention are 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.
As shown in fig. 1, the method for constructing a risk assessment quantification model based on real-time event driving provided by the present invention includes the following steps:
s1, dynamically triggering risk quantitative evaluation of the equipment by a real-time event;
s2, obtaining risk assessment data of the device, specifically, the risk assessment data includes: equipment state, equipment basic information and equipment fault cases;
s3, evaluating the state of the equipment according to the risk evaluation data to obtain the current state of the equipment;
s4, counting according to the historical equipment state evaluation result of the equipment to obtain the average fault probability P of the equipment in each state;
s5, obtaining the importance of the equipment according to the risk assessment data, wherein the importance of the equipment represents a comprehensive quantitative value of the equipment;
specifically, the importance of the equipment is obtained according to the basic information of the equipment;
s6, obtaining the loss degree of the equipment fault according to the risk assessment data, wherein the loss degree of the equipment fault represents the comprehensive loss degree caused by the equipment having the risk accident event;
specifically, the equipment fault loss degree is obtained according to the equipment fault case;
s7, obtaining a result value LE of fault loss according to the importance and the loss degree;
s8, normalizing the result value and the fault probability to obtain a risk value R of the equipment;
and S9, dividing the subsection intervals according to the risk values to obtain the control levels and the operation and inspection strategies corresponding to different devices.
According to the method for constructing the risk assessment quantification model based on the real-time event drive, risk quantification assessment on equipment is dynamically triggered through the real-time event, risk assessment data of the equipment are obtained, the importance, the loss degree, the consequence value and the risk value of the equipment are calculated according to the risk assessment data of the equipment, then, the segmented intervals are divided according to the risk value, control levels and operation and inspection strategies corresponding to different equipment are obtained, the risk state of the equipment can be monitored in real time through the control levels and the operation and inspection strategies, monitoring on a power grid is achieved through insistence of the equipment, and powerful data support is provided for comprehensive calculation and analysis of possible subsequent power grid hazard events.
In one embodiment, the real-time event is the real-time acquisition of power transmission and transformation equipment data.
In addition, the real-time event also comprises environmental data of the power transmission and transformation equipment; the power transmission and transformation equipment comprises: main transformer, circuit breaker and transmission line.
The real-time event is a condition for triggering risk assessment, and the access of the multivariate real-time risk assessment data is acquired from the power transmission and transformation equipment data and the power transmission and transformation equipment environment data, so that the quick response capability of power transmission and transformation state monitoring is greatly improved, and the change of the data can be timely sensed and quickly processed when the conditions such as defects, power protection and supply periods, special patrol periods, typhoon lightning and other meteorological disasters are found, and the dynamic risk assessment assistant decision judgment is realized.
In one embodiment, the risk assessment data comprises: device status, device basic information, and device failure case.
In one embodiment, the state device is obtained from an intranet and extranet business system storing multi-source data of power transmission and transformation.
In one embodiment, in step S3, the state of the device is evaluated according to the state of the device, so as to obtain the current state of the device; the method specifically comprises the following steps:
s31, monitoring the electric transmission and transformation equipment for multiple times through the electric transmission and transformation monitoring device/system, acquiring all state data of the electric transmission and transformation equipment, carrying out state marking on all the state data, and then storing all the state data into a database;
s32, real-time monitoring is carried out on the power transmission and transformation equipment through the power transmission and transformation monitoring device/system, and the current state of the equipment is obtained;
s33, optimizing the BP neural network model by adopting a CS (cuckoo search) algorithm to obtain an optimized BP neural network model according to the constructed BP neural network model;
specifically, an input layer neuron of the optimized BP neural network model is the current state of the equipment, and an output neuron is the current state of the equipment with a mark; and obtaining the number of the neurons of the optimized BP neural network model through the dimension of the CS algorithm, wherein the dimension is E multiplied by C + C + C multiplied by T + T, E is the number of the neurons of the input layer, C is the number of the neurons of the hidden layer, and T is the number of the neurons of the output layer.
S34, training and verifying the optimized BP neural network model by using all state data;
and S35, inputting the current state of the equipment into the optimized BP neural network model to obtain the current state of the equipment.
In one embodiment, the mean failure probability calculation formula of the device is as follows:
in the formula (1), P is the mean failure probability of the equipment, f is the equipment state, and t isfTotal time of failure of the equipment in a certain state, TfThe service life of the equipment under a certain condition (including failure time and maintenance time).
In one embodiment, the importance factor includes: equipment value, load class, and equipment status.
Specifically, the step S5 of obtaining the importance of the device by processing the basic information of the device through a factor analysis method includes the following steps:
s51, obtaining the importance factor of the equipment;
specifically, the elements of importance include: equipment value, load class and equipment status;
s52, carrying out level division on the equipment value, the load level and the equipment status at all, and then carrying out statistics through an Excel table;
s53, calculating the weight of the element of the importance degree by adopting a factor analysis method;
s54, multiplying each of the importance factors by the corresponding weight, and adding the result;
specifically, the importance of the device is the weight of the first element × the first element + the weight of the second element × the second element + the weight of the third element × the third element.
In one embodiment, the loss factor comprises: equipment cost loss degree, personal safety loss degree and electric power safety degree.
Specifically, the loss degree is a weighted sum of the loss degrees caused by the elements of each loss degree.
Specifically, the step S6 of obtaining the loss degree of the equipment fault according to the risk assessment data includes the following steps:
s61, obtaining the loss degree element of the equipment, wherein the loss degree element comprises: equipment cost loss degree, personal safety loss degree and electric power safety degree;
s62, grading the loss degree elements of the equipment;
s63, calculating the weight of the loss degree element by adopting an information quantity method;
s64, the weight of the loss degree element is the sum of the weights corresponding to the loss degree elements.
Specifically, loss values and occurrence probabilities of loss elements are preset as shown in table 1.
Table 1:
in one embodiment, step S53 uses SPSSAU to calculate the weight, and the corresponding Excel table of step S52 is set according to SPSSAU; similarly, the weight of the element for calculating the degree of loss may be calculated by SPSSAU.
The method according to claim 1, wherein in one embodiment, the consequence value is equal to the importance of the equipment multiplied by the loss of the equipment.
In one embodiment, the risk value is calculated by the formula: and R is LE multiplied by P, wherein R is a risk value, LE is a consequence value, and P is importance.
According to one embodiment, the management and control level of the equipment is divided into four levels, namely, a level I (major risk), a level II (major risk), a level III (general risk) and a level IV (normal risk), according to the risk value of the equipment, and the risk values are sequentially represented by red, orange, yellow and green, and specifically, a risk value division table is shown in table 2.
Table 2:
one embodiment further includes step S10, correcting the state characterized by the risk value and the current state obtained without step S35, and if the state characterized by the risk value and the current state are not consistent with each other, modifying and retraining the optimized BP neural network model based on the calculation.
A real-time event driven risk assessment quantification model construction system applying the real-time event driven risk assessment quantification model construction method of claims 1-8, comprising: a real-time event driving module, a current state evaluation module, an equipment average failure probability module, an importance degree module, a loss degree module, an outcome value module, a risk value module and a management and control level and operation and inspection strategy module,
the real-time event driving module is used for dynamically triggering risk quantitative evaluation on the equipment according to a real-time event and acquiring risk evaluation data of the equipment;
the current state evaluation module is used for carrying out state evaluation on the equipment according to the risk evaluation data to obtain the current state of the equipment;
the equipment mean fault probability module is used for carrying out statistics according to the historical equipment state evaluation result of the equipment to obtain the equipment mean fault probability in each state;
the importance degree module is used for obtaining the importance degree of the equipment according to the risk assessment data;
the loss degree module is used for obtaining the loss degree of the equipment fault according to the risk assessment data;
the consequence value module is used for obtaining the consequence value of the fault loss according to the importance and the loss degree;
the risk value module is used for carrying out standardization according to the consequence value and the fault probability to obtain a risk value of the equipment;
and the control grade and operation inspection strategy module is used for dividing the segmented intervals according to the risk values to obtain control grades and operation inspection strategies corresponding to different devices.
It will be apparent to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely illustrated, and in practical applications, the above function distribution may be performed by different functional units and modules as needed, that is, the internal structure of the mobile terminal is divided into different functional units or modules to perform all or part of the above described functions. Each functional module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional modules are only used for distinguishing one functional module from another, and are not used for limiting the protection scope of the application. The specific working process of the module in the mobile terminal may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above disclosure is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or modifications within the technical scope of the present invention, and shall be covered by the scope of the present invention.
Claims (9)
1. The method for constructing the risk assessment quantification model based on real-time event driving is characterized by comprising the following steps of:
dynamically triggering risk quantitative evaluation on the equipment by a real-time event;
acquiring risk assessment data of the equipment;
evaluating the state of the equipment according to the risk evaluation data to obtain the current state of the equipment;
counting according to the historical equipment state evaluation result of the equipment to obtain the average fault probability of the equipment in each state;
obtaining the importance of the equipment according to the risk assessment data;
obtaining the loss degree of the equipment fault according to the risk assessment data;
obtaining a fault loss consequence value according to the importance and the loss degree;
normalizing the consequence value and the fault probability to obtain a risk value of the equipment;
and dividing the subsection interval according to the risk value to obtain the control grade and the operation and inspection strategy corresponding to different equipment.
2. The method for constructing the risk assessment quantification model based on the real-time event driving as claimed in claim 1, wherein the real-time event is the real-time acquisition of the data of the power transmission and transformation equipment.
3. The method according to claim 1, wherein the risk assessment data comprises: device status, device basic information, and device failure case.
4. The method for constructing the risk assessment quantification model based on the real-time event driving as claimed in claim 1, wherein the mean failure probability calculation formula of the equipment is as follows:
in the formula (1), P is the mean failure probability of the equipment, f is the equipment state, and t isfTotal time of failure of the equipment in a certain state, TfIs the service life of the device in a certain state.
5. The method according to claim 1, wherein the importance factor comprises: equipment value, load class, and equipment status.
6. The method according to claim 1, wherein the loss degree element comprises: equipment cost loss degree, personal safety loss degree and electric power safety degree.
7. The method for constructing a quantitative model for risk assessment based on real-time event driving according to claim 1, wherein the consequence value is equal to the importance degree of the equipment multiplied by the loss degree of the equipment.
8. The method for constructing a risk assessment quantification model based on real-time event driving according to claim 1, wherein the risk value is calculated by the formula: and R is LE multiplied by P, wherein R is a risk value, LE is a consequence value, and P is importance.
9. A real-time event-driven risk assessment quantification model construction system applying the real-time event-driven risk assessment quantification model construction method according to claims 1 to 8, comprising:
the real-time event driving module is used for dynamically triggering risk quantitative evaluation on the equipment according to a real-time event and acquiring risk evaluation data of the equipment;
the current state evaluation module is used for carrying out state evaluation on the equipment according to the risk evaluation data to obtain the current state of the equipment;
the equipment mean fault probability module is used for carrying out statistics according to historical equipment state evaluation results of the equipment to obtain equipment mean fault probability in each state;
the importance module is used for obtaining the importance of the equipment according to the risk assessment data;
the loss degree module is used for obtaining the loss degree of the equipment fault according to the risk assessment data;
the consequence value module is used for obtaining the consequence value of the fault loss according to the importance and the loss degree;
the risk value module is used for standardizing the result value and the fault probability to obtain a risk value of the equipment; and
and the management and control level and operation inspection strategy module is used for dividing the subsection interval according to the risk value to obtain management and control levels and operation inspection strategies corresponding to different devices.
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| CN114625086A (en) * | 2022-03-14 | 2022-06-14 | 安徽碳鑫科技有限公司 | Real-time production index monitoring system for pulverized coal gasification device |
| CN116341919A (en) * | 2023-05-26 | 2023-06-27 | 南京南瑞信息通信科技有限公司 | Data and model combined driving safety risk assessment and early warning method |
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