Tunnel construction machinery matching configuration method and equipment and readable storage medium

Abstract

The invention relates to the technical field of tunnel engineering construction, in particular to a tunnel construction machinery matching configuration method and device and a readable storage medium, and the method comprises the steps: constructing a structured construction element knowledge graph based on engineering full life cycle data; establishing a multi-objective optimization model taking the construction period, the cost, the construction quality and the construction safety as core objectives; based on the structured data support of the construction element knowledge graph, performing iterative optimization on the multi-target optimization model by adopting a multi-target intelligent optimization algorithm, and finally generating a Pareto frontier solution set; and based on a fuzzy analytic hierarchy process, screening out an optimal mechanical matching scheme from the Pareto frontier solution set. According to the method, the problems that traditional mechanical configuration depends on experience, process coupling analysis is insufficient, and multi-target collaboration is poor are solved, scientization of mechanical type selection, number configuration and parameter design is achieved, construction efficiency is improved, and cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel engineering construction, and in particular to a tunnel construction mechanical matching configuration method, equipment and readable storage medium. BACKGROUND

[0002] In traditional tunnel engineering construction, mechanical selection and configuration mainly rely on the experience judgment of project management personnel, which has significant limitations: on the one hand, the key data such as parameters, efficiency and cost of construction machinery (such as rock drill, loader, dump truck, etc.) are scattered in historical engineering records, and lack of systematic resource library support, which makes it difficult to quickly compare the performance differences of different equipment during selection; on the other hand, the timing constraints between processes (such as the connection time of drilling and blasting, and the matching relationship between out-of-pit equipment and excavation progress) are not quantitatively modeled, which often leads to project delay or cost overrun due to idle or overloading of equipment.

[0003] In recent years, with the rapid development of tunnel construction mechanization and intelligent technology, the industry is undergoing a transformation from "manual experience driven" to "equipment technology driven": 1) Intelligent equipment accelerates research and application: a large number of intelligent construction equipment has emerged in the market, such as intelligent rock drilling jumbo (integrating automatic navigation and hole planning functions), unmanned loader (realizing remote control through 5G communication), electric dump truck (with real-time energy consumption monitoring and intelligent scheduling interface). These devices are significantly superior to traditional devices in terms of operating efficiency (such as intelligent rock drilling jumbo drilling accuracy ±2cm, 30% higher than traditional devices), safety (unmanned devices reduce personnel exposure risk) and energy consumption (electric devices reduce energy consumption by 40% compared to fuel vehicles), and the update iteration cycle is shortened to 2-3 years (traditional device update cycle is about 5-8 years).

[0004] 2) New challenges to configuration method brought by equipment replacement: the introduction of intelligent equipment brings new parameter dimensions (such as intelligent level, data interface compatibility, software upgrade cost) and economic indicators (such as software subscription fee, data service cost, intelligent operation and maintenance cost), but the existing construction organization design lacks a special resource library for intelligent equipment, and cannot scientifically calculate the comprehensive cost comparison between "traditional equipment + manual" and "intelligent equipment + digital management" (such as the long-term economy of 30% higher initial procurement cost of intelligent equipment, but 25% lower later operation and maintenance cost).

[0005] Although intelligent equipment has been gradually promoted, its selection and application still face the following key problems: 1) Resource library missing: In existing construction organization design, mechanical selection mainly relies on the experience mode of "equipment manual + historical cases", and a full-type, full-life cycle resource library (including parameter performance, cost composition, applicable scene, maintenance period, etc.) covering traditional equipment and intelligent equipment is not established, resulting in difficulty in quickly matching engineering requirements when selecting (such as five-grade surrounding rock requiring high-torque rock drilling equipment, but unable to quickly screen intelligent rock drilling jumbo models that meet geological conditions).

[0006] 2) Unscientific economic comparison: The "technology premium" and "long-term return" of intelligent equipment lack quantitative tools. For example, a project plans to select intelligent rock drilling jumbo (unit price 8 million yuan, annual operation and maintenance fee 500,000 yuan) or traditional rock drilling jumbo (unit price 5 million yuan, annual operation and maintenance fee 80 million yuan), which needs to calculate net present value (NPV) in multiple dimensions such as shortened construction period (20% efficiency improvement of intelligent equipment), reduction of labor cost (reduction of 2 operators per shift), safety benefits (reduction of accident probability), but existing technology can only do qualitative analysis and cannot provide comparable quantitative results.

[0007] 3) Coupling analysis of process lags: The operation mode of intelligent equipment is different from that of traditional equipment (such as unmanned loader that can realize "loading-transportation-unloading" whole process unmanned through collaborative algorithm, reducing waiting time), but existing research does not include the collaborative characteristics of intelligent equipment into the process connection model, resulting in that the configuration scheme still follows the time sequence rules of traditional equipment, and the efficiency advantage of intelligent equipment is not fully utilized.

[0008] Based on the above, the present application provides a systematic configuration method that integrates multi-source data, supports comparison between traditional and intelligent equipment, and quantifies economic efficiency and process coupling, to solve the technical problems existing in the prior art. SUMMARY

[0009] The present application aims to provide a tunnel construction mechanical matching configuration method, equipment and readable storage medium, and the specific technical solutions are as follows: A tunnel construction mechanical matching configuration method, comprising the following steps: S1, based on the whole life cycle data of the project, a structured construction element knowledge graph is constructed; S2, a multi-objective optimization model is established with construction period, cost, construction quality and construction safety as core objectives; S3, based on the structured data support of the construction element knowledge graph, a multi-objective intelligent optimization algorithm is used to iteratively optimize the multi-objective optimization model, and finally a Pareto frontier solution set is generated; S4, the optimal mechanical matching scheme is selected from the Pareto frontier solution set based on fuzzy analytic hierarchy process.

[0010] Further, in S1, the construction element knowledge graph comprises: entity modules, including construction process entities, mechanical equipment entities and geological information entities; relationship modules, including process connection relationships, mechanical coordination relationships, efficiency influence relationships and construction method relationships; attribute modules, including dynamic attributes and static attributes.

[0011] Further, S2 is specifically: S2.1, confirming the construction period, the construction period being specifically: The main construction process sequence on the tunnel construction critical chain is: rock drilling and blasting, excavation and transportation, primary support and secondary lining, and the construction period is represented as: ; Wherein: T is the total duration; represents the set of tunnel construction processes; represents the duration of each construction process, represents the th process; S2.2, confirming the project cost, the cost being represented as: ; Wherein: is the total cost; represents the direct cost of the project, represents the indirect cost of the project, specifically represented as: ; ; Wherein: represents the direct cost of each process; is the unit indirect cost coefficient; represents the th process; S2.3, establishing the coupling relationship between construction quality and construction period, specifically: ; Wherein: represents the construction quality of each process; represents the operation duration of each process; and are fitting coefficients; S2.4, establishing the coupling relationship between construction safety and cost, specifically: ; Wherein: is the current safety level of the process , is the current safety level of the process Inherent safety level estimation value; Increase ratio estimation value of process safety level due to ensuring safety cost investment; Minimum increase ratio estimation value of safety level due to ensuring safety cost investment; Maximum increase ratio estimation value of safety level due to ensuring safety cost investment; Minimum ensuring safety cost of process ; Maximum ensuring safety cost of process ; Ensuring safety cost of process ; S2.5, a multi-objective optimization model is established, specifically: ; In the above formula, Construction quality; Safety level; Weight of construction quality of each process; The constraint conditions are: Construction period is within the specified period: , The construction period specified in the specific tunnel engineering project contract; Construction quality is higher than the minimum level required by all parties: , The quality level required by the tunnel construction owner for tunnel construction; Construction safety meets the safety level requirement: , The safety level required by the tunnel construction owner for tunnel construction; Cost is lower than the amount specified in the contract: , The construction cost amount specified in the specific tunnel engineering project contract.

[0012] Further, S3 is specifically: S3.1, input the geological report of the engineering project, and output specific process methods and construction process information based on the construction element knowledge graph; S3.2, perform coding design, and for each process according to the construction process output by the construction element knowledge graph, sequentially code in three segments, the format is [machine type] [quantity] [parameter model]; S3.3, input the coding information into the multi-objective optimization model, and use a multi-objective intelligent optimization algorithm to perform iterative calculation to generate a Pareto frontier solution set, that is, to obtain multiple sets of construction resource allocation schemes that satisfy the multi-objective balance of cost, construction period, construction quality and construction safety.

[0013] Further, in the coding process, based on the equipment resource library and the relationship model constructed based on the construction element knowledge graph, multi-dimensional parameter matching is performed on the coding information.

[0014] Further, in S4, the optimal mechanical matching scheme is selected from the Pareto frontier solution set based on the fuzzy analytic hierarchy process, specifically, a fuzzy analytic hierarchy evaluation model is used to quantitatively score the multiple sets of construction resource allocation schemes, including: S4.1, a multi-dimensional evaluation index system including target adaptability, economy, process synergy and risk controllability is constructed; S4.2, based on expert experience and historical engineering data, the weights of each index are determined by the fuzzy scale method; S4.3, the index performance of each set of construction resource allocation scheme is quantitatively fuzzy, and the index quantitative value is obtained; S4.4, calculate the comprehensive score, comprehensive score = Σ index weight × index quantitative value; select the scheme with the highest comprehensive score as the optimal construction resource allocation scheme of the project, that is, select the optimal mechanical matching scheme.

[0015] Further, the fuzzy analytic hierarchy evaluation model is constructed as follows: 1) establish a layered analysis model; 2) compare the elements in the same layer of the layered analysis model with each other, and construct the judgment matrix of the upper layer; 3) calculate the weight of the judgment matrix and perform consistency check; 4) establish evaluation grade standard, judge the indexes in the engineering data to be evaluated, and establish membership matrix; 5) based on the membership matrix, establish the evaluation matrix; 6) based on the evaluation matrix, determine the evaluation grade, and combine the evaluation grade standard to obtain the grade of the construction efficiency evaluation.

[0016] An electronic device, the device comprising a memory and a processor, the memory storing a computer program; the processor executes the computer program to realize the tunnel construction mechanical matching configuration method as described above.

[0017] A readable storage medium, the readable storage medium storing a computer program, the processor executing the computer program to realize the tunnel construction mechanical matching configuration method as described above.

[0018] The technical scheme of the application has the following beneficial effects: The application provides a tunnel construction machinery matching configuration method, which quantifies construction element correlation by constructing a construction element knowledge graph, and realizes collaborative optimization of construction period, cost, construction quality and construction safety through a multi-objective optimization model, solves the problems of traditional mechanical configuration relying on experience, insufficient process coupling analysis and poor multi-objective collaboration, realizes the scientization of mechanical selection, quantity configuration and parameter design, improves construction efficiency and reduces cost, and selects an optimal scheme through a fuzzy analytic hierarchy process, and takes into account theoretical rationality and engineering applicability.

[0019] In addition to the purposes, features and advantages described above, the application has other purposes, features and advantages. The application will be further described below with reference to the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0020] The drawings that form part of the application are used to provide further understanding of the application, and the schematic embodiments of the application and the description thereof are used to explain the application and do not constitute an improper limitation on the application. In the drawings: Figure 1 is a flowchart of the tunnel construction machinery matching configuration method in the application; Figure 2 is an encoding schematic diagram; Figure 3 is a schematic diagram of a layered analysis model. DETAILED DESCRIPTION

[0021] The embodiments of the application will be described in detail below with reference to the drawings, but the application can be implemented in various different ways as defined and covered.

[0022] Embodiment: Referring to Figure 1 , the embodiment provides a tunnel construction machinery matching configuration method, including the following steps: S1, based on engineering full life cycle data, a structured construction element knowledge graph is constructed, and the structured construction element knowledge graph is an entity-relation-attribute construction element knowledge graph containing engineering geological information, processes, mechanical and resource constraints.

[0023] The construction element knowledge graph includes an entity module, a relationship module and an attribute module, specifically: 1) the entity module includes construction process entities, mechanical equipment entities and geological information entities; wherein: ① the construction process entities include typical tunnel construction core processes and their sub-processes, such as rock drilling and blasting, excavation and transportation, primary support and secondary lining; ② the mechanical equipment entities include the types and technical parameters of more than 20 types of drilling and blasting construction machinery and equipment, such as rock drilling jumbo, loader, dump truck and concrete wet spraying machine; ③Geological information entity: Record the geological parameters that directly affect the construction technology such as the grade of surrounding rock, the distribution of ground stress, the development degree of fault, and the external constraints such as resource constraint entity (covering the time limit requirement such as total time limit quota), cost limit (such as total budget cost), site space limit (such as the size of hole section) and so on.

[0024] 2) Relationship module, including process connection relationship, mechanical coordination relationship, efficiency influence relationship and construction method relationship; wherein: ①Process connection relationship: Clear the minimum process interval time between processes (such as 30 minutes of ventilation and smoke after blasting), serial / parallel operation mode (such as partial parallel of out-soil transportation and initial support), and process logic adjustment rules after the intervention of intelligent equipment (such as shortening the loading waiting time by using unmanned loader); ②Mechanical coordination relationship: Quantify the number of mechanical matching (such as 1 drilling jumbo needs to be matched with 2-3 loaders), coordination operation rules (such as the loading-transportation matching rules of dump truck and loader), and device interaction logic (such as the parameter synchronization rules of intelligent drilling jumbo and BIM system); ③Efficiency influence relationship: Establish a quantitative correlation model between mechanical performance parameters (such as drilling efficiency of drilling jumbo and loading efficiency of loader) and process operation efficiency (such as 8% reduction of process duration for every 10% improvement of drilling efficiency); ④Construction method relationship: Map the construction technology and method corresponding to different geological information (such as five-grade surrounding rock needs to adopt "three-step seven-step excavation method", which corresponds to specific mechanical configuration requirements).

[0025] 3) Attribute module, including dynamic attribute and static attribute, wherein: ①Dynamic attribute: Parameters that change dynamically with the length of equipment operation, working condition state or operation progress (such as real-time failure rate of machinery, skill level of operation personnel, real-time consumption rate of materials); ②Static attribute: Inherent technical parameters of equipment / process (such as rated power of drilling jumbo, bucket capacity of loader, standard work hour quota of process).

[0026] S2, establish a multi-objective optimization model with construction duration, cost, construction quality and construction safety as the core objectives; specifically including: S2.1, confirm the construction duration, which is specifically: The main construction process sequence on the critical chain of tunnel construction is: drilling and blasting, excavation and transportation, initial support and secondary lining, and the construction duration is represented as: ; Wherein: is the total duration; represents the set of tunnel construction processes; represents the duration of each construction process, represents the first process;

[0027] S2.2, confirm the project cost, the cost is divided into direct cost and indirect cost; the direct cost is: labor cost, mechanical cost, material cost and wind, water, electricity and firework cost; the indirect cost is: management fee and tax and other costs; the cost spending is different for different construction period. The cost is represented as: ; wherein: is the total cost; represents the direct cost of the project, represents the indirect cost of the project, which is specifically represented as: ; ; wherein: represents the direct cost of each process; is the unit indirect cost coefficient; represents the total process.

[0028] S2.3, the construction quality is a qualitative index, and a quantification system needs to be established, therefore, the coupling relationship between construction quality and construction period is established, which is specifically: ; wherein: represents the construction quality of each process; represents the operation time length of each process; and are fitting coefficients;

[0029] S2.4, construction safety is a qualitative index, and a quantification system needs to be established, therefore, the coupling relationship between construction safety and cost is established, which is specifically: ; wherein: is the current safety level of the process , is the inherent safety level estimate value of the process ; is the safety level estimate value of the process ; is the safety level estimate value of the process ; is the minimum guarantee safety cost of the process ; is the minimum guarantee safety cost of the process the highest guaranteed safety cost of the construction; the guaranteed safety cost of the construction process ;

[0030] S2.5, a multi-objective optimization model is established, specifically: ; In the above formula, is the construction quality; is the safety level; is the weight of the construction quality of each process; The constraint conditions are: The construction period is within the specified period: , is the construction period specified in the specific tunnel project contract; The construction quality is higher than the minimum level required by all parties: , is the quality level required by the tunnel construction owner of the tunnel construction; The construction safety meets the safety level requirement: , is the safety level required by the tunnel construction owner of the tunnel construction; The cost is lower than the amount specified in the contract: , is the construction cost amount specified in the specific tunnel project contract.

[0031] S3, structured data support based on construction element knowledge graph, using a multi-objective intelligent optimization algorithm (such as NSGA-III optimization algorithm) to iteratively optimize the multi-objective optimization model, and finally generating a Pareto (Pareto) frontier solution set; specifically: S3.1, input the geological report of the project, and output the specific process method and construction process information based on the construction element knowledge graph; S3.2, coding design, according to the construction process output by the construction element knowledge graph, each process is coded in turn in three segments, the format is [machine type] [quantity] [parameter model], see Figure 2 ; Specifically, based on the equipment resource library (corresponding to the mechanical equipment entity in the entity module, covering equipment parameters, process constraints and use scenarios, etc. Multi-dimensional data) and relationship model (corresponding to the relationship module, defining the association rules and collaboration logic of equipment-process-target) constructed by the construction element knowledge graph, the coding information is matched in multiple dimensions (including equipment type, quantity, performance parameters and process demand adaptability verification).

[0032] S3.3, input the encoding information into the multi-objective optimization model, and use the multi-objective intelligent optimization algorithm to perform iterative calculation to generate a Pareto frontier solution set, i.e. a series of non-dominated solutions under the constraints of construction duration, cost, construction quality and construction safety, reflecting the trade-off relationship between multiple objectives, and the solution set corresponds to specific encoding information (such as the combination of mechanical type encoding, quantity encoding and parameter model encoding), i.e. a construction resource allocation scheme that meets the balance of multiple objectives.

[0033] S4, after obtaining a plurality of construction resource allocation schemes that meet the balance of multiple objectives of construction duration, cost, construction quality and construction safety through the multi-objective optimization model, the optimal allocation scheme for the actual needs of the project needs to be further selected; in this embodiment, the optimal mechanical matching scheme is selected from the Pareto frontier solution set based on fuzzy analytic hierarchy process (FAHP), specifically, the optimal mechanical matching scheme is selected from the Pareto frontier solution set based on the fuzzy analytic hierarchy process evaluation model, specifically, the fuzzy analytic hierarchy process evaluation model is used to quantitatively score the plurality of construction resource allocation schemes, including: S4.1, a multi-dimensional evaluation index system including target adaptability, economy, process synergy and risk controllability is constructed; S4.2, based on expert experience and historical engineering data, the weights of each index are determined by fuzzy scale method; S4.3, the index performance of each group of construction resource allocation schemes is quantitatively fuzzy (such as using 0-1 interval value to represent the "good-bad" degree), and the index quantization value is obtained; S4.4, calculate the comprehensive score, comprehensive score = Σ index weight x index quantization value; select the scheme with the highest comprehensive score as the optimal construction resource allocation scheme for the project, i.e. select the optimal mechanical matching scheme.

[0034] In this embodiment, the fuzzy analytic hierarchy process evaluation model is constructed as follows: 1) Establish a layered analysis model; The required evaluation questions can be decomposed into multiple related factors by literature review method, expert interview method, questionnaire survey method, etc. According to the characteristics of the factors and their correlation, they are stratified into target layer, criterion layer and index layer, as shown in Figure 3 The upper factors are affected by the lower factors, and the factors in each layer are relatively independent, forming a multi-layer progressive hierarchical structure model.

[0035] 2) Compare the elements in the same layer of the layered analysis model with each other to construct the judgment matrix of the upper layer; Use 1-9 scale, as shown in Table 1, to compare the elements in the same layer with each other to construct the judgment matrix of the upper layer. For example, as shown in Figure 3As shown, the comparison matrix of the target layer A is constructed, and the next layer criterion layer The comparison matrix of the target layer A is constructed, and the next layer criterion layer The comparison matrix of the target layer A is constructed, and the next layer criterion layer The comparison matrix of the target layer A is constructed, and the next layer criterion layer The comparison matrix of the target layer A is constructed, and the next layer criterion layer The comparison matrix of the target layer A is constructed, and the next layer criterion layer The comparison matrix of the target layer A is constructed, and the next layer criterion layer The comparison matrix of the target layer A is constructed, and the next layer criterion layer The comparison matrix of the target layer A is constructed, and the next layer criterion layer

[0036] Table 1-9 scale table

[0037] According to the 1-9 scale, the comparison results are valued as shown in Table 2. Table 2 Value assignment table for comparison results

[0038] The results of the two-by-two comparison in Table 2 are constructed into a judgment matrix , where represents the number of elements; . 3) Calculate the weight of the judgment matrix and perform consistency check;

[0039] ① Calculate the weight (the calculation process of the weight is the fuzzy scale method): The column vectors in the judgment matrix are normalized to matrix B : ; ; The arithmetic mean vector of matrix is calculated: ; ; where is the weight of element ; ② Consistency check Because the elements are compared two by two in the process of constructing the judgment matrix, and the values are assigned according to the 1-9 scale, there is a certain subjectivity, which affects the size of the weight vector. Therefore, the judgment results in the judgment matrix should be checked to see if they are reasonable.

[0040] First, calculate the maximum eigenvalue :​​ ; Then calculate the consistency ratio. : ; in, ; As a random consistency index, its value depends only on the order of the judgment matrix. The relevant values ​​are shown in Table 3. Table 3. Random Consistency Index Values

[0041] when A value less than 0.1 indicates that the consistency test has been passed; otherwise, it indicates that the pairwise comparison results are not objective and accurate, and the judgment matrix needs to be readjusted until the consistency test is passed.

[0042] 4) Establish evaluation grading standards, evaluate the indicators in the engineering data to be evaluated, and establish a membership matrix; Literature reviews and expert consultations were conducted to provide a detailed description of each indicator element in the layered structure analysis model. For the target layer, each indicator was evaluated and categorized into different evaluation levels, with established evaluation standards. Common level classifications include excellent, good, average, and poor. The established levels are represented by V. ; in, Indicates shared ownership Each rating level.

[0043] Using the established evaluation grading criteria, the indicators in the engineering data to be evaluated are assessed, and a membership matrix is ​​established using mathematical permutation methods. ;

[0044] ; Indicates the first V obtained from each element j The evaluation score is used to determine the rating level. Indicates the first Each rating level.

[0045] 5) Establish an evaluation matrix based on the membership matrix; ① First-level fuzzy comprehensive evaluation matrix Will Normalization is performed to obtain the matrix. Then the matrix Multiplying by the corresponding index layer weight vector yields : ; The first-level fuzzy comprehensive evaluation matrix is: : ; ② Second-level fuzzy comprehensive evaluation matrix Will Multiplying the matrix by the corresponding criterion layer weight vector yields the second-level fuzzy comprehensive evaluation matrix. Y : ; in Indicates rating level V j The proportion.

[0046] 6) Based on the evaluation matrix, establish the evaluation level, and combine the evaluation level standards to obtain the evaluation level of the project construction efficiency.

[0047] In the evaluation level V, each level is assigned a certain numerical range, and the average value of each range is set to... This indicates that the level is V. j The number of assigned values, i.e., the evaluation level V, can be represented as a matrix. G : ; Then its evaluation level for: ; For a single value, Substitute the value into the established evaluation grading criteria. The evaluation level corresponding to the numerical range in which the value lies is the evaluation level of the construction efficiency of the project.

[0048] The tunnel construction machinery configuration method provided by this invention quantifies the association of construction elements by constructing a knowledge graph of construction elements, and achieves coordinated optimization of construction period, cost, construction quality, and construction safety through a multi-objective optimization model. This solves the problems of traditional machinery configuration relying on experience, insufficient process coupling analysis, and poor multi-objective coordination. It realizes the scientification of machinery selection, quantity configuration, and parameter design, improves construction efficiency, and reduces costs. Furthermore, it selects the optimal solution through fuzzy hierarchical analysis, taking into account both theoretical rationality and engineering applicability.

[0049] The present invention also provides an electronic device corresponding to the above embodiments. The device may be a processing device for a client, such as a mobile phone, a laptop, a tablet computer, a desktop computer, etc., to execute the methods of the above embodiments.

[0050] The device of the embodiment comprises a memory, a processor and a computer program stored on the memory; the processor executes the computer program on the memory to realize the steps of the method of the above embodiment.

[0051] In some implementations, the memory can be a high-speed random access memory (RAM), and can also include a non-volatile memory, such as at least one disk memory.

[0052] In some implementations, the processor can be a central processing unit (CPU), a digital signal processor (DSP), or various types of general-purpose processors, without limitation.

[0053] The application also provides a medium corresponding to the above embodiment, which is a readable storage medium, and a computer program / instruction is stored on the readable storage medium. When the computer program / instruction is executed by the processor, the steps of the method of the above embodiment are realized.

[0054] The computer readable storage medium can be a tangible device that maintains and stores instructions for use by an instruction execution device. The computer readable storage medium can be, for example but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0055] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROMs, optical storage, etc.) containing computer usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, such as object-oriented programming languages Java and interpreted scripting languages JavaScript, etc.

[0056] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the flowcharts and / or block diagrams. Figure 1 one flow or multiple flows and / or blocks Figure 1means for performing the function specified by the block or blocks.

[0057] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operational steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, thus the instructions executed on the computer or other programmable data processing devices provide processes for implementing the flows Figure 1 one or more flows and / or blocks Figure 1 steps of a function specified by the block or blocks.

[0058] The above only is the preferred embodiment of the present application, and is not used to limit the present application, for those skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for configuring and assembling tunnel construction machinery, characterized in that, Includes the following steps: S1. Based on the entire life cycle data of the project, construct a structured knowledge graph of construction elements; S2. Establish a multi-objective optimization model with construction period, cost, construction quality and construction safety as the core objectives; S3. Based on the structured data support of the construction element knowledge graph, a multi-objective intelligent optimization algorithm is used to iteratively optimize the multi-objective optimization model, and finally generate the Pareto front solution set; S4. The optimal mechanical matching scheme is selected from the Pareto front solution set based on the fuzzy hierarchical analysis method.

2. The method for configuring and supporting tunnel construction machinery according to claim 1, characterized in that, In S1, the construction element knowledge graph includes: The entity module includes construction process entities, mechanical equipment entities, and geological information entities; The relationship module includes relationships between work processes, mechanical coordination, efficiency impact, and construction methods. The properties module includes dynamic properties and static properties.

3. The method for configuring and supporting tunnel construction machinery according to claim 1, characterized in that, S2 specifically refers to: S2.1, confirming the construction period, which is specifically as follows: The main construction sequence in the critical chain of tunnel construction is: rock drilling and blasting, excavation and transportation, initial support and secondary lining. Therefore, the construction period is expressed as: ； in: T Total construction period; This represents the collection of all construction processes involved in tunnel construction. This indicates the duration of each construction process. Indicates the first One process; S2.2 Confirm project costs, which are expressed as follows: ； in: Total cost; Indicates the direct costs of the project. Indirect costs of the project are represented as follows: ； ； in: This represents the direct cost of each process. The unit indirect cost coefficient; Indicates shared ownership One process; S2.3 Establish the coupling relationship between construction quality and construction period, specifically as follows: ； in: This refers to the construction quality of each process. This indicates the processing time for each step of the process; and These are the fitting coefficients; S2.

4. Establish the coupling relationship between construction safety and cost, specifically as follows: ； in: For process The current security level, For process The inherent level of security estimate; The percentage increase in process safety level due to guaranteed safety cost investment; This is an estimate of the minimum percentage increase in safety level due to the investment in assurance safety costs. This represents the estimated maximum percentage increase in safety level due to the investment in ensuring safety costs. For process The lowest guaranteed security cost; For process The highest guaranteed safety cost; For process The cost of ensuring safety; S2.

5. Establish a multi-objective optimization model, specifically as follows: ； In the above formula, For construction quality; For safety level; The weighting of the construction quality for each process; The constraints are: The construction period is within the stipulated time limit: , The construction period stipulated in the contract for a specific tunnel engineering project; The construction quality exceeds the minimum requirements of all parties: , The quality level required by the tunnel construction owner for the tunnel construction; Construction safety meets safety level requirements: , The safety level required by the tunnel construction owner for tunnel construction; The cost is lower than the amount stipulated in the contract: , This refers to the construction cost amount stipulated in the contract for a specific tunnel engineering project.

4. The method for configuring and supporting tunnel construction machinery according to claim 2, characterized in that, S3 specifically refers to: S3.1 Input the geological report of the project, and output specific technology and construction procedures based on the construction element knowledge graph; S3.

2. Conduct coding design. According to the construction procedures output by the construction element knowledge graph, each procedure is coded in three segments in sequence, with the format being [machine type][quantity][parameter model]. S3.3 Input the encoded information into the multi-objective optimization model, use the multi-objective intelligent optimization algorithm for iterative calculation, generate the Pareto front solution set, that is, obtain multiple sets of construction resource allocation schemes that satisfy the balance of multiple objectives such as cost, construction period, construction quality and construction safety.

5. A method for configuring and supporting tunnel construction machinery according to claim 4, characterized in that, During the coding process, the equipment resource library and relationship model built based on the construction element knowledge graph are used to perform multi-dimensional parameter matching on the coded information.

6. The method for configuring and supporting tunnel construction machinery according to claim 4, characterized in that, In S4, the optimal mechanical matching scheme is selected from the Pareto front solution set based on fuzzy hierarchical analysis. Specifically, a fuzzy hierarchical analysis evaluation model is used to quantitatively score multiple sets of construction resource allocation schemes, including: S4.1 Construct a multi-dimensional evaluation index system that includes target adaptability, economy, process synergy and risk controllability; S4.

2. Based on expert experience and historical engineering data, the weights of each indicator are determined using the fuzzy scaling method. S4.

3. Fuzzy quantification is performed on the performance indicators of each group of construction resource allocation schemes to obtain the quantitative values ​​of the indicators; S4.4 Calculate the comprehensive score, which is calculated as Σ index weight × index quantification value. Select the scheme with the highest comprehensive score as the optimal construction resource allocation scheme for the project, i.e., select the optimal machinery matching scheme.

7. A method for configuring and supporting tunnel construction machinery according to claim 6, characterized in that, The fuzzy hierarchical analysis evaluation model is constructed as follows: 1) Establish a layered analysis model; 2) Perform pairwise comparisons on elements in the same layer of the layered analysis model to construct a judgment matrix for the layer above it; 3) Calculate the weights of the judgment matrix and perform a consistency check; 4) Establish evaluation grading standards, evaluate the indicators in the engineering data to be evaluated, and establish a membership matrix; 5) Establish an evaluation matrix based on the membership matrix; 6) Based on the evaluation matrix, establish the evaluation level, and combine the evaluation level standards to obtain the evaluation level of the project construction efficiency.

8. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program; the processor executes the computer program to implement the tunnel construction machinery configuration method as described in any one of claims 1-7.

9. A readable storage medium, characterized in that, The readable storage medium stores a computer program, and the processor executes the computer program to implement the tunnel construction machinery matching configuration method as described in any one of claims 1-7.

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