Tunnel vault anomaly processing method and device and computer equipment

Abstract

This application relates to a method, apparatus, and computer equipment for handling anomalies in tunnel arches. The method includes: acquiring anomaly detection results and current structural inspection information of the tunnel arch; identifying the anomaly distribution information of each anomaly type based on the anomaly detection results; identifying structural stress anomalies in the tunnel arch based on the current structural inspection information; and generating an anomaly construction adjustment plan for the tunnel arch based on the anomaly distribution information of each anomaly type and the structural stress anomaly information; after completing the anomaly adjustment of the tunnel arch based on the anomaly construction adjustment plan; generating anomaly quality inspection results for the tunnel arch through a construction quality inspection strategy; and generating a quality adjustment plan for the tunnel arch based on the anomaly quality inspection results. This method improves the rationality, accuracy, and effectiveness of anomaly handling in tunnel arches.

Description

Technical Field

[0001] This application relates to the field of construction quality inspection technology, and in particular to a method, apparatus and computer equipment for handling anomalies in tunnel arches. Background Technology

[0002] During tunnel construction, the quality of the arch concrete is crucial to structural safety. However, traditional manual inspection methods rely on experience and are difficult to implement comprehensively and accurately in real time. Due to the complexity of construction in the arch area, voids (i.e., incomplete filling of concrete) and air bubbles (residual air inside the concrete) are prone to occur during pouring. These weaken the structural strength, increase the risk of water seepage, and affect the tunnel's durability. Therefore, improving the accuracy of anomaly detection during tunnel construction is a current research focus.

[0003] Existing technologies often use uniformly distributed piezoelectric sensors to detect pressure at various construction locations on the tunnel arch, thereby obtaining information on the abnormal distribution of the tunnel arch. However, this method can only identify the locations where abnormalities exist, but for less experienced workers, it is still difficult to understand how to repair and adjust the tunnel arch, resulting in poor handling of abnormalities in the tunnel arch. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for handling anomalies in tunnel arches, in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for handling anomalies in a tunnel arch, including:

[0006] Obtain the anomaly detection results of the tunnel arch and the current structural detection information of the tunnel arch, and identify the anomaly distribution information of each anomaly type based on the anomaly detection results of the tunnel arch;

[0007] Based on the current structural detection information, identify the structural stress anomaly information of the tunnel arch, and generate an abnormal construction adjustment plan for the tunnel arch based on the anomaly distribution information of each anomaly type and the structural stress anomaly information;

[0008] After completing the abnormal adjustment of the tunnel arch based on the abnormal construction adjustment plan, the current detection information of the tunnel arch is collected, and based on the current detection information of the tunnel arch, an abnormal quality detection result of the tunnel arch is generated through a construction quality detection strategy; based on the abnormal quality detection result, a quality adjustment plan for the tunnel arch is generated.

[0009] Optionally, the step of identifying the anomaly distribution information of each anomaly type based on the anomaly detection results of the tunnel arch includes:

[0010] The anomaly detection results are broken down into sub-anomaly detection results for each anomaly type;

[0011] Based on the sub-anomaly detection results of each anomaly type, the sub-distribution range of each anomaly degree of each anomaly type is identified through an anomaly image recognition network;

[0012] The sub-distribution range of each anomaly severity for each anomaly type is used as the anomaly distribution information for each anomaly type.

[0013] Optionally, identifying structural stress anomalies in the tunnel arch based on the current structural detection information includes:

[0014] Based on the current structural detection information, the three-dimensional force distribution information of the tunnel arch is identified;

[0015] Based on the three-dimensional stress distribution information, the abnormal stress direction of each abnormal stress range of the tunnel arch and the abnormal stress distribution information of each abnormal stress range are identified through the abnormal stress analysis model.

[0016] The abnormal force direction and abnormal force distribution information of each of the abnormal force ranges are used as the structural force anomaly information of the tunnel arch.

[0017] Optionally, generating an abnormal construction adjustment plan for the tunnel arch based on the abnormal distribution information of each of the abnormal types and the abnormal structural stress information includes:

[0018] For each anomaly type, based on the sub-distribution range of each anomaly degree of the anomaly type and the force range of each anomaly, the anomaly cause of each sub-distribution range and the target anomaly force range that has a force influence relationship with each sub-distribution range are identified through a force cause analysis strategy.

[0019] Based on the causes of anomalies in each of the sub-distribution ranges, the anomaly handling scheme database is queried to obtain the target anomaly handling scheme for each of the sub-distribution ranges. For each sub-distribution range, based on the anomaly type, the anomaly degree, and the target anomaly stress range of each sub-distribution range, a construction adjustment scheme for the sub-distribution range is generated through the target anomaly handling scheme.

[0020] The construction adjustment plans for all sub-distribution areas shall be taken as the abnormal construction adjustment plans for the tunnel arch.

[0021] Optionally, after completing the abnormal adjustment of the tunnel arch based on the abnormal construction adjustment plan, the current detection information of the tunnel arch is collected, and based on the current detection information of the tunnel arch, an abnormal quality detection result of the tunnel arch is generated through a construction quality detection strategy, including:

[0022] After completing the abnormal adjustment of the tunnel arch based on the abnormal construction adjustment plan, the new stress distribution information of the tunnel arch and the piezoelectric sensing distribution information of the tunnel arch are obtained.

[0023] Based on the new force distribution information and the piezoelectric sensing distribution information, the abnormality type and the degree of abnormality of the mass anomaly distribution information of the tunnel arch are identified.

[0024] The anomaly type and the degree of anomaly in the quality anomaly distribution information are used as the abnormal quality detection results of the tunnel arch.

[0025] Optionally, generating a quality adjustment scheme for the tunnel arch based on the abnormal quality detection results includes:

[0026] Based on the anomaly type and the anomaly degree of the quality anomaly distribution information, the target quality anomaly ranges that need to be adjusted are selected.

[0027] Based on the degree of sub-anomaly of each of the target quality anomaly ranges and the anomaly type of each of the target quality anomaly ranges, a sub-quality adjustment scheme corresponding to each of the target quality anomaly ranges is generated through a quality control strategy.

[0028] The sub-quality adjustment schemes corresponding to all target quality anomaly ranges are used as the quality adjustment schemes for the tunnel arch.

[0029] Secondly, this application also provides an anomaly handling device for a tunnel vault, comprising:

[0030] The acquisition module is used to acquire the anomaly detection results of the tunnel arch and the current structural detection information of the tunnel arch, and based on the anomaly detection results of the tunnel arch, identify the anomaly distribution information of each anomaly type;

[0031] The identification module is used to identify structural stress anomaly information of the tunnel arch based on the current structural detection information, and to generate an abnormal construction adjustment plan for the tunnel arch based on the anomaly distribution information of each anomaly type and the structural stress anomaly information.

[0032] The generation module is used to collect the current detection information of the tunnel arch after the abnormal adjustment of the tunnel arch is completed based on the abnormal construction adjustment plan, and generate the abnormal quality detection result of the tunnel arch based on the current detection information of the tunnel arch and through the construction quality detection strategy; and generate the quality adjustment plan of the tunnel arch based on the abnormal quality detection result.

[0033] Optionally, the acquisition module is specifically used for:

[0034] The anomaly detection results are broken down into sub-anomaly detection results for each anomaly type;

[0035] Based on the sub-anomaly detection results of each anomaly type, the sub-distribution range of each anomaly degree of each anomaly type is identified through an anomaly image recognition network;

[0036] The sub-distribution range of each anomaly severity for each anomaly type is used as the anomaly distribution information for each anomaly type.

[0037] Optionally, the identification module is specifically used for:

[0038] Based on the current structural detection information, the three-dimensional force distribution information of the tunnel arch is identified;

[0039] Based on the three-dimensional stress distribution information, the abnormal stress direction of each abnormal stress range of the tunnel arch and the abnormal stress distribution information of each abnormal stress range are identified through the abnormal stress analysis model.

[0040] The abnormal force direction and abnormal force distribution information of each of the abnormal force ranges are used as the structural force anomaly information of the tunnel arch.

[0041] Optionally, the identification module is specifically used for:

[0042] For each anomaly type, based on the sub-distribution range of each anomaly degree of the anomaly type and the force range of each anomaly, the anomaly cause of each sub-distribution range and the target anomaly force range that has a force influence relationship with each sub-distribution range are identified through a force cause analysis strategy.

[0043] Based on the causes of anomalies in each of the sub-distribution ranges, the anomaly handling scheme database is queried to obtain the target anomaly handling scheme for each of the sub-distribution ranges. For each sub-distribution range, based on the anomaly type, the anomaly degree, and the target anomaly stress range of each sub-distribution range, a construction adjustment scheme for the sub-distribution range is generated through the target anomaly handling scheme.

[0044] The construction adjustment plans for all sub-distribution areas shall be taken as the abnormal construction adjustment plans for the tunnel arch.

[0045] Optionally, the generation module is specifically used for:

[0046] After completing the abnormal adjustment of the tunnel arch based on the abnormal construction adjustment plan, the new stress distribution information of the tunnel arch and the piezoelectric sensing distribution information of the tunnel arch are obtained.

[0047] Based on the new force distribution information and the piezoelectric sensing distribution information, the abnormality type and the degree of abnormality of the mass anomaly distribution information of the tunnel arch are identified.

[0048] The anomaly type and the degree of anomaly in the quality anomaly distribution information are used as the abnormal quality detection results of the tunnel arch.

[0049] Optionally, the generation module is specifically used for:

[0050] Based on the anomaly type and the anomaly degree of the quality anomaly distribution information, the target quality anomaly ranges that need to be adjusted are selected.

[0051] Based on the degree of sub-anomaly of each of the target quality anomaly ranges and the anomaly type of each of the target quality anomaly ranges, a sub-quality adjustment scheme corresponding to each of the target quality anomaly ranges is generated through a quality control strategy.

[0052] The sub-quality adjustment schemes corresponding to all target quality anomaly ranges are used as the quality adjustment schemes for the tunnel arch.

[0053] Thirdly, this application provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described in any one of the first aspects.

[0054] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0055] Fifthly, this application provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0056] The aforementioned method, apparatus, and computer equipment for handling anomalies in tunnel arches acquire anomaly detection results and current structural inspection information of the tunnel arches. Based on the anomaly detection results, they identify the anomaly distribution information of each anomaly type. Based on the current structural inspection information, they identify structural stress anomaly information of the tunnel arches. Based on the anomaly distribution information of each anomaly type and the structural stress anomaly information, they generate an anomaly construction adjustment plan for the tunnel arches. After completing the anomaly adjustment of the tunnel arches based on the anomaly construction adjustment plan, they collect the current inspection information of the tunnel arches and, based on the current inspection information, generate anomaly quality inspection results for the tunnel arches through a construction quality inspection strategy. Based on the anomaly quality inspection results, they generate a quality adjustment plan for the tunnel arches. This solution, when conducting anomaly analysis, not only analyzes the anomaly detection results of the tunnel arch but also combines them with the current structural inspection information of the tunnel arch. It comprehensively analyzes the anomaly adjustment plan for the tunnel arch from two perspectives: abnormal stress and actual inspection results. This ensures that adjustments to tunnel anomalies are made while maintaining normal tunnel stress, avoiding secondary damage to the overall tunnel structure or increasing the degree of structural anomalies caused by adjustments to voids or air bubbles. This effectively ensures the accuracy of adjustments to voids and air bubbles in the tunnel. Finally, after anomaly adjustments, this solution allows for secondary quality inspection to compensate for and correct any anomalies. This not only avoids causing abnormal damage to other stresses or structures in the tunnel but also effectively improves the rationality, accuracy, and effectiveness of anomaly handling for the tunnel arch. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a flowchart illustrating an anomaly handling method for the tunnel arch in one embodiment;

[0059] Figure 2 This is a flowchart illustrating an example of anomaly handling in a tunnel vault in one embodiment;

[0060] Figure 3 This is a structural block diagram of an anomaly handling device for a tunnel arch in one embodiment;

[0061] Figure 4This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0063] The tunnel arch anomaly handling method provided in this application embodiment can be applied to a tunnel arch anomaly handling system. This system can be applied to a terminal, which can be, but is not limited to, various personal computers, laptops, mid-range computers, etc. When performing anomaly analysis, the terminal not only analyzes the anomaly detection results of the tunnel arch but also combines the current structural detection information of the tunnel arch. It comprehensively analyzes the anomaly construction adjustment plan for the tunnel arch from two perspectives: abnormal stress and actual detection results. This ensures that construction adjustments are made while maintaining normal tunnel stress, avoiding secondary damage to the overall tunnel structure or increasing the degree of structural anomalies caused by adjusting voids or air bubbles. This effectively ensures the accuracy of construction adjustments for voids and air bubbles in the tunnel. Finally, after adjusting the construction anomaly, this solution can also perform a secondary quality inspection to compensate for and correct any anomalies in the construction adjustments. This effectively improves the rationality, accuracy, and effectiveness of anomaly handling for the tunnel arch while avoiding abnormal damage to other stresses and structures in the tunnel.

[0064] In one exemplary embodiment, such as Figure 1 As shown, a method for handling anomalies in a tunnel arch is provided. Taking the application of this method to a terminal as an example, the method includes the following steps S101 to S103. Wherein:

[0065] Step S101: Obtain the anomaly detection results of the tunnel arch and the current structural detection information of the tunnel arch, and identify the anomaly distribution information of each anomaly type based on the anomaly detection results of the tunnel arch.

[0066] In this embodiment, the terminal responds to the information upload operation of the staff, acquiring piezoelectric sensing data fed back by piezoelectric sensors evenly distributed at various locations on the tunnel arch. Then, the terminal distributes the sensing data according to their corresponding locations to obtain the anomaly detection result of the tunnel arch. This anomaly detection result is the distribution information of the piezoelectric sensing data distribution on the tunnel arch. Next, the terminal uses a point cloud scanning device to perform a three-dimensional structural scan of the constructed portion of the tunnel arch, obtaining three-dimensional structural data of the tunnel arch, and using this three-dimensional structural data as the current structural detection information of the tunnel arch. Finally, based on the anomaly detection result of the tunnel arch, the terminal identifies the anomaly distribution information of various anomaly types. These anomaly types are the types of anomalies existing in the tunnel arch, including but not limited to voiding, bubble formation, fracture, and delamination. The terminal first identifies the piezoelectric sensing distribution range corresponding to different anomaly types based on the distribution information of the piezoelectric sensing data. Then, through an anomaly image recognition network, it identifies the sub-distribution ranges of each anomaly degree within the piezoelectric sensing distribution range of each anomaly type, thereby obtaining the anomaly distribution information for each anomaly type. This anomaly image recognition network is a convolutional neural network based on deep learning. The specific recognition process will be explained in detail later.

[0067] Step S102: Based on the current structural detection information, identify the structural stress anomaly information of the tunnel arch, and generate an abnormal construction adjustment plan for the tunnel arch based on the anomaly distribution information of each anomaly type and the structural stress anomaly information.

[0068] In this embodiment, the terminal identifies structural stress anomalies in the tunnel arch based on current structural detection information. Based on the anomaly distribution information of each anomaly type and the structural stress anomaly information, it generates an anomaly construction adjustment plan for the tunnel arch. The structural stress anomaly information is obtained by performing stress analysis on the current three-dimensional structural data of the tunnel arch using a mechanical distribution model as the analysis model. This identifies the stress conditions at various locations within the tunnel arch, thus identifying anomalies. These anomalies can be caused by excessive abnormal gravity accumulation, pressure from uncured concrete in certain areas, or structural anomalies due to slab detachment, structural fracture, or improper construction. The terminal then uses the range corresponding to different stress anomalies as the structural stress anomaly information; the specific identification process will be explained in detail later. The anomaly construction adjustment plan combines the structural stress anomalies and the anomaly distribution information of each anomaly type to generate a construction adjustment strategy corresponding to the anomaly distribution information of each anomaly type. The presence of voids, bubbles, fractures, and faults may not only be due to adjustments needed during construction at that specific location, but may also be due to anomalies in the structures connected to that location. These anomalies may have caused the location to be passively subjected to abnormal forces such as tension, compression, and delamination, resulting in various types of abnormal conditions. Therefore, by combining the range of anomaly distribution information with structural stress anomaly information, a construction adjustment plan is generated for each anomaly distribution. This ensures that during construction adjustments, the anomaly distribution information of that type can be eliminated, while also ensuring that the overall structure of the tunnel arch is free of anomalies. This avoids the problem of secondary damage to the overall tunnel arch structure caused by adjusting the anomaly distribution information of certain types.

[0069] Step S103: After completing the abnormal adjustment of the tunnel arch based on the abnormal construction adjustment plan, collect the current detection information of the tunnel arch, and generate the abnormal quality detection result of the tunnel arch based on the current detection information of the tunnel arch through the construction quality detection strategy; generate the quality adjustment plan of the tunnel arch based on the abnormal quality detection result.

[0070] In this embodiment, after the terminal completes the abnormal adjustment of the tunnel arch based on the abnormal construction adjustment plan, it generates abnormal quality detection results of the tunnel arch through a construction quality detection strategy, and generates a quality adjustment plan for the tunnel arch based on the abnormal quality detection results. This construction quality detection strategy involves re-collecting the stress distribution and piezoelectric data of the tunnel arch, and re-detecting structural abnormalities and abnormalities of various types within the tunnel arch. This allows for a re-evaluation of whether any other abnormalities remain after the abnormal adjustment, considering structural and abnormality types. Finally, the terminal generates the quality adjustment plan for the tunnel arch based on the evaluation results to ensure a secondary elimination of abnormalities in the tunnel arch. The specific generation process will be explained in detail later.

[0071] Based on the above scheme, the anomaly analysis not only utilizes the anomaly detection results of the tunnel arch but also combines them with the current structural inspection information of the tunnel arch. It comprehensively analyzes the anomaly adjustment plan for the tunnel arch from two perspectives: abnormal stress and actual inspection results. This ensures that adjustments to tunnel anomalies are made while maintaining normal tunnel stress, avoiding secondary damage to the overall tunnel structure or increasing the degree of structural anomalies caused by adjustments to voids or air bubbles. This effectively ensures the accuracy of adjustments to voids and air bubbles in the tunnel. Finally, after adjusting for anomalies, this scheme allows for secondary quality inspection to compensate for and correct any anomalies. This not only avoids causing abnormal damage to other stresses and structures in the tunnel but also effectively improves the rationality, accuracy, and effectiveness of anomaly handling for the tunnel arch.

[0072] Optionally, based on the anomaly detection results of the tunnel arch, the anomaly distribution information of each anomaly type is identified, including: splitting the anomaly detection results into sub-anomaly detection results of each anomaly type; based on the sub-anomaly detection results of each anomaly type, identifying the sub-distribution range of each anomaly degree of each anomaly type through an anomaly image recognition network; and using the sub-distribution range of each anomaly degree of each anomaly type as the anomaly distribution information of each anomaly type.

[0073] In this embodiment, the terminal uses a preset piezoelectric sensing data range corresponding to each anomaly type. From the distribution information of this piezoelectric sensing data, it filters the sub-distribution information of each piezoelectric sensing data range and uses this sub-distribution information as the sub-anomaly detection result for the anomaly type corresponding to that piezoelectric sensing data range. Specifically, since the deviation between the piezoelectric sensing data ranges corresponding to different anomaly types is large, there are no cases where the piezoelectric sensing data ranges of two anomaly types are adjacent, similar, or overlapping. Therefore, the terminal directly identifies the sub-distribution range corresponding to each anomaly type through the piezoelectric sensing data range filtering method, which can efficiently, accurately, and comprehensively filter the distribution range of each anomaly type.

[0074] Then, based on the sub-anomaly detection results of each anomaly type, the terminal uses an anomaly image recognition network to identify the sub-distribution ranges of different anomaly degrees for each anomaly type. Specifically, for each anomaly type, the terminal inputs the corresponding sub-distribution information into the anomaly image recognition network to identify the distribution ranges corresponding to different anomaly degrees within the sub-distribution information of that anomaly type, which are then used as the sub-distribution ranges for each anomaly degree. Before dividing the ranges, the anomaly image recognition network is trained using different anomaly degrees for each anomaly type and the corresponding piezoelectric sensing data distribution information. This allows it to directly identify the image ranges corresponding to different anomaly degrees within the piezoelectric sensing data distribution information (i.e., sub-distribution information) for different anomaly types.

[0075] Finally, the terminal uses the sub-distribution range of each anomaly severity for each anomaly type as the anomaly distribution information for each anomaly type.

[0076] Based on the above scheme, by dividing the piezoelectric sensing data range, the sub-distribution information corresponding to each anomaly type can be quickly identified. Then, the anomaly image recognition network trained by this scheme is used to divide each sub-distribution information into sub-distribution ranges with different degrees of anomaly, thereby improving the recognition efficiency and accuracy of the anomaly distribution information for each anomaly type.

[0077] Optionally, based on the current structural inspection information, identify structural stress anomalies in the tunnel arch, including: identifying the three-dimensional stress distribution information of the tunnel arch based on the current structural inspection information; identifying the abnormal stress direction and abnormal stress distribution information of each abnormal stress range in the tunnel arch based on the three-dimensional stress distribution information and through an abnormal stress analysis model; and using the abnormal stress direction and abnormal stress distribution information of each abnormal stress range as the structural stress anomalies in the tunnel arch.

[0078] In this embodiment, the terminal identifies the three-dimensional stress distribution information of the tunnel arch based on the current structural detection information. Specifically, firstly, the terminal performs three-dimensional modeling processing on the acquired three-dimensional structural data using a three-dimensional modeling program to obtain a three-dimensional structural model of the tunnel arch. This three-dimensional modeling program can be any program that can perform three-dimensional modeling based on three-dimensional point cloud scan data; this solution does not impose any limitations. Then, the terminal meshes the three-dimensional structural model to obtain sub-mesh models. Based on each sub-mesh model, the terminal performs force, stress, and strain analysis using a three-dimensional stress analysis model to obtain the stress information of each sub-mesh model. Finally, the terminal maps the stress information of each sub-mesh model, after marking the stress distribution using the three-dimensional stress analysis model, onto the three-dimensional structural model to obtain the three-dimensional stress distribution information of the tunnel arch. The three-dimensional stress analysis model is a finite element method (FEM) model.

[0079] The terminal, based on three-dimensional stress distribution information, identifies the abnormal force direction and distribution information of each abnormal force range in the tunnel arch through an abnormal stress analysis model. This abnormal stress analysis model is a graph neural network (GNN), which captures the abnormal stress conditions of each sub-grid node by relying on the topological relationship corresponding to the gridded three-dimensional stress distribution information constructed above. These abnormal stress conditions include, but are not limited to, damage stress, fracture stress, fatigue damage, and buckling instability. When analyzing the above-mentioned extracted abnormal conditions, the terminal assists the graph neural network with traditional mechanical analysis models for comprehensive analysis. These traditional mechanical analysis models include, but are not limited to, damage mechanics models, fracture mechanics models, fatigue damage models, and buckling instability models. Specifically, the terminal uses a traditional mechanical analysis model to perform mechanical analysis on the three-dimensional force distribution information corresponding to each sub-grid model, obtaining the force analysis results for each sub-grid model. Then, using a graph neural network (GNN), combined with the force analysis results of each sub-grid model, a comprehensive force analysis is performed on the force situation of the entire tunnel arch, thereby obtaining the abnormal force direction and abnormal force distribution information for each abnormal force range. The abnormal force direction is the force direction of each abnormal force range, and the abnormal force distribution information is the force distribution information corresponding to each sub-grid model within that abnormal force range.

[0080] Finally, the terminal uses the abnormal force direction and abnormal force distribution information of each abnormal force range as the structural force anomaly information of the tunnel arch.

[0081] Based on the above scheme, by splitting the structure into sub-grids and performing stress analysis on different grids, and then comprehensively analyzing the structural stress anomaly information of the entire tunnel arch, the accuracy and comprehensiveness of the analysis of structural stress anomaly information are improved.

[0082] Optionally, based on the anomaly distribution information of each anomaly type and the structural stress anomaly information, an anomaly construction adjustment plan for the tunnel arch is generated, including: for each anomaly type, based on the sub-distribution ranges of each anomaly degree of the anomaly type and each anomaly stress range, using a stress cause analysis strategy, identifying the anomaly causes of each sub-distribution range and each target anomaly stress range that has a stress influence relationship with each sub-distribution range; based on the anomaly causes of each sub-distribution range, querying the anomaly handling plan database to obtain the target anomaly handling plan for each sub-distribution range, and for each sub-distribution range, based on the anomaly type of the sub-distribution range, the anomaly degree of the sub-distribution range, and each target anomaly stress range of the sub-distribution range, using the target anomaly handling plan, generating a construction adjustment plan for the sub-distribution range; and using the construction adjustment plans for all sub-distribution ranges as the anomaly construction adjustment plan for the tunnel arch.

[0083] In this embodiment, for each anomaly type, the terminal, based on the sub-distribution ranges of each anomaly degree and each anomaly force range, identifies the anomaly causes of each sub-distribution range and the target anomaly force ranges that have a force influence relationship with each sub-distribution range through a force cause analysis strategy. Specifically, for each sub-distribution range, the terminal filters out the anomaly force ranges that are adjacent to, intersect with, or overlap with that sub-distribution range as the target anomaly force ranges that have a force influence relationship with that sub-distribution range. Specifically, the execution process of this force cause analysis strategy is as follows: The terminal presets the correspondence between the abnormality degree ranges of different abnormality types and the abnormal causes. Then, based on the abnormality type and abnormality degree of each sub-distribution range, the terminal first identifies each initial abnormal cause corresponding to the sub-distribution range through the above correspondence. Each initial abnormal cause includes the abnormal force situation of one or more abnormal force ranges. Then, based on the abnormal force situation of each target abnormal force range that has a force influence relationship with the sub-distribution range, the terminal selects the initial abnormal causes that contain the abnormal force situation of each target abnormal force range as the abnormal cause of the sub-distribution range.

[0084] Then, based on the causes of anomalies in each sub-distribution area, the terminal queries the anomaly handling scheme database to obtain the target anomaly handling scheme for each sub-distribution area. For each sub-distribution area, based on the anomaly type, anomaly severity, and target anomaly stress range, a construction adjustment scheme is generated using the target anomaly handling scheme. This anomaly handling scheme database includes multiple anomaly handling schemes and their corresponding anomaly causes. The terminal then uses the anomaly causes for each sub-distribution area, along with the aforementioned correspondences, to select the anomaly handling scheme corresponding to that anomaly cause as the target anomaly handling scheme for that sub-distribution area. Each anomaly handling scheme includes an anomaly handling lookup table. The table header includes the anomaly type, anomaly severity range, target anomaly stress range, and a construction adjustment scheme; that is, each construction adjustment scheme corresponds to one anomaly type, one anomaly severity range, and one anomaly stress range. This anomaly handling lookup table is a compilation of numerous experimental analyses, expert experience, and conventional construction adjustment schemes by the staff. The terminal combines the anomaly type, the anomaly degree, and the abnormal stress range of each target in the sub-distribution range to query the comparison table for construction adjustment schemes that meet the above information, and uses these schemes as the construction adjustment schemes for the sub-distribution range.

[0085] Finally, the terminal uses all the construction adjustment plans for the sub-distribution ranges as the abnormal construction adjustment plans for the tunnel arch.

[0086] Based on the above scheme, by combining the target abnormal stress range, abnormality type, and abnormality degree, the abnormality cause of the sub-distribution range is comprehensively analyzed. Then, the target abnormality handling scheme of the sub-distribution range and the target abnormality acceptance range of the sub-distribution range are analyzed respectively. In this way, the adjustment accuracy of each sub-distribution range is effectively improved while ensuring that the stress structure of the tunnel arch is not affected.

[0087] Optionally, after completing the abnormal adjustment of the tunnel arch based on the abnormal construction adjustment plan, the current detection information of the tunnel arch is collected, and based on the current detection information of the tunnel arch, an abnormal quality detection result of the tunnel arch is generated through the construction quality detection strategy. This includes: after the staff completes the abnormal adjustment of the tunnel arch based on the abnormal construction adjustment plan, obtaining the new stress distribution information of the tunnel arch and the piezoelectric sensing distribution information of the tunnel arch; based on the new stress distribution information and the piezoelectric sensing distribution information, identifying the abnormal type and the abnormal degree of the abnormal distribution information of the quality of the tunnel arch; and using the abnormal type and the abnormal degree of the abnormal distribution information of the quality as the abnormal quality detection result of the tunnel arch.

[0088] In this embodiment, after the staff completes the abnormal adjustment of the tunnel arch based on the abnormal construction adjustment plan, the terminal acquires the new stress distribution information of the tunnel arch and the piezoelectric sensing distribution information of the tunnel arch. The new stress distribution information is based on the three-dimensional structural model of the tunnel arch obtained above. This analysis examines the new stress distribution information of each sub-mesh model of the tunnel arch, and then maps the stress distribution information of each sub-mesh model onto the three-dimensional structural model of the tunnel arch using the finite element model constructed in this scheme to obtain the three-dimensional stress distribution information. Then, the terminal re-acquires the piezoelectric sensing data transmitted by piezoelectric sensors located at different positions, and arranges the piezoelectric sensing data according to different positions to obtain the piezoelectric sensing data distribution information of the tunnel arch, which is then used as the piezoelectric sensing distribution information.

[0089] Based on the new force distribution information and piezoelectric sensing distribution information, the terminal identifies the anomaly type and degree of the mass anomaly distribution information of the tunnel arch. The identification method for this anomaly type and degree is the same as that described above. Finally, the terminal uses the anomaly type and degree of the mass anomaly distribution information as the abnormal quality detection result of the tunnel arch. This mass anomaly distribution information refers to the distribution range of piezoelectric data belonging to different anomaly types, or the distribution range of force distribution information belonging to different abnormal force conditions.

[0090] Based on the above scheme, by reacquiring the stress distribution information and piezoelectric sensing data, the anomaly type and degree of each quality anomaly distribution information can be re-identified, thereby improving the accuracy of quality anomaly detection of the tunnel arch.

[0091] Optionally, based on the abnormal quality detection results, a quality adjustment plan for the tunnel arch is generated, including: screening each target quality anomaly range that needs quality adjustment based on the anomaly type and the anomaly degree of the quality anomaly distribution information; generating sub-quality adjustment plans corresponding to each target quality anomaly range through quality control strategies based on the sub-anomaly degree and anomaly type of each target quality anomaly range; and using the sub-quality adjustment plans corresponding to all target quality anomaly ranges as the quality adjustment plan for the tunnel arch.

[0092] In this embodiment, the terminal filters out target quality anomaly ranges that require quality adjustment based on the anomaly type and the anomaly degree of the quality anomaly distribution information. Specifically, the terminal selects quality anomaly distribution information that belongs to a preset target anomaly type and has an anomaly degree greater than a preset anomaly degree range, as the target quality anomaly range.

[0093] Then, based on the degree of sub-anomaly and the anomaly type of each target quality anomaly range, the terminal generates a sub-quality adjustment plan corresponding to each target quality anomaly range through a quality control strategy. This quality control strategy combines a stress cause analysis strategy with an anomaly handling plan database to first identify the causes of anomalies in each target quality anomaly range, then identify the corresponding construction adjustment plan, and finally use this construction adjustment plan as a sub-quality adjustment plan.

[0094] Finally, the terminal uses the sub-quality adjustment schemes corresponding to all target quality anomalies as the quality adjustment schemes for the tunnel arch.

[0095] Based on the above scheme, by identifying the sub-quality adjustment schemes corresponding to each target quality anomaly range, the tunnel arch can be repaired and improved in a secondary manner, avoiding the quality anomaly problem of single repair and effectively improving the repair accuracy of the tunnel arch.

[0096] This application also provides an example of anomaly handling for tunnel vaults, such as... Figure 2 As shown, the specific processing procedure includes the following steps:

[0097] Step S201: Obtain the anomaly detection results of the tunnel arch and the current structural detection information of the tunnel arch.

[0098] Step S202: The anomaly detection results are broken down into sub-anomaly detection results for each anomaly type.

[0099] Step S203: Based on the sub-anomaly detection results of each anomaly type, the sub-distribution range of each anomaly degree of each anomaly type is identified through an anomaly image recognition network.

[0100] Step S204: The sub-distribution range of each anomaly degree for each anomaly type is used as the anomaly distribution information for each anomaly type.

[0101] Step S205: Based on the current structural detection information, identify the three-dimensional force distribution information of the tunnel arch.

[0102] Step S206: Based on the three-dimensional force distribution information, the abnormal force direction and abnormal force distribution information of each abnormal force range of the tunnel arch are identified through the abnormal force analysis model.

[0103] Step S207: The abnormal force direction and abnormal force distribution information of each abnormal force range are used as the structural force anomaly information of the tunnel arch.

[0104] Step S208: For each anomaly type, based on the sub-distribution range of each anomaly degree of the anomaly type and the force range of each anomaly, the anomaly cause of each sub-distribution range and the target anomaly force range that has a force influence relationship with each sub-distribution range are identified through the force cause analysis strategy.

[0105] Step S209: Based on the causes of anomalies in each sub-distribution range, query the anomaly handling scheme database to obtain the target anomaly handling scheme for each sub-distribution range. For each sub-distribution range, based on the anomaly type, anomaly degree, and target anomaly stress range of each sub-distribution range, generate a construction adjustment scheme for the sub-distribution range through the target anomaly handling scheme.

[0106] Step S210: Treat all construction adjustment plans for the sub-distribution ranges as abnormal construction adjustment plans for the tunnel arch.

[0107] In step S211, after the staff completes the abnormal adjustment of the tunnel arch based on the abnormal construction adjustment plan, they obtain the new stress distribution information of the tunnel arch and the piezoelectric sensing distribution information of the tunnel arch.

[0108] Step S212: Based on the new force distribution information and the piezoelectric sensing distribution information, identify the anomaly type and the degree of anomaly of the mass anomaly distribution information of the tunnel arch.

[0109] Step S213: The anomaly type and the degree of anomaly in the quality anomaly distribution information are used as the anomaly quality detection results of the tunnel arch.

[0110] Step S214: Based on the anomaly type and the degree of anomaly in the quality anomaly distribution information, filter out the target quality anomaly ranges that need to be adjusted.

[0111] Step S215: Based on the sub-anomaly degree of each target quality anomaly range and the anomaly type of each target quality anomaly range, a sub-quality adjustment scheme corresponding to each target quality anomaly range is generated through a quality control strategy.

[0112] Step S216: Use the sub-quality adjustment schemes corresponding to all target quality anomaly ranges as the quality adjustment schemes for the tunnel arch.

[0113] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0114] Based on the same inventive concept, this application also provides a tunnel arch anomaly handling device for implementing the above-described tunnel arch anomaly handling method. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of the one or more tunnel arch anomaly handling device embodiments provided below can be found in the limitations of the tunnel arch anomaly handling method described above, and will not be repeated here.

[0115] In one exemplary embodiment, such as Figure 3 As shown, an anomaly handling device for a tunnel vault is provided, comprising: an acquisition module 310, an identification module 320, and a generation module 330, wherein:

[0116] The acquisition module 310 is used to acquire the anomaly detection results of the tunnel arch and the current structural detection information of the tunnel arch, and to identify the anomaly distribution information of each anomaly type based on the anomaly detection results of the tunnel arch.

[0117] The identification module 320 is used to identify the structural stress anomaly information of the tunnel arch based on the current structural detection information, and to generate an abnormal construction adjustment plan for the tunnel arch based on the anomaly distribution information of each anomaly type and the structural stress anomaly information.

[0118] The generation module 330 is used to collect the current detection information of the tunnel arch after completing the abnormal adjustment of the tunnel arch based on the abnormal construction adjustment plan, and generate the abnormal quality detection result of the tunnel arch based on the current detection information of the tunnel arch and through the construction quality detection strategy; and generate the quality adjustment plan of the tunnel arch based on the abnormal quality detection result.

[0119] Optionally, the acquisition module 310 is specifically used for:

[0120] The anomaly detection results are broken down into sub-anomaly detection results for each anomaly type;

[0121] Based on the sub-anomaly detection results of each anomaly type, the sub-distribution range of each anomaly degree of each anomaly type is identified through an anomaly image recognition network;

[0122] The sub-distribution range of each anomaly severity for each anomaly type is used as the anomaly distribution information for each anomaly type.

[0123] Optionally, the identification module 320 is specifically used for:

[0124] Based on the current structural detection information, the three-dimensional force distribution information of the tunnel arch is identified;

[0125] Based on the three-dimensional stress distribution information, the abnormal stress direction of each abnormal stress range of the tunnel arch and the abnormal stress distribution information of each abnormal stress range are identified through the abnormal stress analysis model.

[0126] The abnormal force direction and abnormal force distribution information of each of the abnormal force ranges are used as the structural force anomaly information of the tunnel arch.

[0127] Optionally, the identification module 320 is specifically used for:

[0128] For each anomaly type, based on the sub-distribution range of each anomaly degree of the anomaly type and the force range of each anomaly, the anomaly cause of each sub-distribution range and the target anomaly force range that has a force influence relationship with each sub-distribution range are identified through a force cause analysis strategy.

[0129] Based on the causes of anomalies in each of the sub-distribution ranges, the anomaly handling scheme database is queried to obtain the target anomaly handling scheme for each of the sub-distribution ranges. For each sub-distribution range, based on the anomaly type, the anomaly degree, and the target anomaly stress range of each sub-distribution range, a construction adjustment scheme for the sub-distribution range is generated through the target anomaly handling scheme.

[0130] The construction adjustment plans for all sub-distribution areas shall be taken as the abnormal construction adjustment plans for the tunnel arch.

[0131] Optionally, the generation module 330 is specifically used for:

[0132] After completing the abnormal adjustment of the tunnel arch based on the abnormal construction adjustment plan, the new stress distribution information of the tunnel arch and the piezoelectric sensing distribution information of the tunnel arch are obtained.

[0133] Based on the new force distribution information and the piezoelectric sensing distribution information, the abnormality type and the degree of abnormality of the mass anomaly distribution information of the tunnel arch are identified.

[0134] The anomaly type and the degree of anomaly in the quality anomaly distribution information are used as the abnormal quality detection results of the tunnel arch.

[0135] Optionally, the generation module 330 is specifically used for:

[0136] Based on the anomaly type and the anomaly degree of the quality anomaly distribution information, the target quality anomaly ranges that need to be adjusted are selected.

[0137] Based on the degree of sub-anomaly of each of the target quality anomaly ranges and the anomaly type of each of the target quality anomaly ranges, a sub-quality adjustment scheme corresponding to each of the target quality anomaly ranges is generated through a quality control strategy.

[0138] The sub-quality adjustment schemes corresponding to all target quality anomaly ranges are used as the quality adjustment schemes for the tunnel arch.

[0139] The various modules in the aforementioned anomaly handling device for the tunnel arch can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0140] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements an anomaly handling method for a tunnel vault. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0141] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0142] In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of an anomaly handling method for a tunnel vault.

[0143] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program implementing the steps of an exception handling method for a tunnel vault when executed by a processor.

[0144] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of an exception handling method for a tunnel vault.

[0145] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0146] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0147] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0148] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for handling anomalies in a tunnel arch, characterized in that, The method includes: Obtain the anomaly detection results of the tunnel arch and the current structural detection information of the tunnel arch, and identify the anomaly distribution information of each anomaly type based on the anomaly detection results of the tunnel arch; Based on the current structural detection information, identify the structural stress anomaly information of the tunnel arch, and generate an abnormal construction adjustment plan for the tunnel arch based on the anomaly distribution information of each anomaly type and the structural stress anomaly information; After completing the abnormal adjustment of the tunnel arch based on the abnormal construction adjustment plan, the current detection information of the tunnel arch is collected, and based on the current detection information of the tunnel arch, an abnormal quality detection result of the tunnel arch is generated through a construction quality detection strategy; based on the abnormal quality detection result, a quality adjustment plan for the tunnel arch is generated.

2. The method according to claim 1, characterized in that, The anomaly distribution information for each anomaly type, identified based on the anomaly detection results of the tunnel arch, includes: The anomaly detection results are broken down into sub-anomaly detection results for each anomaly type; Based on the sub-anomaly detection results of each anomaly type, the sub-distribution range of each anomaly degree of each anomaly type is identified through an anomaly image recognition network; The sub-distribution range of each anomaly severity for each anomaly type is used as the anomaly distribution information for each anomaly type.

3. The method according to claim 1, characterized in that, The step of identifying structural stress anomalies in the tunnel arch based on the current structural detection information includes: Based on the current structural detection information, the three-dimensional force distribution information of the tunnel arch is identified; Based on the three-dimensional stress distribution information, the abnormal stress direction of each abnormal stress range of the tunnel arch and the abnormal stress distribution information of each abnormal stress range are identified through the abnormal stress analysis model. The abnormal force direction and abnormal force distribution information of each of the abnormal force ranges are used as the structural force anomaly information of the tunnel arch.

4. The method according to claim 3, characterized in that, The method for generating an abnormal construction adjustment plan for the tunnel arch based on the abnormal distribution information of each of the aforementioned abnormal types and the structural stress abnormality information includes: For each anomaly type, based on the sub-distribution range of each anomaly degree of the anomaly type and the force range of each anomaly, the anomaly cause of each sub-distribution range and the target anomaly force range that has a force influence relationship with each sub-distribution range are identified through a force cause analysis strategy. Based on the causes of anomalies in each of the sub-distribution ranges, the anomaly handling scheme database is queried to obtain the target anomaly handling scheme for each of the sub-distribution ranges. For each sub-distribution range, based on the anomaly type, the anomaly degree, and the target anomaly stress range of each sub-distribution range, a construction adjustment scheme for the sub-distribution range is generated through the target anomaly handling scheme. The construction adjustment plans for all sub-distribution areas shall be taken as the abnormal construction adjustment plans for the tunnel arch.

5. The method according to claim 1, characterized in that, After completing the abnormal adjustment of the tunnel arch based on the abnormal construction adjustment plan, the current detection information of the tunnel arch is collected, and based on the current detection information of the tunnel arch, an abnormal quality detection result of the tunnel arch is generated through a construction quality detection strategy, including: After completing the abnormal adjustment of the tunnel arch based on the abnormal construction adjustment plan, the new stress distribution information of the tunnel arch and the piezoelectric sensing distribution information of the tunnel arch are obtained. Based on the new force distribution information and the piezoelectric sensing distribution information, the abnormality type and the degree of abnormality of the mass anomaly distribution information of the tunnel arch are identified. The anomaly type and the degree of anomaly in the quality anomaly distribution information are used as the abnormal quality detection results of the tunnel arch.

6. The method according to claim 5, characterized in that, The step of generating a quality adjustment plan for the tunnel arch based on the abnormal quality detection results includes: Based on the anomaly type and the anomaly degree of the quality anomaly distribution information, the target quality anomaly ranges that need to be adjusted are selected. Based on the degree of sub-anomaly of each of the target quality anomaly ranges and the anomaly type of each of the target quality anomaly ranges, a sub-quality adjustment scheme corresponding to each of the target quality anomaly ranges is generated through a quality control strategy. The sub-quality adjustment schemes corresponding to all target quality anomaly ranges are used as the quality adjustment schemes for the tunnel arch.

7. An anomaly handling device for a tunnel arch, characterized in that, The device includes: The acquisition module is used to acquire the anomaly detection results of the tunnel arch and the current structural detection information of the tunnel arch, and based on the anomaly detection results of the tunnel arch, identify the anomaly distribution information of each anomaly type; The identification module is used to identify structural stress anomaly information of the tunnel arch based on the current structural detection information, and to generate an abnormal construction adjustment plan for the tunnel arch based on the anomaly distribution information of each anomaly type and the structural stress anomaly information. The generation module is used to generate abnormal quality inspection results of the tunnel arch after completing the abnormal adjustment of the tunnel arch based on the abnormal construction adjustment plan, and to generate a quality adjustment plan for the tunnel arch based on the abnormal quality inspection results.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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