Intelligent construction method and system for highway super-large section tunnel

Abstract

The invention discloses an intelligent construction method and system for a highway super-large-section tunnel. The method comprises the steps that a drilling parameter set of a tunnel face is collected in the drilling process of the highway super-large-section tunnel; determining multiple groups of paired drilling parameters according to the drilling parameter set, and determining drilling parameters of different sections; based on the drilling parameter set and the drilling parameters of the different sections, the overall surrounding rock level of the tunnel and the surrounding rock levels of the different sections are determined through a pre-trained surrounding rock grading model based on machine learning; and according to the overall surrounding rock level and the surrounding rock levels of the different sections, a corresponding tunnel supporting scheme is generated, so that intelligent construction of a tunnel is achieved. According to the embodiment of the invention, the method can achieve the quick evaluation of the surrounding rock level through the combination of artificial intelligence, generates a corresponding supporting scheme, and improves the safety and efficiency of construction.

Description

Technical Field

[0001] The present invention belongs to the technical field of tunnel construction, and in particular to an intelligent construction method and system for a highway super-large-section tunnel. Background Art

[0002] Tunnel engineering is essentially a geological project. During the construction of the tunnel, various geological environments will be encountered, and various geological disasters will inevitably be faced during the excavation process. The construction of the tunnel will be carried out underground, so there are many uncertain safety factors. During the tunnel construction process, landslides are one of the most common safety accidents in the tunnel construction process.

[0003] Once a tunnel project has a safety accident such as a landslide, it will not only delay the construction period and significantly increase the project cost, but also cause personal injury or even life threat to construction workers and technicians. Therefore, support measures are very critical in the tunnel construction process. The more effective the support measures are, the higher the safety will be, but the construction cost will be greatly increased, the construction progress will become slow, and it will even affect the size of the tunnel itself. Therefore, balancing the safety and cost of support measures is a problem that needs to be faced. Timely and effective support measures usually rely on accurate surrounding rock classification and expert experience. How to determine support measures through accurate surrounding rock classification during construction and reduce dependence on expert experience is a problem that needs to be solved at present. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for intelligent construction of super-large cross-section highway tunnels to address the deficiencies in the prior art. It can combine artificial intelligence to achieve rapid assessment of the surrounding rock grade, generate corresponding support plans, and improve construction safety and efficiency.

[0005] An embodiment of the present application provides a method for intelligently constructing a highway tunnel with a super-large cross-section, the method comprising: Collect drilling parameter sets of the tunnel face during the drilling process of a highway super-large section tunnel; Determine multiple sets of paired drilling parameters according to the drilling parameter set, and determine drilling parameters for different sections; Based on the drilling parameter set and the drilling parameters of the different sections, using a pre-trained surrounding rock classification model based on machine learning, determine the overall surrounding rock grade of the tunnel and the surrounding rock grades of the different sections; According to the overall surrounding rock grade and the surrounding rock grades of different sections, a corresponding tunnel support scheme is generated to realize intelligent construction of the tunnel.

[0006] Optionally, the acquisition of the drilling parameter set of the tunnel face includes: The tunnel face and its surrounding area are divided into multiple sub-areas according to the actual geological characteristics and drilling progress, wherein each sub-area is responsible for collecting parameter data of each drilling parameter in the sub-area, and the types and quantities of drilling parameters collected in each sub-area are the same. The division of the sub-areas takes into account the uniformity of the rock formation and the changes in the surrounding rock, so as to record the drilling characteristics of each sub-area; The parameter data of the drilling parameters collected in each sub-area are combined to form the drilling parameter set of the final tunnel face.

[0007] Optionally, determining multiple sets of paired drilling parameters according to the drilling parameter set, and determining drilling parameters of different sections, includes: In the sub-area, a paired parameter generation mechanism is established according to the drilling parameters of the adjacent sub-areas, and a sliding window algorithm is used to gradually slide the window and collect the drilling parameters of the adjacent sub-areas to generate a paired drilling parameter set; converting the drilling parameters of each paired sub-area into a feature vector, and mapping the feature vector into a nonlinear feature space; In the nonlinear feature space, a clustering algorithm is applied to classify all feature vectors to obtain a feature vector of each classification, and the drilling parameters corresponding to the feature vector of each class are determined as the drilling parameters of each section.

[0008] Optionally, in the sub-area, according to the drilling parameters of the adjacent sub-areas, a pairing parameter generation mechanism is established, and a sliding window algorithm is used to gradually slide the window and collect the drilling parameters of the adjacent sub-areas to generate a pairing drilling parameter set, including: Creating a sliding window, the size of which is set by the user, and the sliding window comprising a plurality of adjacent sub-areas, so that each sliding can obtain complete drilling parameter information of the sub-areas; In each time step, the window position of the sliding window is moved by one grid each time, and the drilling parameters of each sub-area contained in the current window are obtained; The drilling parameters of each sub-area are sorted into a vector form and standardized, and the standardized drilling parameters of each pair of adjacent sub-areas in the window are similarly judged; If it is judged that the similarity is less than the preset similarity threshold, the pairing relationship of the pair of adjacent sub-areas is recorded, and the drilling parameters of the pair of adjacent sub-areas are recorded in the pairing drilling parameter set. The pairing relationship of all adjacent sub-areas is continuously obtained through the sliding window, and finally a complete set of pairing drilling parameter sets is formed for analyzing the overall construction status and surrounding rock characteristics.

[0009] Optionally, the determining the overall surrounding rock grade of the tunnel and the surrounding rock grades of different sections based on the drilling parameter set and the drilling parameters of different sections using a pre-trained surrounding rock classification model based on machine learning includes: Inputting the drilling parameter set into a pre-trained first surrounding rock classification model based on machine learning to determine the overall surrounding rock grade of the tunnel, wherein the first surrounding rock classification model is trained based on a historical drilling parameter set and a corresponding historical overall surrounding rock grade; The drilling parameters of the different sections are respectively input into a pre-trained second surrounding rock classification model based on machine learning to determine the surrounding rock grades of the different sections of the tunnel, wherein the second surrounding rock classification model is trained based on the historical drilling parameters of the different sections and the historical surrounding rock grades corresponding to the different sections; Performing weighted processing on the surrounding rock grades of the different sections to obtain a weighted overall surrounding rock grade; The final overall surrounding rock grade of the tunnel is determined according to the overall surrounding rock grade determined by the first surrounding rock classification model and the weighted overall surrounding rock grade.

[0010] Another embodiment of the present application provides an intelligent construction system for a highway super-large cross-section tunnel, the system comprising: The acquisition module is used to collect the drilling parameter set of the tunnel face during the drilling process of the highway super-large section tunnel; A first determination module, used to determine multiple sets of paired drilling parameters according to the drilling parameter set, and determine drilling parameters of different sections; A second determination module is used to determine the overall surrounding rock grade of the tunnel and the surrounding rock grades of different sections based on the drilling parameter set and the drilling parameters of different sections using a pre-trained surrounding rock classification model based on machine learning; A generation module is used to generate a corresponding tunnel support plan according to the overall surrounding rock level and the surrounding rock levels of different sections to achieve intelligent construction of the tunnel.

[0011] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0012] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0013] Compared with the prior art, the present invention provides an intelligent construction method for a highway super-large section tunnel, which collects a drilling parameter set of the heading face during the drilling process of the highway super-large section tunnel; determines multiple sets of paired drilling parameters according to the drilling parameter set, and determines the drilling parameters of different sections; based on the drilling parameter set and the drilling parameters of the different sections, uses a pre-trained surrounding rock classification model based on machine learning to determine the overall surrounding rock grade of the tunnel and the surrounding rock grades of different sections; generates a corresponding tunnel support plan according to the overall surrounding rock grade and the surrounding rock grades of the different sections to realize intelligent construction of the tunnel, thereby being able to combine artificial intelligence to realize rapid evaluation of the surrounding rock grade, generate a corresponding support plan, and improve construction safety and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A hardware structure block diagram of a computer terminal for an intelligent construction method of a highway super-large cross-section tunnel provided by an embodiment of the present invention; Figure 2 A schematic diagram of a process for intelligently constructing a highway super-large cross-section tunnel provided by an embodiment of the present invention; Figure 3 A schematic structural diagram of an intelligent construction system for a highway super-large cross-section tunnel provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0016] The embodiment of the present invention firstly provides a method for intelligently constructing a highway tunnel with a super-large cross-section. The method can be applied to electronic devices, such as computer terminals, specifically ordinary computers, etc.

[0017] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal of a highway super-large cross-section tunnel intelligent construction method provided by an embodiment of the present invention. Figure 1 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0018] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any one of the intelligent construction methods for highway super-large-section tunnels.

[0019] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0020] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any one of the intelligent construction methods for super-large-section highway tunnels.

[0021] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0022] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0023] See also Figure 2 The embodiment of the present invention provides a method for intelligently constructing a highway tunnel with a super-large cross-section, which may include the following steps: S201, collecting a drilling parameter set of the tunnel face during the drilling process of a highway super-large cross-section tunnel; In the construction of highway super-large section tunnels, the face is the most advanced operation area in the construction process and is directly affected by the external geological environment. By collecting drilling parameter sets in this area, a series of key construction data can be obtained, such as drilling speed, torque, drilling pressure, penetration, etc. Real-time monitoring of these parameters provides a basis for subsequent data analysis and surrounding rock classification, and can reflect the characteristics and change trends of the current surrounding rock. Therefore, collecting the drilling parameter set of the face is one of the core links in realizing intelligent construction, ensuring dynamic control of the construction status.

[0024] Collecting the drilling parameter set of the tunnel face can not only track various changes in the construction process in real time, but also help engineers to find potential problems in time and take corresponding measures to ensure construction safety. This process provides an important basis for the subsequent surrounding rock level assessment and support plan generation, thereby optimizing the construction plan, reducing construction costs, and improving engineering efficiency. Therefore, in-depth understanding and utilization of drilling parameter sets are of great significance to promoting the intelligent development of tunnel construction.

[0025] Specifically, to collect the drilling parameter set of the tunnel face, the tunnel face and its surrounding area can be gridded and divided into multiple sub-areas according to the actual geological characteristics and drilling progress, wherein each sub-area is responsible for collecting parameter data of each drilling parameter in the sub-area, and the types and quantities of drilling parameters collected in each sub-area are the same. The division of the sub-areas takes into account the uniformity of the rock formation and the changes in the surrounding rock, so as to record the drilling characteristics of each sub-area; In the construction of highway super-large section tunnels, grid division of the face and its surrounding areas is the basic step for parameter collection. By dividing the excavation area into multiple sub-areas according to the actual geological characteristics and drilling progress, each sub-area can systematically collect various drilling parameters. This division is not arbitrary, but must comprehensively consider the uniformity of the rock formation, geological characteristics and surrounding rock changes to ensure that representative drilling characteristics can be recorded in different sub-areas.

[0026] The introduction of grid division can effectively improve the understanding of the geological conditions of the tunnel face and ensure the comprehensiveness and consistency of data in different areas. This method not only helps to analyze the surrounding rock characteristics of each sub-area in the future, but also provides important data support for the formulation of more scientific support plans. Through standardized regional division, the construction team can conduct real-time monitoring of specific parameters for each sub-area, thereby achieving more efficient construction management.

[0027] When implementing grid division, it is first necessary to conduct on-site geological exploration to collect geological data of the tunnel face and its surroundings, including important information such as rock type, strength, fracture distribution, and groundwater level. Based on this data, engineers can use GIS (geographic information system) software to visualize the tunnel face and develop a reasonable division plan. During the division process, the tunnel face and its surrounding areas are usually divided into several small blocks (sub-areas) of equal or similar size, such as a circular sub-area with a diameter of 2 meters, to ensure that each sub-area is sufficiently representative in terms of geological characteristics.

[0028] After the division is completed, each sub-area will be equipped with the same drilling parameter acquisition equipment, such as pressure sensors, displacement sensors and temperature sensors, to ensure that the drilling parameter data of each sub-area can be collected in real time and accurately during the drilling process. It is worth noting that the division of sub-areas should be tracked and adjusted in a timely manner to adapt to changes in construction progress and surrounding rock conditions. For example, when the surrounding rock conditions of a sub-area change significantly, the number of sub-areas can be increased, or the division of sub-areas can be adjusted to facilitate more accurate data processing.

[0029] The parameter data of the drilling parameters collected in each sub-area are combined to form the drilling parameter set of the final tunnel face.

[0030] After dividing multiple sub-areas and obtaining drilling parameters independently, the next step is to integrate these parameters to form a complete set of tunnel face drilling parameters. This process is crucial and involves aggregating and organizing the data from each sub-area and forming a unified format for subsequent analysis and processing.

[0031] By integrating the parameters of each sub-area, an overall view of the construction status can be formed, providing a systematic data basis for the evaluation of surrounding rock characteristics. This approach ensures that the parameters of each sub-area can be compared and analyzed with each other, reducing inconsistencies and improving data reliability. Ultimately, this integration work provides a solid foundation for the training of machine learning models and the evaluation of surrounding rock levels.

[0032] When integrating drilling parameters, it is first necessary to conduct a preliminary quality check on the data collected in each sub-area to ensure the accuracy and completeness of the data. This process usually includes removing noise and outliers, and supplementing missing data. After data cleaning is completed, a central database is established using a spreadsheet or database tool to collect and store parameter data for all sub-areas.

[0033] Next, the drilling parameters of all sub-areas are entered into the database in a unified format. For example, they can be classified by parameter type (such as drilling speed, pressure, penetration, etc.), each parameter has its standard unit, and the corresponding timestamp and sub-area number are recorded. Through data management software, it is possible to ensure that the data of various parameters are continuous in time and effectively track the drilling progress of each sub-area.

[0034] After the data integration is completed, engineers will use statistical analysis and visualization tools to analyze the aggregated data to extract key features and trends. These analysis results can not only help the construction team understand the changes in geological conditions in a timely manner, but also provide data support for subsequent surrounding rock assessment and support plans. For example, by normalizing the parameters of different sub-areas, it is possible to identify which sub-areas have more dangerous surrounding rock conditions, so that corresponding reinforcement measures can be taken in advance.

[0035] S202, determining multiple sets of paired drilling parameters according to the drilling parameter set, and determining drilling parameters for different sections; During tunnel construction, changes in drilling parameters can reflect important information such as changes in surrounding geological conditions, the working status of construction equipment, and construction progress. Therefore, by effectively pairing the drilling parameters of the face and different sections, it is helpful to identify and analyze the construction performance at different construction stages and under different geological conditions. By establishing multiple sets of paired drilling parameters, construction personnel can better understand and grasp the dynamic changes in the construction process, so as to make timely adjustments and optimizations. This pairing is not limited to parameters within the same time period, but also takes into account parameter changes in adjacent time periods, ensuring that the formed parameter set can cover more comprehensive construction status information.

[0036] Through the paired analysis of drilling parameters in different sections and sub-areas, an in-depth analysis of the geological surrounding rock state can be achieved. This process enables the construction team to more accurately evaluate the surrounding rock characteristics when facing complex geological conditions, thereby formulating a more reasonable and scientific construction plan. At the same time, this can also improve the safety and efficiency of the overall tunnel construction, reduce construction risks, and ensure the smooth progress of the project. In addition, good data pairing not only provides support for current construction, but also lays the foundation for future engineering optimization and technical improvements.

[0037] Specifically, a paired parameter generation mechanism may be established within the sub-area according to the drilling parameters of the adjacent sub-areas, and a sliding window algorithm may be used to gradually slide the window and collect the drilling parameters of the adjacent sub-areas to generate a paired drilling parameter set; The core of this step is to use the sliding window algorithm to establish connections between sub-areas and collect drilling parameters of adjacent sub-areas. This method can effectively capture the continuity and variability of the drilling process, thereby generating multiple interrelated paired parameters. By adopting a sliding window, it can be ensured that the data collected each time is complete information within a time period, which maximizes the representativeness and accuracy of the collected data.

[0038] Paired analysis of drilling parameters in adjacent sub-areas not only makes the relationship between the sub-areas closer, but also provides a basis for comprehensive evaluation of the construction status of the entire tunnel. By analyzing these paired parameters, the construction team can clearly identify the changes in surrounding rock characteristics in different areas, and then formulate corresponding construction strategies and support measures. This mechanism can effectively reduce blindness in the construction process, improve response speed, and ensure efficient and safe tunnel construction.

[0039] Specifically, a sliding window may be created, the size of which is set by the user, and the sliding window includes a plurality of adjacent sub-areas, so that each sliding operation can obtain complete drilling parameter information of the sub-areas; In this step, the user sets the size of the sliding window according to actual needs. The window contains multiple adjacent sub-areas to efficiently collect relevant drilling parameters during the construction process. The design of the sliding window allows complete information of adjacent sub-areas to be obtained at each slide, ensuring the continuity and integrity of the data, which is helpful for subsequent analysis and decision-making.

[0040] By flexibly setting the size of the sliding window, the system can adapt to different construction environments and requirements, improving the pertinence and flexibility of data collection. At the same time, the design of the window containing multiple sub-areas can ensure that the acquired data is more comprehensive, thereby improving the accuracy and effectiveness of subsequent analysis. This provides important data support for scheduling and decision-making during the construction process.

[0041] First, the user can set the size of the sliding window according to the actual drilling operation requirements, for example, set the window to cover 5 adjacent sub-areas. Assume that at a tunnel construction site, the sub-areas are A, B, C, D, and E. When creating a window, the system will initialize the coverage of A, B, C, D, and E. At this window position, when the construction workers perform drilling operations, the system will collect the drilling parameters of these 5 sub-areas in real time. If the window size is set to 5, when the window starts to slide, the window position will be moved to B, C, D, E, and F, so that each slide can obtain the latest drilling parameters of each sub-area. In this way, the sliding window can continuously update the sub-area information contained therein, thereby ensuring that the collected data is complete and suitable for subsequent analysis.

[0042] In each time step, the window position of the sliding window is moved by one grid each time, and the drilling parameters of each sub-area contained in the current window are obtained; In this step, the system will automatically slide the window position according to the set time step, sliding one grid at a time, so as to obtain the drilling parameters of each sub-area in the current window. This method ensures that the data can be updated in real time at each slide, accurately reflecting the construction status and parameter changes in different time periods.

[0043] By sliding the window according to the time step, dynamic monitoring of drilling parameters can be achieved. This process can capture changes in construction in a timely manner, helping managers to quickly identify problems and make adjustments. At the same time, this dynamic data acquisition method provides more abundant time series data for subsequent analysis, supporting real-time monitoring and evaluation.

[0044] In actual operation, assuming that the set time step is 1 hour, the system will move the sliding window one grid to the right every hour. For example, if the current window covers A, B, C, D and E, after 1 hour, the window will move to B, C, D, E and F. At this time, the system begins to collect the drilling parameters of each sub-area in the current window, including drilling rate, pressure, torque, etc. During the collection process, the system will automatically record the various parameters of each sub-area at that time point, providing a basis for subsequent data processing and analysis. This method ensures that all parameters are compared based on the same time step, which is helpful for subsequent similarity judgment and other analysis.

[0045] The drilling parameters of each sub-area are sorted into a vector form and standardized, and the standardized drilling parameters of each pair of adjacent sub-areas in the window are similarly judged; In this step, the system organizes the collected drilling parameters of each sub-area into vector form and performs standardization. The purpose of this step is to convert the data of different sub-areas into comparable standards, so as to make subsequent similarity judgments and make data analysis more scientific and accurate.

[0046] By arranging the drilling parameters into vectors and standardizing them, unfair comparisons caused by different magnitudes of the parameters can be eliminated. This step lays the foundation for similarity judgments between adjacent sub-areas, ensuring that more realistic and effective analysis results can be obtained in subsequent comparisons, which helps to better understand the problems and their causes in construction.

[0047] In the specific implementation, the system first organizes the drilling parameters (such as drilling rate, pressure, etc.) of each sub-area into vector form. For example, assuming that the drilling rates of sub-areas A, B, and C in the current window are 6 m / h, 4 m / h, and 5 m / h, respectively, the organized vectors will be A = [6, x, y], B = [4, x, y], and C = [5, x, y] (where x and y are other parameters). Next, the system will standardize these vectors, for example, using the Z-score method, so that the mean of each parameter is 0 and the standard deviation is 1. After standardization, the resulting vectors will be A', B', and C'. Next, the system will evaluate the parameter similarity of each pair of adjacent sub-areas by calculating similarity indicators between adjacent sub-areas, such as cosine similarity or Euclidean distance. In this way, it is possible to effectively identify which sub-areas have similar parameters, which provides a basis for subsequent pairing relationship records.

[0048] If it is judged that the similarity is less than the preset similarity threshold, the pairing relationship of the pair of adjacent sub-areas is recorded, and the drilling parameters of the pair of adjacent sub-areas are recorded in the pairing drilling parameter set. The pairing relationship of all adjacent sub-areas is continuously obtained through the sliding window, and finally a complete set of pairing drilling parameter sets is formed for analyzing the overall construction status and surrounding rock characteristics.

[0049] In this step, based on the previous similarity judgment results, if the system finds that the similarity of a pair of adjacent sub-areas is less than the preset similarity threshold, these sub-areas and their drilling parameters will be recorded in the paired drilling parameter set. Such a pairing relationship helps to further analyze different sub-areas and identify potential problems and their impact on construction.

[0050] By recording the pairing relationships with similarities less than the preset threshold, significant differences between different sub-areas can be found. This process helps the construction management team to deeply analyze the surrounding rock characteristics and the overall construction status, so as to adjust the construction plan in time, optimize resource allocation, and ensure the smooth progress and safety of the project.

[0051] During specific implementation, the system will set a similarity threshold, such as 0.7. After the similarity judgment is completed, assuming that the similarity between A and B is 0.6, the pair of sub-areas meets the recording conditions. The system will automatically record the identification of adjacent sub-areas A and B, their respective standardized parameter vectors and similarity scores in the paired drilling parameter set. This set can be in the form of a database or a data table, recording the pairing information of each pair of adjacent sub-areas, including their drilling rate, pressure value, etc. As the sliding window continues to move, the system will continuously update and expand this set. When all the pairing relationship records are completed, engineers can use this set for analysis, such as comparing the construction efficiency and surrounding rock characteristics of different sub-areas, so as to formulate targeted treatment plans, and ultimately improve construction safety and efficiency.

[0052] converting the drilling parameters of each paired sub-area into a feature vector, and mapping the feature vector into a nonlinear feature space; The main task of this step is to convert the drilling parameters into feature vectors for further analysis and processing. The process of mapping to nonlinear feature space can help reveal more complex and potential relationships between parameters in high-dimensional space.

[0053] Converting drilling parameters into feature vectors and mapping them to nonlinear feature space not only provides a basis for subsequent cluster analysis, but also enhances the model's ability to learn complex nonlinear relationships. This process can improve the accuracy of subsequent machine learning algorithms, provide richer and more accurate feature data for surrounding rock classification models, and thus promote the evaluation of surrounding rock levels and the optimization of support schemes.

[0054] After the data is sorted, a feature vector is first created for each paired sub-area. For example, parameters such as drilling speed, drilling pressure, and displacement can be used as features to form a multi-dimensional feature vector in each adjacent sub-area. Then, these feature vectors are mapped to a nonlinear feature space through a specific mapping function (such as a polynomial kernel function or a radial basis function). During the mapping process, the system constructs a new feature combination based on the relationship between the original features in order to more accurately capture the nonlinear relationship between the parameters.

[0055] In the nonlinear feature space, a clustering algorithm is applied to classify all feature vectors to obtain a feature vector of each classification, and the drilling parameters corresponding to the feature vector of each class are determined as the drilling parameters of each section.

[0056] The feature vectors are classified by clustering algorithm, aiming to identify and group drilling parameters with similar characteristics. The successful implementation of this step can make the drilling parameters of different sections clearly divided, providing an accurate basis for the subsequent surrounding rock level assessment.

[0057] Clustering similar feature vectors into one category helps identify the commonalities and differences between different sections during the drilling process. This analysis result can guide the construction team to formulate more targeted construction plans and support measures for different areas. Ultimately, the feature vectors obtained for each classification will be directly used to evaluate and optimize the surrounding rock characteristics of the tunnel, which is of great significance to improving overall construction safety and efficiency.

[0058] In the cluster analysis stage, you can choose common clustering algorithms, such as K-means clustering, hierarchical clustering, or DBSCAN. According to your needs, set the clustering parameters, such as the number of clusters or the distance measurement method. Then, input the previously converted feature vector into the selected clustering algorithm.

[0059] The algorithm will group the feature vectors according to their similarities. Each clustering result will form an independent category, and each category will contain its corresponding drilling parameters. By focusing on the characteristics of each cluster, the section drilling parameters represented by the feature vector of this class can be determined. This process requires verification and adjustment of the clustering results to ensure the validity and accuracy of the classification results, and finally form a clear set of section drilling parameters to support subsequent surrounding rock assessment and support design.

[0060] S203, based on the drilling parameter set and the drilling parameters of the different sections, using a pre-trained surrounding rock classification model based on machine learning, determining the overall surrounding rock grade of the tunnel and the surrounding rock grades of the different sections; In this method, the drilling parameter set and drilling parameters of different sections collected during the drilling process of highway super-large section tunnels are used to determine the overall surrounding rock grade of the tunnel using a pre-trained machine learning-based surrounding rock classification model. This process involves inputting two types of parameters into the trained model, which can identify and predict the properties and behavior of the surrounding rock after learning a large amount of historical data. In the model operation, different input features, such as drilling rate, pressure and torque, are used for feature extraction and predictive analysis, and finally the surrounding rock grade of the tunnel as a whole and in different sections is output, providing a basis for the formulation of subsequent support plans.

[0061] The key to this step is to automate the complex surrounding rock classification process through the application of machine learning models, thereby improving the scientificity and efficiency of decision-making during the construction process. By analyzing multiple sets of drilling parameters, the nature and level of the surrounding rock can be judged more accurately, thus providing an important reference basis for the construction process and ensuring the safety and effectiveness of the construction. In addition, through this data-driven approach, the surrounding rock classification model can be continuously optimized to provide more reliable data support and guidance for similar construction in the future.

[0062] Specifically, the drilling parameter set may be input into a pre-trained first surrounding rock classification model based on machine learning to determine the overall surrounding rock grade of the tunnel, wherein the first surrounding rock classification model is trained based on a historical drilling parameter set and a corresponding historical overall surrounding rock grade; In this step, the collected drilling parameter set will be used as input data to the pre-trained first surrounding rock classification model. The model is built based on historical drilling parameters and corresponding surrounding rock grades. Through comprehensive learning of historical data, the model can identify the complex relationship between drilling parameters and surrounding rock grades, and can use new data to make inferences and derive the overall surrounding rock classification of the tunnel.

[0063] By inputting data into a professional machine learning model, the accuracy and efficiency of surrounding rock level assessment can be greatly improved. Compared with the traditional experience-based assessment method, the machine learning model can quickly process large amounts of data and extract key features that affect the properties of the surrounding rock, providing a more scientific and reasonable decision-making basis for construction management. In addition, this method helps to achieve real-time monitoring and dynamic adjustment during tunnel construction, improving engineering safety.

[0064] In this process, the first thing to do is to organize and preprocess the collected drilling parameter sets, including drilling rate, torque, pressure and other data. Then, format these data into the input format required by the model and send them to the first surrounding rock classification model. Assume that in the past construction, historical data of multiple tunnel projects were collected, including the surrounding rock grades corresponding to each project. After learning and training these historical data, the model can make judgments based on the input drilling parameter characteristics. For example, if the drilling rate represented by the input data is high and the pressure fluctuation is small, the model will infer that the surrounding rock grade in this area may be high based on previous learning. The processed output data will give an overall classification of the surrounding rock grade, such as grade 1 (excellent), grade 2 (good), grade 3 (medium), grade 4 (poor), etc., for engineers to refer to for subsequent construction plans and support measures.

[0065] The drilling parameters of the different sections are respectively input into a pre-trained second surrounding rock classification model based on machine learning to determine the surrounding rock grades of the different sections of the tunnel, wherein the second surrounding rock classification model is trained based on the historical drilling parameters of the different sections and the historical surrounding rock grades corresponding to the different sections; In this step, the drilling parameters of different sections are input into the trained second surrounding rock classification model to evaluate the surrounding rock level of each section. The second surrounding rock classification model is specially trained based on the historical data of different sections, and can specifically analyze the surrounding rock characteristics and their changes of each section under specific conditions. Through this process, the surrounding rock level obtained provides specific technical support for processing different sections.

[0066] By processing the drilling parameters of different sections independently, we can more accurately understand the surrounding rock properties of each section and its impact on construction. This evaluation helps to formulate more reasonable support plans and construction strategies, thereby improving the safety and effectiveness of the entire construction process. In addition, the use of machine learning models for evaluation is more efficient and accurate than traditional methods, and can provide timely feedback on changes in the surrounding rock status of each section, facilitating dynamic adjustments by the construction team.

[0067] In the implementation of the steps, the construction team first needs to classify and organize the drilling parameters of different sections, including the drilling rate, pressure, torque and other parameters of each section collected in sections. Then, these data will be formatted and input into the trained second surrounding rock classification model. Assuming that a certain section shows high drilling pressure and low drilling rate during construction, according to the historical learning of the model, it may be judged to have a low surrounding rock grade and need to be strengthened. After the evaluation of each section is completed, the model will give the surrounding rock grade of the section based on the input parameters, so that engineers can formulate specific optimized construction plans for different sections to ensure the smooth progress of construction.

[0068] Performing weighted processing on the surrounding rock grades of the different sections to obtain a weighted overall surrounding rock grade; In this step, the surrounding rock grades of each section obtained by the second surrounding rock classification model are weighted to generate a weighted overall surrounding rock grade. This process takes into account the different importance of the surrounding rock of each section. Through weighted evaluation, the surrounding rock conditions of the entire tunnel can be determined more accurately, providing more effective data support for the formulation of the construction plan.

[0069] The overall surrounding rock level after weighting is more representative and can more realistically reflect the surrounding rock characteristics of the entire tunnel. This method can help the construction team optimize the construction process, rationally allocate resources, and ensure construction safety and efficiency under complex geological conditions. At the same time, the use of weighted evaluation can make the model more flexible and adaptable in practical applications.

[0070] When implementing weighted processing, the construction team needs to first determine the weight of each section, which is usually based on factors such as the geological characteristics of the section, historical construction conditions, or the importance assessment of the entire tunnel. Assume that a section is given a higher weight due to geological complexity, while other relatively simple sections are given a lower weight. Next, the surrounding rock grade of each section is multiplied by its weight to obtain a weighted value. Then, the weighted values ​​of all sections are added together to finally obtain a weighted overall surrounding rock grade. Assume that if the grade of section 1 is grade 1 and the weight is 0.4; the grade of section 2 is grade 2 and the weight is 0.6, then the overall grade is calculated as overall grade = 1 × 0.4 + 2 × 0.6 = 1.6, and the grade can be understood as good to above (i.e., leaning towards excellent). The results obtained will provide a scientific basis for subsequent tunnel support schemes.

[0071] The final overall surrounding rock grade of the tunnel is determined according to the overall surrounding rock grade determined by the first surrounding rock classification model and the weighted overall surrounding rock grade.

[0072] In the last step, the overall rock mass grade obtained by the first rock mass classification model is combined with the overall rock mass grade after weighting to comprehensively evaluate and determine the final overall rock mass grade of the tunnel. This process aims to integrate the results obtained by different models to form a more accurate and comprehensive assessment, providing a direct basis for the formulation of construction strategies.

[0073] By combining the results of different models, the possible bias of a single model can be eliminated in the evaluation, improving the accuracy and reliability of the overall evaluation. This integration step ensures that in the complex tunnel construction environment, the construction team can develop a highly targeted and highly operational construction plan, further improving project safety and construction efficiency.

[0074] In this implementation process, it is first necessary to compare and analyze the overall surrounding rock grade obtained by the first surrounding rock classification model with the weighted overall surrounding rock grade. The construction team will use certain rules or algorithms, such as a simple weighted average or maximum method, to determine the final overall surrounding rock grade. Assuming that the overall surrounding rock grade obtained by the first model is "good", and the overall surrounding rock grade obtained by the weighted model is "medium", a compromise assessment may be finally obtained, and the decision is "good-medium". This comprehensive evaluation result will provide a direct reference basis for subsequent construction decisions, ensuring that the construction process is more scientific and reasonable.

[0075] S204, generating a corresponding tunnel support scheme according to the overall surrounding rock grade and the surrounding rock grades of different sections to achieve intelligent construction of the tunnel.

[0076] According to the overall surrounding rock grade and the surrounding rock grade of different sections, this method aims to generate corresponding tunnel support schemes to achieve intelligent construction of tunnels. Specifically, by analyzing the determined surrounding rock grade, the construction team can identify the surrounding rock characteristics of different sections and the overall tunnel, thus providing a basis for subsequent support design. The support scheme will target the surrounding rock conditions of each section to ensure that the designed support structure can meet safety requirements and improve construction efficiency, ultimately forming a systematic and intelligent tunnel construction process.

[0077] The generated tunnel support scheme has important practical significance. First, by using machine learning models to evaluate the surrounding rock level, the construction party can make more scientific support decisions based on data, thereby improving construction safety and reducing potential risks and accident rates. Secondly, the tailor-made support scheme can optimize resource allocation, reduce unnecessary material waste, and improve construction progress, ultimately reducing the overall cost of the project. At the same time, this construction method based on intelligent technology also provides a reference standard for future tunnel construction and promotes the advancement of industry technology.

[0078] In the specific implementation process, the construction team will first conduct a systematic analysis based on the overall surrounding rock level and the surrounding rock level of different sections obtained in the previous steps. Assuming that the overall surrounding rock is assessed as "good" and a specific section is assessed as "poor", the construction team needs to formulate a corresponding support plan for the "poor" section. The specific operation steps can be as follows:

[0079] 1. Data integration and evaluation: Collect the overall and individual section rock mass levels and integrate them into a comprehensive evaluation report. This includes the specific parameters of each section, the historical rock mass status and the current evaluation results. In this report, the sections that need special attention and their surrounding rock characteristics are clearly pointed out, such as possible cracks, loose soil or hydrological conditions in the section.

[0080] 2. Preliminary design of support scheme: Determine the support type based on the assessment report. If a section is assessed as "poor", the team may choose steel support and shotcrete, while for "good" sections, simple prestressed anchor support can be used. The team will calculate the size, quantity and material of the support structure in detail to ensure the effectiveness and stability of the support scheme.

[0081] 3. Dynamic adjustment and feedback mechanism: After the initial design of the support scheme is completed, the construction team needs to establish a dynamic feedback mechanism. During the tunnel construction process, the drilling parameters and surrounding rock behavior are monitored in real time. If the surrounding rock conditions of a certain section are found to have changed, such as increased pressure or a slowdown in drilling speed, it is necessary to immediately feedback to the support design, and the support scheme may need to be adjusted. For example, if the monitoring results show that the surrounding rock strength of a certain section has decreased, the construction personnel may need to strengthen the support, increase support points, or use higher-strength materials.

[0082] 4. Comprehensive technology and material selection: When formulating support solutions, consider using new materials and intelligent sensor technologies to improve the safety and durability of the overall structure. For example, IoT sensors can be embedded to monitor the state of the surrounding rock in real time and feed the data back to the central control system so that the construction team can make necessary adjustments during the process. At the same time, advanced simulation technology can be applied to simulate and test the support solution to evaluate its feasibility and effectiveness.

[0083] Through the above steps, a comprehensive tunnel support solution is formed, which not only ensures the safety and efficiency of construction, but also improves the overall construction management level through intelligent means, and better copes with complex geological and environmental conditions. This strategy also provides experience and foundation for future intelligent tunnel construction.

[0084] It can be seen that during the drilling process of the super-large section highway tunnel, the drilling parameter set of the heading face is collected; multiple sets of paired drilling parameters are determined according to the drilling parameter set, and the drilling parameters of different sections are determined; based on the drilling parameter set and the drilling parameters of the different sections, the overall surrounding rock grade of the tunnel and the surrounding rock grades of different sections are determined using a pre-trained surrounding rock classification model based on machine learning; according to the overall surrounding rock grade and the surrounding rock grade of the different sections, a corresponding tunnel support scheme is generated to realize the intelligent construction of the tunnel, so that artificial intelligence can be combined to realize a rapid assessment of the surrounding rock grade, generate a corresponding support scheme, and improve the safety and efficiency of construction.

[0085] Another embodiment of the present invention provides a highway super-large cross-section tunnel intelligent construction system, see Figure 3 , the system may include: The acquisition module 301 is used to acquire a drilling parameter set of the tunnel face during the drilling process of the highway super-large cross-section tunnel; A first determination module 302, configured to determine multiple sets of paired drilling parameters according to the drilling parameter set, and determine drilling parameters for different sections; A second determination module 303 is used to determine the overall surrounding rock grade of the tunnel and the surrounding rock grades of different sections based on the drilling parameter set and the drilling parameters of different sections using a pre-trained surrounding rock classification model based on machine learning; The generation module 304 is used to generate a corresponding tunnel support scheme according to the overall surrounding rock level and the surrounding rock levels of different sections to achieve intelligent construction of the tunnel.

[0086] It can be seen that during the drilling process of the super-large section highway tunnel, the drilling parameter set of the heading face is collected; multiple sets of paired drilling parameters are determined according to the drilling parameter set, and the drilling parameters of different sections are determined; based on the drilling parameter set and the drilling parameters of the different sections, the overall surrounding rock grade of the tunnel and the surrounding rock grades of different sections are determined using a pre-trained surrounding rock classification model based on machine learning; according to the overall surrounding rock grade and the surrounding rock grade of the different sections, a corresponding tunnel support scheme is generated to realize the intelligent construction of the tunnel, so that artificial intelligence can be combined to realize a rapid assessment of the surrounding rock grade, generate a corresponding support scheme, and improve the safety and efficiency of construction.

[0087] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0088] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S201, collecting a drilling parameter set of the tunnel face during the drilling process of a highway super-large cross-section tunnel; S202, determining multiple sets of paired drilling parameters according to the drilling parameter set, and determining drilling parameters for different sections; S203, based on the drilling parameter set and the drilling parameters of the different sections, using a pre-trained surrounding rock classification model based on machine learning, determining the overall surrounding rock grade of the tunnel and the surrounding rock grades of the different sections; S204, generating a corresponding tunnel support scheme according to the overall surrounding rock grade and the surrounding rock grades of different sections to achieve intelligent construction of the tunnel.

[0089] It can be seen that during the drilling process of the super-large section highway tunnel, the drilling parameter set of the heading face is collected; multiple sets of paired drilling parameters are determined according to the drilling parameter set, and the drilling parameters of different sections are determined; based on the drilling parameter set and the drilling parameters of the different sections, the overall surrounding rock grade of the tunnel and the surrounding rock grades of different sections are determined using a pre-trained surrounding rock classification model based on machine learning; according to the overall surrounding rock grade and the surrounding rock grade of the different sections, a corresponding tunnel support scheme is generated to realize the intelligent construction of the tunnel, so that artificial intelligence can be combined to realize a rapid assessment of the surrounding rock grade, generate a corresponding support scheme, and improve the safety and efficiency of construction.

[0090] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0091] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0092] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S201, collecting a drilling parameter set of the tunnel face during the drilling process of a highway super-large cross-section tunnel; S202, determining multiple sets of paired drilling parameters according to the drilling parameter set, and determining drilling parameters for different sections; S203, based on the drilling parameter set and the drilling parameters of the different sections, using a pre-trained surrounding rock classification model based on machine learning, determining the overall surrounding rock grade of the tunnel and the surrounding rock grades of the different sections; S204, generating a corresponding tunnel support scheme according to the overall surrounding rock grade and the surrounding rock grades of different sections to achieve intelligent construction of the tunnel.

[0093] It can be seen that during the drilling process of the super-large section highway tunnel, the drilling parameter set of the heading face is collected; multiple sets of paired drilling parameters are determined according to the drilling parameter set, and the drilling parameters of different sections are determined; based on the drilling parameter set and the drilling parameters of the different sections, the overall surrounding rock grade of the tunnel and the surrounding rock grades of different sections are determined using a pre-trained surrounding rock classification model based on machine learning; according to the overall surrounding rock grade and the surrounding rock grade of the different sections, a corresponding tunnel support scheme is generated to realize the intelligent construction of the tunnel, so that artificial intelligence can be combined to realize a rapid assessment of the surrounding rock grade, generate a corresponding support scheme, and improve the safety and efficiency of construction.

[0094] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A method for intelligently constructing a highway tunnel with a super-large cross-section, characterized in that: The method comprises: Collect drilling parameter sets of the tunnel face during the drilling process of a highway super-large section tunnel; Determine multiple sets of paired drilling parameters according to the drilling parameter set, and determine drilling parameters for different sections; Based on the drilling parameter set and the drilling parameters of the different sections, using a pre-trained surrounding rock classification model based on machine learning, determine the overall surrounding rock grade of the tunnel and the surrounding rock grades of the different sections; According to the overall surrounding rock grade and the surrounding rock grades of different sections, a corresponding tunnel support scheme is generated to realize intelligent construction of the tunnel.

2. The method according to claim 1, characterized in that The drilling parameter set of the acquisition face includes: The tunnel face and its surrounding area are divided into multiple sub-areas according to the actual geological characteristics and drilling progress, wherein each sub-area is responsible for collecting parameter data of each drilling parameter in the sub-area, and the types and quantities of drilling parameters collected in each sub-area are the same. The division of the sub-areas takes into account the uniformity of the rock formation and the changes in the surrounding rock, so as to record the drilling characteristics of each sub-area; The parameter data of the drilling parameters collected in each sub-area are combined to form the drilling parameter set of the final tunnel face.

3. The method according to claim 2, characterized in that Determining multiple sets of paired drilling parameters according to the drilling parameter set, and determining drilling parameters of different sections, includes: In the sub-area, a paired parameter generation mechanism is established according to the drilling parameters of the adjacent sub-areas, and a sliding window algorithm is used to gradually slide the window and collect the drilling parameters of the adjacent sub-areas to generate a paired drilling parameter set; converting the drilling parameters of each paired sub-area into a feature vector, and mapping the feature vector into a nonlinear feature space; In the nonlinear feature space, a clustering algorithm is applied to classify all feature vectors to obtain a feature vector of each classification, and the drilling parameters corresponding to the feature vector of each class are determined as the drilling parameters of each section.

4. The method according to claim 3, characterized in that In the sub-area, according to the drilling parameters of the adjacent sub-areas, a pairing parameter generation mechanism is established, and a sliding window algorithm is used to gradually slide the window and collect the drilling parameters of the adjacent sub-areas to generate a pairing drilling parameter set, including: Creating a sliding window, the size of which is set by the user, and the sliding window comprising a plurality of adjacent sub-areas, so that each sliding can obtain complete drilling parameter information of the sub-areas; In each time step, the window position of the sliding window is moved by one grid each time, and the drilling parameters of each sub-area contained in the current window are obtained; The drilling parameters of each sub-area are sorted into a vector form and standardized, and the standardized drilling parameters of each pair of adjacent sub-areas in the window are similarly judged; If it is judged that the similarity is less than the preset similarity threshold, the pairing relationship of the pair of adjacent sub-areas is recorded, and the drilling parameters of the pair of adjacent sub-areas are recorded in the pairing drilling parameter set. The pairing relationship of all adjacent sub-areas is continuously obtained through the sliding window, and finally a complete set of pairing drilling parameter sets is formed for analyzing the overall construction status and surrounding rock characteristics.

5. The method according to claim 4, characterized in that The method of determining the overall surrounding rock grade of the tunnel and the surrounding rock grades of different sections based on the drilling parameter set and the drilling parameters of different sections by using a pre-trained surrounding rock grade model based on machine learning includes: Inputting the drilling parameter set into a pre-trained first surrounding rock classification model based on machine learning to determine the overall surrounding rock grade of the tunnel, wherein the first surrounding rock classification model is trained based on a historical drilling parameter set and a corresponding historical overall surrounding rock grade; The drilling parameters of the different sections are respectively input into a pre-trained second surrounding rock classification model based on machine learning to determine the surrounding rock grades of the different sections of the tunnel, wherein the second surrounding rock classification model is trained based on the historical drilling parameters of the different sections and the historical surrounding rock grades corresponding to the different sections; Performing weighted processing on the surrounding rock grades of the different sections to obtain a weighted overall surrounding rock grade; The final overall surrounding rock grade of the tunnel is determined according to the overall surrounding rock grade determined by the first surrounding rock classification model and the weighted overall surrounding rock grade.

6. An intelligent construction system for highway super-large section tunnels, characterized in that: The system comprises: The acquisition module is used to collect the drilling parameter set of the tunnel face during the drilling process of the highway super-large section tunnel; A first determination module, used to determine multiple sets of paired drilling parameters according to the drilling parameter set, and determine drilling parameters of different sections; A second determination module is used to determine the overall surrounding rock grade of the tunnel and the surrounding rock grades of different sections based on the drilling parameter set and the drilling parameters of different sections using a pre-trained surrounding rock classification model based on machine learning; A generation module is used to generate a corresponding tunnel support plan according to the overall surrounding rock level and the surrounding rock levels of different sections to achieve intelligent construction of the tunnel.

7. The system according to claim 6, characterized in that The acquisition module is specifically used for: The tunnel face and its surrounding area are divided into multiple sub-areas according to the actual geological characteristics and drilling progress, wherein each sub-area is responsible for collecting parameter data of each drilling parameter in the sub-area, and the types and quantities of drilling parameters collected in each sub-area are the same. The division of the sub-areas takes into account the uniformity of the rock formation and the changes in the surrounding rock, so as to record the drilling characteristics of each sub-area; The parameter data of the drilling parameters collected in each sub-area are combined to form the drilling parameter set of the final tunnel face.

8. The system according to claim 7, characterized in that The first determining module is specifically configured to: In the sub-area, a paired parameter generation mechanism is established according to the drilling parameters of the adjacent sub-areas, and a sliding window algorithm is used to gradually slide the window and collect the drilling parameters of the adjacent sub-areas to generate a paired drilling parameter set; converting the drilling parameters of each paired sub-area into a feature vector, and mapping the feature vector into a nonlinear feature space; In the nonlinear feature space, a clustering algorithm is applied to classify all feature vectors to obtain a feature vector of each classification, and the drilling parameters corresponding to the feature vector of each class are determined as the drilling parameters of each section.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 5 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 5.

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