CN114017049B - Multi-Agent theory-based collaborative design and manufacturing method for core component of shield tunneling equipment - Google Patents
Multi-Agent theory-based collaborative design and manufacturing method for core component of shield tunneling equipment Download PDFInfo
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 69
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- 230000005641 tunneling Effects 0.000 title claims abstract description 32
- 239000008358 core component Substances 0.000 title claims abstract description 21
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
- E21D9/00—Tunnels or galleries, with or without linings; Methods or apparatus for making thereof; Layout of tunnels or galleries
- E21D9/06—Making by using a driving shield, i.e. advanced by pushing means bearing against the already placed lining
- E21D9/08—Making by using a driving shield, i.e. advanced by pushing means bearing against the already placed lining with additional boring or cutting means other than the conventional cutting edge of the shield
- E21D9/087—Making by using a driving shield, i.e. advanced by pushing means bearing against the already placed lining with additional boring or cutting means other than the conventional cutting edge of the shield with a rotary drilling-head cutting simultaneously the whole cross-section, i.e. full-face machines
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Abstract
The invention provides a Multi-Agent theory-based collaborative design and manufacturing method for a core component of shield tunneling equipment, which comprises the following steps: dividing the design and manufacture of the core component of the shield tunneling equipment into a system digital model selection design part and a system production part and constructing a digital database; analyzing the relation between the operation and maintenance knowledge and the collaborative design and manufacture elements; modeling and optimizing a system digital model selection design and a system production process in a modularization mode; introducing a Multi-Agent theory to carry out individual Agents on the operation and maintenance knowledge and the elements of the design and manufacture process; and achieving a collaborative interaction strategy of description, detection correction and system correction based on self-adaptation and self-organization of the Multi-Agent system. The method adopts system dynamics simulation to carry out system verification, and effectively realizes the collaborative design and manufacture of the core component of the shield tunneling equipment.
Description
Technical Field
The invention relates to the field of large-scale shield tunneling production and manufacture and Multi-Agent cooperation, and particularly relates to a Multi-Agent theory-based shield tunneling equipment core component cooperation design and manufacture method.
Background
The shield machine is core equipment in infrastructure, is a mark for measuring the manufacturing level of national underground construction equipment, is widely applied to the practical field of various underground tunneling projects, has a huge structure, is difficult to manage by customized discrete manufacturing, multi-site complex environment operation and maintenance and multi-body data fusion, has low collaborative integration degree of design, manufacturing/operation and maintenance services, and is limited in equipment effectiveness. In the prior art, the influence of operation and maintenance factors is not considered, information islands exist at three ends of design, manufacture and operation and maintenance, actual operation and maintenance information cannot be fed back to influence the decision of the design and manufacture ends, a multi-element design and manufacture cooperation mechanism of a core component is unclear to cause cooperation difficulty, the method has the characteristics of strong dynamic property and randomness, multiple uncertain factors and the like, the problems of slow abnormal response, poor decision real-time property and low system stability are brought to the design and manufacture, the cooperation difficulty situation is caused, the multi-element cooperation design and manufacture mechanism driven by the operation and maintenance is not formed, and the manufacture efficiency, the manufacture quality and the manufacture cost of the shield tunneling core equipment are greatly influenced.
Disclosure of Invention
The invention aims to provide a Multi-Agent theory-based collaborative design and manufacturing method for a core component of shield tunneling equipment, so as to solve the problems in the background technology.
Aiming at the problem that the cooperation is difficult due to unclear Multi-element design and manufacture cooperation mechanism of a core component of shield tunneling equipment, the relation between operation and maintenance service and cooperation manufacture elements such as component selection, part design, processing technology, equipment state and the like is cleared, the potential mapping relation between relevant operation performance parameters and working conditions of the key component of the tunneling equipment is researched, a cross-organization, cross-platform and Multi-field-point design and manufacture cooperation unified control mode is researched, an operation and maintenance knowledge-driven equipment design and manufacture Multi-element cooperation mechanism is constructed based on a Multi-Agent system method, the optimization of a Multi-element target cooperation manufacture machine model is realized by considering the constraints of highest efficiency, optimal quality and lowest cost, and the model is verified by using system dynamics simulation.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for designing and manufacturing a core component of shield tunneling equipment in a coordinated manner based on a Multi-Agent theory is characterized by comprising the following steps:
step 3, establishing a design and manufacturing model: converting elements in the design and manufacturing process into individual agents by using a Multi-Agent theory;
and 4, completing the cooperative strategy through distributed Multi-Agent learning interaction and state correction based on the adaptivity and self-organization of the agents.
The system production in the step 1 comprises the production of a driving system and the assembly of a complete machine.
In the step 2, a grey correlation analysis method is adopted to analyze and express the relationship between the operation and maintenance service and the elements such as component selection, part design, processing technology, equipment state and the like and the potential mapping relationship between the related performance parameters and the working conditions of the key components of the tunneling equipment; and (3) determining a parent sequence changing along with time according to a certain rule, taking the change of each evaluation object along with time as a subsequence, calculating the correlation degree of each subsequence and the parent sequence, and obtaining a conclusion according to the correlation size.
In the step 3, the design, manufacture and operation and maintenance elements are respectively regarded as individual agents. Formally defining a set I to represent a finite set of agents, and for convenience we number each Agent in the set as 1,2, …, n, where n | I |; a single Agent has a decision tree of the single Agent so as to complete the cooperative interaction of the multiple agents.
The specific steps of creating the Multi-Agent cooperation strategy in the step 4 are as follows:
a. modeling the system S as a semantic structure M according to the step 3, and describing required elements P1, P2, … and Pn as formulas psi 1, psi 2, … and psi n respectively;
b. and (3) verification: judging whether the system meets the required attribute by detecting whether M-psi is zero;
c. and (3) model correction: if M ≠ ψ, then the Agent performs a learning interaction to generate a new model M to approach ψ;
d. and (3) updating the system: the design and manufacture end generates new operation to convert S into a new system S with model M.
Compared with the prior art, the invention has the beneficial effects that:
the invention analyzes the influence of operation and maintenance knowledge on multiple factors in the design and manufacturing process by using a grey correlation analysis method, clearly and intuitively reflects the correlation of the operation and maintenance on the design and manufacturing end, and breaks through an information island.
The invention adopts a cooperative mechanism based on the Multi-Agent theory, and can intelligently complete the feedback guidance of operation and maintenance knowledge to a design and manufacture end due to the learning capability and the self-organizing self-adapting capability of the Agent, thereby achieving data sharing, knowledge embedding, resource coordination and feedback execution.
Drawings
FIG. 1 is a schematic diagram of the basic collaboration framework of the method of the present invention;
FIG. 2 is a schematic diagram of an exemplary digitization database of the present invention;
FIG. 3 is a schematic diagram of an example of an individual Agent policy tree according to the present invention;
FIG. 4 is a schematic diagram of a collaborative interaction strategy between individual agents according to the present invention;
FIG. 5 is a schematic diagram of a Multi-Agent system cooperation strategy in the present invention.
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.
As shown in fig. 1 to 5, the present embodiment describes a cooperative design and manufacturing method for a core component of a shield tunneling device based on a Multi-Agent theory, which includes the following steps:
step (1), designing, manufacturing and dividing: dividing the design and manufacture of a core component of shield tunneling equipment into a system digital model selection design part and a system production part and constructing a digital database;
step (2), element extraction and analysis: analyzing potential mapping relations between operation and maintenance and working condition information in the shield tunneling process and various elements in the design and manufacturing process;
step (3), establishing a design and manufacture model: converting elements in the system into individual agents by using a Multi-Agent theory;
and (4) completing a cooperative strategy through distributed Multi-Agent learning interaction and state correction based on the adaptivity and self-organization of the agents.
And (2) the core components of the shield tunneling equipment in the step (1) comprise a cutter head, a cutter, a main bearing, a speed reducer, a main driving motor and the like if aiming at a main driving system. Wherein the system model selection design part comprises: the method comprises the following steps of cutter head design, main bearing model selection, speed reducer model selection, main motor model selection, frequency converter model selection, a rotary joint and the like; the system production part comprises: the method comprises the steps of body processing, accessory purchasing, cutter head assembly, quality detection, complete machine assembly and the like, and is shown in figure 2.
In the step (2), a grey correlation analysis method is adopted to analyze and express the relationship between the operation and maintenance service and the factors such as component selection, part design, processing technology, equipment state and the like, and the potential mapping relationship between the relevant performance parameters and the working conditions of the key components of the tunneling equipment. For the shield tunneling main driving system, construction is carried out under different working conditions in the service life period, and the parameters of torque, rotating speed and the like of a cutter head inevitably have different influences on the operation of the cutter head. And analyzing the influence of part of operation and maintenance elements on the service life duration of the equipment during operation by using a grey correlation analysis method.
TABLE 1 on-site operation and maintenance and design parameter table
| Length of operation | Environmental variables | Rotational speed of cutter head | Torque of | Opening ratio of cutter head |
| 9300h | Sand and pebble stratum (soil quality coefficient 1) | 2.2rpm | 6000kN·m | 40％ |
| 8500h | Dense mudstone (soil property coefficient 0.75) | 1.5rpm | 4200kN·m | 30％ |
| 8700h | Soft sandy soil layer (soil texture coefficient 0.8) | 1.7rpm | 5100kN·m | 38％ |
1) Reference series reflecting system behavior characteristics and comparison series affecting system behavior are determined according to table 1. The sequence of data reflecting the behavior of the system is called the reference sequence (mother sequence). The data sequence consisting of factors that influence the behavior of the system is called the comparison sequence (subsequence). In this example the equipment duration (x) 0 ) Is a parent sequence, an environmental variable (x) 1 ) Initial selection of the rotational speed of the cutter head (x) 2 ) Initial selection torque (x) 3 ) Cutter head opening ratio (x) 4 ) Is a subsequence.
2) The variables are preprocessed (dimensionless, variable range reduction and simplified computation). Preprocessing each index of the parent sequence and the subsequence, firstly solving the mean value of each index, and dividing each element in the index by the corresponding mean value.
As in table 1.
TABLE 2. dimensionless formulation
3) And calculating the correlation coefficient of each parameter in each subsequence and the corresponding parameter of the mother sequence.
4) And calculating the degree of association.
Can obtain r 1 ＝0.6404，r 2 ＝0.5524，r 3 ＝0.6419，r 4 ＝0.5819.
And (3) converting elements in design, manufacture, operation and maintenance into individual agents by using a Multi-Agent theory. Formally defining a set I denotes a finite set of agents, and for convenience we number each Agent in the set as 1,2, …, n, where n | I |. As shown in FIG. 3, a single Agent has its own decision tree to accomplish the collaborative interaction of multiple agents, such as cutterhead design, and the Agent selects a design drawing according to the decision tree to decide design parameters. Individual agents create internal representations of other Agent inputs observed and link them to learning actions that are beneficial to them. If the observed input is not highly correlated with the current, it will not be noticed.
The Agent decision algorithm is as follows:
Observe O s(t) form S (t) using the observation function
Subtract S (t) -S (t’) using the difference function
Compose E s(t) using the event function
Look for N (t) using introspective search
Repeat(for each N i (t)∈N(t))
Repeat(for each I j (t)∈I(t))
Attention＝maxI j (t)
Create a Motivation by Attention.
in the step (4), in each decision period T-0, 1,2, …, T-1, the Agent of the whole system makes a decision and performs an action. The joint action of all agents causes the system to transition from one state to another and the system enters the next decision cycle. In the next decision cycle, each agent observes the respective inputs, then makes the decision and performs the operation, as shown in FIG. 4.
The specific steps for creating the Multi-Agent cooperation strategy are as follows:
1) modeling the system S into a semantic structure M according to the step (3), and enabling the required element P 1 ，P 2 ，…，P n Respectively described as the formula psi 1 ，ψ 2 ，…，ψ n ；
2) And (3) verification: and judging whether the system meets the required attribute by detecting whether M ═ ψ.
3) And (3) model correction: if M ≠ ψ, a learning interaction by the Agent generates a new model M that approaches ψ.
4) And (3) updating the system: the design and manufacturing end generates new operations, and converts S into a new system S with a model of M.
In conclusion, the Multi-Agent theory-based collaborative design and manufacturing method for the core components of the shield tunneling equipment can realize the Multi-element collaborative design and manufacturing of the operation and maintenance drive of the shield tunneling main drive system.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It should be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and the above-described embodiments and descriptions are only preferred examples of the invention and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (5)
1. A Multi-Agent theory-based collaborative design and manufacturing method for core components of shield tunneling equipment is characterized by comprising the following steps:
step 1, designing, manufacturing and dividing: dividing the design and manufacture of a core component of shield tunneling equipment into a system digital model selection design part and a system production part and constructing a digital database;
step 2, element extraction and analysis: analyzing potential mapping relations between operation and maintenance and working condition information in the shield tunneling process and various elements in the design and manufacturing process;
step 3, establishing a design and manufacture model: converting elements in the design and manufacturing process into individual agents by using a Multi-Agent theory;
and 4, completing the cooperative strategy through distributed Multi-Agent learning interaction and state correction based on the adaptivity and self-organization of the agents.
2. The Multi-Agent theory-based shield tunneling equipment core component collaborative design manufacturing method according to claim 1, characterized in that: the system production in the step 1 comprises the production of a driving system and the assembly of a complete machine.
3. The Multi-Agent theory-based shield tunneling equipment core component collaborative design manufacturing method according to claim 1, characterized in that: in the step 2, a grey correlation analysis method is adopted to analyze and express the relation between the operation and maintenance service and component selection, part design, processing technology and equipment state factors and the potential mapping relation between the relevant performance parameters and working conditions of the key components of the tunneling equipment; and (3) determining a parent sequence changing along with time according to a certain rule, taking the change of each evaluation object along with time as a subsequence, calculating the correlation degree of each subsequence and the parent sequence, and obtaining a conclusion according to the correlation size.
4. The Multi-Agent theory-based shield tunneling equipment core component collaborative design manufacturing method according to claim 1, characterized in that: in the step 3, the design, manufacture and operation and maintenance elements are respectively regarded as individual agents; formally defining a set I to represent a finite set of agents, each Agent in the set being numbered 1,2, …, n, where n | I |; a single Agent has a decision tree of the single Agent so as to complete the cooperative interaction of the multiple agents.
5. The Multi-Agent theory-based collaborative design manufacturing method for the core component of the shield tunneling equipment according to claim 1 is characterized in that: the specific steps of creating the Multi-Agent cooperation strategy in the step 4 are as follows:
a. modeling the system S as a semantic structure M according to the step 3, and describing required elements P1, P2, … and Pn as formulas psi 1, psi 2, … and psi n respectively;
b. and (3) verification: judging whether the system meets the required attribute by detecting whether M-psi is zero;
c. and (3) model correction: if M ≠ ψ, then the Agent performs a learning interaction to generate a new model M to approach ψ;
d. and (3) updating the system: the design and manufacture end generates new operation to convert S into a new system S with model M.
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