CN115907565A - 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
- Publication number
- CN115907565A CN115907565A CN202310108775.XA CN202310108775A CN115907565A CN 115907565 A CN115907565 A CN 115907565A CN 202310108775 A CN202310108775 A CN 202310108775A CN 115907565 A CN115907565 A CN 115907565A
- Authority
- CN
- China
- Prior art keywords
- safety
- index
- diversion tunnel
- fuzzy
- obtaining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/20—Hydro energy
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
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 structural safety evaluation result and the time sequence data by adopting a dynamic Bayesian network model to obtain 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 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, 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 and device, electronic equipment and a storage medium.
Background
China has uneven water resource distribution and obvious annual precipitation difference. The problem of water resource shortage in the northwest arid water-deficient and water-deficient areas seriously affects the development of local economy and the improvement of living standard. In order to solve the problem of uneven space-time distribution of water resources and meet the requirements of water-deficient and water-deficient areas on production, life, ecology and the like, cross-basin long-distance hydraulic engineering invested in construction in China plays an important role, wherein a diversion tunnel is an important component of a water delivery system, and the safety of a diversion tunnel lining structure is an important guarantee for safe operation of the diversion tunnel lining structure.
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 safety evaluation indexes are provided at home and abroad, qualitative judgment is mainly used, and accuracy is lacked; the safety state of the diversion tunnel is difficult to be reflected comprehensively, and the development trend of the safety state of the diversion tunnel cannot be predicted at the same time.
Disclosure of Invention
In view of the above, the present invention provides a diversion tunnel structure safety evaluation method, device, electronic device, and storage medium, which implement quantitative evaluation of diversion tunnel structure safety, improve accuracy and predictability of diversion tunnel structure safety evaluation, further eliminate potential safety hazards in advance, and avoid accidents.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:
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 safety evaluation instruction comprises index detection data, a fuzzy judgment matrix and time sequence data of each safety index corresponding to the diversion tunnel to be evaluated; the time series data represents historical detection data of each safety index;
obtaining 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 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 structure 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 embodiment, the processing the structural security evaluation result and the time-series data by using 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 includes the probability that each of the security indicators is damaged to a different security level;
and obtaining the prediction result through a dynamic Bayesian network model according to the structure safety evaluation result and the transition probability distribution information.
In an optional implementation manner, the obtaining the index weight according to the fuzzy judgment matrix includes:
obtaining a judgment weight according to each fuzzy judgment matrix; the fuzzy judgment matrixes correspond to the judgment weights one by one, and the judgment weights represent the relative importance degree of each fuzzy judgment matrix in the structure 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 safety indexes after the summary;
and obtaining the index weight according to the group decision fuzzy matrix.
In an optional embodiment, the obtaining a determination weight according to each of the fuzzy determination matrices includes:
obtaining a normalized relative distance vector, an optimal solution of the safety index and a 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 index represents the minimum value of the judgment error of each safety index; the worst solution of the safety index represents the maximum value of the judgment 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 the fuzzy weight 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, comprising:
the receiving module is used for receiving a structural safety evaluation instruction of the diversion tunnel; the safety evaluation instruction comprises index detection data, a fuzzy judgment matrix and time sequence data of each safety index corresponding to the diversion tunnel to be evaluated; the time series data represents historical detection data of each safety index;
the evaluation module is used for obtaining 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 is also 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 structure 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 structure safety evaluation result and the transition probability distribution information.
In an optional embodiment, the fuzzy judgment matrix is multiple, and the evaluation module is specifically configured to:
obtaining a judgment weight according to each fuzzy judgment matrix; the fuzzy judgment matrixes correspond to the judgment weights one by one, and the judgment weights represent the relative importance degree of each fuzzy judgment matrix in the structure 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 safety indexes after the summary;
and obtaining the index weight according to the group decision fuzzy matrix.
In a third aspect, the present invention provides an electronic device, which includes a memory and a processor, where the memory is used for storing a computer program, and the processor is used for executing the diversion tunnel structure safety evaluation method according to any one of the foregoing embodiments when the computer program is called.
In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the diversion tunnel structure safety evaluation method according to any one of the foregoing embodiments.
Compared with the prior art, the diversion tunnel structure safety evaluation method, the diversion tunnel structure safety evaluation device, the electronic equipment and the storage medium provided by the embodiment of the invention receive the structure safety evaluation instruction of the diversion tunnel; the safety evaluation instruction comprises index detection data, a fuzzy judgment matrix and time sequence data of each safety index corresponding to the diversion tunnel to be evaluated; the time sequence data represents 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; and 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 according to 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 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 hazard is eliminated in advance, and the accident risk is avoided.
In order to make the aforementioned and other objects, features and advantages of the present invention 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 required in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 shows a schematic flow chart of a method for evaluating the safety of a diversion tunnel structure provided by the embodiment of the present invention.
Fig. 2 shows a schematic diagram of a security index system.
Fig. 3 shows a sub-step flow diagram of step S105 in 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 in fig. 1.
Fig. 6 shows a sub-step flow diagram of steps 1021 and 1023 of fig. 5.
Fig. 7 shows a schematic representation of the development of a diversion tunnel risk probability distribution.
Fig. 8 shows a schematic diagram of the water 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 the embodiment of the invention.
Fig. 10 is a block diagram of an electronic device provided by an embodiment of the invention.
Icon: 100-an electronic device; 110-a memory; 120-a processor; 130-a communication module; 200-a diversion tunnel structure safety evaluation device; 201-a receiving module; 202-an evaluation module; 203-prediction module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of 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 present invention, 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 derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
It is noted that relational terms such as "first" and "second," and the like, may be 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. Also, 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 phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
In order to ensure the water for life and production, china builds a large amount of hydraulic engineering, wherein the diversion tunnel plays a key role, so the safety problem of the diversion tunnel is also of great importance. Once the diversion tunnel has safety problems, serious accidents are often caused. For example, the piping accident of the downstream dam caused by tunnel collapse of the curve pavilion reservoir brings life and property loss to local people. Therefore, carry out the structure safety evaluation to diversion tunnel to master diversion tunnel operational aspect, it is very necessary to provide corresponding basis for diversion tunnel maintenance protection.
However, the existing evaluation method mainly focuses on the stress-strain condition of the tunnel lining, is difficult to comprehensively and accurately reflect the safety state of the diversion tunnel, and cannot predict the damage condition of the diversion tunnel.
Based on the above, the embodiment of the invention provides a diversion tunnel structure safety evaluation method and device, electronic equipment and a storage node. Realize the quantitative evaluation of diversion tunnel structure safety, improve the accuracy and the predictability of diversion tunnel structure safety evaluation, and then get rid of the potential safety hazard in advance, avoid the occurence of failure.
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The method and the device for evaluating the safety of the diversion tunnel structure provided by the embodiment of the invention are applied to electronic equipment, and the electronic equipment can be, but is not limited to, personal Computers (PCs), notebook computers or servers and other electronic equipment with computing capability. According to the scheme, the establishment of a safety index system is relied on when the safety evaluation of the diversion tunnel structure is carried out. Therefore, a security index system needs to be established on the electronic device in advance.
The following describes a method for reviewing the safety of the diversion tunnel structure provided by the embodiment of the present invention based on a safety index system established in an electronic device. Referring to fig. 1, fig. 1 shows a schematic diagram of a diversion tunnel structure safety evaluation method provided by an embodiment of the present invention, the method includes the following steps:
step S101, receiving a structural safety evaluation instruction of the diversion tunnel;
the safety evaluation instruction comprises index detection data, fuzzy judgment matrixes and time sequence data of each safety index corresponding to the diversion tunnel to be evaluated;
wherein the time series data represents historical detection data of each safety index.
In the embodiment of the invention, after the electronic equipment receives the structural safety assessment 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 assessed in the structural safety assessment instruction of the diversion tunnel are obtained.
Specifically, the index detection data of the safety index can be acquired in the modes of detection, monitoring, manual operation or intelligent inspection by an underwater robot, and the data can reflect the real-time damage condition of the diversion tunnel in the operation period from different aspects. According to the analysis of the index detection data of the safety index, the overall structural safety condition of the diversion tunnel to be evaluated can be obtained.
Since many safety indexes affecting the diversion tunnel are provided, 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 as shown in fig. 2, and if the safety index system for evaluating the safety of the diversion tunnel structure comprises three levels of safety indexes, the first level of safety indexes is used for evaluating the safety of the diversion tunnel structure. The second-level safety index is subordinate to the first-level safety index, and the structural safety of the diversion tunnel is monitored from seven dimensions of lining cracks, water leakage, material degradation, lining cavities, lining deformation, lining stripping and operating environment respectively. The third level safety indexes are respectively subordinate to the second level safety indexes of seven dimensionalities. The present invention is not limited to the selection of the safety index and the specific use mode.
And step S102, obtaining index weight according to the fuzzy judgment matrix.
Wherein the index weight characterizes 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 index is compared by adopting an analytic hierarchy process and an expert consultation mode, so that key influence factors are screened out and important attention is paid. Therefore, a fuzzy judgment matrix is issued when the structural safety evaluation of the diversion tunnel is carried out, and the fuzzy judgment matrix is elaborately designed by field experts and used for calculating the importance among safety indexes, namely the expert fuzzy judgment matrix.
Specifically, the expert fuzzy judgment matrix is used for comparing the importance of the next-level safety indexes belonging to the same safety index.
And step S103, 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;
in the embodiment of the invention, the safety grade of the diversion tunnel safety index is divided into five grades A, B, C, D and E, 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 represented as the 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, and the assumed correspondence is as shown in table 1:
TABLE 1
| Risk rating | A | B | C | D | E |
| Value of risk | 0.8—1 | 0.6—0.8 | 0.4—0.6 | 0.2—0.4 | <=0.2 |
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 thereto.
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;
in the embodiment of the invention, the risk probability distribution of the subordinate last-level safety index can be obtained according to the risk probability distribution information and the index weight of each level of safety index, and the structural safety comment result of the diversion tunnel is finally obtained by calculating upwards step by step in a similar manner.
Step S105, processing the structure 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.
Therefore, the diversion tunnel structure safety evaluation method provided by the embodiment of the invention receives a diversion tunnel structure safety evaluation instruction on the electronic equipment; the safety evaluation instruction comprises index detection data, a fuzzy judgment matrix and time sequence data of each safety index corresponding to the diversion tunnel to be evaluated; the time sequence data represents 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; and 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 according to 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 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 hazard is eliminated in advance, and the accident risk is avoided.
In practical application, what everyone pays attention to is not only the safe state of the diversion tunnel at the current moment, but also needs to predict the development trend of diversion tunnel structure safety according to historical detection data of the diversion tunnel, intervene and maintain in advance, and avoid accidents.
Optionally, in order to prevent the water diversion tunnel from getting ill, the dynamic bayesian network model is built in the method for predicting the development trend of the safety of the water diversion tunnel structure. Referring to fig. 3, the sub-steps of step S105 may include:
step S1051, obtain and shift the probability distribution information according to the time series data; the transition probability distribution information contains the probability that each security indicator is compromised to a different security level.
In the embodiment of the invention, the dynamic Bayesian network model divides the risk development process of the safety index system into time slices with the same time interval, and describes the interrelation between indexes of adjacent time slices by adopting transition probability. The network structure of dynamic bayes in each time slice is the same as the security index system, as shown in figure 4. And inputting the time sequence data of each safety index into the dynamic Bayesian network, and automatically learning through different algorithms to obtain corresponding transition probability distribution information.
And step S1052, obtaining a prediction result through the dynamic Bayesian network model according to the structure safety evaluation result and the transition probability distribution information.
In the embodiment of the invention, the dynamic Bayesian network is trained by combining the structure safety evaluation result and the transfer probability distribution information, and a dynamic Bayesian network model for performing structure safety evaluation on the diversion tunnel to be evaluated is established.
According to the established dynamic Bayesian network model, each safety index and the whole structure safety risk probability distribution information of the diversion tunnel after a certain time can be calculated, and meanwhile, a comprehensive risk value can be calculated to carry out diversion tunnel risk comprehensive evaluation, so that the development trend of diversion tunnel structure safety is predicted.
Optionally, in order to improve comprehensiveness and accuracy of evaluation, multiple experts may be requested to provide a fuzzy judgment matrix, the structural safety evaluation of the diversion tunnel is a step-by-step evaluation 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. Next, a third-level safety index will be described as an example. Referring to fig. 5, when there are a plurality of fuzzy decision matrices, the sub-step of step S102 may include:
step S1021, obtaining a judgment weight according to each fuzzy judgment matrix; the fuzzy judgment matrix corresponds to the judgment weight one by one.
The judgment weight represents the relative importance degree of each fuzzy judgment matrix in the structure safety evaluation process.
In the embodiment of the invention, a fuzzy chromatographic analysis method is adopted to compare the importance of a third-level safety index belonging to the same second-level safety index in an expert consultation mode, if s experts are consulted, the fuzzy judgment matrixes comprising the third-level safety index in parameters sent to electronic equipment are s in total, and the fuzzy judgment matrix of a t-th expert is as follows:
wherein the content of the first and second substances,
the fuzzy number of the relative importance judgment result of the ith safety index and the jth safety index by the tth expert is shown, and n is the number of the safety indexes.
Step S1022, a group decision fuzzy matrix is obtained according to each fuzzy judgment matrix and each corresponding judgment weight.
And the fuzzy matrix of the group decision represents the fuzzy number of the relative importance judgment result among the summarized safety indexes.
Step S1023, index weight is obtained according to the group decision fuzzy matrix.
Optionally, in order to reduce an evaluation error caused by the fuzzy determination matrix of the security index, the method includes calculating a determination weight of the fuzzy determination matrix by introducing a normalized relative distance vector, and referring to fig. 6 on the basis of fig. 5, the sub-step of step S1021 may include:
step 10211, obtaining the normalized relative distance vector, the optimal solution of 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 judgment error of each safety index; the worst solution of the safety index represents the maximum value of the judgment error of each safety index.
In the embodiment of the invention, the fuzzy judgment matrix is used
Each fuzzy number in the fuzzy number can be defuzzified by using a truncation methodObtaining a de-blurring matrix>
Comprises the following steps:
wherein Cv is a v-cut set of the corresponding fuzzy number, inf is a infimum and sup is an supremum;
is a fuzzy number
The results obtained after defuzzification.
According to the defuzzification matrix, a safety index theoretical weight matrix is obtained by calculation through a feature vector method and is as follows:
the safety index theoretical weight matrix is theoretically calculated according to the defuzzification matrix and is used for verifying the error size of the fuzzy judgment matrix of an expert.
Is the theoretical weight of the ith safety index judged by the tth expert, and the value of i is greater or less than>
。
According to the theoretical weight of the safety index, a consistent judgment matrix corresponding to the fuzzy judgment matrix can be constructed as follows:
wherein, each element in the consistency judgment matrix is obtained by calculation according to the theoretical weight of the safety index, and the specific formula is as follows:
wherein the content of the first and second substances,
is the ratio of the theoretical weight of the ith safety index and the jth safety index judged by the tth expert.
To obtain an error level of the fuzzy judgment matrix, a normalized relative distance vector between the fuzzy judgment matrix and the consensus judgment matrix is calculated
The formula is as follows:
wherein the content of the first and second substances,
the judgment error is the judgment error when the ith expert judges the relative importance of the ith safety index; />
The judgment error normalization result is obtained when the ith expert judges the relative importance of the ith safety index. />
Based on the normalized relative distance vector of each fuzzy judgment matrix, constructing an optimal solution according to a TOPSIS method
And the worst solution->
The formula is as follows:
wherein the content of the first and second substances,
,/>
。
and step 10212, obtaining a judgment weight according to the normalized relative distance vector, the optimal solution and the worst solution.
In an embodiment of the invention, the vector is based on normalized relative distance
And optimal solution>
And obtaining the grey correlation degree between the two, wherein the formula is as follows:
wherein the content of the first and second substances,
is a normalized relative distance vector->
And optimal solution>
The degree of grey correlation between the two,
is the minimum difference between the normalized relative distance vector and the optimal solution, < > is>
Is the maximum difference between the normalized relative distance vector and the optimal solution, is>
For the resolution factor, 0.5 is generally used.
Obtaining the gray correlation degree of the worst solution according to the normalized relative distance vector and the worst solution, wherein the formula is as follows:
wherein the content of the first and second substances,
is a normalized relative distance vector->
And the worst solution->
Gray degree of association between->
Is the minimum difference between the normalized relative distance vector and the worst solution, < > is>
Is the maximum difference between the normalized relative distance vector and the worst solution, is>
For the resolution factor, 0.5 is generally used.
Obtaining the judgment weight of each fuzzy judgment matrix according to the grey correlation
The formula is as follows:
wherein the content of the first and second substances,
is a normalized relative distance vector->
And optimal solution +>
The degree of grey correlation between the two,
is the normalized relative distance vector->
And the worst solution->
Grey correlation degree between.
In order to comprehensively refer to the evaluation judgment results of each expert, based on the fuzzy judgment matrix and the judgment weight, a weighted average method can be used to obtain a group decision matrix, and the formula is as follows:
wherein the content of the first and second substances,
the fuzzy number is the relative importance judgment result of the ith safety index and the jth safety index after the evaluation judgment results of each expert are integrated.
Optionally, in order to obtain a more accurate index weight of each security index, referring to fig. 6 on the basis of fig. 5, the sub-step of step S1023 may include:
and 10231, obtaining the fuzzy weight of each safety index according to the group decision fuzzy matrix.
In the embodiment of the invention, a fuzzy least square method is used to obtain the index weight fuzzy number of each safety index according to the group decision fuzzy matrix
。
Wherein the content of the first and second substances,
step S10232, the index weight of each safety index is obtained according to the fuzzy weight of each safety index.
In the embodiment of the invention, the fuzzy number of the index weight is defuzzified by an intercept method to obtain the index weight of each safety index
。
Specifically, it is assumed that the risk probability distribution information of the third-level safety index of the diversion tunnel is defaulted to a column vector, that is, the column vector is
Based on the corresponding index weight ^ obtained in the step S10232>
The current risk probability distribution information R of the subordinate second-level safety index can be obtained, and the formula is as follows:
similarly, the risk probability distribution information of the first-layer index in the index system, namely the overall risk probability distribution information of the diversion tunnel, can be obtained through calculation 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, lining cracks are taken as an example for illustration.
Specifically, referring to the safety certification regulations for hydraulic tunnels (SL/T790-2020), the crack width can be divided into five safety levels, and the correspondence between the crack width and the safety level is shown in table 2:
TABLE 2
| Level of security | A | B | C | D | E |
| Crack width/mm | >0.2 | 0.15-0.2 | 0.1-0.15 | 0.05-0.1 | <0.05 |
And the electronic equipment receives and records the index detection data of the lining cracks, and detects 18 cracks 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 [ 0/18 5/18 10/18], namely [ 0.00.17 0.28.55 ].
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 condition of each third-level safety index is shown in table 3.
TABLE 3
| A | B | C | D | | |
| Crack location | |||||
| 0 | 0 | 0 | 0 | 1 | |
| Length of crack | 0.04 | 0.17 | 0.24 | 0.37 | 0.17 |
| Width of crack | 0.02 | 0.11 | 0.20 | 0.52 | 0.15 |
| Density of |
0 | 0 | 0 | 0 | 1 |
| Direction of |
0 | 0 | 0 | 1 | 0 |
| Depth of |
0 | 0 | 0 | 0 | 1 |
| |
0 | 0.05 | 0.05 | 0.63 | 0.27 |
| |
0 | 0 | 0 | 0 | 1 |
| pH value of | 0 | 0 | 0 | 0 | 1 |
| Location of |
0 | 0 | 0 | 0 | 1 |
| |
0 | 0 | 0 | 0 | 1 |
| Diameter of |
0 | 0 | 0 | 0 | 1 |
| Depth of |
0 | 0 | 0 | 0 | 1 |
| |
0 | 0 | 0 | 0 | 1 |
| Thickness of |
0 | 0 | 0 | 0 | 1 |
| Loss rate of |
0 | 0 | 0 | 0 | 1 |
| Thickness of steel bar |
0 | 0 | 0 | 0 | 1 |
| Depth of |
0 | 0 | 0 | 0 | 1 |
| |
0 | 0 | 0 | 0 | 1 |
| Area of |
0 | 0 | 0 | 0 | 1 |
| Depth of |
0 | 0 | 0 | 0 | 1 |
| Rate of |
0 | 0 | 0 | 0 | 1 |
| Amount of |
0 | 0 | 0 | 0 | 1 |
| |
0 | 0 | 0 | 0 | 1 |
| |
0 | 0 | 0 | 0 | 1 |
| Flow rate of |
0 | 0 | 0 | 0 | 1 |
Index weights of the second-level and third-level safety indexes are obtained by adopting an analytic hierarchy process in an expert consultation manner, and are shown in table 4.
TABLE 4
Specifically, taking lining cracks as an example, according to index detection results of corresponding six third-level safety indexes, obtaining the risk probability distribution information of the lining cracks in the subordinate second-level safety indexes as follows:
similarly, the risk probability distribution information of other second-level safety indexes and the first-level safety indexes can be obtained, and the specific risk probability distribution information is shown in table 5.
TABLE 5
| A | B | C | D | | |
| Diversion tunnel | |||||
| 0 | 0.01 | 0.02 | 0.08 | 0.89 | |
| Lining cracks | 0.01 | 0.05 | 0.08 | 0.29 | 0.57 |
| |
0 | 0.02 | 0.02 | 0.21 | 0.75 |
| Degradation of |
0 | 0 | 0 | 0 | 1 |
| |
0 | 0 | 0 | 0 | 1 |
| Deformation of lining | 0 | 0 | 0 | 0 | 1 |
| |
0 | 0 | 0 | 0 | 1 |
| |
0 | 0 | 0 | 0 | 1 |
As can be seen from table 5, the risk probability distribution information of the diversion tunnel is [0,0.01,0.02,0.08,0.89], the risk value corresponding to each safety level in table 1 is combined, and in consideration of conservative estimation of the safety state, the risk value of each safety level is the maximum value of the corresponding range, 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 level E safety level. The specific calculation process of the comprehensive risk value of the diversion tunnel to be evaluated is as follows:
taking the structure safety evaluation result of the diversion tunnel to be evaluated as the prior probability of the dynamic Bayesian network, obtaining the transfer probability distribution information according to the time sequence data of each safety index, establishing a dynamic Bayesian network model for the diversion tunnel structure safety evaluation, 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.
The time slice interval is assumed to be 1 year, and the change situation of the risk probability distribution information of the four diversion channels is predicted according to the dynamic Bayesian network model, as shown in FIG. 7.
According to the analysis, the development trend of the comprehensive risk value of the first-level safety index and the second-level safety index lining cracks 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 continuously increases with time, and increases to 0.8 after about 20 years, and at this time, corresponding repair measures need to be taken. From the increasing trend of risk probability, the normal speed is higher in the initial stage of operation, the constant speed is approximately kept to increase in the middle stage, and the increasing speed in the later stage is gradually reduced. Therefore, the safety inspection of the diversion tunnel is required to be carried out as early as possible, the problems are found and solved in time, and the safety accidents are 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. Diversion tunnel structure safety evaluation device 200 is applied to electronic equipment, and diversion tunnel structure safety evaluation device 200 contains receiving module 201, evaluation module 202 and 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 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 203 is used for processing the structure 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.
Optionally, the evaluation module 202 is specifically configured to obtain transition probability distribution information according to the time series data; the transition probability distribution information contains the probability that each safety index is damaged to different safety levels; and obtaining a prediction result through a dynamic Bayesian network model according to the structure safety evaluation result and the transition probability distribution information.
Optionally, when there are multiple fuzzy judgment matrices, the evaluation module 202 is specifically configured to obtain a judgment weight according to each fuzzy judgment matrix; the fuzzy judgment matrixes correspond to the judgment weights one by one, and the judgment weights represent the relative importance degree of each fuzzy judgment matrix in the structure safety evaluation process; obtaining a group decision fuzzy matrix according to each fuzzy judgment matrix and each corresponding judgment weight; representing fuzzy numbers of relative importance judgment results among the summarized safety indexes by using a group decision fuzzy matrix; and obtaining index weight according to the group decision fuzzy matrix.
Optionally, the evaluation module 202 is specifically configured to obtain the normalized relative distance vector, the optimal solution of the safety index, and the worst solution of the safety index according to the fuzzy judgment matrix; 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 judgment error of each safety index; the worst solution of the safety index represents the maximum value of the judgment error of each safety index; and obtaining the 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 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.
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, processor 120, and communication module 130 are in direct or indirect electrical communication with one another to enable the transfer or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines.
The memory 110 is used to store programs or data. The Memory 110 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 120 is used to read/write data or programs stored in the memory 110 and perform corresponding functions. For example, when the computer program stored in the memory 110 is executed by the processor 120, the diversion tunnel structure safety evaluation method disclosed in the above embodiments can be implemented.
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 configuration shown in fig. 10 is merely a schematic diagram of the configuration of the electronic device 100, and that the electronic device 100 may 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 present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by the processor 120, the method for evaluating the safety of the diversion tunnel structure disclosed in the foregoing embodiments is implemented.
In summary, the diversion tunnel structure safety evaluation method, the diversion tunnel structure safety evaluation device, the electronic device and the storage medium provided by the embodiment of the invention receive a diversion tunnel structure safety evaluation instruction; the safety evaluation instruction comprises index detection data, a fuzzy judgment matrix and time sequence data of each safety index corresponding to the diversion tunnel to be evaluated; the time sequence data represents 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; and 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 according to 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 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 hazard is eliminated in advance, and the accident risk is avoided.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures 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, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the present invention or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for evaluating the structural safety of a diversion tunnel is characterized by comprising the following steps:
receiving a structural safety evaluation instruction of the diversion tunnel; the safety evaluation instruction comprises index detection data, a fuzzy judgment matrix and time sequence data of each safety index corresponding to the diversion tunnel to be evaluated; the time series data represents historical detection data of each safety index;
obtaining 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 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 structure 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.
2. The method for evaluating the safety of the water diversion tunnel structure according to claim 1, wherein the step of processing the structural safety evaluation result and the time series data by using a dynamic bayesian network model to obtain a prediction result comprises the steps of:
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 structure safety evaluation result and the transition probability distribution information.
3. The method for evaluating the safety of the diversion tunnel structure according to claim 1, wherein the fuzzy judgment matrix is a plurality of fuzzy judgment matrices, and obtaining the index weight according to the fuzzy judgment matrix comprises:
obtaining a judgment weight according to each fuzzy judgment matrix; the fuzzy judgment matrixes correspond to the judgment weights one by one, and the judgment weights represent the relative importance degree of each fuzzy judgment matrix in the structure 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 safety indexes after the summary;
and obtaining the index weight according to the group decision fuzzy matrix.
4. The diversion tunnel structure safety evaluation method according to claim 3, wherein obtaining a judgment weight according to each fuzzy judgment matrix comprises:
obtaining a normalized relative distance vector, an optimal solution of the safety index and a 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 judgment error of each safety index; the worst solution of the safety index represents the maximum value of the judgment error of each safety index;
and obtaining the judgment weight according to the normalized relative distance vector, the optimal solution and the worst solution.
5. The diversion tunnel structure safety evaluation method according to claim 3, wherein the obtaining the index weight according to the group decision fuzzy matrix comprises:
obtaining the fuzzy weight 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.
6. 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 a structural safety evaluation instruction of the diversion tunnel; the safety evaluation instruction comprises index detection data, a fuzzy judgment matrix and time sequence data of each safety index corresponding to the diversion tunnel to be evaluated; the time sequence data represents historical detection data of each safety index;
the evaluation module is used for obtaining 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 is also 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 structure 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.
7. The diversion tunnel structure safety evaluation device of claim 6, wherein 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 structure safety evaluation result and the transition probability distribution information.
8. The diversion tunnel structure safety evaluation device of claim 6, wherein the fuzzy judgment matrix is a plurality of, and the evaluation module is specifically configured to:
obtaining a judgment weight according to each fuzzy judgment matrix; the fuzzy judgment matrixes correspond to the judgment weights one by one, and the judgment weights represent the relative importance degree of each fuzzy judgment matrix in the structure 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 safety indexes after the summary;
and obtaining the index weight according to the group decision fuzzy matrix.
9. An electronic device, characterized in that the electronic device comprises a memory for storing a computer program and a processor for executing the diversion tunnel structure safety evaluation method according to any one of claims 1-5 when the computer program is called.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the diversion tunnel structure safety evaluation method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310108775.XA CN115907565B (en) | 2023-02-14 | 2023-02-14 | Diversion tunnel structure safety evaluation method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310108775.XA CN115907565B (en) | 2023-02-14 | 2023-02-14 | Diversion tunnel structure safety evaluation method and device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115907565A true CN115907565A (en) | 2023-04-04 |
| CN115907565B CN115907565B (en) | 2023-06-23 |
Family
ID=86489928
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310108775.XA Active CN115907565B (en) | 2023-02-14 | 2023-02-14 | Diversion tunnel structure safety evaluation method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115907565B (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| 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 |
| 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 |
| US20200148395A1 (en) * | 2018-01-26 | 2020-05-14 | Dalian University Of Technology | Method for prediction of key performance parameters of aero-engine in transition condition |
| 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 |
| CN111985804A (en) * | 2020-08-18 | 2020-11-24 | 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 |
| 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 |
-
2023
- 2023-02-14 CN CN202310108775.XA patent/CN115907565B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200148395A1 (en) * | 2018-01-26 | 2020-05-14 | Dalian University Of Technology | Method for prediction of key performance parameters of aero-engine in transition condition |
| 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 |
| 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 |
| 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 |
| CN111985804A (en) * | 2020-08-18 | 2020-11-24 | 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 |
| 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 (8)
| Title |
|---|
| KANG LIU,等: "Dynamic Bayesian Network Method for Structural Safety Evaluation of Diversion Tunnel", 《PROCEEDINGS OF THE 39TH IAHR WORLD CONGRESS》, pages 4746 - 4755 * |
| LI LI, 等: "Tunnel collapse risk assessment based on improved quantitative theory III and EW-AHP coupling weight", 《SCIENTIFIC REPORTS》, vol. 12, pages 1 - 19 * |
| 廖兴旺: "基于改进TOPSIS法的耐压测试***风险评估", 《项目管理技术》, vol. 21, no. 1, pages 121 - 125 * |
| 杨威，等: "模糊环境下基于TOPSIS的部分权重信息多属性群决策方法", 《模糊***与数学》, vol. 28, no. 2, pages 145 - 147 * |
| 杨敏,等: "基于因果贝叶斯网络的风险建模与分析", 《工业工程》, vol. 19, no. 5, pages 122 - 125 * |
| 虢小燕: "城市综合管廊运营与维护阶段的风险管理研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》, no. 4, pages 038 - 977 * |
| 贾玲: "基于组合权重及模糊理论的光伏组件健康状况评估", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》, no. 1, pages 042 - 1387 * |
| 青奎: "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", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》, no. 2, pages 034 - 61 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115907565B (en) | 2023-06-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111256754B (en) | Concrete dam long-term operation safety early warning method | |
| CN109508827B (en) | Drilling accident early warning method based on time recursion neural network | |
| CN106503807B (en) | Improved RCM analysis method and mobile equipment integrity evaluation system based on same | |
| Kleiner et al. | Comparison of four models to rank failure likelihood of individual pipes | |
| CN108470095B (en) | TBM (tunnel boring machine) propulsion prediction method based on data-driven radial basis function model | |
| CN112101811A (en) | Water supply network pipe explosion risk prediction method and system | |
| CN111369056B (en) | Geological disaster prediction method and electronic equipment | |
| Hou et al. | Modeling vehicle load for a long-span bridge based on weigh in motion data | |
| CN115907565A (en) | Diversion tunnel structure safety evaluation method and device, electronic equipment and storage medium | |
| CN111882238A (en) | Gantry crane structure health assessment method based on cloud model and EAHP | |
| CN117191147A (en) | Flood discharge dam water level monitoring and early warning method and system | |
| CN116306062A (en) | Intelligent monitoring method, system, device and storage medium for construction engineering | |
| CN113269384B (en) | Method for early warning health state of river system | |
| Kim et al. | Computational platform for probabilistic optimum monitoring planning for effective and efficient service life management | |
| Yang et al. | Multistage warning indicators of concrete dam under influences of random factors | |
| Nabizadehdarabi | Reliability of bridge superstructures in Wisconsin | |
| Yusof et al. | Markov chain model for predicting pitting corrosion damage in offshore pipeline | |
| CN111341396A (en) | Method and system for evaluating material corrosion safety in atmospheric environment | |
| CN112504934B (en) | Concrete dam seepage pressure prediction and monitoring threshold determination method | |
| Li et al. | A comprehensive health diagnosis method for expansive soil slope protection engineering based on supervised and unsupervised learning | |
| CN113742814B (en) | Dam safety early warning method, dam safety early warning device, computer equipment and storage medium | |
| Wang et al. | Study on safety evaluation of Earth-Rock Dam based on Fuzzy AHP | |
| Gao et al. | Fusing multi-source quality statistical data for construction risk assessment and warning based on deep learning | |
| Collins | Towards Multiple Model Approach to Bridge Deterioration | |
| Li et al. | Neural network prediction of energy demand and supply in China |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |