Bridge and tunnel inspection maintenance management system and method based on three-dimensional panorama and GIS map

Abstract

The invention discloses a bridge and tunnel inspection and maintenance management system and method based on a three-dimensional panorama and a GIS map, and relates to the field of bridge and tunnel inspection and maintenance. The method comprises the following steps: processing and fusing collected bridge and tunnel multi-dimensional environment data to obtain preliminary three-dimensional map data, performing super-resolution enhancement on a turbid shadow area to construct a high-precision three-dimensional digital model, and fusing the model with GIS data to obtain a digital twin data volume; microcosmic spectrum features are extracted, spectrum power authenticity judgment is carried out, and a health state time sequence is generated; planning an autonomous inspection path of an inspection machine, and introducing a quantum annealing algorithm to optimize scheduling and solve an optimal path; and constructing a knowledge graph associated historical maintenance record and an expert experience library, and mining a hidden association rule to generate a maintenance suggestion. Through fusion of multi-source data and intelligent analysis and optimization technologies, high-precision inspection, accurate damage evaluation and intelligent maintenance decision making are realized, and the intelligent level of bridge and tunnel management is improved.

Description

Technical Field

[0001] The present invention relates to the field of bridge and tunnel inspection and maintenance, and in particular to a bridge and tunnel inspection and maintenance management system and method based on three-dimensional panoramic view and GIS map. Background Art

[0002] As an important part of modern transportation infrastructure, the structural safety of bridges and tunnels is directly related to public safety and the stable operation of transportation networks. Traditional bridge and tunnel inspection and maintenance mainly rely on manual visual inspection and regular maintenance, which has problems such as low efficiency, limited coverage, and strong subjectivity. In recent years, with the application of technologies such as 3D modeling, geographic information systems, and artificial intelligence, bridge and tunnel management has gradually developed in the direction of digitalization and intelligence. However, the existing technology still has many shortcomings, such as: insufficient accuracy of 3D modeling, lack of dynamics and objectivity in health status assessment, inefficient inspection path planning, insufficient level of intelligent maintenance decision-making, and weak data security and traceability.

[0003] In response to the above problems, existing technologies have attempted to optimize through single technology improvements, but have failed to systematically solve the collaborative problems of data quality, dynamic analysis, path planning, and knowledge mining. Therefore, there is an urgent need for a bridge and tunnel maintenance management method that integrates high-precision 3D modeling, dynamic health assessment, intelligent path optimization, and knowledge graph-driven to improve the automation and intelligence level of the entire process. Summary of the invention

[0004] Based on the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a bridge and tunnel inspection and maintenance management system and method based on three-dimensional panoramic and GIS maps to solve the above-mentioned technical problems.

[0005] To achieve the above object, the present invention provides the following technical solution: a bridge and tunnel inspection and maintenance management method based on three-dimensional panoramic view and GIS map, comprising: The collected multi-dimensional environmental data of bridges and tunnels are processed and integrated to obtain preliminary three-dimensional map data, and the turbidity area is super-resolution enhanced to build a high-precision three-dimensional digital model, and the model is integrated with GIS data to obtain a digital twin data body; Extract microscopic spectrum features, identify the authenticity of spectrum power, and generate health status time series; Plan the autonomous inspection path of the inspection machine and introduce the quantum annealing algorithm to optimize the scheduling and solve the optimal path; Build a knowledge graph to associate historical maintenance records with expert experience base, and mine implicit association rules to generate maintenance suggestions.

[0006] The present invention is further configured that the method comprises: Use drones, laser scanners, and panoramic cameras to collect multi-dimensional data and build multi-dimensional environmental data of bridges and tunnels; Use dynamic SLAM to fuse the collected multi-dimensional environmental data of bridges and tunnels to generate preliminary three-dimensional map data; The preliminary three-dimensional map is screened according to the turbidity function to obtain the turbidity area; The graph neural network is used to perform super-resolution enhancement on the turbidity area data, replacing the original data to obtain complete high-quality data, and then the Poisson surface reconstruction algorithm is used to perform surface fitting on the complete high-quality data to obtain a high-precision three-dimensional model; The high-precision three-dimensional model is integrated with GIS to realize digital twin and obtain the digital twin data body.

[0007] The present invention is further configured as follows: the turbidity function construction logic: extracting local topological inconsistency and spectrum phase aliasing index for a specified area, and obtaining a turbidity function using the local topological inconsistency and the spectrum phase aliasing index; The logic of constructing the spectrum phase aliasing index is as follows: perform two-dimensional Fourier transform on the spatial domain image to obtain spectrum data, calculate the phase spectrum based on the spectrum data, calculate the gradient of the phase spectrum to obtain the phase gradient, and define the weighted ratio of the phase gradient to the spectrum amplitude as the spectrum phase aliasing index; Turbidity function calculation logic: ,in, is the turbidity function, is the local topological inconsistency, is the spectrum phase aliasing index; Local topological inconsistency calculation logic: ,in, is the local topological inconsistency of the current data point, is the current data point, is the neighbor set of the current data point, is the number of neighbor points, It is the sum of the number of neighbor points of all neighbor points; Spectrum phase aliasing index calculation logic: ,in, is the spectral phase aliasing index, is the spectrum amplitude, is the phase spectrum, is the phase spectrum gradient; Spectrum data calculation logic: ,in, is the spectrum data, is the number of rows of input data, is the number of columns of input data, is the intensity value of the input data, is the frequency domain coordinate, is an imaginary unit, is the angular frequency factor of the Fourier transform.

[0008] The present invention is further configured as follows: the turbidity area screening logic: ,in, is the turbidity area, is the current data point, To set the threshold; Digital twin data volume calculation logic: ,in, For the digital twin data body, is the fusion mapping operator, For high-precision three-dimensional models, It is geographic information data of GIS.

[0009] The present invention is further configured to specifically include: Use a two-stream Transformer network architecture to fuse image and point cloud features to locate the damaged area on the digital twin data volume; Performing Fourier transform on the damaged area to obtain spectrum data of the damaged area; Calculate the power spectrum density of the spectrum data of the damaged area, construct a micro-spectral feature vector set, and judge the authenticity of the damaged area based on the obtained micro-spectral feature vector set; Predict health trends based on the identification results combined with the damaged area and spectrum data; Damage area spectrum data calculation logic: ,in, is the spectrum data of the damaged area, For the damaged area, is the intensity of the pixel in the damaged area, is the frequency domain coordinate, is the imaginary unit representing the complex nature of the Fourier transform, is the angular frequency scaling factor; Power spectral density calculation logic: ,in, is the power spectral density, is the spectrum data of the damaged area; Extract key information from power spectral density and microscopic spectrum features to form a feature vector set: ,in, is the microscopic spectrum feature vector set, is the real part mean, is the mean of the imaginary part, is the average power spectral density, is the standard deviation of the power spectrum; Spectrum power authenticity judgment logic: , is the classifier, , is the weight matrix, , is the bias vector, is the activation function, Output activation function, the judgment logic is: ,in, To determine the results, To have real damage, It is a false detection.

[0010] The present invention is further configured as follows: the health trend calculation logic: , where the health trend calculation logic is: ,in, For current health status, For the health status of the next moment, is the health transfer coefficient, is the feature map of damage information, For the damaged area, is the element-wise product, It is element-by-element addition; according to the health trend calculation formula, the complete health status time series is obtained recursively .

[0011] The present invention is further configured that the method comprises: For the predicted damaged area smaller than the final healthy state threshold, an A* search algorithm is used to generate a set of candidate paths; The candidate path set is screened and optimized using the quantum annealing scheduling operator to obtain the optimal inspection path; The optimal inspection path is passed to the inspection system to autonomously allocate inspection machines, and the data is recorded to generate inspection records after the inspection is completed; Candidate path calculation logic: ,in, is the candidate path, For the search algorithm, As a starting point, is the target point, Construct a set of candidate paths for the weighted graph of the environment map ; Optimal inspection path calculation logic: ,in, is the optimal inspection path, is the candidate path, is the quantum annealing scheduling operator, and its calculation logic is: ,in, is the total number of path nodes, To adjust the parameters, Candidate path exist Local cost of the node, inspection record: ,in, is the timestamp, For device information.

[0012] The present invention is further configured that the method comprises: Extract information from historical maintenance records and expert experience to build a knowledge graph: Use graph neural network to mine implicit rules on the constructed knowledge graph to obtain a rule set; Generate maintenance recommendations by combining health status with rule sets; Maintenance suggestion generation logic: ,in, For maintenance recommendations, is the best matching rule, and the calculation logic is: , The final health state, is the weight, is a set of rules, where For rules The best health status value applicable, Provides maintenance advice for the association; is the applicability calculation function, and the calculation logic is: ,in, is the smoothing parameter.

[0013] The present invention is further configured that the method further includes the preservation and traceability of bridge and tunnel data: calculating a hash value for a set of bridge and tunnel status, and combining blockchain storage to ensure data traceability and non-tamperability; Construct a bridge-tunnel status collection: ,in, is the bridge-tunnel state set, For the digital twin data body, For inspection records, is the health status time series, For maintenance suggestions; hash value calculation logic: ,in, is the hash value, Calculates the hash value.

[0014] The present invention also provides a bridge and tunnel inspection and maintenance management system based on a three-dimensional panoramic view and a GIS map, the system comprising: Data acquisition and model building module: The collected multi-dimensional environmental data of bridges and tunnels are processed and integrated to obtain preliminary three-dimensional map data, super-resolution enhancement is performed on the turbidity area to build a high-precision three-dimensional digital model, and the model is integrated with GIS data to obtain a digital twin data body; Damage detection and health trend prediction module: extracts microscopic spectrum features, determines the authenticity of spectrum power, and generates health status time series; Inspection path construction and optimization module: plans the autonomous inspection path of the inspection machine, introduces quantum annealing algorithm to optimize the scheduling and solve the optimal path; Maintenance suggestion generation module: construct a knowledge graph to associate historical maintenance records with the expert experience library, and mine implicit association rules to generate maintenance suggestions.

[0015] The present invention provides a bridge and tunnel inspection and maintenance management system and method based on a three-dimensional panoramic view and GIS map. The method obtains preliminary three-dimensional map data by processing and fusing the collected multi-dimensional environmental data of the bridge and tunnel, performs super-resolution enhancement on the turbidity area to construct a high-precision three-dimensional digital model, and fuses the model with GIS data to obtain a digital twin data body; extracts microscopic spectrum features, performs spectrum power authenticity judgment, and generates a health status time series; plans the autonomous inspection path of the inspection machine, introduces a quantum annealing algorithm to optimize the scheduling and solve the optimal path; constructs a knowledge graph to associate historical maintenance records with an expert experience library, and mines implicit association rules to generate maintenance suggestions. The beneficial effects produced include: High-precision three-dimensional digital modeling improves the accuracy of bridge and tunnel inspections: Multi-dimensional environmental data of bridges and tunnels is obtained through multi-source sensors such as drones, laser scanners, and panoramic cameras, and dynamic SLAM technology is used for data fusion to build a preliminary three-dimensional map to improve the integrity and real-time performance of spatial information. Local topological inconsistency analysis and spectrum phase aliasing index calculation methods are used to accurately identify turbidity areas, and super-resolution reconstruction is performed using a generative adversarial network to enhance data accuracy and ultimately generate a high-precision three-dimensional digital model. Combined with GIS geographic information data, the construction of digital twins of bridges and tunnels is realized, providing intuitive and accurate visualization support for inspection and maintenance, and improving the level of intelligent management of bridges and tunnels.

[0016] Spectrum analysis and health assessment to improve the accuracy of damage detection: Fourier transform is used to extract microscopic spectrum features, and combined with power spectrum density analysis, a spectrum power authenticity discrimination model is constructed to accurately identify the true state of structural damage and reduce false detection and missed detection rates. Through damage contour extraction and microscopic spectrum feature analysis, a health status time series is established, and a recursive formula is used to calculate the health trend, to achieve long-term health monitoring and prediction of bridge and tunnel structures, provide a scientific and reasonable early warning mechanism, and effectively avoid safety hazards caused by sudden damage.

[0017] Intelligent maintenance suggestions driven by knowledge graphs improve the scientific nature of maintenance decisions: Using knowledge graph construction methods, integrating historical maintenance records and expert experience bases, mining implicit association rules between bridge and tunnel damage and maintenance measures, and building an intelligent maintenance knowledge base. Analyze health status data through graph neural networks, automatically match the optimal maintenance strategy, and provide targeted maintenance suggestions based on the long-term health trend of bridge and tunnel structures to improve the scientific nature and accuracy of maintenance decisions. The maintenance strategy has self-learning capabilities and can be continuously optimized as data accumulates, improving the intelligence level of inspection and maintenance and reducing the need for manual intervention.

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 A flowchart of a bridge and tunnel inspection and maintenance management method based on a three-dimensional panoramic view and a GIS map is shown as an exemplary embodiment of the present invention; Figure 2 The present invention is a schematic structural diagram of a bridge and tunnel inspection and maintenance management system based on a three-dimensional panoramic view and a GIS map, showing an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0021] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0022] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0023] Embodiment 1 A bridge and tunnel inspection and maintenance management method based on 3D panoramic view and GIS map, such as Figure 1 As shown, including: The collected multi-dimensional environmental data of bridges and tunnels are processed and integrated to obtain preliminary three-dimensional map data, and the turbidity area is super-resolution enhanced to build a high-precision three-dimensional digital model, and the model is integrated with GIS data to obtain a digital twin data body; Extract microscopic spectrum features, identify the authenticity of spectrum power, and generate health status time series; Plan the autonomous inspection path of the inspection machine and introduce the quantum annealing algorithm to optimize the scheduling and solve the optimal path; Build a knowledge graph to associate historical maintenance records with expert experience base, and mine implicit association rules to generate maintenance suggestions.

[0024] The present invention is further configured that the method comprises: Use drones, laser scanners, and panoramic cameras to collect multi-dimensional data and build multi-dimensional environmental data of bridges and tunnels; Use dynamic SLAM to fuse the collected multi-dimensional environmental data of bridges and tunnels to generate preliminary three-dimensional map data; The preliminary three-dimensional map is screened according to the turbidity function to obtain the turbidity area; The graph neural network is used to perform super-resolution enhancement on the turbidity area data, replacing the original data to obtain complete high-quality data, and then the Poisson surface reconstruction algorithm is used to perform surface fitting on the complete high-quality data to obtain a high-precision three-dimensional model; The high-precision three-dimensional model is integrated with GIS to realize the digital twin and obtain the digital twin data body. Specifically, the inspection machine such as drone is used to collect information on the bridge and tunnel. The drone mainly collects local RGB image data, infrared image data, and low-precision point cloud data, among which: the local RGB image data can be used to observe the damaged area, range, and type, the infrared image data can be used to observe the temperature field distribution caused by the uneven temperature caused by hollowing and leakage, and the low-precision point cloud data can be used to observe the large volume and contour of the target or help to roughly model the vegetation obstruction area; the laser scanner mainly collects high-precision three-dimensional point cloud data and reflection intensity data, among which: the high-precision three-dimensional point cloud data can provide collection information including sub-millimeter precision point coordinates, and the reflection intensity can help to identify the difference in material properties and the rusted area; the panoramic camera mainly collects 360° spherical image data and partial depth information, which is mainly used for blind-angle visual recording and relative position marking of diseases. It is an existing technology to use dynamic SLAM to fuse multidimensional data and generate three-dimensional map data. It is a common technical means used by technicians in this field and will not be repeated here.

[0025] The present invention is further configured as follows: the turbidity function construction logic: extracting local topological inconsistency and spectrum phase aliasing index for a specified area, and obtaining a turbidity function using the local topological inconsistency and the spectrum phase aliasing index; The logic of constructing the spectrum phase aliasing index is as follows: perform two-dimensional Fourier transform on the spatial domain image to obtain spectrum data, calculate the phase spectrum based on the spectrum data, calculate the gradient of the phase spectrum to obtain the phase gradient, and define the weighted ratio of the phase gradient to the spectrum amplitude as the spectrum phase aliasing index; Turbidity function calculation logic: ,in, is the turbidity function, is the local topological inconsistency, is the spectrum phase aliasing index; Local topological inconsistency calculation logic: ,in, is the local topological inconsistency of the current data point, is the current data point, is the neighbor set of the current data point, is the number of neighbor points, It is the sum of the number of neighbor points of all neighbor points; Spectrum phase aliasing index calculation logic: ,in, is the spectral phase aliasing index, is the spectrum amplitude, is the phase spectrum, is the phase spectrum gradient; Spectrum data calculation logic: ,in, is the spectrum data, is the number of rows of input data, is the number of columns of input data, is the intensity value of the input data, is the frequency domain coordinate, is an imaginary unit, is the angular frequency factor of the Fourier transform. Specifically, the turbidity function Combined with local topological inconsistencies and spectral phase aliasing index , used to distinguish high-quality data from turbidity areas, using the S-type function to ensure smooth judgment of data quality, so that the turbidity is between The local topological inconsistency between The geometric anomalies of the laser point cloud are captured by calculating the sum of the number of neighbor points of all neighbor points and neighbor points, and the spectrum phase aliasing index By observing the phase, the state of the image is judged. The larger the value, the more it means that the phase information of the image is disturbed, which may be a turbid area. For example, the motion blur of the image will cause high-frequency phase confusion, and water stains or shadows will cause low-frequency phase distortion. These fluctuations will cause drastic changes in the phase, so a higher value will be obtained. The S-type function is used to constrain the final data range. Between; the neighbor set of the current pixel in the local topological inconsistency formula Represents pixel The surrounding adjacent point set, the number of neighbor points Represents the connectivity of the pixel in the local topology, the number of neighbors of the pixel's neighbors Indicates adjacent points The local topological complexity of the pixel; this formula measures the ratio of the number of neighbors of a pixel to the sum of the number of neighbors of all its neighboring points. The larger the ratio, the more abnormal the topological structure of the point, and the more likely it is low-quality data; the phase spectrum in the spectral phase aliasing index Represents the phase information in the spectrum data, which is related to the geometric structure of the image and is obtained by calculating the inverse tangent of the spectrum data. The specific calculation formula is: , among which, and are the real and imaginary parts of the spectrum, respectively, and the phase spectrum gradient It is used to measure the rate of change of phase in the frequency domain. It is approximated by the Sobel operator or the finite difference method. The specific calculation formula is: , used to detect the clarity of the image. The phase change of a clear image is smoother, while the phase gradient of a blurred image is more drastic. The spectrum amplitude It is the absolute value of the spectrum data, indicating the energy intensity of each frequency component. Its calculation formula is: , is used to normalize the phase gradient, ensuring The index can reflect the phase change trend and will not be affected by the overall energy size. If the amplitude is large, it means that the frequency component contributes more to the image and the aliasing effect may be small. If the amplitude is small, it means that the frequency component contributes less, but if the phase gradient is large, it may be an artifact or data anomaly. The spectrum data It is obtained by Fourier transforming the image or signal. It is a complex matrix containing the real part and the imaginary part , which represents the energy distribution of the signal at different frequencies, is a prior art and will not be described in detail here.

[0026] The present invention is further configured as follows: the turbidity area screening logic: ,in, is the turbidity area, is the current data point, To set the threshold; Digital twin data volume calculation logic: ,in, For the digital twin data body, is the fusion mapping operator, For high-precision three-dimensional models, It is the geographic information data of GIS. Specifically, the turbidity area screening is performed by calculating the turbidity function of the regional image. If the turbidity value of the area is less than the set threshold Determine that the data point belongs to a low-quality image and classify it as a turbid area. The threshold is usually set between , adjusted according to experience; the data fusion of digital twin data bodies belongs to the existing technology and is a commonly used technical means for those skilled in the art, so no further elaboration is required here.

[0027] The present invention is further configured to specifically include: Use a two-stream Transformer network architecture to fuse image and point cloud features to locate the damaged area on the digital twin data volume; Performing Fourier transform on the damaged area to obtain spectrum data of the damaged area; Calculate the power spectrum density of the spectrum data of the damaged area, construct a micro-spectral feature vector set, and judge the authenticity of the damaged area based on the obtained micro-spectral feature vector set; Predict health trends based on the identification results combined with the damaged area and spectrum data; Damage area spectrum data calculation logic: ,in, is the spectrum data of the damaged area, For the damaged area, is the intensity of the pixel in the damaged area, is the frequency domain coordinate, is the imaginary unit representing the complex nature of the Fourier transform, is the angular frequency scaling factor; Power spectral density calculation logic: ,in, is the power spectral density, is the spectrum data of the damaged area; Extract key information from power spectral density and microscopic spectrum features to form a feature vector set: ,in, is the microscopic spectrum feature vector set, is the real part mean, is the mean of the imaginary part, is the average power spectral density, is the standard deviation of the power spectrum; Spectrum power authenticity judgment logic: , is the classifier, , is the weight matrix, , is the bias vector, is the activation function, Output activation function, the judgment logic is: ,in, To determine the results, To have real damage, Specifically, the spectrum data of the damaged area is the frequency domain data obtained by performing a two-dimensional Fourier transform on the damaged area, which is a prior art and will not be described in detail here; the power spectrum density It is used to measure the power distribution of the signal at different frequencies, reflecting the energy characteristics of the signal; the real part mean It represents the average value of the real part of the spectrum data in the area, reflecting the low-frequency component energy distribution in the damaged area, which is related to the overall morphology of the damage. A higher value indicates better structural integrity, while a lower value means more serious damage. The imaginary part mean It represents the average value of the imaginary part of the obtained spectrum data in the area, reflecting the energy distribution of high-frequency components in the damaged area, which is related to the edge characteristics and texture details. A larger value indicates that there are more high-frequency details in the damaged area, such as sharper edges of cracks. If the value is too low, it means that the damaged area lacks high-frequency information, which means that the damage boundary is blurred. Average power spectral density is the average value of the power spectrum, indicating the total energy intensity of the signal, the average power spectrum density High means the signal energy in the damaged area is strong, the crack is deep or the boundary is clear, and the average power spectrum density is Low means the energy of the damaged area is low, the crack is shallow or the boundary is fuzzy; the power spectrum standard deviation It is used to measure the discrete degree of spectrum energy in the damaged area and quantify the uniformity of energy distribution. The specific calculation formula is: ,in, is the frequency domain matrix size. The higher the value, the more uneven the tiled energy distribution is, the higher the complexity of the damaged area is, and the more diverse the damage forms are. It is the first layer weight matrix used to linearly transform the input features to adjust the influence to prevent the gradient from disappearing or exploding. The value is usually or between, It is the first layer bias term used to help the neural network fit nonlinear relationships and prevent over-reliance on the weight matrix , the initial value is usually set to a small random number and optimized through training, The second-layer weight matrix is ​​used to perform weighted calculations on the output of the first layer to obtain the final classification score. The initial value is usually set to a small random number and optimized through training. The bias vector Used to adjust the classification decision boundary. The initial value is usually 0 and adjusted during training; output activation function Is the Sigmoid function, used to compress the output value to between.

[0028] The present invention is further configured as follows: the health trend calculation logic: ,in, For current health status, For the health status of the next moment, is the health transfer coefficient, is the feature map of damage information, For accurate damage contour, is the element-wise product, It is element-by-element addition; according to the formula, the complete health status time series is obtained recursively . Specifically, current health status represents the health status of the system at time t, Indicates complete health, Indicates total damage, healthy transfer coefficient Used to measure the impact of past health status on the future. The healthier the state, the less impact it will suffer; feature mapping of damage information Represents the effect of injury on health status, usually normalized to , indicating the degree of damage, activation function Is the Sigmoid function, used to compress the output value to Between; by recursively calculating the health status at the next moment, a complete health status time series is generated.

[0029] The present invention is further configured to generate a set of candidate paths using an A* search algorithm for the predicted damaged area that is less than the final health state threshold; The candidate path set is screened and optimized using the quantum annealing scheduling operator to obtain the optimal inspection path; The optimal inspection path is passed to the inspection system to autonomously allocate inspection machines, and the data is recorded to generate inspection records after the inspection is completed; Candidate path calculation logic: ,in, is the candidate path, For the search algorithm, As a starting point, is the target point, Construct a set of candidate paths for the weighted graph of the environment map ; Optimal inspection path calculation logic: ,in, is the optimal inspection path, is the candidate path, is the quantum annealing scheduling operator, and its calculation logic is: ,in, is the total number of path nodes, To adjust the parameters, Candidate path exist Local cost of the node, inspection record: ,in, is the timestamp, For device information. Specifically, the candidate path It is the inspection path calculated from the starting point to the target point, which is an optional solution for the inspection robot and a weighted graph of the environment map. Used to calculate the path cost, , It is a checkpoint in the environment. is the path between nodes, is the path cost, such as distance, obstacle weight; quantum annealing scheduling operator It is used to optimize path selection, calculate the global cost of the path, select the path with the smallest cost as the optimal inspection path, and the local cost Includes: path length cost, energy consumption cost, safety cost, time cost, environmental adaptability, and adjustment parameters Determines the degree of influence of local cost on the total cost, and its value range is .0, adjust the value according to different scenarios.

[0030] The present invention is further configured to extract information from historical maintenance records and expert experience to construct a knowledge graph: Use graph neural network to mine implicit rules on the constructed knowledge graph to obtain a rule set; Generate maintenance recommendations by combining health status with rule sets; Maintenance suggestion generation logic: ,in, For maintenance recommendations, is the best matching rule, and the calculation logic is: , The final health state, is the weight, is a set of rules, where For rules The best health status value applicable, Provides maintenance advice for the association; is the applicability calculation function, and the calculation logic is: ,in, is a smoothing parameter. Specifically, the knowledge graph is a structured knowledge representation method, which includes entities and relationships between entities. The constructed knowledge graph nodes include: bridge and tunnel components, damage types, maintenance measures, and environmental factors; among them, the bridge and tunnel component nodes include attributes: component type, casting material, design life, manufacturing year, bridge and tunnel to which they belong, and current health status; the damage type node includes attributes: damage type, damage length, damage width, damage location, and discovery time; the maintenance measures node includes attributes: maintenance method, maintenance cost, validity period, and construction conditions; the environmental factor node includes attributes: environment type, degree of impact, and scope of impact; the use of graph neural networks for implicit rule mining is an existing technology and a common technical means used by those skilled in the art, so I will not elaborate on it here; the rule set includes all the rules mined using the graph neural network, each rule has an associated health state and a corresponding maintenance suggestion, and the best match is selected by matching the predicted final health state with the health state in the rule set, and the maintenance suggestion feedback corresponding to the rule is selected; the weight Indicates the importance of the rule, and its value range is usually ,in It means the most important. represents the least important smoothing parameter Indicates the effect of controlling health status differences on suitability, and common values ​​are , the smaller the value, the more sensitive the matching degree.

[0031] The present invention is further configured that the method further includes the preservation and traceability of bridge and tunnel data: calculating a hash value for a set of bridge and tunnel status, and combining blockchain storage to ensure data traceability and non-tamperability; Construct a bridge-tunnel status collection: ,in, is the bridge-tunnel state set, For the digital twin data body, For inspection records, is the health status time series, For maintenance suggestions; hash value calculation logic: ,in, is the hash value, It is a hash value calculation function. Specifically, the hash value is a unique identifier of fixed length, which indicates the uniqueness of the data. Whenever the bridge and tunnel status set is updated, its hash value will be calculated, and the hash value and its corresponding data will be stored in the blockchain. Each new data point will form a new big block to join the blockchain. Once the data enters the blockchain, it will not be tampered with or deleted, which ensures the plasticity and credibility of the data.

[0032] Embodiment 2 See also Figure 2 The exemplary bridge and tunnel inspection and maintenance management system based on three-dimensional panoramic view and GIS map includes: Data acquisition and model building module: The collected multi-dimensional environmental data of bridges and tunnels are processed and integrated to obtain preliminary three-dimensional map data, super-resolution enhancement is performed on the turbidity area to build a high-precision three-dimensional digital model, and the model is integrated with GIS data to obtain a digital twin data body; Damage detection and health trend prediction module: extracts microscopic spectrum features, determines the authenticity of spectrum power, and generates health status time series; Inspection path construction and optimization module: plans the autonomous inspection path of the inspection machine, introduces quantum annealing algorithm to optimize the scheduling and solve the optimal path; Maintenance suggestion generation module: construct a knowledge graph to associate historical maintenance records with the expert experience library, and mine implicit association rules to generate maintenance suggestions.

[0033] It should be noted that the bridge and tunnel inspection and maintenance management system based on three-dimensional panoramic view and GIS map provided in the above embodiment and the bridge and tunnel inspection and maintenance management method based on three-dimensional panoramic view and GIS map provided in the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment and will not be repeated here. In actual application, the bridge and tunnel inspection and maintenance management system based on three-dimensional panoramic view and GIS map provided in the above embodiment can distribute the above functions to different functional modules as needed, that is, divide the internal structure of the system into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0034] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0035] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0036] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0037] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0038] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0039] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0040] In the several embodiments provided in the present application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0041] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0042] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0043] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0044] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A bridge and tunnel inspection and maintenance management method based on three-dimensional panoramic view and GIS map, characterized in that: include: The collected multi-dimensional environmental data of bridges and tunnels are processed and integrated to obtain preliminary three-dimensional map data, and the turbidity area is super-resolution enhanced to build a high-precision three-dimensional digital model, and the model is integrated with GIS data to obtain a digital twin data body; Extract microscopic spectrum features, identify the authenticity of spectrum power, and generate health status time series; Plan the autonomous inspection path of the inspection machine and introduce the quantum annealing algorithm to optimize the scheduling and solve the optimal path; Build a knowledge graph to associate historical maintenance records with expert experience base, and mine implicit association rules to generate maintenance suggestions.

2. A bridge and tunnel inspection and maintenance management method based on three-dimensional panoramic view and GIS map according to claim 1, characterized in that: The method comprises: Use drones, laser scanners, and panoramic cameras to collect multi-dimensional data and build multi-dimensional environmental data of bridges and tunnels; Use dynamic SLAM to fuse the collected multi-dimensional environmental data of bridges and tunnels to generate preliminary three-dimensional map data; The preliminary three-dimensional map is screened according to the turbidity function to obtain the turbidity area; The graph neural network is used to perform super-resolution enhancement on the turbidity area data, replacing the original data to obtain complete high-quality data, and then the Poisson surface reconstruction algorithm is used to perform surface fitting on the complete high-quality data to obtain a high-precision three-dimensional model; The high-precision three-dimensional model is integrated with GIS to realize digital twin and obtain the digital twin data body.

3. A bridge and tunnel inspection and maintenance management method based on three-dimensional panoramic view and GIS map according to claim 2, characterized in that: Turbidity function construction logic: extract local topological inconsistency and spectral phase aliasing index for the specified area, and use local topological inconsistency and spectral phase aliasing index to obtain turbidity function; The logic of constructing the spectrum phase aliasing index is as follows: perform two-dimensional Fourier transform on the spatial domain image to obtain spectrum data, calculate the phase spectrum based on the spectrum data, calculate the gradient of the phase spectrum to obtain the phase gradient, and define the weighted ratio of the phase gradient to the spectrum amplitude as the spectrum phase aliasing index; Turbidity function calculation logic: ,in, is the turbidity function, is the local topological inconsistency, is the spectrum phase aliasing index; Local topological inconsistency calculation logic: ,in, is the local topological inconsistency of the current data point, is the current data point, is the neighbor set of the current data point, is the number of neighbor points, It is the sum of the number of neighbor points of all neighbor points; Spectrum phase aliasing index calculation logic: ,in, is the spectral phase aliasing index, is the spectrum amplitude, is the phase spectrum, is the phase spectrum gradient; Spectrum data calculation logic: ,in, is the spectrum data, is the number of rows of input data, is the number of columns of input data, is the intensity value of the input data, is the frequency domain coordinate, is an imaginary unit, is the angular frequency factor of the Fourier transform.

4. A bridge and tunnel inspection and maintenance management method based on three-dimensional panoramic view and GIS map according to claim 3, characterized in that: Turbidity area screening logic: ,in, is the turbidity area, is the current data point, To set the threshold; Digital twin data volume calculation logic: ,in, For the digital twin data body, is the fusion mapping operator, For high-precision three-dimensional models, It is geographic information data of GIS.

5. The bridge and tunnel inspection and maintenance management method based on three-dimensional panoramic view and GIS map according to claim 1 is characterized in that: Specifically include: Use a two-stream Transformer network architecture to fuse image and point cloud features to locate the damaged area on the digital twin data volume; Performing Fourier transform on the damaged area to obtain spectrum data of the damaged area; Calculate the power spectrum density of the spectrum data of the damaged area, construct a micro-spectral feature vector set, and judge the authenticity of the damaged area based on the obtained micro-spectral feature vector set; Predict health trends based on the identification results combined with the damaged area and spectrum data; Damage area spectrum data calculation logic: ,in, is the spectrum data of the damaged area, For the damaged area, is the intensity of the pixel in the damaged area, is the frequency domain coordinate, is the imaginary unit representing the complex nature of the Fourier transform, is the angular frequency scaling factor; Power spectral density calculation logic: ,in, is the power spectral density, is the spectrum data of the damaged area; Extract key information from power spectral density and microscopic spectrum features to form a feature vector set: ,in, is the microscopic spectrum feature vector set, is the real part mean, is the mean of the imaginary part, is the average power spectral density, is the standard deviation of the power spectrum; Spectrum power authenticity judgment logic: , is the classifier, , is the weight matrix, , is the bias vector, is the activation function, Output activation function, the judgment logic is: ,in, To determine the results, To have real damage, It is a false detection.

6. A bridge and tunnel inspection and maintenance management method based on three-dimensional panoramic view and GIS map according to claim 5, characterized in that: Health trend calculation logic: ,in, For current health status, For the health status of the next moment, is the health transfer coefficient, is the feature map of damage information, For the damaged area, is the element-wise product, It is element-by-element addition; according to the health trend calculation formula, the complete health status time series is obtained recursively .

7. The bridge and tunnel inspection and maintenance management method based on three-dimensional panoramic view and GIS map according to claim 1 is characterized in that: The method comprises: For the predicted damaged area smaller than the final healthy state threshold, an A* search algorithm is used to generate a set of candidate paths; The candidate path set is screened and optimized using the quantum annealing scheduling operator to obtain the optimal inspection path; The optimal inspection path is passed to the inspection system to autonomously allocate inspection machines, and the data is recorded to generate inspection records after the inspection is completed; Candidate path calculation logic: ,in, is the candidate path, For the search algorithm, As a starting point, is the target point, Construct a set of candidate paths for the weighted graph of the environment map ; Optimal inspection path calculation logic: ,in, is the optimal inspection path, is the candidate path, is the quantum annealing scheduling operator, and its calculation logic is: ,in, is the total number of path nodes, To adjust the parameters, Candidate path exist Local cost of the node, inspection record: ,in, is the timestamp, For device information.

8. The bridge and tunnel inspection and maintenance management method based on three-dimensional panoramic view and GIS map according to claim 1 is characterized in that: The method comprises: Extract information from historical maintenance records and expert experience to build a knowledge graph: Use graph neural network to mine implicit rules on the constructed knowledge graph to obtain a rule set; Generate maintenance recommendations by combining health status with rule sets; Maintenance suggestion generation logic: ,in, For maintenance recommendations, is the best matching rule, and the calculation logic is: , The final health state, is the weight, is a set of rules, where For rules The best health status value applicable, Provides maintenance advice for the association; is the applicability calculation function, and the calculation logic is: ,in, is the smoothing parameter.

9. A bridge and tunnel inspection and maintenance management method based on three-dimensional panoramic view and GIS map according to claim 1, characterized in that: The method also includes the preservation and traceability of bridge and tunnel data: calculating a hash value for a set of bridge and tunnel status, and combining blockchain storage to ensure data traceability and non-tamperability; Construct a bridge-tunnel status collection: ,in, is the bridge-tunnel state set, For the digital twin data body, For inspection records, is the health status time series, For maintenance suggestions; hash value calculation logic: ,in, is the hash value, Calculates the hash value.

10. A bridge and tunnel inspection and maintenance management system based on three-dimensional panoramic view and GIS map, used to implement a bridge and tunnel inspection and maintenance management method based on three-dimensional panoramic view and GIS map as described in any one of claims 1 to 9, characterized in that: include: Data acquisition and model building module: The collected multi-dimensional environmental data of bridges and tunnels are processed and integrated to obtain preliminary three-dimensional map data, the turbidity area is super-resolution enhanced to build a high-precision three-dimensional digital model, and the model is integrated with GIS data to obtain a digital twin data body; Damage detection and health trend prediction module: extracts microscopic spectrum features, determines the authenticity of spectrum power, and generates health status time series; Inspection path construction and optimization module: plans the autonomous inspection path of the inspection machine, introduces quantum annealing algorithm to optimize the scheduling and solve the optimal path; Maintenance suggestion generation module: construct a knowledge graph to associate historical maintenance records with the expert experience library, and mine implicit association rules to generate maintenance suggestions.

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