A rail transit construction operation guidance method and system based on set pair analysis

Abstract

The present invention belongs to the technical field of rail transit construction. The present invention discloses a rail transit construction operation guidance method and system based on set pair analysis, including obtaining a construction operation process and a matching risk source list based on a construction operation target, and extracting a set of risk assessment indicators corresponding to different construction risks; determining the evaluation indicator weights of each risk assessment indicator based on a combined weighting method, and matching the risk assessment level corresponding to the risk assessment indicator through the maximum correlation; determining the evaluation indicator weights corresponding to the current risk assessment indicator based on the risk assessment level, and determining the risk level of the current rail transit construction operation; formulating a specific risk prevention and control plan, adjusting and optimizing the prior knowledge base used in the construction operation process and the project progress based on the risk prevention and control plan, combining the virtual dynamic construction corresponding to the three-dimensional construction platform, importing the dynamic playback display based on the timeline in the virtual reality device, and refreshing the three-dimensional construction platform in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of rail transit construction, and more specifically, to a rail transit construction operation guidance method and system based on set pair analysis. Background Art

[0002] As the urbanization process accelerates, rail transit has become the key to easing traffic pressure. However, the multiple risks faced during the construction process, such as safety, environmental protection, and progress, as well as technological progress and information needs, make the formulation of scientific guidance plans particularly important. In addition to comprehensively considering multiple risk assessment indicators, providing a scientific basis for risk management, and using advanced technologies to improve the efficiency and accuracy of construction management;

[0003] For example, the Chinese patent application with the publication number CN116822808A discloses a rail transit construction management method and system based on automatic request and cancel point. Through the mode of unified management of construction conditions, automatic judgment of plan conditions by the system and automatic approval of points, unified management of construction conditions is achieved, and batch release of construction plans that meet the same construction conditions is achieved. At present, in rail transit construction, improvements are mainly made to existing problems existing at the construction site. In fact, rail transit construction involves multiple links and aspects, including line design and construction, station building design, vehicle operation and management, communication equipment installation and commissioning, etc. There are many risks in the construction process, such as safety risks, environmental risks, progress risks, etc. These risks may have a serious impact on the safety of life and property of construction personnel, the surrounding environment and the construction progress.

[0004] With the continuous development of information technology, rail transit construction is also facing the demand for informatization and intelligence. By introducing advanced technical means, such as set pair analysis and big data analysis, scientific and systematic guidance is provided to ensure the smooth progress of the construction process and the realization of high standards of construction quality.

[0005] In view of this, the present invention provides a rail transit construction operation guidance method and system based on set pair analysis. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a rail transit construction operation guidance method and system based on set pair analysis.

[0007] According to a first aspect of the present invention, a rail transit construction operation guidance method based on set pair analysis is provided, comprising the following steps:

[0008] Constructing a three-dimensional construction platform based on the rail transit construction environment, extracting construction operation targets corresponding to each target area based on the three-dimensional construction platform, and obtaining a construction operation process matching the construction operation targets based on the construction operation targets;

[0009] Based on the construction operation process, a matching risk source list is searched in the prior knowledge base, and each construction risk content in the risk source list is reviewed based on the prior knowledge, and risk assessment indicators corresponding to different construction risks are extracted, and a risk assessment indicator set is generated based on the risk assessment indicator integration;

[0010] Determine the evaluation index weight of each risk assessment index in the risk assessment index set based on the combined weighting method;

[0011] Call the pre-trained set pair analysis model to perform correlation analysis on the set pairs of risk assessment indicator sets and result sets, obtain the correlation between the risk assessment indicator and the risk assessment level, and match the risk assessment level corresponding to the risk assessment indicator based on the maximum correlation;

[0012] Determine the evaluation index weight corresponding to the current risk assessment index based on the risk assessment level, conduct a comprehensive evaluation of the current risk assessment index, and determine the risk level of the current rail transit construction operation;

[0013] According to the results of the risk level analysis, a specific risk prevention and control plan is formulated. Based on the risk prevention and control plan, the prior knowledge base used in the construction operation process and project progress is adjusted and optimized. Combined with the virtual dynamic construction corresponding to the 3D construction platform, the virtual reality equipment is imported into the timeline-based dynamic playback display to refresh the 3D construction platform in real time.

[0014] As a preferred solution of the first aspect of the present invention, the acquisition logic of the construction operation process is:

[0015] Collect environmental data corresponding to the rail transit construction environment based on remote sensing technology, the environmental data including construction terrain data, geological conditions, building distribution and traffic routes;

[0016] Conduct 3D modeling based on environmental data, build a virtual model of the rail transit construction environment, annotate the environmental data corresponding to the rail transit construction environment based on the virtual model, and obtain a 3D construction platform that simulates the real construction environment;

[0017] On the three-dimensional construction platform, the construction area is divided into different target areas, and for each target area, the corresponding construction operation target is extracted;

[0018] According to the extracted construction operation objectives, the matching construction operation process is searched in the prior knowledge base.

[0019] As a preferred solution of the first aspect of the present invention, the logic for obtaining the risk assessment indicator is:

[0020] The risk source list includes at least one historical construction operation data, and the historical construction operation data includes at least one construction node corresponding to a construction operation target, historical operation data corresponding to the construction node, and a historical operation index;

[0021] Slice the historical operation data to obtain a risk processing result, which includes several risk assessment indicators of Q risk types, corresponding risk classification levels and risk operation indexes, Q∈N*;

[0022] Used to perform timestamp rewriting processing on a number of risk assessment indicators of each risk type according to a preset rewriting rule; perform secondary sharding processing on a number of risk assessment indicators of each risk type after the timestamp rewriting to obtain a secondary risk processing result, wherein the secondary risk processing result includes G secondary risk assessment indicators, corresponding secondary risk classification levels and secondary risk operation indexes, G∈N*;

[0023] The secondary risk assessment indicator is mapped to at least one construction node corresponding to the corresponding construction work target.

[0024] As a preferred scheme of the first aspect of the present invention, the risk assessment indicator set is a set of risk assessment indicators subdivided according to different requirements of the construction environment and construction methods. The risk assessment indicators include market risk indicators, technical risk indicators, financial risk indicators, construction environment risk indicators, management risk indicators and construction safety risk indicators.

[0025] As a preferred solution of the first aspect of the present invention, the historical operation data, risk processing results and secondary risk processing results in the construction nodes are backed up, and the backed up historical operation data, risk processing results and secondary risk processing results in the construction nodes are stored in a priori knowledge base.

[0026] As a preferred solution of the first aspect of the present invention, the logic for obtaining the evaluation index weight is:

[0027] Obtain a set of risk assessment indicators, and collect target data associated with the risk assessment indicators based on a historical database and expert prior knowledge;

[0028] According to the expert scoring data, a judgment matrix is ​​constructed and optimized using rough set theory. The subjective weight of each risk assessment indicator is calculated using the optimized judgment matrix.

[0029] The objective weight of each risk assessment indicator is calculated using the entropy method;

[0030] According to the combination rules, the subjective weight and the objective weight are coupled to obtain the final comprehensive weight; based on the comprehensive weight and the target data in the risk assessment indicator set, the risk assessment indicators of the project are initially evaluated to obtain the evaluation indicator weights of each risk assessment indicator.

[0031] As a preferred solution of the first aspect of the present invention, the logic for obtaining the risk level analysis result is:

[0032] The risk assessment level is divided into K risk levels based on the construction operation process, K∈N*;

[0033] The weight of the evaluation index corresponding to each risk assessment index is determined according to the impact of the risk assessment level on the construction operation process:

[0034] The evaluation index weights corresponding to each risk assessment index are accumulated to obtain the comprehensive evaluation results of the risk assessment index;

[0035] According to the comprehensive assessment results, the risk level analysis results of current rail transit construction operations are divided into corresponding risk assessment levels.

[0036] As a preferred solution of the first aspect of the present invention, the logic for obtaining the risk prevention and control plan is:

[0037] Based on the risk level analysis results, a priori knowledge base is entered, and databases of different cloud service platforms are associated with the priori knowledge base. Based on the network learning model, operation risk prevention and control features are extracted from a large number of historical construction operation processes with correct field classification to form an operation risk prevention and control feature list;

[0038] Conduct similar risk prevention and control analysis based on the operational risk prevention and control feature list to obtain matching risk prevention and control case analysis;

[0039] The timestamps of risk prevention and control cases are rewritten according to the preset rewriting rules to generate a risk prevention and control plan.

[0040] According to a second aspect of the present invention, the present invention provides a rail transit construction operation guidance system based on set pair analysis, which is based on the implementation of the first aspect and includes a construction platform construction module, a construction review module, a risk initial assessment module, a set pair analysis module, a risk assessment module and a construction guidance module, and each module is connected by wire and / or wirelessly;

[0041] A construction platform construction module constructs a three-dimensional construction platform based on the rail transit construction environment, extracts construction work targets corresponding to each target area based on the three-dimensional construction platform, obtains a construction work process matching the construction work target based on the construction work target; and sends the construction work process to the construction review module;

[0042] The construction review module searches for a matching risk source list in the prior knowledge base based on the construction operation process, reviews each construction risk content in the risk source list based on the prior knowledge, extracts risk assessment indicators corresponding to different construction risks, generates a risk assessment indicator set based on risk assessment indicator integration, and sends the risk assessment indicator set to the risk initial assessment module and the set pair analysis module;

[0043] The initial risk assessment module determines the evaluation indicator weight of each risk assessment indicator in the risk assessment indicator set based on the combined weighting method, and sends the evaluation indicator weight of the risk assessment indicator to the risk assessment module;

[0044] The set pair analysis module calls the pre-trained set pair analysis model to perform correlation analysis on the set pairs of risk assessment indicator sets and result sets, obtains the correlation between risk assessment indicators and risk assessment levels, matches the risk assessment levels corresponding to the risk assessment indicators based on the maximum correlation, and sends the risk assessment levels corresponding to the risk assessment indicators to the risk assessment module;

[0045] The risk assessment module determines the evaluation index weight corresponding to the current risk assessment index based on the risk assessment level, conducts a comprehensive assessment of the current risk assessment index, determines the risk level of the current rail transit construction operation, and sends the risk level to the construction guidance module;

[0046] The construction guidance module formulates a specific risk prevention and control plan based on the risk level analysis results. Based on the risk prevention and control plan, it adjusts and optimizes the prior knowledge base used in the construction operation process and project progress. Combined with the virtual dynamic construction corresponding to the 3D construction platform, it imports the timeline-based dynamic playback display into the virtual reality equipment and refreshes the 3D construction platform in real time.

[0047] According to a third aspect of the present invention, there is provided an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0048] The processor calls the computer program stored in the memory, and the computer executes the first aspect.

[0049] The technical effects and advantages of a rail transit construction operation guidance method and system based on set pair analysis of the present invention are as follows:

[0050] The present invention uses set pair analysis technology to accurately assess construction risks, effectively prevent safety accidents, and ensure the safety of construction workers and the smooth progress of the project. At the same time, it uses advanced three-dimensional video comparison methods to provide construction workers with real-time and accurate construction guidance, optimize the construction process, reduce resource waste, and significantly improve construction efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a schematic diagram of the structure of a rail transit construction operation guidance system based on set pair analysis of the present invention;

[0052] Figure 2 This is a flow chart of an integrated secure hosting operation method based on hybrid cloud of the present invention;

[0053] Figure 3 The figure is a schematic structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Example 1

[0056] The present invention provides Figure 1 A rail transit construction operation guidance method based on set pair analysis is shown, comprising a construction platform construction module 100, a construction review module 200, a risk initial assessment module 300, a set pair analysis module 400, a risk assessment module 500 and a construction guidance module 600, wherein each module is connected by wire and / or wirelessly;

[0057] The construction platform construction module 100 constructs a three-dimensional construction platform based on the rail transit construction environment, extracts the construction work targets corresponding to each target area based on the three-dimensional construction platform, obtains the construction work process matching the construction work target based on the construction work target; and sends the construction work process to the construction review module 200;

[0058] Specifically, the acquisition logic of the construction operation process is:

[0059] Collect environmental data corresponding to the rail transit construction environment based on remote sensing technology, the environmental data including construction terrain data, geological conditions, building distribution and traffic routes;

[0060] Conduct 3D modeling based on environmental data, build a virtual model of the rail transit construction environment, annotate the environmental data corresponding to the rail transit construction environment based on the virtual model, and obtain a 3D construction platform that simulates the real construction environment;

[0061] On the three-dimensional construction platform, the construction area is divided into different target areas, and for each target area, the corresponding construction operation target is extracted;

[0062] According to the extracted construction operation objectives, the matching construction operation process is searched in the prior knowledge base.

[0063] The construction review module 200 searches for a matching risk source list in a priori knowledge base based on the construction operation process, reviews each construction risk content in the risk source list based on priori knowledge, extracts risk assessment indicators corresponding to different construction risks, generates a risk assessment indicator set based on risk assessment indicator integration, and sends the risk assessment indicator set to the risk initial assessment module 300 and the set pair analysis module 400;

[0064] It should be noted that the prior knowledge base includes historical construction data, expert experience data and industry standard data to assist in determining the severity and possibility of risks; the construction process in the prior knowledge base and the prior knowledge base are used to identify, review and evaluate the sources of risks in the construction process. Based on the construction process, potential sources of risk are reviewed and evaluated;

[0065] The risk source list is compiled through a joint investigation of the on-site environment by the construction unit, risk control center and risk management professional unit, and after content review by multiple professional experts. It itself has certain industry professional guidance significance.

[0066] Specifically, the logic for obtaining the risk assessment indicators is as follows:

[0067] The risk source list includes at least one historical construction operation data, and the historical construction operation data includes at least one construction node corresponding to a construction operation target, historical operation data corresponding to the construction node, and a historical operation index;

[0068] Slice the historical operation data to obtain a risk processing result, which includes several risk assessment indicators of Q risk types, corresponding risk classification levels and risk operation indexes, Q∈N*;

[0069] Used to perform timestamp rewriting processing on a number of risk assessment indicators of each risk type according to a preset rewriting rule; perform secondary sharding processing on a number of risk assessment indicators of each risk type after the timestamp rewriting to obtain a secondary risk processing result, wherein the secondary risk processing result includes G secondary risk assessment indicators, corresponding secondary risk classification levels and secondary risk operation indexes, G∈N*;

[0070] The secondary risk assessment indicator is mapped to at least one construction node corresponding to the corresponding construction work target.

[0071] Specifically, the risk assessment index set is a set of risk assessment indicators that are subdivided according to the different needs of the construction environment and construction methods. The risk assessment indicators include market risk indicators, technical risk indicators, capital risk indicators, construction environment risk indicators, management risk indicators and construction safety risk indicators;

[0072] Market risk indicators include market demand indicators, price fluctuation indicators and policy adjustment indicators. Price fluctuation indicators are manifested as declines in project returns caused by fluctuations in raw material prices, changes in labor costs, reductions in market demand or changes in policy orientations. When assessing market risks, attention should be paid to market dynamics, policy trends and industry competition situations so as to adjust project strategies in a timely manner.

[0073] Technical risk indicators include design feasibility indicators, construction technology strength indicators and equipment selection indicators; during project execution, technical risk indicators may lead to construction delays, cost overruns or substandard quality. When assessing technical risks, the feasibility of the technical solution, the strength of the technical team and the adequacy of technical reserves should be examined.

[0074] Financial risk indicators include financing difficulty indicators, capital flow ratio and exchange rate fluctuation degree. In actual applications, engineering projects usually require a large amount of capital investment. Financial risks may cause the project's capital chain to break and affect the project progress. When assessing financial risks, attention should be paid to the project's financing channels, capital plans and cost control capabilities.

[0075] Construction environment risk indicators include geological condition indicators, climate condition indicators and social environment indicators. Complex geology, harsh climate or unstable social environment may have an adverse impact on construction. When assessing construction environment risks, detailed on-site surveys should be conducted to understand the geological conditions, climate characteristics and local social environment in order to formulate targeted response measures.

[0076] Management risk indicators include management organizational structure, decision-making process progress and communication process progress. Poor management may lead to inefficient projects, waste of resources and poor team collaboration. When assessing management risks, attention should be paid to the project team's management level, decision-making efficiency and communication and coordination capabilities.

[0077] Construction safety risk indicators include construction site safety protection indicators, personnel safety training indicators and emergency plan indicators. When assessing construction safety risks, attention should be paid to construction site safety management, personnel training and the formulation and implementation of emergency plans.

[0078] In summary, the risk assessment index set covers all kinds of risks that may be encountered in engineering project management. However, many risk assessments cannot be quantified in actual applications. Therefore, scientific risk assessment is needed so that construction units can timely discover and respond to potential risks to ensure the smooth progress and successful completion of the project. At the same time, the risk assessment index set also provides an important basis for project decision-making, which helps construction units make wise decisions.

[0079] More specifically, the historical operation data, risk processing results and secondary risk processing results in the construction nodes are backed up, and the backed-up historical operation data, risk processing results and secondary risk processing results in the construction nodes are stored in a priori knowledge base.

[0080] The initial risk assessment module 300 determines the evaluation indicator weight of each risk assessment indicator in the risk assessment indicator set based on the combined weighting method, and sends the evaluation indicator weight of the risk assessment indicator to the risk assessment module 500;

[0081] It should be noted that the combined weighting method is a comprehensive weighting method that combines subjective weighting method and objective weighting method. Subjective weighting method mainly relies on the knowledge and experience of experts to make judgments, such as analytic hierarchy process (AHP) and Delphi method, while objective weighting method is calculated based on the statistical characteristics of data, such as entropy weight method and principal component analysis method. Considering the subjective judgment of experts and using objective data information at the same time makes the weight distribution more scientific and reasonable. Second, the combined weighting method of analytic hierarchy process coupled with entropy value method optimized by rough set theory

[0082] Specifically, the logic for obtaining the evaluation index weights is as follows:

[0083] Obtain a set of risk assessment indicators, and collect target data associated with the risk assessment indicators based on a historical database and expert prior knowledge;

[0084] According to the expert scoring data, a judgment matrix is ​​constructed and optimized using rough set theory. The subjective weight of each risk assessment indicator is calculated using the optimized judgment matrix.

[0085] The objective weight of each risk assessment indicator is calculated using the entropy method;

[0086] According to the combination rules, the subjective weight and the objective weight are coupled to obtain the final comprehensive weight; based on the comprehensive weight and the target data in the risk assessment indicator set, the risk assessment indicators of the project are initially evaluated to obtain the evaluation indicator weights of each risk assessment indicator.

[0087] The set pair analysis module 400 calls a pre-trained set pair analysis model to perform correlation analysis on the set pairs of the risk assessment indicator set and the result set, obtains the correlation between the risk assessment indicator and the risk assessment level, matches the risk assessment level corresponding to the risk assessment indicator based on the maximum correlation, and sends the risk assessment level corresponding to the risk assessment indicator to the risk assessment module 500;

[0088] Specifically, the construction logic of the set pair analysis model is:

[0089] Based on historical training data of the distributed risk assessment system, the historical training data includes a set of risk assessment indicators constituting a risk assessment indicator set and a set of results constituting a risk assessment level;

[0090] Use 70% of the historical training data as the parameter training set and 30% of the historical training data as the parameter test set to build a set pair analysis model;

[0091] The parameter training set is input into the set to train the analysis model, so as to obtain the correlation between the indicator set and the result set as the output, the actual correlation as the prediction target, and the minimization of the sum of the prediction accuracy of all risk assessment indicators as the training target; wherein the calculation formula for prediction accuracy is: , where P is the number of the risk assessment indicator, zk is the prediction accuracy, is the predicted correlation corresponding to the Pth risk assessment indicator, is the actual correlation corresponding to the Pth risk assessment indicator; the set pair analysis model is tested using the parameter test set until the sum of the prediction accuracies reaches convergence and the training is stopped.

[0092] It should be noted here that: by training and testing the set-pair analysis model, determining parameters such as the number of training iterations and learning rate, and performing model evaluation and tuning, the accuracy of the correlation between the output risk assessment indicator set and the risk assessment level can be ensured.

[0093] The risk assessment module 500 determines the evaluation index weight corresponding to the current risk assessment index based on the risk assessment level, conducts a comprehensive evaluation on the current risk assessment index, determines the risk level analysis result of the current rail transit construction operation, and sends the risk level analysis result to the construction guidance module 600;

[0094] Specifically, the logic for obtaining the risk level analysis results is as follows:

[0095] The risk assessment level is divided into K risk levels based on the construction operation process, K∈N*; illustratively, when K=5, the risk levels include high risk, medium risk, low risk, normal and good;

[0096] The weight of the evaluation index corresponding to each risk assessment indicator is determined according to the degree of influence of the risk assessment level on the construction operation process: Generally, the higher the risk level, the greater the corresponding weight.

[0097] The evaluation index weights corresponding to each risk assessment index are accumulated to obtain the comprehensive evaluation results of the risk assessment index; based on the comprehensive evaluation results, the risk level analysis results of the current rail transit construction operations are divided into corresponding risk assessment levels.

[0098] The construction guidance module 600 formulates a specific risk prevention and control plan based on the risk level analysis results, adjusts and optimizes the prior knowledge base used in the construction operation process and project progress based on the risk prevention and control plan, combines the virtual dynamic construction corresponding to the three-dimensional construction platform, imports the virtual reality device to dynamically play and display based on the timeline, and refreshes the three-dimensional construction platform in real time;

[0099] It should be noted that when formulating the risk prevention and control plan, the entire construction project is taken as the evaluation object, and the safety risk of the high slope construction of the entire line cutting is evaluated according to the scale of the project construction, geological conditions, project characteristics, inducing factors, construction environment, data integrity, etc., and the risk level is determined and control measures are proposed. Different methods and means are used to ensure that there will be no conflict when applying, and multiple methods can be effectively combined. In addition, virtual dynamic construction is combined with specific projects for empirical analysis, so as to verify that the application of set pair analysis in rail transit construction safety risk analysis can provide scientific decision-making support for construction safety and help construction units effectively identify, evaluate and control safety risks during construction.

[0100] Specifically, the logic for obtaining the risk prevention and control plan is as follows:

[0101] Based on the risk level analysis results, a priori knowledge base is entered, and databases of different cloud service platforms are associated with the priori knowledge base. Based on the network learning model, operation risk prevention and control features are extracted from a large number of historical construction operation processes with correct field classification to form an operation risk prevention and control feature list;

[0102] Conduct similar risk prevention and control analysis based on the operational risk prevention and control feature list to obtain matching risk prevention and control case analysis;

[0103] The timestamps of risk prevention and control cases are rewritten according to the preset rewriting rules to generate a risk prevention and control plan.

[0104] Example 2

[0105] like Figure 2 As shown, the part not described in detail in this embodiment is as described in Example 1. This embodiment provides a rail transit construction operation guidance method based on set pair analysis, including the following steps:

[0106] The following steps are involved:

[0107] Constructing a three-dimensional construction platform based on the rail transit construction environment, extracting construction operation targets corresponding to each target area based on the three-dimensional construction platform, and obtaining a construction operation process matching the construction operation targets based on the construction operation targets;

[0108] Based on the construction operation process, a matching risk source list is searched in the prior knowledge base, and each construction risk content in the risk source list is reviewed based on the prior knowledge, and risk assessment indicators corresponding to different construction risks are extracted, and a risk assessment indicator set is generated based on the risk assessment indicator integration;

[0109] Determine the evaluation index weight of each risk assessment index in the risk assessment index set based on the combined weighting method;

[0110] Call the pre-trained set pair analysis model to perform correlation analysis on the set pairs of risk assessment indicator sets and result sets, obtain the correlation between the risk assessment indicator and the risk assessment level, and match the risk assessment level corresponding to the risk assessment indicator based on the maximum correlation;

[0111] Determine the evaluation index weight corresponding to the current risk assessment index based on the risk assessment level, conduct a comprehensive evaluation of the current risk assessment index, and determine the risk level of the current rail transit construction operation;

[0112] According to the results of the risk level analysis, a specific risk prevention and control plan is formulated. Based on the risk prevention and control plan, the prior knowledge base used in the construction operation process and project progress is adjusted and optimized. Combined with the virtual dynamic construction corresponding to the 3D construction platform, the virtual reality equipment is imported into the timeline-based dynamic playback display to refresh the 3D construction platform in real time.

[0113] The acquisition logic of the construction operation process:

[0114] Collect environmental data corresponding to the rail transit construction environment based on remote sensing technology, the environmental data including construction terrain data, geological conditions, building distribution and traffic routes;

[0115] Conduct 3D modeling based on environmental data, build a virtual model of the rail transit construction environment, annotate the environmental data corresponding to the rail transit construction environment based on the virtual model, and obtain a 3D construction platform that simulates the real construction environment;

[0116] On the three-dimensional construction platform, the construction area is divided into different target areas, and for each target area, the corresponding construction operation target is extracted;

[0117] According to the extracted construction operation objectives, the matching construction operation process is searched in the prior knowledge base.

[0118] The logic for obtaining the risk assessment indicators is as follows:

[0119] The risk source list includes at least one historical construction operation data, and the historical construction operation data includes at least one construction node corresponding to a construction operation target, historical operation data corresponding to the construction node, and a historical operation index;

[0120] Slice the historical operation data to obtain a risk processing result, which includes several risk assessment indicators of Q risk types, corresponding risk classification levels and risk operation indexes, Q∈N*;

[0121] Used to perform timestamp rewriting processing on a number of risk assessment indicators of each risk type according to a preset rewriting rule; perform secondary sharding processing on a number of risk assessment indicators of each risk type after the timestamp rewriting to obtain a secondary risk processing result, wherein the secondary risk processing result includes G secondary risk assessment indicators, corresponding secondary risk classification levels and secondary risk operation indexes, G∈N*;

[0122] The secondary risk assessment indicator is mapped to at least one construction node corresponding to the corresponding construction work target.

[0123] The risk assessment indicator set is a set of risk assessment indicators that are subdivided according to the different needs of the construction environment and construction methods. The risk assessment indicators include market risk indicators, technical risk indicators, financial risk indicators, construction environment risk indicators, management risk indicators and construction safety risk indicators.

[0124] The historical operation data, risk processing results and secondary risk processing results in the construction nodes are backed up, and the backed up historical operation data, risk processing results and secondary risk processing results in the construction nodes are stored in a priori knowledge base.

[0125] The logic for obtaining the evaluation index weights is as follows:

[0126] Obtain a set of risk assessment indicators, and collect target data associated with the risk assessment indicators based on a historical database and expert prior knowledge;

[0127] According to the expert scoring data, a judgment matrix is ​​constructed and optimized using rough set theory. The subjective weight of each risk assessment indicator is calculated using the optimized judgment matrix.

[0128] The objective weight of each risk assessment indicator is calculated using the entropy method;

[0129] According to the combination rules, the subjective weight and the objective weight are coupled to obtain the final comprehensive weight; based on the comprehensive weight and the target data in the risk assessment indicator set, the risk assessment indicators of the project are initially evaluated to obtain the evaluation indicator weights of each risk assessment indicator.

[0130] The logic for obtaining the risk level analysis results is as follows:

[0131] The risk assessment level is divided into K risk levels based on the construction operation process, K∈N*;

[0132] The weight of the evaluation index corresponding to each risk assessment index is determined according to the impact of the risk assessment level on the construction operation process:

[0133] The evaluation index weights corresponding to each risk assessment index are accumulated to obtain the comprehensive evaluation results of the risk assessment index;

[0134] Based on the comprehensive assessment results, the risk level analysis results of current rail transit construction operations are divided into corresponding risk assessment levels.

[0135] The logic for obtaining the risk prevention and control plan is as follows:

[0136] Based on the risk level analysis results, a priori knowledge base is entered, and databases of different cloud service platforms are associated with the priori knowledge base. Based on the network learning model, operation risk prevention and control features are extracted from a large number of historical construction operation processes with correct field classification to form an operation risk prevention and control feature list;

[0137] Conduct similar risk prevention and control analysis based on the operational risk prevention and control feature list to obtain matching risk prevention and control case analysis;

[0138] The timestamps of risk prevention and control cases are rewritten according to the preset rewriting rules to generate a risk prevention and control plan.

[0139] Example 3

[0140] An electronic device according to an exemplary embodiment includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0141] The processor executes the above-mentioned rail transit construction operation guidance method based on set pair analysis by calling the computer program stored in the memory.

[0142] Figure 3It is a structural diagram of an electronic device provided in an embodiment of the present application. The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor to implement the stock algorithm trading method based on deep neural network provided in the above-mentioned various method embodiments. The electronic device may also include other components for realizing the functions of the device. For example, the electronic device may also have components such as wired or wireless network interfaces and input and output interfaces for input and output. The embodiments of the present application are not described in detail here.

[0143] Example 4

[0144] According to an exemplary embodiment, a computer-readable storage medium is shown, on which a rewritable computer program is stored;

[0145] When the computer program is executed on a computer device, the computer device executes the above-mentioned rail transit construction operation guidance method based on set pair analysis.

[0146] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0147] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0148] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0149] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0150] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0151] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0152] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0153] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0154] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A rail transit construction operation guidance method based on set pair analysis, characterized in that: The following steps are involved: Constructing a three-dimensional construction platform based on the rail transit construction environment, extracting construction operation targets corresponding to each target area based on the three-dimensional construction platform, and obtaining a construction operation process matching the construction operation targets based on the construction operation targets; Based on the construction operation process, a matching risk source list is searched in the prior knowledge base, and each construction risk content in the risk source list is reviewed based on the prior knowledge, and risk assessment indicators corresponding to different construction risks are extracted, and a risk assessment indicator set is generated based on the risk assessment indicator integration; The logic for obtaining the risk assessment indicators is as follows: The risk source list includes at least one historical construction operation data, and the historical construction operation data includes at least one construction node corresponding to a construction operation target, historical operation data corresponding to the construction node, and a historical operation index; Slice the historical operation data to obtain a risk processing result, which includes several risk assessment indicators of Q risk types, corresponding risk classification levels and risk operation indexes, Q∈N*; Used to rewrite timestamps of several risk assessment indicators of each risk type according to preset rewriting rules; Perform secondary sharding processing on several risk assessment indicators of each risk type after the timestamp is rewritten to obtain a secondary risk processing result, wherein the secondary risk processing result includes G secondary risk assessment indicators, corresponding secondary risk classification levels and secondary risk operation indexes, G∈N*; Mapping the secondary risk assessment indicator to at least one construction node corresponding to the corresponding construction operation target; Determine the evaluation index weight of each risk assessment index in the risk assessment index set based on the combined weighting method; Call the pre-trained set pair analysis model to perform correlation analysis on the set pairs of risk assessment indicator sets and result sets, obtain the correlation between risk assessment indicators and risk assessment levels, and match the risk assessment levels corresponding to the risk assessment indicators based on the maximum correlation; Determine the evaluation index weight corresponding to the current risk assessment index based on the risk assessment level, conduct a comprehensive evaluation of the current risk assessment index, and determine the risk level of the current rail transit construction operation; According to the results of the risk level analysis, a specific risk prevention and control plan is formulated. Based on the risk prevention and control plan, the prior knowledge base used in the construction operation process and project progress is adjusted and optimized. Combined with the virtual dynamic construction corresponding to the 3D construction platform, the virtual reality equipment is imported into the dynamic playback display based on the timeline to refresh the 3D construction platform in real time.

2. A rail transit construction work guidance method based on set pair analysis according to claim 1, characterized in that: The acquisition logic of the construction operation process: Collect environmental data corresponding to the rail transit construction environment based on remote sensing technology, the environmental data including construction terrain data, geological conditions, building distribution and traffic routes; Conduct 3D modeling based on environmental data, build a virtual model of the rail transit construction environment, annotate the environmental data corresponding to the rail transit construction environment based on the virtual model, and obtain a 3D construction platform that simulates the real construction environment; On the three-dimensional construction platform, the construction area is divided into different target areas, and for each target area, the corresponding construction operation target is extracted; According to the extracted construction operation objectives, the matching construction operation process is searched in the prior knowledge base.

3. A rail transit construction work guidance method based on set pair analysis according to claim 2, characterized in that: The risk assessment indicator set is a set of risk assessment indicators that are subdivided according to the different needs of the construction environment and construction methods. The risk assessment indicators include market risk indicators, technical risk indicators, financial risk indicators, construction environment risk indicators, management risk indicators and construction safety risk indicators.

4. The rail transit construction work guidance method based on set pair analysis according to claim 3 is characterized in that: The historical operation data, risk processing results and secondary risk processing results in the construction nodes are backed up, and the backed up historical operation data, risk processing results and secondary risk processing results in the construction nodes are stored in a priori knowledge base.

5. The rail transit construction work guidance method based on set pair analysis according to claim 4 is characterized in that: The logic for obtaining the evaluation index weights is as follows: Obtain a set of risk assessment indicators, and collect target data associated with the risk assessment indicators based on a historical database and expert prior knowledge; According to the expert scoring data, a judgment matrix is ​​constructed and optimized using rough set theory. The subjective weight of each risk assessment indicator is calculated using the optimized judgment matrix. The objective weight of each risk assessment indicator is calculated using the entropy method; According to the combination rules, the subjective weight and the objective weight are coupled to obtain the final comprehensive weight; According to the comprehensive weight and the target data in the risk assessment indicator set, the risk assessment indicators of the project are initially evaluated to obtain the evaluation indicator weights of each risk assessment indicator.

6. A rail transit construction work guidance method based on set pair analysis according to claim 5, characterized in that: The logic for obtaining the risk level analysis results is as follows: The risk assessment level is divided into K risk levels based on the construction operation process, K∈N*; The weight of the evaluation index corresponding to each risk assessment index is determined according to the impact of the risk assessment level on the construction operation process: The evaluation index weights corresponding to each risk assessment index are accumulated to obtain the comprehensive evaluation results of the risk assessment index; According to the comprehensive assessment results, the risk level analysis results of current rail transit construction operations are divided into corresponding risk assessment levels.

7. A rail transit construction work guidance method based on set pair analysis according to claim 6, characterized in that: The logic for obtaining the risk prevention and control plan is as follows: Based on the risk level analysis results, a priori knowledge base is entered, and databases of different cloud service platforms are associated with the priori knowledge base. Based on the network learning model, operation risk prevention and control features are extracted from a large number of historical construction operation processes with correct field classification to form an operation risk prevention and control feature list; Conduct similar risk prevention and control analysis based on the operational risk prevention and control feature list to obtain matching risk prevention and control case analysis; The timestamps of risk prevention and control cases are rewritten according to the preset rewriting rules to generate a risk prevention and control plan.

8. A rail transit construction operation guidance system based on set pair analysis, which is implemented based on a rail transit construction operation guidance method based on set pair analysis according to any one of claims 1 to 7, characterized in that: It comprises a construction platform building module (100), a construction review module (200), an initial risk assessment module (300), a set analysis module (400), a risk assessment module (500) and a construction guidance module (600), wherein each module is connected via wired and / or wireless connections; A construction platform construction module (100) constructs a three-dimensional construction platform based on the rail transit construction environment, extracts construction work targets corresponding to each target area based on the three-dimensional construction platform, obtains a construction work process matching the construction work target based on the construction work target; and sends the construction work process to a construction review module (200); The construction review module (200) searches for a matching risk source list in a priori knowledge base based on the construction operation process, reviews each construction risk content in the risk source list based on the priori knowledge, extracts risk assessment indicators corresponding to different construction risks, generates a risk assessment indicator set based on risk assessment indicator integration, and sends the risk assessment indicator set to the risk initial assessment module 300 and the set pair analysis module (400); The initial risk assessment module (300) determines the evaluation indicator weight of each risk assessment indicator in the risk assessment indicator set based on a combined weighting method, and sends the evaluation indicator weight of the risk assessment indicator to the risk assessment module (500); The set pair analysis module (400) calls a pre-trained set pair analysis model to perform correlation analysis on the set pairs of the risk assessment indicator set and the result set, obtains the correlation between the risk assessment indicator and the risk assessment level, matches the risk assessment level corresponding to the risk assessment indicator based on the maximum correlation, and sends the risk assessment level corresponding to the risk assessment indicator to the risk assessment module (500); The risk assessment module (500) determines the evaluation indicator weight corresponding to the current risk assessment indicator based on the risk assessment level, conducts a comprehensive evaluation on the current risk assessment indicator, determines the risk level of the current rail transit construction operation, and sends the risk level to the construction guidance module (600); The construction guidance module (600) formulates a specific risk prevention and control plan based on the risk level analysis results, adjusts and optimizes the prior knowledge base used in the construction operation process and project progress based on the risk prevention and control plan, combines the virtual dynamic construction corresponding to the three-dimensional construction platform, imports the timeline-based dynamic playback display into the virtual reality device, and refreshes the three-dimensional construction platform in real time.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the rail transit construction operation guidance method based on set pair analysis as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

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