CN115907565B - Diversion tunnel structure safety evaluation method and device, electronic equipment and storage medium - Google Patents
Diversion tunnel structure safety evaluation method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides a diversion tunnel structure safety evaluation method, a diversion tunnel structure safety evaluation device, electronic equipment and a storage medium, wherein index weights are obtained according to a fuzzy judgment matrix; obtaining index risk probability distribution information according to all index detection data; obtaining a structural safety evaluation result of the diversion tunnel to be evaluated according to the index risk probability distribution information and the index weight; and processing the structure safety evaluation result and the time sequence data by adopting a dynamic Bayesian network model to obtain the development trend of the structure safety state of the diversion tunnel to be evaluated. Therefore, the safety level of the diversion tunnel is quantitatively evaluated through the current risk probability distribution and the relative importance degree of various safety indexes of the diversion tunnel, and the risk probability development trend prediction is carried out by combining the historical detection data, so that the accuracy of the safety evaluation of the diversion tunnel structure can be improved, effective risk management is carried out, the potential safety hazards are eliminated in advance, and the accident occurrence risk is avoided.
Description
Technical Field
The invention relates to the field of diversion tunnels, in particular to a diversion tunnel structure safety evaluation method, a diversion tunnel structure safety evaluation device, electronic equipment and a storage medium.
Background
The water resource distribution of China is uneven, and the annual precipitation difference is obvious. The problem of water resource shortage in northwest arid water-deficient areas seriously affects the development of local economy and the improvement of living standard. In order to solve the problem of uneven spatial and temporal distribution of water resources and meet the requirements of production, life, ecology and the like in water-deficient areas, great investment and construction of China is introduced into the long-distance hydraulic engineering of cross-river areas such as Qin engineering, north and south projects and the like to play an important role, wherein a diversion tunnel is an important component part of a water delivery system, and the safety of a lining structure of the diversion tunnel is an important guarantee for safe operation of the diversion tunnel.
Because the diversion tunnel has more damage types and complicated damage causes, the safety evaluation method of other hydraulic structures is difficult to be directly applied to the safety evaluation of the diversion tunnel. At present, although a large number of indexes for safety evaluation are provided at home and abroad, qualitative judgment is mainly adopted, and accuracy is lacking; it is difficult to comprehensively reflect the safety state of the diversion tunnel, and the development trend of the safety state of the diversion tunnel cannot be predicted.
Disclosure of Invention
In view of the above, the invention aims to provide a diversion tunnel structure safety evaluation method, a device, electronic equipment and a storage medium, which are used for realizing quantitative evaluation of diversion tunnel structure safety, improving accuracy and predictability of diversion tunnel structure safety evaluation, further eliminating potential safety hazards in advance and avoiding accidents.
In order to achieve the above object, the technical scheme adopted by the embodiment of the invention is as follows:
in a first aspect, the invention provides a diversion tunnel structure safety evaluation method, which comprises the following steps:
receiving a structural safety evaluation instruction of the diversion tunnel; the structural safety evaluation instruction comprises index detection data, a fuzzy judgment matrix and time sequence data of the diversion tunnel to be evaluated corresponding to each safety index; the time sequence data represents the historical detection data of each safety index;
obtaining index weights according to the fuzzy judgment matrix; the index weight represents the relative importance degree of each safety index in the structural safety evaluation process;
obtaining index risk probability distribution information according to all the index detection data; the index risk probability distribution information comprises the distribution condition of each safety index at different safety levels;
obtaining a structural safety evaluation result of the diversion tunnel to be evaluated according to the index risk probability distribution information and the index weight;
processing the structural safety evaluation result and the time sequence data by adopting a dynamic Bayesian network model to obtain a prediction result; and the prediction result represents the development trend of the structural safety state of the diversion tunnel to be evaluated.
In an optional implementation manner, the processing the structural safety evaluation result and the time sequence data by adopting a dynamic bayesian network model to obtain a prediction result includes:
obtaining transition probability distribution information according to the time sequence data; the transition probability distribution information comprises the probability that each safety index is damaged to different safety levels;
and obtaining the prediction result through a dynamic Bayesian network model according to the structural safety evaluation result and the transition probability distribution information.
In an optional embodiment, the fuzzy judgment matrix is multiple, and the obtaining the index weight according to the fuzzy judgment matrix includes:
obtaining judgment weights according to each fuzzy judgment matrix; the fuzzy judgment matrixes are in one-to-one correspondence with the judgment weights, and the judgment weights represent the relative importance degree of each fuzzy judgment matrix in the structural safety evaluation process;
obtaining a group decision fuzzy matrix according to each fuzzy judgment matrix and each corresponding judgment weight; the fuzzy matrix of the group decision represents the fuzzy number of the relative importance judgment result among the summarized safety indexes;
And obtaining the index weight according to the group decision fuzzy matrix.
In an optional embodiment, the obtaining a judgment weight according to each fuzzy judgment matrix includes:
obtaining an optimal solution of the normalized relative distance vector and the safety index and a worst solution of the safety index according to the fuzzy judgment matrix; the normalized relative distance vector characterizes the judgment error of the fuzzy judgment matrix for each safety index; the optimal solution of the safety indexes represents the minimum value of the judgment error of each safety index; the worst solution of the safety indexes represents the maximum value of the judging error of each safety index;
and obtaining the judgment weight according to the normalized relative distance vector, the optimal solution and the worst solution.
In an optional embodiment, the obtaining the index weight according to the group decision fuzzy matrix includes:
obtaining fuzzy weights of each safety index according to the group decision fuzzy matrix;
and obtaining the index weight of each safety index according to the fuzzy weight of each safety index.
In a second aspect, the present invention provides a diversion tunnel structure safety evaluation device, the device comprising:
The receiving module is used for receiving the structural safety evaluation instruction of the diversion tunnel; the structural safety evaluation instruction comprises index detection data, a fuzzy judgment matrix and time sequence data of the diversion tunnel to be evaluated corresponding to each safety index; the time sequence data represents the historical detection data of each safety index;
the evaluation module is used for obtaining index weights according to the fuzzy judgment matrix; the index weight represents the relative importance degree of each safety index in the structural safety evaluation process;
the evaluation module is further used for obtaining index risk probability distribution information according to all the index detection data; the index risk probability distribution information comprises the distribution condition of each safety index at different safety levels;
the evaluation module is further used for obtaining a structural safety evaluation result of the diversion tunnel to be evaluated according to the index risk probability distribution information and the index weight;
the prediction module is used for processing the structural safety evaluation result and the time sequence data by adopting a dynamic Bayesian network model to obtain a prediction result; and the prediction result represents the development trend of the structural safety state of the diversion tunnel to be evaluated.
In an alternative embodiment, the prediction module is specifically configured to:
obtaining transition probability distribution information according to the time sequence data; the transition probability distribution information comprises the probability that each safety index is damaged to different safety levels;
and obtaining the prediction result through a dynamic Bayesian network model according to the structural safety evaluation result and the transition probability distribution information.
In an optional embodiment, the fuzzy judgment matrix is a plurality of, and the evaluation module is specifically configured to:
obtaining judgment weights according to each fuzzy judgment matrix; the fuzzy judgment matrixes are in one-to-one correspondence with the judgment weights, and the judgment weights represent the relative importance degree of each fuzzy judgment matrix in the structural safety evaluation process;
obtaining a group decision fuzzy matrix according to each fuzzy judgment matrix and each corresponding judgment weight; the fuzzy matrix of the group decision represents the fuzzy number of the relative importance judgment result among the summarized safety indexes;
and obtaining the index weight according to the group decision fuzzy matrix.
In a third aspect, the present invention provides an electronic device comprising a memory for storing a computer program and a processor for executing the diversion tunnel construction safety evaluation method according to any one of the preceding embodiments when the computer program is invoked.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the diversion tunnel structure safety evaluation method according to any one of the preceding embodiments.
Compared with the prior art, the method, the device, the electronic equipment and the storage medium for evaluating the safety of the diversion tunnel structure provided by the embodiment of the invention receive the structure safety evaluation instruction of the diversion tunnel; the structural safety evaluation instruction comprises index detection data, fuzzy judgment matrix and time sequence data of the diversion tunnel to be evaluated corresponding to each safety index; the time sequence data represents the historical detection data of each safety index; obtaining index weight according to the fuzzy judgment matrix; the index weight represents the relative importance degree of each safety index in the structural safety evaluation process; obtaining index risk probability distribution information according to all index detection data; the index risk probability distribution information comprises the distribution condition of each safety index at different safety levels; obtaining a structural safety evaluation result of the diversion tunnel to be evaluated according to the index risk probability distribution information and the index weight; processing the structure safety evaluation result and the time sequence data by adopting a dynamic Bayesian network model to obtain a prediction result; the prediction result represents the development trend of the structural safety state of the diversion tunnel to be evaluated. Therefore, the safety level of the diversion tunnel is quantitatively evaluated through the current risk probability distribution and the relative importance degree of various safety indexes of the diversion tunnel, and the risk probability development trend prediction is carried out by combining the historical detection data, so that the accuracy of the safety evaluation of the diversion tunnel structure can be improved, effective risk management is carried out, the potential safety hazards are eliminated in advance, and the accident occurrence risk is avoided.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 shows a schematic flow chart of a drainage tunnel structure safety evaluation method provided by an embodiment of the invention.
Fig. 2 shows a schematic representation of a safety index system.
Fig. 3 shows a sub-step flow diagram of step S105 of fig. 1.
Fig. 4 shows a schematic diagram of a dynamic bayesian network structure.
Fig. 5 shows a sub-step flow diagram of step S102 of fig. 1.
Fig. 6 shows a sub-step flow diagram of steps 1021 and 1023 of fig. 5.
Fig. 7 shows a schematic diagram of the development of the diversion tunnel risk probability distribution.
FIG. 8 shows a schematic diagram of diversion tunnel and lining crack structure safety evaluation prediction.
FIG. 9 shows a block schematic diagram of a diversion tunnel structure safety evaluation device provided by an embodiment of the invention.
Fig. 10 shows a block schematic diagram of an electronic device according to an embodiment of the present invention.
Icon: 100-an electronic device; 110-memory; a 120-processor; 130-a communication module; 200-a diversion tunnel structure safety evaluation device; 201-a receiving module; 202-an evaluation module; 203-a prediction module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
It is noted that relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In order to ensure domestic and production water, a large number of hydraulic engineering is built in China, wherein the diversion tunnel plays a key role, and then the safety problem of the diversion tunnel is also important. Once the diversion tunnel has a safety problem, serious accidents are often caused. For example, the piping accident of the downstream dam caused by the collapse of the tunnel in the tretay reservoir brings life and property loss to the local common people. Therefore, structural safety evaluation is carried out on the diversion tunnel, so that the operation condition of the diversion tunnel is mastered, and a corresponding basis is necessary for maintenance and protection of the diversion tunnel.
However, the current evaluation method mainly focuses on the stress strain condition of the tunnel lining, so that the safety state of the diversion tunnel is difficult to comprehensively and accurately reflect, and meanwhile, the damage condition of the diversion tunnel cannot be predicted.
Based on the above, the embodiment of the invention provides a drainage tunnel structure safety evaluation method, a drainage tunnel structure safety evaluation device, electronic equipment and a storage node. The quantitative evaluation of the safety of the diversion tunnel structure is realized, the accuracy and the predictability of the safety evaluation of the diversion tunnel structure are improved, and then the potential safety hazard is eliminated in advance, so that the accident is avoided.
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The security evaluation method and the security evaluation device for the diversion tunnel structure are applied to electronic equipment, wherein the electronic equipment can be, but is not limited to, personal Computers (PC), notebook computers or servers and other electronic equipment with computing capacity. The scheme is used for establishing a safety index system when the safety evaluation of the diversion tunnel structure is carried out. It is therefore necessary to pre-establish a security index hierarchy on the electronic device in advance.
The method for commenting the safety of the diversion tunnel structure provided by the embodiment of the invention is explained based on an established safety index system in electronic equipment. Referring to fig. 1, fig. 1 shows a schematic diagram of a method for evaluating safety of a diversion tunnel structure according to an embodiment of the present invention, where the method includes the following steps:
S101, receiving a structural safety evaluation instruction of a diversion tunnel;
the structural safety evaluation instruction comprises index detection data, a fuzzy judgment matrix and time sequence data of the diversion tunnel to be evaluated corresponding to each safety index;
wherein the time series data characterizes historical detection data of each safety index.
In the embodiment of the invention, after the electronic equipment receives the structural safety evaluation instruction of the diversion tunnel, index detection data, a fuzzy judgment matrix and time sequence data of each safety index corresponding to the diversion tunnel to be evaluated in the structural safety evaluation instruction of the diversion tunnel are acquired.
Specifically, the index detection data of the safety index can be obtained through detection, monitoring, manual or intelligent inspection of the underwater robot, and the data can reflect real-time damage conditions of the diversion tunnel during operation from different aspects. According to analysis of index detection data of the safety index, the overall structural safety condition of the diversion tunnel to be evaluated can be obtained.
In order to facilitate management of the safety indexes of the diversion tunnel, in the embodiment of the invention, different damage types of the diversion tunnel are comprehensively considered, a safety index system which can be established by electronic equipment is shown as a figure 2, and the safety index system for evaluating the safety of the diversion tunnel structure is assumed to comprise three levels of safety indexes, wherein the first level of safety index is used for evaluating the safety of the diversion tunnel structure. The second-level safety index belongs to the first-level safety index, and monitors the structural safety of the diversion tunnel from seven dimensions of lining cracks, water leakage, material degradation, lining hollowness, lining deformation, lining peeling and operation environment. The third-level safety indexes are respectively subordinate to the second-level safety indexes of seven dimensions. The present invention is not limited to the selection of the safety index and the specific usage.
Step S102, obtaining index weights according to the fuzzy judgment matrix.
The index weight represents the relative importance degree of each safety index in the structural safety evaluation process.
In the embodiment of the invention, in order to more accurately evaluate the influence of each safety index on the safety of the diversion tunnel, the importance of the safety indexes is compared by adopting a hierarchical analysis method through an expert consultation mode, so that key influence factors are screened out for important attention. Therefore, when the structural safety evaluation of the diversion tunnel is carried out, a fuzzy judgment matrix is issued, and the fuzzy judgment matrix is carefully designed by field experts and is used for calculating the importance among safety indexes, namely the expert fuzzy judgment matrix.
Specifically, the expert fuzzy judgment matrix is used for carrying out importance comparison on the next-stage safety indexes belonging to the same safety index.
And step S103, obtaining index risk probability distribution information according to all the index detection data.
The index risk probability distribution information comprises distribution conditions of each safety index at different safety levels;
in the embodiment of the invention, the safety grades of the safety indexes of the diversion tunnel are divided into A, B, C, D, E five grades, wherein A is the most dangerous grade, E is the most safe grade, and the dangerous degree is gradually reduced from A to E. Each safety index in the index system can evaluate specific index detection data according to corresponding evaluation criteria, and the evaluation result is embodied as risk probability distribution of the index in five levels.
Specifically, in order to comprehensively reflect the security levels of a certain security index, risk values are set for five security levels, assuming that the correspondence is as shown in table 1:
TABLE 1
It should be noted that, the security level of each security index may be preset in the electronic device, or may be issued along with the index monitoring data. The present invention is not limited to this.
Step S104, obtaining a structural safety evaluation result of the diversion tunnel to be evaluated according to the index risk probability distribution information and the index weight;
according to the embodiment of the invention, the risk probability distribution of the subordinate upper-level safety indexes can be obtained according to the risk probability distribution information and the index weight of each-level safety index, and the structural safety comment result of the diversion tunnel is finally obtained by calculating the risk probability distribution of the subordinate upper-level safety indexes step by step in a similar manner.
Step S105, processing the structural safety evaluation result and the time sequence data by adopting a dynamic Bayesian network model to obtain a prediction result;
the prediction result represents the development trend of the structural safety state of the diversion tunnel to be evaluated.
Therefore, the structural safety evaluation method of the diversion tunnel provided by the embodiment of the invention receives the structural safety evaluation instruction of the diversion tunnel on the electronic equipment; the structural safety evaluation instruction comprises index detection data, fuzzy judgment matrix and time sequence data of the diversion tunnel to be evaluated corresponding to each safety index; the time sequence data represents the historical detection data of each safety index; obtaining index weight according to the fuzzy judgment matrix; the index weight represents the relative importance degree of each safety index in the structural safety evaluation process; obtaining index risk probability distribution information according to all index detection data; the index risk probability distribution information comprises the distribution condition of each safety index at different safety levels; obtaining a structural safety evaluation result of the diversion tunnel to be evaluated according to the index risk probability distribution information and the index weight; processing the structure safety evaluation result and the time sequence data by adopting a dynamic Bayesian network model to obtain a prediction result; the prediction result represents the development trend of the structural safety state of the diversion tunnel to be evaluated. Therefore, the safety level of the diversion tunnel is quantitatively evaluated through the current risk probability distribution and the relative importance degree of various safety indexes of the diversion tunnel, and the risk probability development trend prediction is carried out by combining the historical detection data, so that the accuracy of the safety evaluation of the diversion tunnel structure can be improved, effective risk management is carried out, the potential safety hazards are eliminated in advance, and the accident occurrence risk is avoided.
In practical application, attention is paid to the safety state of the diversion tunnel at the current moment, and the safety development trend of the diversion tunnel structure is predicted according to the historical detection data of the diversion tunnel, so that the diversion tunnel structure is intervened in advance to be maintained, and accidents are avoided.
Optionally, in order to prevent the occurrence of the safety problem, the dynamic Bayesian network model is constructed to predict the development trend of the safety of the diversion tunnel structure. Referring to fig. 3 on the basis of fig. 1, the substeps of step S105 may include:
step S1051, obtaining transition probability distribution information according to time sequence data; the transition probability distribution information contains the probability that each security indicator will be compromised to a different security level.
In the embodiment of the invention, a dynamic Bayesian network model divides the risk development process of a safety index system into time slices with the same time interval, and adopts transition probability to describe the interrelationship between indexes of adjacent time slices. The network structure of the dynamic bayesian in each time slice is the same as the security index system, as shown in fig. 4. And inputting the time sequence data of each safety index into a dynamic Bayesian network, and automatically learning through different algorithms to obtain corresponding transition probability distribution information.
Step S1052, obtaining a prediction result through a dynamic Bayesian network model according to the structural safety evaluation result and the transition probability distribution information.
In the embodiment of the invention, a dynamic Bayesian network is trained by combining a structure safety evaluation result and transition probability distribution information, and a dynamic Bayesian network model for carrying out structure safety evaluation on a diversion tunnel to be evaluated is established.
According to the established dynamic Bayesian network model, the safety indexes and the overall structural safety risk probability distribution information of the diversion tunnel after a certain time can be calculated, and meanwhile, the comprehensive risk value can be calculated to carry out comprehensive assessment on the risk of the diversion tunnel, so that the prediction of the development trend of the structural safety of the diversion tunnel is realized.
Optionally, in order to improve the comprehensiveness and accuracy of the evaluation, multiple experts can be asked to provide a fuzzy judgment matrix, the structural safety evaluation of the diversion tunnel is performed from bottom to top, the relative importance degree of the third-level safety index is evaluated first, the relative importance degree of the second-level safety index is evaluated, and finally the structural safety evaluation result of the diversion tunnel is obtained. The third level safety index will be described below as an example. Referring to fig. 5, when the fuzzy judgment matrix is plural on the basis of fig. 1, the substeps of step S102 may include:
Step S1021, obtaining judgment weights according to each fuzzy judgment matrix; the fuzzy judgment matrix corresponds to the judgment weights one by one.
The judging weight represents the relative importance degree of each fuzzy judging matrix in the structural safety evaluation process.
In the embodiment of the invention, the importance of third-level safety indexes belonging to the same second-level safety index is compared by adopting a fuzzy chromatography analysis method through an expert consultation mode, and s fuzzy judgment matrixes including the third-level safety indexes in parameters issued to electronic equipment are s in total under the assumption that s experts are consulted, wherein the fuzzy judgment matrixes of the t-th expert are as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
the fuzzy number of the t-th expert on the relative importance judgment result of the i-th safety index and the j-th safety index is the number of the safety indexes, and n is the number of the safety indexes.
Step S1022, obtaining a group decision fuzzy matrix according to each fuzzy judgment matrix and each corresponding judgment weight.
The group decision fuzzy matrix represents fuzzy numbers of relative importance judgment results among the summarized safety indexes.
Step S1023, obtaining index weight according to the group decision fuzzy matrix.
Optionally, in order to reduce the evaluation error caused by the fuzzy judgment matrix of the safety index, the judgment weight of the fuzzy judgment matrix is calculated by introducing the normalized relative distance vector, and referring to fig. 6 on the basis of fig. 5, the substep of step S1021 may include:
Step 10211, obtaining the optimal solution of the normalized relative distance vector and the safety index and the worst solution of the safety index according to the fuzzy judgment matrix.
The normalized relative distance vector represents the judgment error of the fuzzy judgment matrix for each safety index; the optimal solution of the safety indexes represents the minimum value of the judging error of each safety index; the worst solution of the safety indexes represents the maximum value of the judgment error of each safety index.
In the embodiment of the invention, for the fuzzy judgment matrix
Each fuzzy number can be defuzzified by using a truncated set method to obtain a defuzzified matrix +.>
The method comprises the following steps:
wherein Cv is v truncated set of corresponding fuzzy numbers, inf is lower definite, and sup is upper definite;
is the fuzzy number
The result obtained after deblurring.
According to the defuzzification matrix, calculating by using a feature vector method to obtain a safety index theoretical weight matrix, wherein the safety index theoretical weight matrix is as follows:
the safety index theoretical weight matrix is calculated theoretically according to the defuzzification matrix and is used for verifying the error of the fuzzy judgment matrix of the expert.
Is the theoretical weight of the ith safety index judged by the t-th expert, and the value of i is +.>
。
According to the theoretical weight of the safety index, a consistency judgment matrix corresponding to the fuzzy judgment matrix can be constructed as follows:
Wherein, each element in the consistency judgment matrix is calculated according to the theoretical weight of the safety index, and the specific formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is the ratio of the theoretical weight of the ith safety index and the jth safety index judged by the t-th expert.
To obtain the error level of the fuzzy judgment matrix, calculating the normalized relative between the fuzzy judgment matrix and the consistent judgment matrixDistance vector
The formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is the judgment error when the t-th expert judges the relative importance of the i-th safety index; />
The method is a judgment error normalization result when the t-th expert judges the relative importance of the i-th safety index.
Based on normalized relative distance vector of each fuzzy judgment matrix, constructing an optimal solution according to TOPSIS method
And worst solution->
The formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
,/>
。
step 10212, obtaining judgment weights according to the normalized relative distance vector, the optimal solution and the worst solution.
In the embodiment of the invention, the relative distance vector is normalized
And optimal solution->
The gray correlation between the two is obtained, and the formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is normalized relative distance vector +.>
And optimal solution->
The degree of grey-color correlation between the two, 
Is the minimum difference between normalized relative distance vector and optimal solution, +.>
Is the maximum difference between the normalized relative distance vector and the optimal solution, +.>
For the resolution factor, 0.5 is generally taken.
According to the normalized relative distance vector and the worst solution, the gray correlation degree of the worst solution is obtained, and the formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is normalized relative distance vector +.>
And worst solution->
The degree of grey-color correlation between the two,
is the minimum difference between normalized relative distance vector and worst solution,/and>
is the maximum difference between the normalized relative distance vector and the worst solution,/and>
for the resolution factor, 0.5 is generally taken.
Obtaining the judgment weight of each fuzzy judgment matrix according to the gray correlation degree
The formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is normalized relative distance vector +.>
And optimal solution->
The degree of grey-color correlation between the two,
is normalized relative distance vector +.>
And worst solution->
Gray correlation between.
In order to comprehensively reference the evaluation and judgment results of each expert, a weighted average method can be used to obtain a group decision matrix based on the fuzzy judgment matrix and the judgment weight, and the formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
the fuzzy number of the ith safety index and the jth safety index relative importance judgment result after the evaluation judgment result of each expert is synthesized.
Optionally, in order to obtain a more accurate index weight of each safety index, referring to fig. 6, the substeps of step S1023 may include:
step 10231, obtaining the fuzzy weight of each security index according to the fuzzy matrix of the group decision.
In the embodiment of the inventionObtaining the index weight fuzzy number of each safety index according to the group decision fuzzy matrix by using a fuzzy least square method
。
Wherein, the liquid crystal display device comprises a liquid crystal display device,
step S10232, obtaining the index weight of each safety index according to the fuzzy weight of each safety index.
In the embodiment of the invention, the fuzzy number of the index weight is deblurred by a truncated set method to obtain the index weight of each safety index
。
Specifically, supposing that the risk probability distribution information of the third-stage safety index of the diversion tunnel defaults to be a column vector, namely
Corresponding index weight +.>
The current wind of the subordinate second-level safety index can be obtainedThe risk probability distribution information R is given by:
similarly, the risk probability distribution information of the first layer of indexes in the index system, namely the risk probability distribution information of the whole diversion tunnel, can be calculated step by step. The calculated risk probability distribution information of each safety index reflects the structural safety level of the diversion tunnel to be evaluated under the current condition. Thus, it can be used as the prior probability distribution of the dynamic Bayesian network.
In order to more clearly illustrate the diversion tunnel structure safety evaluation method provided by the embodiment of the application, an example of lining cracks is described below.
Specifically, referring to "hydraulic tunnel safety evaluation procedure" (SL/T790-2020), the crack width may be divided into five safety classes, and the correspondence between the crack width and the safety class is shown in Table 2:
TABLE 2
The electronic equipment receives the record in the index detection data of the lining cracks, and 18 cracks are detected in the diversion tunnel to be evaluated. Wherein, 10E-grade cracks, 5D-grade cracks and 3C-grade cracks, the risk distribution of the crack width index is [0 0.3/18.5/18.10/18 ], namely [0 0 0.17 0.28 0.55].
Similarly, the risk probability distribution information of all the safety indexes can be obtained according to the index detection data of each safety index, the calculation process is not repeated, and the risk probability distribution situation of each third-level safety index is shown in table 3.
TABLE 3 Table 3
And obtaining the index weights of the safety indexes of the second level and the third level by adopting an analytic hierarchy process through an expert consultation mode, as shown in table 4.
TABLE 4 Table 4
Specifically, taking lining cracks as an example, according to index detection results of the six corresponding third-level safety indexes, the risk probability distribution information of the lining cracks in the subordinate second-level safety indexes is obtained as follows:
Similarly, risk probability distribution information of other second-level security indexes and first-level security indexes can be obtained, and specific risk probability distribution information is shown in table 5.
TABLE 5
From table 5, it can be seen that the risk probability distribution information of the diversion tunnel is [0,0.01,0.02,0.08,0.89], and in combination with the risk value corresponding to each safety level in table 1, the risk value of each safety level takes the maximum value of the corresponding range for the conservative estimation of the safety state, so that the comprehensive risk value of the diversion tunnel to be evaluated is 0.2, and finally the structural safety evaluation result of the diversion tunnel to be evaluated is the E-level safety level. The specific calculation process of the comprehensive risk value of the diversion tunnel to be evaluated is as follows:
and taking the structural safety evaluation result of the diversion tunnel to be evaluated as the prior probability of the dynamic Bayesian network, obtaining transition probability distribution information according to the time sequence data of each safety index, establishing a dynamic Bayesian network model for the safety evaluation of the diversion tunnel structure, and predicting the risk probability distribution information of each safety index of the diversion tunnel after a plurality of time slices by using the model.
Assuming that the time slice interval is 1 year, and predicting the change condition of the risk probability distribution information of the diversion tunnel according to a dynamic Bayesian network model, as shown in fig. 7.
According to the analysis, the development trend of the comprehensive risk value of the lining cracks of the first-level safety index and the second-level safety index in the safety index system is shown in fig. 8. As can be seen from fig. 8, the diversion tunnel target belongs to class E, the risk probability is low, but the risk probability is continuously increased along with time, and increases to 0.8 after about 20 years, and corresponding repair measures are needed. From the increasing trend of the risk probability, the normal speed is higher in the early running stage, the medium-term approximately keeps constant-speed growth, and the later-term growth speed is gradually reduced. Therefore, the safety inspection of the diversion tunnel must be carried out early, the problems are found and solved in time, and the occurrence of safety accidents is avoided.
Based on the same inventive concept, the present embodiment further provides a diversion tunnel structure safety evaluation device, please refer to fig. 9, and fig. 9 is a block schematic diagram of a diversion tunnel structure safety evaluation device 200 provided in the embodiment of the present invention. The diversion tunnel structure safety evaluation device 200 is applied to electronic equipment, and the diversion tunnel structure safety evaluation device 200 comprises a receiving module 201, an evaluation module 202 and a prediction module 203.
A receiving module 201, configured to obtain an index weight according to the fuzzy judgment matrix; the index weight characterizes the relative importance degree of each safety index in the structural safety evaluation process.
The evaluation module 202 is configured to obtain an index weight according to the fuzzy judgment matrix; the index weight characterizes the relative importance degree of each safety index in the structural safety evaluation process. Obtaining index risk probability distribution information according to all index detection data; the index risk probability distribution information comprises the distribution condition of each safety index at different safety levels; and obtaining the structural safety evaluation result of the diversion tunnel to be evaluated according to the index risk probability distribution information and the index weight.
The prediction module 203 is configured to process the structural safety evaluation result and the time sequence data by using a dynamic bayesian network model to obtain a prediction result; the prediction result represents the development trend of the structural safety state of the diversion tunnel to be evaluated.
Optionally, the prediction module 203 is specifically configured to obtain transition probability distribution information according to the time sequence data; the transition probability distribution information comprises the probability that each security index is damaged to different security levels; and obtaining a prediction result through a dynamic Bayesian network model according to the structural safety evaluation result and the transition probability distribution information.
Optionally, when the fuzzy judgment matrix is multiple, the evaluation module 202 is specifically configured to obtain a judgment weight according to each fuzzy judgment matrix; the fuzzy judgment matrixes are in one-to-one correspondence with judgment weights, and the judgment weights represent the relative importance degree of each fuzzy judgment matrix in the structural safety evaluation process; obtaining a group decision fuzzy matrix according to each fuzzy judgment matrix and each corresponding judgment weight; the fuzzy matrix of group decision represents the fuzzy number of the relative importance judgment result among the summarized safety indexes; and obtaining index weight according to the group decision fuzzy matrix.
Optionally, the evaluation module 202 is specifically configured to obtain, according to the fuzzy judgment matrix, a normalized relative distance vector, an optimal solution of the safety index, and a worst solution of the safety index; normalizing the relative distance vector to represent the judgment error of the fuzzy judgment matrix for each safety index; the optimal solution of the safety indexes represents the minimum value of the judging error of each safety index; the worst solution of the safety indexes represents the maximum value of the judging error of each safety index; and obtaining judgment weight according to the normalized relative distance vector, the optimal solution and the worst solution.
Optionally, the evaluation module 202 is specifically configured to obtain a fuzzy weight of each security index according to the group decision fuzzy matrix; and obtaining the index weight of each safety index according to the fuzzy weight of each safety index.
Fig. 10 is a block diagram of an electronic device 100 according to an embodiment of the invention. The electronic device 100 may be a Personal Computer (PC), a notebook computer, a server, or the like. The electronic device 100 includes a memory 110, a processor 120, and a communication module 130. The memory 110, the processor 120, and the communication module 130 are electrically connected directly or indirectly to each other to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines.
Wherein the memory 110 is used for storing programs or data. The Memory 110 may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.
The processor 120 is used to read/write data or programs stored in the memory 110 and perform corresponding functions. For example, the diversion tunnel structure safety evaluation method disclosed in the above embodiments may be implemented when the computer program stored in the memory 110 is executed by the processor 120.
The communication module 130 is used for establishing a communication connection between the electronic device 100 and other communication terminals through a network, and for transceiving data through the network.
It should be understood that the structure shown in fig. 10 is merely a schematic diagram of the structure of the electronic device 100, and that the electronic device 100 may also include more or fewer components than shown in fig. 10, or have a different configuration than shown in fig. 10. The components shown in fig. 10 may be implemented in hardware, software, or a combination thereof.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by the processor 120, implements the diversion tunnel structure safety evaluation method disclosed in the above embodiments.
In summary, the method, the device, the electronic equipment and the storage medium for evaluating the safety of the diversion tunnel structure provided by the embodiment of the invention receive the structural safety evaluation instruction of the diversion tunnel; the structural safety evaluation instruction comprises index detection data, fuzzy judgment matrix and time sequence data of the diversion tunnel to be evaluated corresponding to each safety index; the time sequence data represents the historical detection data of each safety index; obtaining index weight according to the fuzzy judgment matrix; the index weight represents the relative importance degree of each safety index in the structural safety evaluation process; obtaining index risk probability distribution information according to all index detection data; the index risk probability distribution information comprises the distribution condition of each safety index at different safety levels; obtaining a structural safety evaluation result of the diversion tunnel to be evaluated according to the index risk probability distribution information and the index weight; processing the structure safety evaluation result and the time sequence data by adopting a dynamic Bayesian network model to obtain a prediction result; the prediction result represents the development trend of the structural safety state of the diversion tunnel to be evaluated. Therefore, the safety level of the diversion tunnel is quantitatively evaluated through the current risk probability distribution and the relative importance degree of various safety indexes of the diversion tunnel, and the risk probability development trend prediction is carried out by combining the historical detection data, so that the accuracy of the safety evaluation of the diversion tunnel structure can be improved, effective risk management is carried out, the potential safety hazards are eliminated in advance, and the accident occurrence risk is avoided.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners as well. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present invention may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. The method for evaluating the safety of the diversion tunnel structure is characterized by comprising the following steps of:
receiving a structural safety evaluation instruction of the diversion tunnel; the structural safety evaluation instruction comprises index detection data, a fuzzy judgment matrix and time sequence data of the diversion tunnel to be evaluated corresponding to each safety index; the time sequence data represents the historical detection data of each safety index; the index detection data represents real-time damage conditions of the diversion tunnel during operation;
obtaining index weights according to the fuzzy judgment matrix; the index weight represents the relative importance degree of each safety index in the structural safety evaluation process;
obtaining index risk probability distribution information according to all the index detection data; the index risk probability distribution information comprises the distribution condition of each safety index at different safety levels;
obtaining a structural safety evaluation result of the diversion tunnel to be evaluated according to the index risk probability distribution information and the index weight;
processing the structural safety evaluation result and the time sequence data by adopting a dynamic Bayesian network model to obtain a prediction result; the prediction result represents the development trend of the structural safety state of the diversion tunnel to be evaluated;
The fuzzy judgment matrix is a plurality of, and the step of obtaining the index weight according to the fuzzy judgment matrix comprises the following steps:
obtaining judgment weights according to each fuzzy judgment matrix; the fuzzy judgment matrixes are in one-to-one correspondence with the judgment weights, and the judgment weights represent the relative importance degree of each fuzzy judgment matrix in the structural safety evaluation process;
obtaining a group decision fuzzy matrix according to each fuzzy judgment matrix and each corresponding judgment weight; the fuzzy matrix of the group decision represents the fuzzy number of the relative importance judgment result among the summarized safety indexes;
obtaining the index weight according to the group decision fuzzy matrix;
the step of obtaining judgment weight according to each fuzzy judgment matrix comprises the following steps:
obtaining a defuzzification matrix and a theoretical weight matrix according to the fuzzy judgment matrix;
obtaining a consistency judgment matrix according to the theoretical weight matrix;
obtaining a normalized relative distance vector according to the deblurring matrix and the consistency judgment matrix; the normalized relative distance vector characterizes the judgment error of the fuzzy judgment matrix for each safety index;
Obtaining an optimal solution of the safety index and a worst solution of the safety index according to the normalized relative distance vector; the optimal solution of the safety indexes represents the minimum value of the judgment error of each safety index; the worst solution of the safety indexes represents the maximum value of the judging error of each safety index;
obtaining the gray correlation degree of the optimal solution according to the normalized relative distance vector and the optimal solution;
obtaining the gray correlation degree of the worst solution according to the normalized relative distance vector and the worst solution;
obtaining the judgment weight according to the optimal solution gray correlation degree and the worst solution gray correlation degree;
the step of processing the structural safety evaluation result and the time sequence data by adopting a dynamic Bayesian network model to obtain a prediction result comprises the following steps:
the dynamic Bayesian network model divides the risk development process of the safety index system into time slices with the same time interval, and adopts transition probability to describe the interrelationship between indexes of adjacent time slices; the network structure of the dynamic Bayes in each time slice is the same as the safety index system;
obtaining transition probability distribution information according to the time sequence data; the transition probability distribution information comprises the probability that each safety index is damaged to different safety levels;
And according to the structural safety evaluation result and the transition probability distribution information, predicting each safety index of the diversion tunnel and the overall risk probability distribution information of the diversion tunnel after a plurality of time slices through a dynamic Bayesian network model, and calculating a comprehensive risk value to perform comprehensive risk evaluation of the diversion tunnel.
2. The diversion tunnel structure safety evaluation method according to claim 1, wherein the obtaining the index weight according to the group decision fuzzy matrix comprises:
obtaining fuzzy weights of each safety index according to the group decision fuzzy matrix;
and obtaining the index weight of each safety index according to the fuzzy weight of each safety index.
3. The utility model provides a diversion tunnel structure safety evaluation device which characterized in that, the device includes:
the receiving module is used for receiving the structural safety evaluation instruction of the diversion tunnel; the structural safety evaluation instruction comprises index detection data, a fuzzy judgment matrix and time sequence data of the diversion tunnel to be evaluated corresponding to each safety index; the time sequence data represents the historical detection data of each safety index; the index detection data represents real-time damage conditions of the diversion tunnel during operation;
The evaluation module is used for obtaining index weights according to the fuzzy judgment matrix; the index weight represents the relative importance degree of each safety index in the structural safety evaluation process;
the evaluation module is further used for obtaining index risk probability distribution information according to all the index detection data; the index risk probability distribution information comprises the distribution condition of each safety index at different safety levels;
the evaluation module is further used for obtaining a structural safety evaluation result of the diversion tunnel to be evaluated according to the index risk probability distribution information and the index weight;
the prediction module is used for processing the structural safety evaluation result and the time sequence data by adopting a dynamic Bayesian network model to obtain a prediction result; the prediction result represents the development trend of the structural safety state of the diversion tunnel to be evaluated;
the fuzzy judgment matrix is multiple, and the evaluation module is further used for obtaining a defuzzification matrix and a theoretical weight matrix according to each fuzzy judgment matrix; obtaining a consistency judgment matrix according to the theoretical weight matrix; obtaining a normalized relative distance vector according to the deblurring matrix and the consistency judgment matrix; the normalized relative distance vector characterizes the judgment error of the fuzzy judgment matrix for each safety index; obtaining an optimal solution of the safety index and a worst solution of the safety index according to the normalized relative distance vector; the optimal solution of the safety indexes represents the minimum value of the judgment error of each safety index; the worst solution of the safety indexes represents the maximum value of the judging error of each safety index; obtaining the gray correlation degree of the optimal solution according to the normalized relative distance vector and the optimal solution; obtaining the gray correlation degree of the worst solution according to the normalized relative distance vector and the worst solution; obtaining judgment weight according to the optimal solution gray correlation degree and the worst solution gray correlation degree; the fuzzy judgment matrixes are in one-to-one correspondence with the judgment weights, and the judgment weights represent the relative importance degree of each fuzzy judgment matrix in the structural safety evaluation process; obtaining a group decision fuzzy matrix according to each fuzzy judgment matrix and each corresponding judgment weight; the fuzzy matrix of the group decision represents the fuzzy number of the relative importance judgment result among the summarized safety indexes; obtaining the index weight according to the group decision fuzzy matrix;
The prediction module is further used for dividing the risk development process of the safety index system into time slices with the same time interval by the dynamic Bayesian network model, and describing the interrelationship between indexes of adjacent time slices by adopting transition probability; the network structure of the dynamic Bayes in each time slice is the same as the safety index system; obtaining transition probability distribution information according to the time sequence data; the transition probability distribution information comprises the probability that each safety index is damaged to different safety levels; and according to the structural safety evaluation result and the transition probability distribution information, predicting each safety index of the diversion tunnel and the overall risk probability distribution information of the diversion tunnel after a plurality of time slices through the dynamic Bayesian network model, and calculating a comprehensive risk value to carry out comprehensive risk evaluation of the diversion tunnel.
4. An electronic device comprising a memory for storing a computer program and a processor for executing the diversion tunnel construction safety evaluation method according to any one of claims 1-2 when the computer program is invoked.
5. A computer-readable storage medium having stored thereon a computer program, which when executed by a processor implements the diversion tunnel construction safety evaluation method according to any one of claims 1-2.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109740800A (en) * | 2018-12-18 | 2019-05-10 | 山东大学 | Suitable for tunnel TBM driving rockburst risk classification and prediction technique and system |
| CN115187016A (en) * | 2022-06-27 | 2022-10-14 | 国网天津市电力公司 | Distribution cable state evaluation method based on analytic hierarchy process and grey correlation degree analysis |
| CN115545440A (en) * | 2022-09-23 | 2022-12-30 | 国网冀北电力有限公司经济技术研究院 | Differential evaluation method for bidding project quantity and project settlement quantity construction cost of power transmission and transformation project |
| CN115563874A (en) * | 2022-10-14 | 2023-01-03 | 张改凤 | Tunnel health degree analysis method and system based on big data |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019144386A1 (en) * | 2018-01-26 | 2019-08-01 | 大连理工大学 | Method for predicting key performance parameters of aviation engine transition state |
| CN108734378A (en) * | 2018-04-04 | 2018-11-02 | 河海大学 | A kind of reservoir operation state of risk groups Decision Method under the conditions of imperfect information |
| CN110059963A (en) * | 2019-04-20 | 2019-07-26 | 北京交通大学 | A kind of tunnel risk evaluating method based on fuzzy polymorphism Bayesian network |
| CN110264030A (en) * | 2019-04-29 | 2019-09-20 | 国网山东省电力公司经济技术研究院 | A kind of new and old kinetic energy conversion effect evaluation method and system |
| CN111401653A (en) * | 2020-03-25 | 2020-07-10 | 华中科技大学 | Tunnel water leakage risk spatial dependency prediction method and prediction system |
| CN111985804B (en) * | 2020-08-18 | 2021-09-10 | 华中科技大学 | Shield approaching existing tunnel safety evaluation method based on data mining and data fusion |
| CN112949202A (en) * | 2021-03-19 | 2021-06-11 | 交通运输部科学研究院 | Bayesian network-based rockburst probability prediction method |
| CN113570226A (en) * | 2021-07-20 | 2021-10-29 | 中交第一公路勘察设计研究院有限公司 | Method for evaluating occurrence probability grade of tunnel water inrush disaster in fault fracture zone |
-
2023
- 2023-02-14 CN CN202310108775.XA patent/CN115907565B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109740800A (en) * | 2018-12-18 | 2019-05-10 | 山东大学 | Suitable for tunnel TBM driving rockburst risk classification and prediction technique and system |
| CN115187016A (en) * | 2022-06-27 | 2022-10-14 | 国网天津市电力公司 | Distribution cable state evaluation method based on analytic hierarchy process and grey correlation degree analysis |
| CN115545440A (en) * | 2022-09-23 | 2022-12-30 | 国网冀北电力有限公司经济技术研究院 | Differential evaluation method for bidding project quantity and project settlement quantity construction cost of power transmission and transformation project |
| CN115563874A (en) * | 2022-10-14 | 2023-01-03 | 张改凤 | Tunnel health degree analysis method and system based on big data |
Non-Patent Citations (6)
| Title |
|---|
| Dynamic Bayesian Network Method for Structural Safety Evaluation of Diversion Tunnel;Kang Liu,等;《Proceedings of the 39th IAHR World Congress》;第4746-4755页 * |
| Tunnel collapse risk assessment based on improved quantitative theory III and EW-AHP coupling weight;Li Li, 等;《Scientific Reports》;第12卷;第1-19页 * |
| 城市综合管廊运营与维护阶段的风险管理研究;虢小燕;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》(第4期);第C038-977页 * |
| 基于改进TOPSIS法的耐压测试系统风险评估;廖兴旺;《项目管理技术》;第21卷(第1期);第121-125页 * |
| 基于模糊综合法的交通基础设施施工阶段安全风险评价研究 ————以某大桥岛隧工程为例;青奎;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》(第2期);第C034-61页 * |
| 基于组合权重及模糊理论的光伏组件健康状况评估;贾玲;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》(第1期);第C042-1387页 * |
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