CN110598966B - Grading method of equipment in risk-guided post-processing facility - Google Patents
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Abstract
The invention provides a grading method of equipment in post-processing facilities of risk guidance, which comprises (1) after an expert j selects the equipment, identifying a decision factor i and constructing a decision level by using a hierarchical analysis processing method; (2) Constructing a Bayesian belief network model by expert j, and judging to obtain P (C ij |S i ) And calculates the security importance P of each decision factor of the target component using the Bayesian belief network (S i =hi); (3) Obtaining evidence E by expert ij And updates P using evidence (S i =hi); (4) Obtaining the weight w of the decision factor i by applying analytic hierarchy process i The method comprises the steps of carrying out a first treatment on the surface of the (5) Based on the probability theory and the certainty theory, a full probability P is obtained (S T =hi), and finally classified. The method provided by the invention can provide basis for grading of post-treatment facilities.
Description
Technical Field
The invention belongs to the technical field of probability safety evaluation of spent fuel aftertreatment facilities, and particularly relates to a grading method of equipment in risk-guided aftertreatment facilities.
Background
Currently the united states nuclear medicine (Nuclear Regulatory Commission, NRC) is working to implement management of Risk guidelines (RI) based on probabilistic security ratings (Probabilistic Safety Assessment, PSA). Security importance Ranking (RISSC) of RI for management and application of RI, RISSC ranks them into two or more groups according to security importance of structure, system or component (SSC) using the point of view of certainty and probability theory. The process is generally divided into three steps as shown in figure 1. The first and second steps are performed by a team working on the RISSC and the third step is performed by an expert group (or comprehensive decision-making group). The two steps performed by the work team are categorized into an initial ranking process; the last step performed by the expert group is categorized as a comprehensive decision making process.
In a first step, by exploiting the perspective of probability theory, the components are divided into two or more stages according to their relative importance to the core damage frequency (Core Damage Frequency, simply CDF). In general, cut set importance (Fussell-Vesely, abbreviated FV) and risk value importance (Risk Achievementworth, abbreviated RAW) are used as quantitative security importance measures. In the second step, the results of the first step are complemented by a qualitative assessment based on a deterministic opinion. In a third step, the expert group uses the views compiled in the initial ranking process in combination with other deterministic views to make discussions and opinion mediations to make the final decision (ranking) qualitatively.
The decision of the expert group is made through discussion and opinion mediation, and the conventional method has the following advantages: the multiple opinions of the experts are considered, and a synergistic effect is possible through discussion and opinion mediation. However, due to the qualitative and linear decision making process, conventional approaches have the following drawbacks: (1) significant time and labor costs; (2) it is difficult to reach a final decision when the expert agrees left; ( 3) Since the linear patterns have overlapping processes (perform grading twice-first, by engineers; second, by an expert group. )
Accordingly, there is a need for a method of grading equipment in a risk-directed post-processing facility that addresses the above-described issues.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a grading method of equipment in post-processing facilities for risk guidance, which enables an expert to participate in early stage by utilizing analytic hierarchy process and Bayesian belief network, and can provide basis for grading the post-processing facilities so as to reduce the running and maintenance costs of related equipment.
In order to achieve the above purpose, the invention adopts the technical scheme that:
a method of grading equipment in a risk-directed post-processing facility, the method comprising:
(1) After the expert j selects the equipment, identifying a decision factor i and constructing a decision level by using a hierarchical analysis processing method;
(2) Constructing a Bayesian belief network model by expert j, and judging to obtain P (C ij |S i ) And calculates the security importance P of each decision factor of the target component using the Bayesian belief network (S i ＝Hi)；
(3) Obtaining evidence E by expert ij And updates P using evidence (S i ＝Hi)；
(4) Obtaining the weight w of the decision factor i by using analytic hierarchy process i ；
(5) Based on the probability theory and the certainty theory, a full probability P is obtained (S T =hi), and finally classified.
Further, the step (1) specifically includes:
after selecting the target equipment, the decision factor i of the probability theory of the equipment is based on fault tree analysis of all accidents related to the equipment, namely an accident Risk; decision factor i of the device's decision is based on qualitative evaluation factors: critical security functions and specific normal operation functions, i.e. qualitative evaluation QA.
Further, the qualitative evaluation QA includes: QA (quality assurance) 1 Maintaining the integrity of the respective enclosures requires the apparatus; QA (quality assurance) 2 The apparatus is needed to clean or reduce the released radioactivity; QA (quality assurance) 3 Auxiliary systems (water supply, power supply, air pressure) require the device.
Further, the constructing the bayesian belief network model by the expert j in the step (2) specifically includes:
creating a Bayesian belief network model comprising a parent variable S i And sub-variable C ij ，S i Decision factor i representing the target component falls into Hi (high security importance rating) or Lo (low security importance rating), C ij Decision factor i representing the target component falls within decision criterion (c) based on the judgment of expert j 1 ,c 2 ,…,c m M is the total number of criteria for decision factor i).
Further, the decision criteria of the probability theory accident risk in the post-processing facility RISSC specifically include:
as a consequence of an accident>10 8 MBq，0<The occurrence frequency is less than or equal to 10 -9 a -1 When the decision criterion is c 4 ；
As a consequence of an accident>10 8 MBq，10 -9 a -1 <The occurrence frequency is less than or equal to 10 -6 a -1 When the decision criterion is c 2 ；
As a consequence of an accident>10 8 MBq，10 -6 a -1 <When the occurrence frequency is the same, the decision criterion is c 1 ；
When 0 is<The accident result is less than or equal to 10 8 MBq，0<The occurrence frequency is less than or equal to 10 -9 a -1 When the decision criterion is c 6 ；
When 0 is<The accident result is less than or equal to 10 8 MBq，10 -9 a -1 <The occurrence frequency is less than or equal to 10 -6 a -1 When the decision criterion is c 5 ；
When 0 is<The accident result is less than or equal to 10 8 MBq，10 -6 a -1 <When the occurrence frequency is the same, the decision criterion is c 3 。
Further, decision criterion c for qualitative evaluation in the post-processing facility RISSC 1 -c 4 The method comprises the following steps:
c 1 : the device is directly required and no other device performs the same or similar functions;
c 2 : the equipment is straightReceiving the request and storing other devices to perform the same or similar functions;
c 3 : the present device is indirectly required (e.g., the device may affect the behavior of a person or the device is directly required by the device, etc.);
c 4 : the present component is not required at all.
Further, in the step (2), the expert j judges to obtain P (C) ij |S i ) And calculates the security importance P of each decision factor of the target component using the Bayesian belief network (S i =hi) specifically includes:
by expert j P (S) i |C ij ) And P (C) ij )；
According to P (S) by Bayes' theorem i |C ij ) Calculation of P (C) ij |S i )；
Calculation of P (S) i =hi), the calculation formula is
P(S i ＝Hi)＝P(S i ＝Hi|C i1 ＝c 1 )P(C i1 ＝c 1 )+…+P(S i ＝Hi|C i1 ＝c m )P(C i1 ＝c m )。
Further, the calculation formula of the full probability in the step (5) is as follows:
P(S T ＝H i )＝w P P(S PT ＝Hi)+w D P(S DT ＝Hi)，
wherein P (S) PT =hi) is a hierarchical probability derived based on PSA; p (S) DT =hi) is a hierarchical probability derived based on qualitative evaluation of the certainty theory; w (w) P 、w D P (S) PT =hi) and P (S DT =hi).
Further, the P (S PT The calculation formula of =hi) is:
wherein P (S) n =hi) (n=1, 2,., N) is the system deriving the risk of the nth accident as belonging to HiProbability.
Further, the P (S DT The calculation formula of =hi) is:
wherein w is 1n 、w 2n Qualitative assessment QA of the nth accident respectively 1 、QA 2 Weight value, w in qualitative assessment 2N+1 To qualitatively evaluate QA 3 Weight value in qualitative evaluation.
Further, the grading standard in the step (5) is as follows:
when the 0< full probability is less than or equal to 0.35, the device is Lo with low safety importance;
when the total probability is 0.35 and is less than or equal to 0.65, the device is Po potential safety importance;
when 0.65< full probability is less than or equal to 1, the device is Hi with high security importance.
The method provided by the invention has the advantages that the expert can participate in early stage by utilizing analytic hierarchy process and Bayesian belief network, and can provide basis for grading the post-processing facilities so as to reduce the operation and maintenance costs of related equipment.
Drawings
FIG. 1 is a flow chart of an embodiment of a conventional RISSC method;
FIG. 2 is a flow chart of a RISSC method based on analytic hierarchy process and Bayesian belief network;
FIG. 3 is a schematic diagram of a decision level of a post-processing facility RISSC based on analytic hierarchy process;
FIG. 4 is a schematic diagram of a Bayesian belief network model of decision factor i;
FIG. 5 is a schematic diagram of decision criteria for risk of an accident in a post-processing facility RISSC.
Detailed Description
In order to make the technical problems solved, the technical scheme adopted and the technical effects achieved by the invention more clear, the technical scheme of the embodiment of the invention will be further described in detail with reference to the accompanying drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
Referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of the method according to the present invention.
A method of grading equipment in a risk-directed post-processing facility, comprising the steps of:
step 101: after the expert j selects the device, the decision factor i is identified and a decision hierarchy is constructed using analytic hierarchy process.
After the expert j (j=1, 2, …, n, n is the total number of experts) selects the target device, the decision factor i of the probability theory of the device is based on the fault tree analysis of all accidents related to the device, namely the accident Risk; decision factor i of the device's decision is based on qualitative evaluation factors: critical security functions and specific normal operation functions, i.e. qualitative evaluation QA.
Specifically, RISSC performs information using both probability theory and certainty theory. Thus, the available probabilistic or deterministic information should be identified. Importance of PSA analyzes information as a probability theory; qualitative evaluation of functional attributes based on SSC is used as information of the certainty.
For systems or equipment of the aftertreatment facility, it is often not difficult to determine the occurrence of possible abnormal events (incipient events), such as nuclear criticality, explosions, leaks, etc. But the cause and progress of the event is more difficult to determine. From the PSA's point of view, these certain possible anomaly events are top events in the fault tree analysis. After the top event is determined, the various causes (failure modes) that lead to this result are found by reasoning, using a fruit-to-factor algorithm. The decision-making factor of the probability theory of the post-processing facility RISSC takes the importance analysis, which results from the independent fault tree analysis of all incidents involved in the target device. For example, a device may be involved in three types of incidents, critical, fire and leakage, and then the combined effects of all three types of incidents should be considered in the RISSC for that device. In the perspective of probability theory, importance of PSA is mainly used for importance analysis, which is FV importance and RAW importance.
The decision factor of the decision of the post-processing facility RISSC is based on a qualitative evaluation as follows: (1) Key safety function-ensuring the integrity of the corresponding containment; ensuring the ability to prevent or mitigate the consequences of accidents that may lead to potential off-site irradiation; (2) Specific normal functioning functions-can lead to an originating event or production interruption.
TABLE 1 decision factors for post-processing facility RISSC
As described in table 1, after the expert j (j=1, 2, …, n, n is the total number of experts) selects the target device, decision factors i (i=1, 2,., l, l are the total number of decision factors) of the target device post-processing facility RISSC are determined according to the above method. Specifically, the qualitative evaluation QA includes: QA (quality assurance) 1 Maintaining the integrity of the respective enclosures requires the apparatus; QA (quality assurance) 2 The apparatus is needed to clean or reduce the released radioactivity; QA (quality assurance) 3 Auxiliary systems (water supply, power supply, air pressure) require the device.
Analytic hierarchy process (The analytic hierarchy process, AHP for short) is a tool that helps decision makers perform multi-criteria decision analysis. Since saath was proposed in the last 70 th century, it has been widely used in a variety of decision fields. In the present invention, the decision problem is RISSC. As shown in fig. 3, the decision problem is decomposed layer by layer into interrelated decision elements, and the AHP builds RISSC with the decision elements hierarchically.
Step 102: constructing a Bayesian belief network model by expert j, and judging to obtain P (C ij |S i ) And calculates the security importance P of each decision factor of the target component using the Bayesian belief network (S i ＝Hi)。
The bayesian belief network model (Bayesian belief networks, BBN for short) strongly aggregates different knowledge sources with various degrees of uncertainty in a strict and efficient mathematical manner.
Referring to fig. 4, a bayesian belief network model is created, the bayesian belief network model containing the parent variable S i And sub-variable C ij And a directional edge between the parent and child variables. S is S i Decision factor i representing the target component falls into Hi (high security importance rating) or Lo (low security importance rating), C ij Decision factor i representing the target component falls within decision criterion (c) based on the judgment of expert j 1 ,c 2 ,…,c m M is the total number of criteria for decision factor i).
Referring to fig. 5, fig. 5 is a decision criterion for post-processing risk of accidents in a facility RISSC. The consequences in the present invention take the most radioactivity the target device may leak; for single plants, however, the frequency of certain nuclear accidents is generally relatively high, which results from the fact that the failure rate of some components of the post-treatment plant (such as the burst leakage of valves) is significantly higher than that of the same components of the nuclear plant, about an order of magnitude. The decision criteria for probability theory accident risk in the post-processing facility RISSC specifically include:
as a consequence of an accident>10 8 MBq，0<The occurrence frequency is less than or equal to 10 -9 a -1 When the decision criterion is c 4 ；
As a consequence of an accident>10 8 MBq，10 -9 a -1 <The occurrence frequency is less than or equal to 10 -6 a -1 When the decision criterion is c 2 ；
As a consequence of an accident>10 8 MBq，10 -6 a -1 <When the occurrence frequency is the same, the decision criterion is c 1 ；
When 0 is<The accident result is less than or equal to 10 8 MBq，0<The occurrence frequency is less than or equal to 10 -9 a -1 When the decision criterion is c 6 ；
When 0 is<The accident result is less than or equal to 10 8 MBq，10 -9 a -1 <The occurrence frequency is less than or equal to 10 -6 a -1 When the decision criterion is c 5 ；
When 0 is<The accident result is less than or equal to 10 8 MBq，10 -6 a -1 <When the occurrence frequency is the same, the decision criterion is c 3 。
Decision criterion c for qualitative evaluation in a post-processing facility RISSC 1 -c 4 The method comprises the following steps:
c 1 : the device is directly required and no other device performs the same or similar functions;
c 2 : the device is directly required and other devices perform the same or similar functions;
c 3 : the present device is indirectly required (e.g., the device may affect the behavior of a person or the device is directly required by the device, etc.);
c 4 : the present component is not required at all.
In the present invention, the safety importance of each decision factor of a component is expressed in terms of the probability that it falls into Hi, denoted as P (S i =hi). Expert j judges to obtain P (C) ij |S i ) And calculates the security importance P of each decision factor of the target component using the Bayesian belief network (S i =hi) specifically includes:
first, P is evaluated by expert j (S i |C ij )。P(S i |C ij ) Is easy to obtain because of the variable S i There are only 2 states, hi or Lo. For example, if P (S i ＝Hi|C ij ＝c 1 ) Is evaluated by a expert, P (S i ＝Lo|C ij ＝c 1 ) Can be derived from expressions 1-P (S i ＝Hi|C ij ＝c 1 ) Is obtained directly from the plant. Thus, P (S) i |C ij ) Can be evaluated by an expert. It should be noted that when expert j evaluates P (S i |C ij ) When P (S) i |C ij ) The evaluation yields a point value. Therefore, P (S) i |C ij ) Is estimated as an interval value. As shown in table 2, the median value of the section values is used as a typical value, and if the median value of the expert-based opinion section values is not suitable, the expert may additionally determine a suitable value.
TABLE 2 section values for expert estimation
Then, according to P (S) by Bayes' theorem i |C ij ) Calculation of P (C) ij |S i ). The calculation formula is as follows:
it is noted that when there is no variable C ij Can be assumed to be P (C ij ＝c 1 )＝,…,＝P(C ij ＝c m )＝1/m。
Finally, the security importance P of each decision factor of the target component is calculated (S i =hi), the calculation formula is:
P(S i ＝Hi)＝P(S i ＝Hi|C i1 ＝c 1 )P(C i1 ＝c 1 )+…+P(S i ＝Hi|C i1 ＝c m )P(C i1 ＝c m )。
wherein P (S) i ＝Lo)＝1-P(S i ＝Hi)。
Step 103: obtaining evidence E by expert ij And updates P using evidence (S i ＝Hi)。
After selecting the target part, for each i, P (S i ) Evidence E can be judged by expert ij Upgrading.
First, if expert 1 determines that decision factor i of the target component falls within c 1 I.e. P * (C i1 )＝(1，0，0，...)，C ij Evidence of (2) is denoted as P * (C ij ) Or E is ij (P * (C ij )≡E ij )，P * (C i1 ) = (1, 0,) to be used as update P (S) i =hi), as follows:
P * (S i ＝Hi)＝P(S i ＝Hi|C i1 ＝c 1 )P * (C i1 ＝c 1 )+…+P(S i ＝Hi|C i1 ＝c m )P * (C i1 ＝c m )
＝P(S i ＝Hi|C i1 ＝c 1 )P * (C i1 ＝c 1 )
·.·P * (C i1 )＝(1，0，0，...)
known P (S) i |C i1 ) And P * (C i1 ＝c 1 ) By using 1, P can be calculated * (S i =hi), the probability updated by evidence is noted as P * . In the pair P (S i ) If expert 2 determines that decision factor i of the target component falls within c 2 I.e. P * (C i2 )＝(0，1，0，...)，P * (S i =hi) is updated by Bayes theorem as follows:
wherein P (C) i2 ＝c 2 |S i ＝Hi)，P(C i2 ＝c 2 |S i =lo) and P * (S i The value of =hi) is known, and P * (S i ＝Lo)＝1-P * (S i =hi). Thus, P * (S i ＝Hi|C i2 ＝c 2 ) Can be calculated. P (S) of the second update i =hi) is P ** (S i =hi) can be represented by P * (S i ＝Hi|C i2 ＝c 2 ) Calculated as follows:
P ** (S i ＝Hi)＝P * ((S i ＝Hi|C i2 ＝c 1 )P * (C i2 ＝c 1 )+…+P * (S i ＝Hi|C i2 ＝c m )P * (C i2 ＝c m )
＝P * (S i ＝Hi|C i2 ＝c 2 )P * (C i2 ＝c 2 )
·.·P * (C i2 )＝(0，1，0，...)
similarly, for each decision factor i, P (S i =hi) can continue updating until all expert judged evidenceAre all used. Therefore, with expert judgment evidence added to the BBN model, a more reasonable posterior P (S) i =hi).
Step 104: obtaining the weight w of the decision factor i by using analytic hierarchy process i 。
Firstly, obtaining input data of a given problem, namely a judgment matrix comprising pairwise comparison of decision elements of each level; secondly, if all the judgment matrixes are provided by different experts, each element of the combined judgment matrix is obtained by the geometric average value of the corresponding elements of all the matrixes; thirdly, the relevant weight value of the decision factor is estimated by a Saath characteristic value method of the joint matrix; finally, the weight value of each bottom layer decision factor is obtained by accumulating the weights of each level of related elements.
For all layers of the AHP, the calculation formula of the weight value wi of the decision factor i is as follows:
wherein w is u/u-1 : the weight of element u relative to its associated last-previous-level element u-1; u (u) i : decision factor i; v: the number of layers between decision factor i and decision problem; ut: decision making problems.
Step 105: based on the probability theory and the certainty theory, a full probability P is obtained (S T =hi), and finally classified.
The calculation formula of the full probability is as follows:
P(S T ＝Hi)＝w P P(S PT ＝Hi)+w D P(S DT ＝Hi)
wherein P (S) PT =hi) is a hierarchical probability derived based on PSA; p (S) DT =hi) is a hierarchical probability derived based on qualitative evaluation of the certainty theory; w (W) P 、W D P (S) PT =hi) and P (S DT =hi).
Said P (S) PT The calculation formula of =hi) is:
wherein P (S) n =hi) (n=1, 2,., N) is the probability that the system derives from the nth accident risk that belongs to Hi.
Said P (S) DT The calculation formula of =hi) is:
wherein W is 1n 、W 2n Qualitative assessment QA of the nth accident respectively 1 、QA 2 Weight value, W in qualitative evaluation 2N+1 To qualitatively evaluate QA 3 Weight value in qualitative evaluation.
The grading criteria are as follows:
when the total probability is more than 0 and less than or equal to 0.35, the equipment is Lo with low safety importance;
when the total probability is more than 0.35 and less than or equal to 0.65, the equipment is of potential safety importance of Po;
when 0.65< full probability is less than or equal to 1, the device is Hi with high security importance.
In a specific embodiment, the present invention is applied to RISSC of critical equipment in the co-decontamination cycle segment of a typical aftertreatment facility.
The equipment of the post-treatment facility originally has three security levels (the lower the level is, the more important) which are classified into a level 1 of the amplification security, a level 2 of the amplification security and a level 3 of the amplification security according to the method of the definite theory, and the corresponding anti-seismic class or the quality assurance level is shown in the following table 3.
TABLE 3 correspondence of the security level to the shock resistance class and the quality assurance level
In the analysis, 31 devices are total, and the original classification belongs to the security level 2. Results of RISSC show that 13 devices belong to Po class, which corresponds to the original class, i.e. results of RISSC agree with the results of the complete certainty; there are 18 devices belonging to the Lo class, which reduces the security level compared to the original class, indicating the conservation of the original class. The results of the RISSC agree with the RISSC experience of the nuclear power plant equipment and the correctness and applicability of the RISSC method herein are also demonstrated laterally. In addition, for the waste liquid evaporator, the original quality assurance grade is grade A, the result of RISSC belongs to Lo grade, and the quality assurance grade can be considered to be reduced so as to reduce the related operation and maintenance cost.
Compared with the prior art, the grading method of the equipment in the post-processing facility for risk guidance provided by the invention has the advantages that the expert early participates in the post-processing facility by utilizing analytic hierarchy process and Bayesian belief network, and the grading method can provide basis for grading the post-processing facility so as to reduce the running and maintenance cost of related equipment.
It will be appreciated by persons skilled in the art that the methods of the present invention are not limited to the examples described in the detailed description, which are provided for the purpose of illustrating the invention and are not intended to limit the invention. Other embodiments will occur to those skilled in the art from a consideration of the specification and practice of the invention as claimed and as claimed in the claims and their equivalents.
Claims (6)
1. A method of grading equipment in a risk-directed post-processing facility, the method comprising:
(1) After the equipment is selected, a decision factor i is identified and a decision level is constructed by using a hierarchical analysis processing method;
(2) Constructing a Bayesian belief network model, and judging to obtain P (C ij |S i ) And calculates the security importance P of each decision factor of the target component using the Bayesian belief network (S i ＝Hi)；
(3) Obtaining evidence E ij And updates P using evidence (S i ＝Hi)；
(4) Obtaining the weight w of the decision factor i by using analytic hierarchy process i ；
(5) Based on the probability theory and the certainty theory, a full probability P is obtained (S T =hi), and finally classified;
after selecting the target equipment, the decision factor i of the probability theory of the equipment is based on fault tree analysis of all accidents related to the equipment, namely an accident Risk; decision factor i of the device's decision is based on qualitative evaluation factors: key safety functions and specific normal operation functions, namely qualitative evaluation QA;
the qualitative evaluation QA includes: QA1 requires the device to maintain the integrity of the respective enclosures; the apparatus is required for QA2 cleaning or for reducing the released radioactivity; the apparatus is needed for a QA3 auxiliary system, which includes water supply, power supply and air compression;
the step (2) of constructing a bayesian belief network model specifically includes:
creating a Bayesian belief network model comprising a parent variable S i And sub-variable C ij ，S i Decision factor i representing the target component falls within either a high security importance rating or a low security importance rating, C ij The decision factor i representing the target component is based on the fact that the judgment falls into the decision criterion;
the calculation formula of the full probability in the step (5) is as follows:
P(S T ＝Hi)＝w P P(S PT ＝Hi)+w D P(S DT ＝Hi)，
wherein P (S) PT =hi) is a hierarchical probability derived based on PSA; p (S) DT =hi) is a hierarchical probability derived based on qualitative evaluation of the certainty theory; w (w) P 、w D P (S) PT =hi) and P (S DT =hi).
2. The method for grading equipment in a risk-directed post-processing facility according to claim 1, wherein the decision criteria for probability-based accident risk in the post-processing facility RISSC specifically comprises:
as a consequence of an accident>10 8 MBq，0<The occurrence frequency is less than or equal to 10 -9 a -1 When the decision criterion is c 4 ；
As a consequence of an accident>10 8 MBq，10 -9 a -1 <The occurrence frequency is less than or equal to 10 -6 a -1 When the decision criterion is c 2 ；
As a consequence of an accident>10 8 MBq，10 -6 a -1 <When the occurrence frequency is the same, the decision criterion is c 1 ；
When 0 is<The accident result is less than or equal to 10 8 MBq，0<The occurrence frequency is less than or equal to 10 -9 a -1 When the decision criterion is c 6 ；
When 0 is<The accident result is less than or equal to 10 8 MBq，10 -9 a -1 <The occurrence frequency is less than or equal to 10 -6 a -1 When the decision criterion is c 5 ；
When 0 is<The accident result is less than or equal to 10 8 MBq，10 -6 a -1 <When the occurrence frequency is the same, the decision criterion is c 3 。
3. The method for grading equipment in a risk-directed post-processing facility according to claim 1, wherein the decision criteria c for qualitative evaluation in the post-processing facility RISSC 1 -c 4 The method comprises the following steps:
c 1 : the device is directly required and no other device performs the same or similar functions;
c 2 : the device is directly required and other devices perform the same or similar functions;
c 3 : the device is indirectly required;
c 4 : the present component is not required at all.
4. The method for grading equipment in a risk-guided post-processing facility according to claim 1, wherein the step (2) evaluates to obtain P (C ij |S i ) And calculates the security importance P of each decision factor of the target component using the Bayesian belief network (S i =hi) specifically includes:
p (S) was evaluated i |C ij ) And P (C) ij )；
According to P (S) by Bayes' theorem i |C ij ) Calculation of P (C) ij |S i )；
Calculation of P (S) i =hi), the calculation formula is
P(S i ＝Hi)＝P(S i ＝Hi|C i1 ＝c 1 )P(C i1 ＝c 1 )+…+P(S i ＝Hi|C i1 ＝c m )P(C i1 ＝c m )。
5. The method of grading equipment in a risk-directed post-processing facility according to claim 1, wherein the calculation formula of P (sdt=hi) is:
wherein w is 1n 、w 2n Qualitative assessment QA of the nth accident respectively 1 、QA 2 Weight value, w in qualitative assessment 2N+1 To qualitatively evaluate QA 3 Weight value in qualitative evaluation.
6. A method of grading equipment in a risk-directed post-processing facility according to claim 1, wherein the grading criteria in step (5) are as follows:
when the 0< full probability is less than or equal to 0.35, the device is Lo with low safety importance;
when the total probability is 0.35 and is less than or equal to 0.65, the device is Po potential safety importance;
when 0.65< full probability is less than or equal to 1, the device is Hi with high security importance.
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