Digitalized simulation checking calculation system and method for repairing and reinforcing ancient building

Abstract

The invention discloses a digital simulation checking calculation system and method for repairing and reinforcing an ancient building, and relates to the technical field of data processing, and the method comprises the steps: carrying out the damage analysis of a three-dimensional model of a first ancient building, and extracting the structure type, material, shape and size of a damaged region; indexing and matching the repairing process knowledge graph in the repairing process library, and inputting damaged data to update material consumption prediction; simulating repairing to generate a second three-dimensional model based on the repairing process number and the predicted material consumption; and comparing the first modeling with the second modeling, calculating the repairing and reinforcing feasibility, and if the repairing and reinforcing feasibility reaches a threshold value, recording a passing identifier and outputting a corresponding scheme, thereby achieving the technical effects of providing accurate and efficient repairing and reinforcing evaluation and improving the repairing accuracy and reliability.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a digital simulation verification system and method for repairing and reinforcing ancient buildings. Background Art

[0002] In the field of ancient building repair and reinforcement, traditional methods mainly rely on manual experience to conduct damage analysis and formulate repair plans, which requires a lot of on-site surveys and manual measurements. The efficiency of damage analysis is low, and its accuracy is limited by the professional level and experience of personnel. The evaluation lacks effective quantitative indicators and methods, making it difficult to conduct a comprehensive and objective evaluation of the repair effect. Summary of the Invention

[0003] The present invention aims to solve the technical problems of low evaluation efficiency and accuracy in the existing technology for the repair and reinforcement of ancient buildings by providing a digital simulation verification system and method for the repair and reinforcement of ancient buildings.

[0004] The technical solution of the present invention to solve the above technical problems is as follows: In a first aspect, the present invention provides a digital simulation verification system for the repair and reinforcement of ancient buildings, wherein the system comprises: The damage analysis module is used to perform damage analysis on the three-dimensional model of the first ancient building and generate damage identification areas.

[0005] The damage feature extraction module is used to extract the damage structure type, damage area material, damage area shape, and damage area size of the damage identification area.

[0006] A process knowledge graph acquisition module is used to input the damaged structure type into the repair process library for primary indexing to obtain a first repair process knowledge graph, wherein any one of the first repair process knowledge graphs includes a first entity, a second entity and a third entity, the first entity is the repair process type number, the second entity is a ternary array, the ternary array includes a material vector representation, a shape vector representation and a size vector representation, the third entity is the material usage, the initial state of the second entity and the third entity is a blank entity, the first entity, the second entity and the third entity are undirectedly connected in sequence, and a preset mapping rule is deployed between the second entity and the third entity.

[0007] The material usage prediction module is used to input the damaged area material, the damaged area shape, and the damaged area size into the second entity when the number of the first repair process knowledge graph is equal to 1, so as to obtain the predicted material usage of the third entity.

[0008] The simulation repair module is used to simulate the repair of the first ancient building three-dimensional model according to the repair process type number and the predicted material usage to obtain the second ancient building three-dimensional model.

[0009] The repair and verification module is used to compare the second three-dimensional model of the ancient building with the first three-dimensional model of the ancient building to perform repair and verification and obtain the feasibility of repair.

[0010] An identification construction module is used to construct a simulation verification pass identification according to the fixing process type number and the predicted material usage when the fixing feasibility is greater than or equal to the fixing feasibility threshold.

[0011] In a second aspect, the present invention provides a digital simulation verification method for the repair and reinforcement of ancient buildings, wherein the method comprises: Conduct damage analysis on the three-dimensional model of the first ancient building and generate damage identification areas.

[0012] Extract the damaged structure type, damaged area material, damaged area shape, and damaged area size of the damaged identification area.

[0013] The damaged structure type is input into the repair process library for primary indexing to obtain a first repair process knowledge graph, wherein any one of the first repair process knowledge graphs includes a first entity, a second entity and a third entity, the first entity is the repair process type number, the second entity is a ternary array, the ternary array includes a material vector representation, a shape vector representation and a size vector representation, the third entity is the material usage, the initial state of the second entity and the third entity is a blank entity, the first entity, the second entity and the third entity are undirectedly connected in sequence, and a preset mapping rule is deployed between the second entity and the third entity.

[0014] When the number of the first repair process knowledge graphs is equal to 1, the material of the damaged area, the shape of the damaged area, and the size of the damaged area are input into the second entity to obtain the predicted material usage of the third entity.

[0015] The first three-dimensional model of the ancient building is simulated and repaired according to the repair process type number and the predicted material usage to obtain the second three-dimensional model of the ancient building.

[0016] Compare the second three-dimensional model of the ancient building with the first three-dimensional model of the ancient building to perform repair verification and obtain the feasibility of repair.

[0017] When the repair feasibility is greater than or equal to the repair feasibility threshold, a simulation verification pass mark is constructed according to the repair process type number and the predicted material usage.

[0018] The present invention has the following beneficial effects: by performing damage analysis on a first ancient building 3D model, damage identification areas are generated; information on the damaged structure type, material, shape, and size is extracted; and a first-level index is performed in a restoration process library based on the damaged structure type to obtain a first restoration process knowledge graph. This knowledge graph includes the restoration process number (first entity), a three-element array representing the material, shape, and size (second entity), and the material usage (third entity). The second and third entities are initially blank and connected via a preset mapping rule. If the unique corresponding knowledge graph is indexed, the damaged material, shape, and size are input into the second entity to generate a material usage prediction value for the third entity; based on the repair process number and the predicted material usage, the three-dimensional model of the first ancient building is simulated and repaired to generate a three-dimensional model of the second ancient building; the first and second models are compared to calculate the feasibility of the repair; if the feasibility reaches the threshold, a simulation verification pass mark is generated, and the repair process number and material usage are recorded. The digital simulation verification system and method for the repair and reinforcement of ancient buildings disclosed in the present invention solve the technical problems of low evaluation efficiency and low evaluation accuracy of the repair and reinforcement of ancient buildings, and achieve the technical effect of providing accurate and efficient repair and reinforcement evaluation and improving the accuracy and reliability of repair. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a structural diagram of the digital simulation verification system for ancient building repair and reinforcement of the present invention; Figure 2 This is a flow chart of the digital simulation calculation method for the repair and reinforcement of ancient buildings according to the present invention.

[0020] In the accompanying drawings, the components represented by the reference numerals are described as follows: Damage analysis module 11, damage feature extraction module 12, process knowledge graph acquisition module 13, material consumption prediction module 14, simulation repair module 15, repair verification module 16, identification construction module 17. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0023] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or illustration". Any embodiment of the present invention described as "for example" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed herein.

[0024] Example 1:

[0025] like Figure 1 As shown, an embodiment of the present invention provides a structural diagram of a digital simulation verification system for the repair and reinforcement of ancient buildings, wherein the system includes: The damage analysis module 11 is used to perform damage analysis on the three-dimensional model of the first ancient building and generate a damage identification area.

[0026] Specifically, first, detailed three-dimensional modeling and damage analysis are carried out on the target ancient building to comprehensively identify the damage characteristics of the first ancient building and generate corresponding identification areas. Among them, the damage analysis includes classifying the damage according to the damage type, such as crack areas, peeling areas, deformation areas, biological erosion, etc.

[0027] Specifically, the generated damage identification area is marked with the damage nature and damage magnitude (area, depth or volume) to indicate the degree of damage; optionally, the damaged area is marked on the three-dimensional model with different colors or textures.

[0028] In some embodiments, the damage analysis module 11 performs the following steps: Through the user end, the first ancient building concept six views are configured; based on the first ancient building three-dimensional modeling, the first ancient building monitoring six views are extracted; the first ancient building concept six views and the first ancient building monitoring six views are compared to obtain the damaged area six views; the damaged area is modeled according to the damaged area six views to obtain a damaged area three-dimensional model; the first ancient building three-dimensional model is marked according to the damaged area three-dimensional model to generate the damaged marked area.

[0029] Specifically, first, the conceptual six views of the ancient building are configured through the user end (including software programs and interactive hardware), including front, back, left, right, top and bottom views. The conceptual six views reflect the ideal state of the ancient building; then, based on the three-dimensional modeling of the first ancient building, the corresponding six monitoring views of the first ancient building are obtained through the same perspective as the above-mentioned conceptual six views. Among them, the three-dimensional modeling of the first ancient building is a three-dimensional model constructed by detailed point cloud data and high-resolution photos of the first ancient building obtained through three-dimensional laser scanning and drone oblique photography technology, reflecting the current status of the ancient building.

[0030] Specifically, the conceptual six views and the monitoring six views are compared. For example, through image difference technology, damaged areas such as cracks on the walls of ancient buildings and missing tiles on the roof are identified to generate six views of the damaged areas. Then, based on the comparison results, the damaged areas are modeled in detail using three-dimensional modeling software to generate a three-dimensional model of the damaged areas. The three-dimensional model of the damaged areas records in detail information such as the width and depth of the cracks, the location and number of missing tiles, etc.

[0031] Furthermore, the damaged area is marked (such as marking based on coordinate mapping) using model editing software in combination with the three-dimensional model of the first ancient building to generate a damaged marking area. The marked model can be visualized in the software. For example, the damaged area is displayed in different colors or transparencies, which makes it easier for repair personnel to intuitively understand the damage of the ancient building.

[0032] In some implementations, the six views of the damaged area are obtained by comparing the first six conceptual views of the ancient building with the first six monitoring views of the ancient building. The execution steps of the damage analysis module 11 further include: Extract the first conceptual view of the first six conceptual views of the ancient building; extract the first monitoring view of the first six monitoring views of the ancient building, wherein the image acquisition perspective and image acquisition parameters of the first monitoring view and the first conceptual view are the same; perform center alignment on the first monitoring view and the first conceptual view to obtain the non-overlapping area and the overlapping area with the first monitoring view in the first conceptual view; perform deviation analysis on the first monitoring view and the first conceptual view of the overlapping area to obtain the overlapping damaged area; add the non-overlapping area and the overlapping damaged area into the first view damaged area, and add the first view damaged area into the six views of the damaged area.

[0033] Specifically, first, through the first ancient building concept six views configured on the user side and the first ancient building monitoring six views extracted based on three-dimensional modeling, any views with corresponding perspectives (such as all front views) are extracted as the first concept view and the first monitoring view, ensuring that the two are completely consistent in image acquisition perspective and parameters; then, the center points and main feature points of the two views (such as building corners, boundary lines) are determined, and the center points and main feature points of the first monitoring view and the first concept view are aligned through translation, rotation and scale transformation, and then the first monitoring view and the first concept view are divided into overlapping areas (matching parts in the two views) and non-overlapping areas (parts that only appear in the first monitoring view or the first concept view) according to the alignment results.

[0034] Specifically, the areas in the first monitoring view that have no corresponding parts with the first conceptual view are marked as missing areas; at the same time, deviation analysis is performed on the areas in the first conceptual view that have no corresponding parts with the first monitoring view, involving comparing the first monitoring view and the first conceptual view in the overlapping area with respect to features such as pixel brightness, color, and texture; a difference analysis is performed on geometric properties such as shape, edge position, and area of ​​the overlapping area; and the parts in the overlapping area with obvious deviations are marked as damaged areas, including cracks, peeling, deformation, etc.; finally, the non-overlapping areas and the overlapping damaged areas are merged to form a complete first view damaged area.

[0035] Furthermore, the above process is repeated to compare and analyze each of the six conceptual views of the first ancient building with the corresponding monitoring view, generate the damaged area of ​​each view one by one, and integrate the damaged area data of the six views into a complete six-view damaged area as the final result of the first ancient building damage assessment.

[0036] Through the above process, the damaged areas of the ancient buildings were accurately identified, providing detailed data support and visual reference for the formulation and implementation of subsequent repair plans, which in turn helped to improve the efficiency and accuracy of the ancient building repair work, reduce interference from human factors, and ensure that the historical value and cultural connotation of the ancient buildings were effectively protected.

[0037] In some implementations, performing deviation analysis on the first monitoring view and the first conceptual view of the overlapping area to obtain an overlapping damaged area, the damage analysis module 11 further includes: The first conceptual view has texture deviation threshold distribution information, color deviation threshold distribution information and shape deviation threshold distribution information; according to the texture deviation threshold distribution information, the color deviation threshold distribution information and the shape deviation threshold distribution information, the deviation binary condition of the overlapping area is configured, wherein the texture deviation threshold, the color deviation threshold and the shape deviation threshold in the deviation binary condition at any position are in a logical AND relationship; based on the deviation binary condition, the first monitoring view and the first conceptual view of the overlapping area are subjected to deviation analysis to obtain the overlapping damaged area.

[0038] Specifically, the texture deviation threshold distribution information, color deviation threshold distribution information and shape deviation threshold distribution information in the first concept view are set in advance through expert knowledge or historical data to ensure that the deviation conditions at different positions are adaptable; among them, the texture deviation threshold distribution information represents the maximum allowable difference range of texture features in the view; the color deviation threshold distribution information represents the maximum allowable difference range of color features; and the shape deviation threshold distribution information represents the maximum allowable difference range of geometric shapes, such as based on edge contour matching or shape feature point deviation.

[0039] Specifically, the deviation binary condition is composed of a logical combination of the deviation thresholds of texture, color, and shape, and all conditions are in a logical "and" relationship, that is, if the above three conditions are met at the same time, it is considered as a non-damaged area; if any condition is not met, it is marked as a damaged area; for example, for any position, the texture deviation is less than or equal to the texture deviation threshold, the color deviation is less than or equal to the color deviation threshold, and the shape deviation is less than or equal to the shape deviation threshold. Through the above-mentioned precise analysis based on the deviation dichotomy condition, the damaged areas in the overlapping areas can be effectively identified, providing a scientific basis for the monitoring and restoration of ancient buildings.

[0040] The damage feature extraction module 12 is used to extract the damage structure type, damage area material, damage area shape, and damage area size of the damage identification area.

[0041] Specifically, based on the regional three-dimensional model of the damaged marked area, the damaged structure type, damaged area material, damaged area shape, and damaged area size are extracted accordingly. Among them, the damaged structure type is obtained through image recognition or neural network model, including component cracking, decay, deformation, insect damage, mortise and tenon joint damage, etc.; the damaged area material is obtained through marked label information and feature information; the damaged area shape is obtained through the damaged area edge coordinates and depth data; and the damaged area size is obtained by scanning column by column.

[0042] By obtaining the above-mentioned damaged structure type, damaged area material, damaged area shape, and damaged area size, a detailed quantitative identification of the damage condition of the ancient building is carried out.

[0043] The process knowledge graph acquisition module 13 is used to input the damaged structure type into the repair process library for primary indexing to obtain a first repair process knowledge graph, wherein any one of the first repair process knowledge graphs includes a first entity, a second entity and a third entity, the first entity is the repair process type number, the second entity is a ternary array, the ternary array includes a material vector representation, a shape vector representation and a size vector representation, the third entity is the material usage, the initial state of the second entity and the third entity is a blank entity, the first entity, the second entity and the third entity are undirectedly connected in sequence, and a preset mapping rule is deployed between the second entity and the third entity.

[0044] Specifically, the repair process library is a database containing various knowledge of ancient building repair and reinforcement processes, which is used to store and retrieve information such as process types, materials, shapes, sizes, etc. related to ancient building repair; in the repair process library, the primary index refers to the index stored directly with the data, which is usually used to quickly retrieve the primary key, that is, to quickly find a specific repair process type.

[0045] Specifically, in the repair process, the knowledge graph includes entities and the relationships between entities, which are used to represent various aspects of the repair process. The entities include the repair process type number, material vector representation, shape vector representation, size vector representation and material usage.

[0046] Specifically, the preset mapping rules refer to the rules used to map one entity to another in the knowledge graph. In the repair process knowledge graph, the preset mapping rules are used to map material vector representation and shape vector representation to material usage to predict the required material usage, provide accurate material preparation for repair work, and reduce resource waste.

[0047] Through the above process, the repair process library and knowledge graph provide comprehensive and systematic technical support for the repair of ancient buildings, ensuring the scientificity and accuracy of the repair work.

[0048] In some embodiments, the step of constructing a preset mapping rule includes: According to the preset fixing process type number, material type record data, shape record data, size record data and material usage identification data are collected, wherein the material usage identification data is generated by taking the average of multiple material usage record data of multiple groups of material type record data, shape record data and size record data of the same mode; the material type record data, the shape record data and the size record data are encoded by an encoder to obtain material vector representation record data, shape vector representation record data and size vector representation record data; the material vector representation record data, the shape vector representation record data and the size vector representation record data are input into a feedforward neural network, supervision is performed through the material usage identification data, and the preset mapping rules are trained.

[0049] Specifically, the preset fixing process type number is a predefined number for identifying a specific fixing process type, and the preset fixing process type number can be used to quickly retrieve relevant process information in the database.

[0050] Specifically, first, according to the preset repair process type number, the material type record data, shape record data, size record data and material usage identification data related to the type are collected, wherein the material usage identification data is the mean value calculated by multiple groups of data with the same modality. For example, for a specific repair process, multiple groups of data with the same material type, shape and size are collected, and the mean value of the material usage corresponding to these data is calculated.

[0051] Specifically, an encoder is used to convert material type, shape and size data into vector representation record data. The encoder can be a simple linear transformation or a complex neural network layer to obtain a vector form suitable for neural network processing.

[0052] Specifically, a feedforward neural network is constructed, and the network structure includes: an input layer for receiving vector representation record data of materials, shapes and sizes; a hidden layer, which contains a nonlinear activation function (such as ReLU) to learn complex mapping relationships; an output layer, which is used to generate corresponding material usage prediction values; then, the material vector representation record data, the shape vector representation record data and the size vector representation record data are spliced ​​into a single input vector and input into the neural network, and the material usage identification data (such as the actual required amount of wood, brick and tile area, etc.) is used as a supervision label for iterative supervised training to minimize the error between the predicted value and the actual material usage identification data, and optimize the preset mapping rules.

[0053] By encoding material, shape and size data into vector representations; using a feedforward neural network to learn the mapping relationship of material usage identification data, and then obtaining preset mapping rules to achieve efficient and automated mapping rule optimization, it provides accurate material usage prediction capabilities for the restoration of damaged areas of ancient buildings, reducing resource waste and costs.

[0054] The material usage prediction module 14 is used to input the damaged area material, the damaged area shape, and the damaged area size into the second entity when the number of the first repair process knowledge graph is equal to 1, to obtain the predicted material usage of the third entity.

[0055] Specifically, the number of the first repair process knowledge graph is equal to 1, which means that there is only one repair process knowledge graph found in the repair process library through the first-level index that completely matches the specific damaged structure type, that is, the first repair process knowledge graph is unique and accurate, and the material type, shape and size data extracted from the damaged identification area are input into the second entity of the knowledge graph, represented as material vector representation, shape vector representation and size vector representation, and then the preset mapping rules are used to process the input material vector representation, shape vector representation and size vector representation through the trained feedforward neural network to generate a predicted value of material usage, and the predicted material usage is stored in the third entity of the knowledge graph to complete the prediction of material usage.

[0056] Through this process, the uniqueness of the knowledge graph is confirmed, ensuring that the repair process used is unique and accurate, avoiding repair failures due to improper process selection. Combined with the accurate material usage prediction based on preset mapping rules, accurate prediction of material usage is achieved, allowing repair personnel to prepare the required materials in advance, avoiding material shortages or surpluses, and reducing resource waste and costs.

[0057] The simulation repair module 15 is used to simulate the repair of the first ancient building three-dimensional model according to the repair process type number and the predicted material usage to obtain the second ancient building three-dimensional model.

[0058] By simulating repairs, the effects of repairs can be predicted before actual repairs, helping repair personnel to discover potential problems in advance and optimize repair plans.

[0059] Specifically, according to the repair process type number and the predicted material usage, the corresponding repair process type (such as reinforcement, replacement, etc.) is simulated and applied to the designated area through computer three-dimensional modeling technology to achieve simulated repair and reinforcement; then, the three-dimensional model of the overall structure after repair is output, including the shape, material information and process type of the marked repair area, to generate a three-dimensional model of the second ancient building.

[0060] The repair and verification module 16 is used to compare the second three-dimensional model of the ancient building with the first three-dimensional model of the ancient building to perform repair and verification to obtain the feasibility of repair.

[0061] Specifically, the three-dimensional model after simulated repair (three-dimensional modeling of the second ancient building) is compared with the original three-dimensional model (three-dimensional modeling of the first ancient building) to evaluate the feasibility and effectiveness of the repair plan, including structural analysis and verification of material usage; illustratively, the shape deviation ratio of the repair area and the original model is compared to obtain the geometric matching degree, which is used to ensure that the shape of the repaired area of ​​the second model and the first model matches the surrounding structure; the material properties such as elastic modulus, density and similarity with the original material are compared to obtain the material compatibility matching degree to verify the compatibility of the materials used in the repaired area with the surrounding areas; through computer simulation analysis methods, the force analysis results of the repaired area are obtained, and compared with the set targets to obtain the mechanical property matching degree to ensure that the repaired area can withstand the set external stress.

[0062] Furthermore, based on the weighted method, the above-mentioned geometric matching, material compatibility matching and mechanical property matching are combined to obtain the feasibility of the repair. The feasibility of the repair is used to quantitatively represent the feasibility and effect of the repair plan.

[0063] The identification construction module 17 is used to construct a simulation verification pass identification according to the fixing process type number and the predicted material usage when the fixing feasibility is greater than or equal to the fixing feasibility threshold.

[0064] Specifically, when the feasibility of repair reaches or exceeds the preset threshold, it can be considered that the current repair plan and the expected material usage are reasonable, and the repaired ancient building that meets the expected goals can be obtained and can be put into practical use.

[0065] In some embodiments, the system further comprises a map scale control unit configured to: When the number of the first repair process knowledge graphs is greater than 1, the damaged area material, the damaged area shape, and the damaged area size are input into the repair process library for secondary indexing to obtain the second repair process knowledge graph; the intersection graph of the first repair process knowledge graph and the second repair process knowledge graph is taken to obtain the third repair process knowledge graph; when the number of the third repair process knowledge graphs is equal to 1, the damaged area material, the damaged area shape, and the damaged area size are input into the second entity execution process; when the number of the first repair process knowledge graphs is equal to 0, or the number of the third repair process knowledge graphs is equal to 0, a repair process null value alarm signal is generated to the user end.

[0066] Specifically, in the repair process library, when the first-level index returns multiple results, that is, the number of the first repair process knowledge graphs is greater than 1, a second index is performed using more detailed conditions (such as the material, shape, and size of the damaged area) to obtain a more accurate second repair process knowledge graph; then, the first repair process knowledge graph is compared with the second repair process knowledge graph to find the knowledge graph that meets both index conditions and obtain the third repair process knowledge graph.

[0067] Specifically, the number of the third repair process knowledge graphs is determined. If the number of the third repair process knowledge graphs is equal to 1, it means that the only matching repair process has been found. At this time, the material, shape, and size data of the damaged area are input into the second entity of the knowledge graph, and the preset process is executed, such as material usage prediction.

[0068] Specifically, if the number of results of the first-level index or intersection graph is 0, or the number of the third repair process knowledge graph is equal to 0, it means that no matching repair process is found, and a repair process null value alarm signal is generated to the user end, prompting the user to perform corresponding operations, such as checking the accuracy of the input information or supplementing the data of the repair process library.

[0069] In the above steps, by obtaining secondary indexes and intersection maps, it is possible to more accurately match the repair process suitable for specific damage conditions, thereby improving the pertinence and effectiveness of the repair plan; when no matching repair process is found, an alarm signal is sent to the user end in time to help the user discover and solve the problem in time, further ensuring the smooth progress of the repair work.

[0070] In some embodiments, the execution steps of the atlas scale control unit further include: When the number of the third fixing process knowledge graphs is greater than 1, the third fixing process knowledge graph is sent to the user end to obtain the user-selected fixing process knowledge graph; and a process is executed according to the user-selected fixing process knowledge graph.

[0071] Specifically, when the number of the third fixing process knowledge graphs is greater than 1, all the knowledge graphs are sent to the user end, so that the user can select a most suitable graph from multiple fixing process knowledge graphs according to his or her professional knowledge or preferences.

[0072] Through the above process, the right of choice is given to the user, which increases the user's participation and decision-making power, ensuring that the repair plan is more in line with the user's actual needs and professional judgment. At the same time, when it is impossible to automatically determine the only optimal repair process, flexibility is provided through user choice, so that the most appropriate repair method can be selected according to the specific situation.

[0073] In some implementations, the damaged area material, the damaged area shape, and the damaged area size are input into the repair process library for secondary indexing to obtain a second repair process knowledge graph, and the execution step of the graph scale control unit further includes: Taking the damaged area material, the damaged area shape and the damaged area size as constraints, the trigger frequency statistics of the first repair process type of the first repair process knowledge graph of the repair process library are performed to obtain the first trigger frequency; until the material of the damaged area, the damaged area shape and the damaged area size are used as constraints, the trigger frequency statistics of the Nth repair process type of the Nth repair process knowledge graph of the repair process library are performed to obtain the Nth trigger frequency; based on the first trigger frequency until the Nth trigger frequency, the repair process knowledge graph with a trigger frequency greater than or equal to the trigger frequency threshold is extracted from the repair process library and added into the second repair process knowledge graph.

[0074] Specifically, first, taking the material, shape and size of the damaged area as constraints, the triggering frequency of each repair process type (from the first to the Nth) in the repair process library is counted. For example, the number of times each repair process type is triggered under the given material type of "wood", shape of "square" and size of "1m×1m" is counted; then, based on the statistically obtained triggering frequency, the repair process knowledge graph with a triggering frequency greater than or equal to the preset threshold is extracted from the repair process library. These knowledge graphs can be considered as repair processes that are highly relevant to the given damaged area; finally, the screened repair process knowledge graph is added to the second repair process knowledge graph, so as to provide a more accurate reference for the subsequent formulation of repair plans.

[0075] In summary, the digital simulation verification system for ancient building repair and reinforcement provided by the present invention has the following technical effects: By performing a damage analysis on the first 3D model of the ancient building, damage identification areas are generated. The damaged structural type, material, shape, and size information are extracted. A primary index is performed in the repair process library based on the damaged structural type, resulting in a first repair process knowledge graph. This knowledge graph contains the repair process number (first entity), a three-element array representing the material, shape, and size (second entity), and the material quantity (third entity). The second and third entities are initially blank and connected via pre-set mapping rules. Once a unique corresponding knowledge graph is indexed, the damaged material, shape, and size are entered into the second entity to generate a predicted material quantity for the third entity. Based on the repair process number and predicted material quantity, a simulated repair of the first 3D model of the ancient building is performed to generate a second 3D model of the ancient building. The first and second models are compared to calculate the repair feasibility. If the feasibility reaches a threshold, a simulation verification pass is generated, and the repair process number and material quantity are recorded. This provides accurate and efficient repair and reinforcement assessments, improving the accuracy and reliability of the repair.

[0076] Example 2:

[0077] Figure 2 This is a flow chart of the digital simulation calculation method for the repair and reinforcement of ancient buildings of the present invention. Figure 1 The structural diagram of the digital simulation verification system for the repair and reinforcement of ancient buildings in the present invention is used to achieve the following Figure 2 The process shown.

[0078] Based on the same concept as the digital simulation verification system for the repair and reinforcement of ancient buildings in the above embodiment, the present invention also provides a digital simulation verification method for the repair and reinforcement of ancient buildings, which includes: Conduct damage analysis on the three-dimensional model of the first ancient building and generate damage identification areas.

[0079] Extract the damaged structure type, damaged area material, damaged area shape, and damaged area size of the damaged identification area.

[0080] The damaged structure type is input into the repair process library for primary indexing to obtain a first repair process knowledge graph, wherein any one of the first repair process knowledge graphs includes a first entity, a second entity and a third entity, the first entity is the repair process type number, the second entity is a ternary array, the ternary array includes a material vector representation, a shape vector representation and a size vector representation, the third entity is the material usage, the initial state of the second entity and the third entity is a blank entity, the first entity, the second entity and the third entity are undirectedly connected in sequence, and a preset mapping rule is deployed between the second entity and the third entity.

[0081] When the number of the first repair process knowledge graphs is equal to 1, the material of the damaged area, the shape of the damaged area, and the size of the damaged area are input into the second entity to obtain the predicted material usage of the third entity.

[0082] The first three-dimensional model of the ancient building is simulated and repaired according to the repair process type number and the predicted material usage to obtain the second three-dimensional model of the ancient building.

[0083] Compare the second three-dimensional model of the ancient building with the first three-dimensional model of the ancient building to perform repair verification and obtain the feasibility of repair.

[0084] When the repair feasibility is greater than or equal to the repair feasibility threshold, a simulation verification pass mark is constructed according to the repair process type number and the predicted material usage.

[0085] In some embodiments, performing damage analysis on the three-dimensional model of the first ancient building to generate a damage identification area includes: Through the user terminal, configure the first six views of the ancient building concept.

[0086] Based on the three-dimensional modeling of the first ancient building, six monitoring views of the first ancient building are extracted.

[0087] The six views of the damaged area are obtained by comparing the first six views of the ancient building concept and the first six views of the ancient building monitoring.

[0088] The damaged area is modeled according to the six views of the damaged area to obtain a three-dimensional model of the damaged area.

[0089] The first three-dimensional model of the ancient building is marked according to the three-dimensional model of the damaged area to generate the damaged marked area.

[0090] In some implementations, comparing the first six conceptual views of the ancient building with the first six monitoring views of the ancient building to obtain the six views of the damaged area includes: A first conceptual view of the six conceptual views of the first ancient building is extracted.

[0091] A first monitoring view of the six monitoring views of the first ancient building is extracted, wherein the first monitoring view has the same image acquisition perspective and image acquisition parameters as the first conceptual view.

[0092] The first monitoring view and the first conceptual view are center-aligned to obtain a non-overlapping area and an overlapping area of ​​the first conceptual view with the first monitoring view.

[0093] A deviation analysis is performed on the first monitoring view and the first conceptual view of the overlapping area to obtain an overlapping damaged area.

[0094] The non-overlapping area and the overlapping damaged area are added to the first view damaged area, and the first view damaged area is added to the damaged area six views.

[0095] Furthermore, performing deviation analysis on the first monitoring view and the first conceptual view of the overlapping area to obtain an overlapping damaged area includes: The first conceptual view has texture deviation threshold distribution information, color deviation threshold distribution information, and shape deviation threshold distribution information.

[0096] According to the texture deviation threshold distribution information, the color deviation threshold distribution information and the shape deviation threshold distribution information, the deviation binary condition of the overlapping area is configured, wherein the texture deviation threshold, the color deviation threshold and the shape deviation threshold in the deviation binary condition at any position are in a logical AND relationship.

[0097] Based on the deviation binary condition, a deviation analysis is performed on the first monitoring view and the first conceptual view of the overlapping area to obtain the overlapping damaged area. A deviation analysis is performed on the first monitoring view and the first conceptual view of the overlapping area to obtain the overlapping damaged area.

[0098] In some embodiments, the step of constructing the preset mapping rule includes: According to the preset fixing process type number, material type record data, shape record data, size record data and material usage identification data are collected, wherein the material usage identification data is generated by taking the average of multiple material usage record data of multiple groups of material type record data, shape record data, and size record data of the same mode.

[0099] The material type record data, the shape record data, and the size record data are encoded by an encoder to obtain material vector representation record data, shape vector representation record data, and size vector representation record data.

[0100] The material vector representation record data, the shape vector representation record data and the size vector representation record data are input into a feedforward neural network, supervision is performed through the material usage identification data, and the preset mapping rules are trained.

[0101] Furthermore, the method further comprises: When the number of the first repair process knowledge graphs is greater than 1, the damaged area material, the damaged area shape, and the damaged area size are input into the repair process library for secondary indexing to obtain the second repair process knowledge graph.

[0102] Take the intersection graph of the first repair process knowledge graph and the second repair process knowledge graph to obtain the third repair process knowledge graph.

[0103] When the number of the third repair process knowledge graph is equal to 1, the material of the damaged area, the shape of the damaged area, and the size of the damaged area are input into the second entity execution process.

[0104] When the number of the first fixing process knowledge graphs is equal to 0, or the number of the third fixing process knowledge graphs is equal to 0, a fixing process null value alarm signal is generated to the user end.

[0105] In some implementations, the method further includes: When the number of the third fixing process knowledge graphs is greater than 1, the third fixing process knowledge graphs are sent to the user end to obtain the fixing process knowledge graph selected by the user.

[0106] The process is executed according to the user-selected repair process knowledge graph.

[0107] In some implementations, inputting the damaged area material, the damaged area shape, and the damaged area size into the repair process library for secondary indexing to obtain a second repair process knowledge graph includes: Taking the damaged area material, the damaged area shape and the damaged area size as constraints, the trigger frequency statistics of the first fixing process type of the first fixing process knowledge graph of the fixing process library are performed to obtain a first trigger frequency.

[0108] Until the Nth repair process type of the Nth repair process knowledge graph of the repair process library is subjected to trigger frequency statistics based on the damaged area material, the damaged area shape, and the damaged area size, the Nth trigger frequency is obtained.

[0109] Based on the first trigger frequency to the Nth trigger frequency, a repair process knowledge graph with a trigger frequency greater than or equal to a trigger frequency threshold is extracted from the repair process library and added into the second repair process knowledge graph.

[0110] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0111] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0115] Although preferred embodiments of the present invention have been described, additional changes and modifications to these embodiments may occur to those skilled in the art once the basic inventive concepts become known.

[0116] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. The digital simulation verification system for the repair and reinforcement of ancient buildings is characterized by: include: A damage analysis module is used to perform damage analysis on the three-dimensional modeling of the first ancient building and generate a damage identification area; A damage feature extraction module, used to extract the damage structure type, damage area material, damage area shape, and damage area size of the damage identification area; A process knowledge graph acquisition module is used to input the damaged structure type into the repair process library for primary indexing to obtain a first repair process knowledge graph, wherein any one of the first repair process knowledge graphs includes a first entity, a second entity and a third entity, the first entity is a repair process type number, the second entity is a ternary array, the ternary array includes a material vector representation, a shape vector representation and a size vector representation, the third entity is a material usage, the initial states of the second entity and the third entity are blank entities, the first entity, the second entity and the third entity are undirectedly connected in sequence, and a preset mapping rule is deployed between the second entity and the third entity; A material usage prediction module, used for inputting the damaged area material, the damaged area shape, and the damaged area size into the second entity when the number of the first fixing process knowledge graph is equal to 1, to obtain the predicted material usage of the third entity; A simulation repair module, used for performing simulation repair on the first ancient building three-dimensional model according to the repair process type number and the predicted material usage to obtain a second ancient building three-dimensional model; A repair and verification module is used to compare the second ancient building three-dimensional modeling and the first ancient building three-dimensional modeling to perform repair and verification to obtain the repair feasibility; An identification construction module is used to construct a simulation verification pass identification according to the fixing process type number and the predicted material usage when the fixing feasibility is greater than or equal to the fixing feasibility threshold.

2. The system according to claim 1, characterized in that Perform damage analysis on the 3D model of the first ancient building and generate a damage identification area. The execution steps include: Through the user end, configure the first ancient building concept six views; Based on the three-dimensional modeling of the first ancient building, extracting six monitoring views of the first ancient building; Comparing the first six conceptual views of the ancient building with the first six monitoring views of the ancient building to obtain six views of the damaged area; Modeling the damaged area according to the six views of the damaged area to obtain a three-dimensional model of the damaged area; The first ancient building three-dimensional model is marked according to the three-dimensional model of the damaged area to generate the damaged marked area.

3. The system according to claim 2, characterized in that Comparing the first six views of the ancient building concept with the first six views of the ancient building monitoring to obtain six views of the damaged area, the execution steps include: Extracting a first conceptual view of the six conceptual views of the first ancient building; Extracting a first monitoring view of the six monitoring views of the first ancient building, wherein the first monitoring view has the same image acquisition viewing angle and image acquisition parameters as the first conceptual view; Performing center alignment on the first monitoring view and the first conceptual view to obtain a non-overlapping area and an overlapping area in the first conceptual view with the first monitoring view; Performing deviation analysis on the first monitoring view and the first conceptual view of the overlapping area to obtain an overlapping damaged area; The non-overlapping area and the overlapping damaged area are added into the first view damaged area, and the first view damaged area is added into the damaged area six views.

4. The system according to claim 3, characterized in that Performing deviation analysis on the first monitoring view and the first conceptual view of the overlapping area to obtain an overlapping damaged area includes: The first concept view has texture deviation threshold distribution information, color deviation threshold distribution information and shape deviation threshold distribution information; According to the texture deviation threshold distribution information, the color deviation threshold distribution information and the shape deviation threshold distribution information, a deviation binary division condition of the overlapped area is configured, wherein the texture deviation threshold, the color deviation threshold and the shape deviation threshold in the deviation binary division condition at any position are in a logical AND relationship; Based on the deviation binary condition, a deviation analysis is performed on the first monitoring view and the first conceptual view of the overlapped area to obtain the overlapped damaged area.

5. The system according to claim 1, wherein: The step of constructing the preset mapping rule comprises: According to the preset fixing process type number, material type record data, shape record data, size record data and material usage identification data are collected, wherein the material usage identification data is generated by taking the average of multiple material usage record data of multiple groups of material type record data, shape record data and size record data of the same mode; Encoding the material type record data, the shape record data, and the size record data through an encoder to obtain material vector representation record data, shape vector representation record data, and size vector representation record data; The material vector characterization record data, the shape vector characterization record data and the size vector characterization record data are input into a feedforward neural network, supervision is performed through the material usage identification data, and the preset mapping rule is trained.

6. The system according to claim 1, wherein: The system also includes a map scale control unit for: When the number of the first repair process knowledge graphs is greater than 1, the damaged area material, the damaged area shape, and the damaged area size are input into the repair process library for secondary indexing to obtain a second repair process knowledge graph; Taking the intersection graph of the first fixing process knowledge graph and the second fixing process knowledge graph to obtain a third fixing process knowledge graph; When the number of the third fixing process knowledge graph is equal to 1, the damaged area material, the damaged area shape, and the damaged area size are input into the second entity execution process; When the number of the first fixing process knowledge graphs is equal to 0, or the number of the third fixing process knowledge graphs is equal to 0, a fixing process null value alarm signal is generated to the user end.

7. The system according to claim 6, characterized in that The execution steps of the atlas scale control unit also include: When the number of the third fixing process knowledge graphs is greater than 1, the third fixing process knowledge graph is sent to the user end to obtain the fixing process knowledge graph selected by the user; The process is executed according to the repair process knowledge graph selected by the user.

8. The system according to claim 6, characterized in that The damaged area material, the damaged area shape, and the damaged area size are input into the repair process library for secondary indexing to obtain a second repair process knowledge graph, and the execution steps include: Taking the damaged area material, the damaged area shape and the damaged area size as constraints, performing trigger frequency statistics on the first fixing process type of the first fixing process knowledge graph of the fixing process library to obtain a first trigger frequency; Until the Nth repair process type of the Nth repair process knowledge graph of the repair process library is subjected to trigger frequency statistics based on the damaged area material, the damaged area shape, and the damaged area size, to obtain the Nth trigger frequency; Based on the first trigger frequency to the Nth trigger frequency, a repair process knowledge graph having a trigger frequency greater than or equal to a trigger frequency threshold is extracted from the repair process library and added into the second repair process knowledge graph.

9. A digital simulation verification method for the repair and reinforcement of ancient buildings, characterized in that: The method is applied to the digital simulation verification system for the repair and reinforcement of ancient buildings according to any one of claims 1 to 8, and the method comprises: Conduct damage analysis on the three-dimensional model of the first ancient building and generate damage identification areas; Extracting the damaged structure type, damaged area material, damaged area shape, and damaged area size of the damaged identification area; Input the damaged structure type into the repair process library for primary indexing to obtain a first repair process knowledge graph, wherein any one of the first repair process knowledge graphs includes a first entity, a second entity and a third entity, the first entity is a repair process type number, the second entity is a ternary array, the ternary array includes a material vector representation, a shape vector representation and a size vector representation, the third entity is a material usage, the initial states of the second entity and the third entity are blank entities, the first entity, the second entity and the third entity are undirectedly connected in sequence, and a preset mapping rule is deployed between the second entity and the third entity; When the number of the first fixing process knowledge graphs is equal to 1, the damaged area material, the damaged area shape, and the damaged area size are input into the second entity to obtain the predicted material usage of the third entity; Performing simulated repair on the first ancient building three-dimensional model according to the repair process type number and the predicted material usage to obtain a second ancient building three-dimensional model; Comparing the second three-dimensional modeling of the ancient building with the first three-dimensional modeling of the ancient building to perform repair verification calculation and obtain the repair feasibility; When the feasibility of the repair is greater than or equal to the threshold value of the feasibility of the repair, a simulation verification pass mark is constructed according to the repair process type number and the predicted material usage.

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