CN114418164A - Intelligent optimization type selection method for shield tunneling equipment based on knowledge management - Google Patents
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Abstract
The invention provides an intelligent optimization type selection method of shield tunneling equipment based on knowledge management, which comprises the following steps: the method comprises the steps of firstly summarizing knowledge and previous model selection rules in the field of shield tunneling equipment design, collecting main parameters of shield tunneling equipment of famous manufacturers, arranging excavated tunnel engineering data, establishing a corresponding knowledge base and a corresponding database, then calling an intelligent reasoning module to determine the structural type of the shield tunneling equipment based on the previous model selection rules and the design knowledge of the model selection knowledge base according to specific working condition parameters and geological parameters, further determining the main parameters of the shield tunneling equipment from the database based on an example reasoning module to obtain a model selection scheme of the shield tunneling equipment, and then finishing final model selection and main parameter optimization of the shield tunneling equipment based on a multi-level multi-index comprehensive evaluation model and an intelligent decision model.
Description
Technical Field
The invention relates to the technical field of shield tunneling equipment engineering, in particular to an intelligent optimization type selection method for shield tunneling equipment based on knowledge management.
Background
Along with the acceleration of urbanization construction, urban population density is continuously increased, a plurality of big cities have some problems such as population expansion, traffic congestion and the like, the reasonable utilization of underground space becomes an effective solution, more and more urban rail and tunnel bridge projects commonly adopt a shield construction technology, the shield construction technology is a construction technology which is integrally propelled according to the predicted direction by adopting tunneling equipment, and the urban railway and tunnel bridge engineering has the advantages of rapidness, safety, high quality, environmental protection and the like. And selecting proper types of shield tunneling equipment according to geological environment, working condition parameters and the like aiming at different construction regions so as to ensure the smooth proceeding of the engineering. The traditional model selection of shield tunneling equipment is to collect geological survey reports, hydrogeology, design files, surrounding environment and other data, to be discussed by domain experts, and to determine the main parameters of the shield tunneling equipment suitable for the engineering according to the past engineering construction experience and scientific reasoning. The traditional model selection method is large in workload, long in evaluation period and manual in evaluation, the evaluation process is lack of scientific judgment, subjective factors are easy to introduce, judgment of model selection results of shield tunneling equipment is not facilitated, and engineering risks are easy to cause. Therefore, a new intelligent optimization model selection method is needed to solve the model selection problem under different geological environments and working condition parameters.
Disclosure of Invention
The invention aims to provide an intelligent optimization type selection method of shield tunneling equipment based on knowledge management, which aims to solve the problems in the background technology.
In order to achieve the purpose, the invention adopts the following technical scheme:
a shield tunneling equipment intelligent optimization model selection method based on knowledge management is characterized by comprising the following steps:
step 1: inputting working condition design parameters and geological survey parameters according to construction requirements;
step 2: calling an intelligent reasoning module according to the input parameters to select the structural type of the shield tunneling equipment with high adaptability from a type selection knowledge base;
and step 3: calling an instance reasoning module, and selecting a shield tunneling equipment model with high similarity to a target instance and main parameters from a database by adopting a similarity calculation method;
and 4, step 4: calling a multi-index type selection evaluation module, constructing a multi-level multi-index evaluation model, and carrying out comprehensive evaluation on the preliminary type selection scheme;
and 5: and calling a multi-index intelligent decision module, determining the type and the main parameters of the shield tunneling equipment according to the multi-index evaluation model, then carrying out satisfaction survey on the selected type and the main parameters based on the intelligent decision subsystem functional module, and returning to carry out optimization on a primary type selection scheme if the type and the main parameters do not meet the requirements.
In the step 2, as the design parameters of the shield tunneling equipment are extremely large and the model selection influence factors are many, the characteristics and the attributes of the design task are extracted according to the geological survey parameters and the working condition design parameters, the intelligent reasoning module is called to simulate the logic thinking mode of human problem solving for knowledge reasoning, and the structural type of the shield tunneling equipment with strong adaptability to the design parameters is selected from the original model selection rules and the design knowledge of the model selection knowledge base.
In the step 3, similarity measurement is carried out on target examples determined by design tasks and source examples in a database from main parameters of shield tunneling equipment produced by home and abroad famous shield manufacturers stored in the database and shield tunneling equipment models which are successfully excavated in different geology and corresponding main parameters by adopting a similarity calculation method based on an example reasoning module, and the examples with the highest similarity are found to determine the models and the main parameters of the shield tunneling equipment. The adopted similarity calculation method comprises the following steps:
assume that the target instance is represented as a vector, a ═ a1,A2,...Ai...An) An example of a source is represented as vector B ═ B (B)1,B2,...Bi...Bn) Similarity between two vectors is expressed by calculating their cosine values, the greater the cosine value, the greater the similarity.
The model selection comprehensive evaluation module based on the multistage multi-index influence of the shield tunneling equipment is constructed in the step 4, and the implementation steps are as follows:
step 4.1: selecting an adaptability difference index capable of fully reflecting different machine types based on a multi-level multi-index comprehensive evaluation method;
step 4.2: and constructing a multi-level multi-index comprehensive evaluation model of the shield tunneling equipment, and determining a multi-index evaluation membership function.
The evaluation index with adaptability difference representativeness selected in the step 4.1 comprises working condition parameters, specifically comprising construction environment, geological conditions, hydrological conditions and operation and maintenance feedback.
The multi-level multi-index comprehensive evaluation model constructed in the step 4.2 comprises the following steps: the design and model selection parameters of the shield tunneling equipment are very many, and geological conditions, working condition parameters and the like have certain influence on the determination of main parameters, for example, the main parameters of the cutter head diameter, the cutter head rotating speed, the main driving power and the opening rate of the shield tunneling equipment in a soft soil stratum and a sand and gravel stratum are different. Therefore, a multi-level multi-index evaluation model is constructed: only the first and second level evaluation indexes are listed.
If the first-order evaluation index is: c ═ D, (D, E, H, F) where D is the geological condition, E is the formation stability, H is the construction environment, and F is the working condition parameter;
d ═ a (weathered rock formation, soft clay formation, sandy gravel formation);
e ═ E (formation permeability coefficient, formation particle fraction, water pressure size);
h ═ low temperature freezing climate, sub-hot climate, warm climate;
f ═ the (tunneling length, section shape, construction period);
the secondary evaluation indexes are as follows: o ═ work efficiency, tunneling performance, function, operational conditions;
and after the model selection is carried out according to the required design parameters and the geological characteristic parameters, the model and the main parameters of the shield tunneling equipment are subjected to first-stage and second-stage evaluation to form a multi-stage multi-index evaluation model of the shield tunneling equipment, wherein the main parameters comprise cutter thrust, rotating speed, torque and driving power.
The membership function of the multi-index evaluation in the step 4.2 is the key of applying a multi-index comprehensive evaluation method, and because the evaluation indexes have different attributes and importance degrees and have no degree of commonality, the evaluation indexes cannot be directly compared, the membership function is evaluated by adopting multiple indexes; using [0,1] to represent an element and a multi-index set, taking '0' to represent that the element is not in the multi-index set, taking '1' to represent that the element is completely in the multi-index set, taking any number between [0,1] to represent the probability of the element in the multi-index set, wherein the probability is the membership degree of the element to the multi-index set; therefore, a multi-index comprehensive evaluation method is used for establishing a multi-index membership function of the influence rule of different machine types on the type selection of the shield tunneling equipment in different geological adaptability difference evaluation indexes.
In the step 5, on the basis of the model selection preliminary scheme evaluation model, a multistage multi-index evaluation model and a multi-index membership function are combined, the model and the main parameters of the shield tunneling equipment are determined on the basis of an intelligent decision module, then the selected model and the main parameters are subjected to adaptive satisfaction judgment on the basis of an intelligent decision subsystem function module, if the model and the main parameters are not in accordance with the requirements, the preliminary model selection scheme is optimized, until the model and the main parameters are in accordance with the requirements, and the intelligent multi-index decision model of the shield tunneling equipment is as follows:
max F(i)＝[P1(i)w1,p2(i)w2,p3(i)w3,...,pj(i)wj,...,pk(i)wk]r (1)
in the formula, pj(i) The adaptability of the preliminarily selected model scheme i to the jth evaluation index;
i＝1,2,3,…k；
wjthe weight of the jth evaluation index;
the formula (1) is used for judging the maximum adaptability of the multiple indexes to the model selected by the shield tunneling equipment and the main parameters under the weight ratio.
Compared with the prior art, the invention has the beneficial effects that:
compared with the traditional method for collecting geological survey reports, hydrogeology, design files, surrounding environment and other data, the intelligent optimization model selection method based on knowledge management is more efficient and accurate in model selection discussed by domain experts, the constructed multi-attribute intelligent decision-making model can carry out satisfaction survey on the determined model selection scheme, and if the model selection scheme does not meet the requirements, the model selection preliminary scheme is returned to optimize until the model selection requirement is met. In addition, an intelligent model selection system is developed based on knowledge management, and the reuse and sharing of the existing model selection knowledge, geological exploration knowledge, working condition knowledge and the like are realized by constructing and calling a knowledge base, so that the model selection efficiency and accuracy of the shield tunneling equipment are improved.
Drawings
FIG. 1 is a flow chart of the invention for selecting the type of shield tunneling equipment determined by experts based on design parameters and geological conditions;
fig. 2 is a flow chart of the intelligent optimization type selection method of the shield tunneling equipment based on knowledge management.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by those skilled in the art without any inventive work based on the embodiments of the present invention belong to the scope of the present invention.
Fig. 2 is a flowchart of a conventional shield model selection method in the prior art.
Example 1
As shown in fig. 1, the embodiment provides an intelligent optimization model selection method for shield tunneling equipment based on knowledge management, which is characterized by comprising the following steps:
step 1: inputting working condition design parameters and geological survey parameters according to construction requirements;
step 2: calling an intelligent reasoning module according to the input parameters to select the structural type of the shield tunneling equipment with high adaptability from a type selection knowledge base;
and step 3: calling an instance reasoning module, and selecting a shield tunneling equipment model with high similarity to a target instance and main parameters from a database by adopting a similarity calculation method;
and 4, step 4: calling a multi-index type selection evaluation module, constructing a multi-level multi-index evaluation model, and carrying out comprehensive evaluation on the preliminary type selection scheme;
and 5: and calling a multi-index intelligent decision module, determining the type and the main parameters of the shield tunneling equipment according to the multi-index evaluation model, then carrying out satisfaction survey on the selected type and the main parameters based on the intelligent decision subsystem functional module, and returning to carry out optimization on a primary type selection scheme if the type and the main parameters do not meet the requirements.
In the step 2, as the design parameters of the shield tunneling equipment are extremely large and the model selection influence factors are many, the characteristics and the attributes of the design task are extracted according to the geological survey parameters and the working condition design parameters, the intelligent reasoning module is called to simulate the logic thinking mode of human problem solving for knowledge reasoning, and the structural type of the shield tunneling equipment with strong adaptability to the design parameters is selected from the original model selection rules and the design knowledge of the model selection knowledge base.
In the step 3, similarity measurement is carried out on target examples determined by design tasks and source examples in a database from main parameters of shield tunneling equipment produced by home and abroad famous shield manufacturers stored in the database and shield tunneling equipment models which are successfully excavated in different geology and corresponding main parameters by adopting a similarity calculation method based on an example reasoning module, and the examples with the highest similarity are found to determine the models and the main parameters of the shield tunneling equipment. The adopted similarity calculation method comprises the following steps:
assume that the target instance is represented as a vector, a ═ a1,A2,...Ai...An) An example of a source is represented as vector B ═ B (B)1,B2,...Bi...Bn) Similarity between two vectors is expressed by calculating their cosine values, the greater the cosine value, the greater the similarity.
The model selection comprehensive evaluation module based on the multistage multi-index influence of the shield tunneling equipment is constructed in the step 4, and the implementation steps are as follows:
step 4.1: selecting an adaptability difference index capable of fully reflecting different machine types based on a multi-level multi-index comprehensive evaluation method;
step 4.2: and constructing a multi-level multi-index comprehensive evaluation model of the shield tunneling equipment, and determining a multi-index evaluation membership function.
The evaluation index with adaptability difference representativeness selected in the step 4.1 comprises working condition parameters, specifically comprising construction environment, geological conditions, hydrological conditions and operation and maintenance feedback.
The multi-level multi-index comprehensive evaluation model constructed in the step 4.2 comprises the following steps: the design and model selection parameters of the shield tunneling equipment are very many, and geological conditions, working condition parameters and the like have certain influence on the determination of main parameters, for example, the main parameters of the cutter head diameter, the cutter head rotating speed, the main driving power and the opening rate of the shield tunneling equipment in a soft soil stratum and a sand and gravel stratum are different. Therefore, a multi-level multi-index evaluation model is constructed: only the first and second level evaluation indexes are listed.
If the first-order evaluation index is: c ═ D, (D, E, H, F) where D is the geological condition, E is the formation stability, H is the construction environment, and F is the working condition parameter;
d ═ a (weathered rock formation, soft clay formation, sandy gravel formation);
e ═ E (formation permeability coefficient, formation particle fraction, water pressure size);
h ═ low temperature freezing climate, sub-hot climate, warm climate;
f ═ the (tunneling length, section shape, construction period);
the secondary evaluation indexes are as follows: o ═ work efficiency, tunneling performance, function, operational conditions;
and after the model selection is carried out according to the required design parameters and the geological characteristic parameters, the model and the main parameters of the shield tunneling equipment are subjected to first-stage and second-stage evaluation to form a multi-stage multi-index evaluation model of the shield tunneling equipment, wherein the main parameters comprise cutter thrust, rotating speed, torque and driving power.
The membership function of the multi-index evaluation in the step 4.2 is the key of applying a multi-index comprehensive evaluation method, and because the evaluation indexes have different attributes and importance degrees and have no degree of commonality, the evaluation indexes cannot be directly compared, the membership function is evaluated by adopting multiple indexes; using [0,1] to represent an element and a multi-index set, taking '0' to represent that the element is not in the multi-index set, taking '1' to represent that the element is completely in the multi-index set, taking any number between [0,1] to represent the probability of the element in the multi-index set, wherein the probability is the membership degree of the element to the multi-index set; therefore, a multi-index comprehensive evaluation method is used for establishing a multi-index membership function of the influence rule of different machine types on the type selection of the shield tunneling equipment in different geological adaptability difference evaluation indexes.
In the step 5, on the basis of the model selection preliminary scheme evaluation model, a multistage multi-index evaluation model and a multi-index membership function are combined, the model and the main parameters of the shield tunneling equipment are determined on the basis of an intelligent decision module, then the selected model and the main parameters are subjected to adaptive satisfaction judgment on the basis of an intelligent decision subsystem function module, if the model and the main parameters are not in accordance with the requirements, the preliminary model selection scheme is optimized, until the model and the main parameters are in accordance with the requirements, and the intelligent multi-index decision model of the shield tunneling equipment is as follows:
max F(i)＝[P1(i)w1,p2(i)w2,p3(i)w3,...,pj(i)wj,...,pk(i)wk]r (1)
in the formula, pj(i) The adaptability of the preliminarily selected model scheme i to the jth evaluation index;
i＝1,2,3,…k；
wjthe weight of the jth evaluation index;
the formula (1) is used for judging the maximum adaptability of the multiple indexes to the model selected by the shield tunneling equipment and the main parameters under the weight ratio.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the preferred embodiments of the invention and described in the specification are only preferred embodiments of the invention and are not intended to limit the invention, and that various changes and modifications may be made without departing from the novel spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. A shield tunneling equipment intelligent optimization model selection method based on knowledge management is characterized by comprising the following steps:
step 1: inputting working condition design parameters and geological survey parameters according to construction requirements;
step 2: calling an intelligent reasoning module according to the input parameters to select the structural type of the shield tunneling equipment with high adaptability from a type selection knowledge base;
and step 3: calling an instance reasoning module, and selecting a shield tunneling equipment model with high similarity to a target instance and main parameters from a database by adopting a similarity calculation method;
and 4, step 4: calling a multi-index type selection evaluation module, constructing a multi-level multi-index evaluation model, and carrying out comprehensive evaluation on the preliminary type selection scheme;
and 5: and calling a multi-index intelligent decision module, determining the type and the main parameters of the shield tunneling equipment according to the multi-index evaluation model, then carrying out satisfaction survey on the selected type and the main parameters based on the intelligent decision subsystem functional module, and returning to carry out optimization on a primary type selection scheme if the type and the main parameters do not meet the requirements.
2. The intelligent optimization type selection method for the shield tunneling equipment based on the knowledge management as claimed in claim 1, wherein the method comprises the following steps: in the step 2, as the design parameters of the shield tunneling equipment are extremely large and the model selection influence factors are many, the characteristics and the attributes of the design task are extracted according to the geological survey parameters and the working condition design parameters, the intelligent reasoning module is called to simulate the logic thinking mode of human problem solving for knowledge reasoning, and the structural type of the shield tunneling equipment with strong adaptability to the design parameters is selected from the original model selection rules and the design knowledge of the model selection knowledge base.
3. The intelligent optimization type selection method for the shield tunneling equipment based on the knowledge management as claimed in claim 1, wherein the method comprises the following steps: in the step 3, similarity measurement is performed on the target examples determined by the design task and the source examples in the database from the main parameters of the shield tunneling equipment produced by home and abroad famous shield manufacturers stored in the database, the models of the shield tunneling equipment which is successfully excavated in different geology and the corresponding main parameters by adopting a similarity calculation method based on the example reasoning module, and the example with the highest similarity is found to determine the model and the main parameters of the shield tunneling equipment, wherein the similarity calculation method comprises the following steps:
assume that the target instance is represented as a vector, a ═ a1,A2,...Ai...An) An example of a source is represented as vector B ═ B (B)1,B2,...Bi...Bn) Similarity between two vectors is expressed by calculating their cosine values, the greater the cosine value, the greater the similarity.
4. The intelligent optimization type selection method for the shield tunneling equipment based on the knowledge management as claimed in claim 1, wherein the method comprises the following steps: the model selection comprehensive evaluation module based on the multistage multi-index influence of the shield tunneling equipment is constructed in the step 4, and the implementation steps are as follows:
step 4.1: selecting an adaptability difference index capable of fully reflecting different machine types based on a multi-level multi-index comprehensive evaluation method;
step 4.2: and constructing a multi-level multi-index comprehensive evaluation model of the shield tunneling equipment, and determining a multi-index evaluation membership function.
5. The intelligent optimization type selection method for shield tunneling equipment based on knowledge management as claimed in claim 4, wherein the method comprises the following steps: the evaluation index with adaptability difference representativeness selected in the step 4.1 comprises working condition parameters, specifically comprising construction environment, geological conditions, hydrological conditions and operation and maintenance feedback.
6. The intelligent optimization type selection method for shield tunneling equipment based on knowledge management as claimed in claim 4, wherein the method comprises the following steps: the multi-level multi-index comprehensive evaluation model constructed in the step 4.2 comprises the following steps: (D, E, H, F) wherein D is geological conditions, E is formation stability, H is construction environment, and F is working condition parameters;
d ═ a (weathered rock formation, soft clay formation, sandy gravel formation);
e ═ E (formation permeability coefficient, formation particle fraction, water pressure size);
h ═ low temperature freezing climate, sub-hot climate, warm climate;
f ═ the (tunneling length, section shape, construction period);
the secondary evaluation indexes are as follows: o ═ work efficiency, tunneling performance, function, operational conditions;
and after the model selection is carried out according to the required design parameters and the geological characteristic parameters, the model and the main parameters of the shield tunneling equipment are subjected to first-stage and second-stage evaluation to form a multi-stage multi-index evaluation model of the shield tunneling equipment, wherein the main parameters comprise cutter thrust, rotating speed, torque and driving power.
7. The intelligent optimization type selection method for shield tunneling equipment based on knowledge management as claimed in claim 4, wherein the method comprises the following steps: the membership function of the multi-index evaluation in the step 4.2 is the key to the application of a multi-index comprehensive evaluation method; using [0,1] to represent an element and a multi-index set, taking '0' to represent that the element is not in the multi-index set, taking '1' to represent that the element is completely in the multi-index set, taking any number between [0,1] to represent the probability of the element in the multi-index set, wherein the probability is the membership degree of the element to the multi-index set; therefore, a multi-index comprehensive evaluation method is used for establishing a multi-index membership function of the influence rule of different machine types on the type selection of the shield tunneling equipment in different geological adaptability difference evaluation indexes.
8. The intelligent optimization type selection method for the shield tunneling equipment based on the knowledge management as claimed in claim 1, wherein the method comprises the following steps: in the step 5, on the basis of the model selection preliminary scheme evaluation model, a multistage multi-index evaluation model and a multi-index membership function are combined, the model and the main parameters of the shield tunneling equipment are determined on the basis of an intelligent decision module, then the selected model and the main parameters are subjected to adaptive satisfaction judgment on the basis of an intelligent decision subsystem function module, if the model and the main parameters are not in accordance with the requirements, the preliminary model selection scheme is optimized, until the model and the main parameters are in accordance with the requirements, and the intelligent multi-index decision model of the shield tunneling equipment is as follows:
max F(i)＝[P1(i)w1,p2(i)w2,p3(i)w3,...,pj(i)wj,...,pk(i)wk]r (1)
in the formula, pj(i) The adaptability of the preliminarily selected model scheme i to the jth evaluation index;
i＝1,2,3,…k；
wjthe weight of the jth evaluation index;
the formula (1) is used for judging the maximum adaptability of the multiple indexes to the model selected by the shield tunneling equipment and the main parameters under the weight ratio.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115130930A (en) * | 2022-08-30 | 2022-09-30 | 矿冶科技集团有限公司 | Non-coal mine tunneling machine equipment model selection method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108491590A (en) * | 2018-03-05 | 2018-09-04 | 北京交通大学 | Shield driving parameter prediction method based on grey system model |
| CN111709650A (en) * | 2020-06-18 | 2020-09-25 | 中铁十一局集团第四工程有限公司 | Coastal complex stratum shield tunneling adaptability evaluation method |
| KR102211421B1 (en) * | 2020-06-17 | 2021-02-02 | 에스케이건설 주식회사 | Method and system for determining tbm control parameters based on prediction geological condition ahead of tunnel face |
-
2021
- 2021-12-01 CN CN202111456267.8A patent/CN114418164A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108491590A (en) * | 2018-03-05 | 2018-09-04 | 北京交通大学 | Shield driving parameter prediction method based on grey system model |
| KR102211421B1 (en) * | 2020-06-17 | 2021-02-02 | 에스케이건설 주식회사 | Method and system for determining tbm control parameters based on prediction geological condition ahead of tunnel face |
| CN111709650A (en) * | 2020-06-18 | 2020-09-25 | 中铁十一局集团第四工程有限公司 | Coastal complex stratum shield tunneling adaptability evaluation method |
Non-Patent Citations (2)
| Title |
|---|
| 何小新;吴庆鸣;: "隧道掘进机选型智能决策支持系统的研究", 铁道学报, vol. 29, no. 03, pages 127 - 131 * |
| 詹金武: ""基于人工智能的TBM选型及掘进适应性评价方法与决策支持系统"", 《中国博士学位论文全文数据库工程科技Ⅱ辑》, vol. 2019, no. 11, pages 034 - 3 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115130930A (en) * | 2022-08-30 | 2022-09-30 | 矿冶科技集团有限公司 | Non-coal mine tunneling machine equipment model selection method |
| CN115130930B (en) * | 2022-08-30 | 2022-11-25 | 矿冶科技集团有限公司 | Non-coal mine tunneling machine equipment model selection method |
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