Quantum entanglement neuromorphic bridge disease holographic monitoring repair method and system

Abstract

Quantum entanglement neuro morphology bridge disease holographic monitoring repair method and system: deploy quantum entanglement sensor array to obtain micro-disease information and fuse infrared sensor, laser radar, industrial camera data, encrypt transmission preprocessing through quantum communication network to generate fusion feature vector, disease category probability, severity score and comprehensive risk index; build bridge three-dimensional coordinate system to reconstruct three-dimensional geometric model, map the index to three-dimensional coordinate system, output three-dimensional disease holographic model through spatial clustering; weighted sum of comprehensive risk index, average severity, structure and traffic importance weight to get repair priority score and sort, under the constraint of cost and construction period, take repair cost, time, risk reduction and life increment as the comprehensive optimization objective, solve the construction control parameter and dynamically evaluate the repair quality to generate structured repair record; continuously collect monitoring data to calculate health indicators, establish degradation model to determine failure probability and remaining life, form maintenance plan and update parameters.

Description

Technical Field

[0001] This invention belongs to the field of health monitoring and repair technology for intelligent infrastructure, specifically relating to a method and system for holographic monitoring and repair of defects in quantum entangled neuromorphic bridges. Background Technology

[0002] As vital transportation infrastructure, the safety and durability of bridges directly impact public safety. With increasing service life, bridges are prone to structural defects such as cracks, corrosion, spalling, and honeycombing due to environmental factors, traffic loads, and material aging. Traditional bridge defect detection methods rely heavily on manual inspections and simple manual measuring tools, such as crack gauges and microscopes. These methods are inefficient, subjective, and prone to missed detections and misjudgments, especially in complex environments such as the bridge substructure, tunnels, and bearing areas, where manual inspections cannot cover all areas and cannot provide comprehensive structural monitoring.

[0003] With the rapid development of sensor technology, computer vision, and artificial intelligence, non-contact sensing technologies such as machine vision, drones, laser scanning, and infrared thermal imaging have been gradually applied to bridge defect detection. These technologies have improved the efficiency of defect detection and, to some extent, reduced manual intervention and improved the objectivity of data. However, existing technologies still face the following technical bottlenecks:

[0004] (1) Insufficient accuracy and sensitivity in disease detection: Traditional sensor technologies, especially two-dimensional image recognition technology, cannot effectively detect minute cracks, early corrosion, or hidden structural diseases. In particular, when the disease is in its early stages, the sensitivity and accuracy of traditional sensors cannot meet the monitoring requirements. Existing technologies mostly rely on vision-based detection, which cannot capture minute structural changes in real time with high precision.

[0005] (2) Insufficient three-dimensional positioning and spatial reconstruction capabilities: Although some technologies have applied sensors such as LiDAR and structured light to model bridges in three dimensions, in complex environments (such as the bottom of the bridge, main cable, tunnel section, etc.), the existing three-dimensional positioning and spatial reconstruction accuracy is insufficient, and the accurate positioning of the damaged area still faces challenges.

[0006] (3) Low level of intelligence and automation: Existing systems usually rely on manual judgment and manual repair decisions, lacking intelligent adaptive repair mechanisms and unable to dynamically adjust repair plans based on monitoring data, resulting in insufficient optimization of repair effects. Most systems fail to make full use of long-term monitoring data to optimize repair strategies.

[0007] (4) Insufficient data fusion capability: Although existing technologies attempt to fuse data from different types of sensors (such as vision, infrared, laser, etc.), the fusion technology of these systems is still in its initial stage and cannot fully integrate various types of data, resulting in insufficient accuracy in disease detection and location. Existing technologies still have significant shortcomings in the depth and accuracy of multi-source data fusion, and cannot achieve comprehensive optimization of disease detection and repair strategies.

[0008] (5) Poor environmental adaptability: Most existing technologies rely on GNSS signals or automated flight modes based on fixed paths, which are easily interfered with in complex environments (such as tunnels, under bridges, etc.), affecting the stability and accuracy of monitoring and repair, thus limiting the adaptability of the system.

[0009] (6) Lack of long-term health monitoring and predictive maintenance capabilities: Existing technologies are usually limited to single-time detection and repair, lacking continuous tracking and maintenance planning for the long-term health of bridges. Existing technologies have failed to effectively combine the evolution and degradation processes of defects for long-term prediction and risk assessment, resulting in bridge maintenance still being mainly passive repair, lacking the ability for proactive prevention and predictive maintenance. Summary of the Invention

[0010] To address the problems existing in the prior art, this invention provides a quantum entangled neuromorphic bridge holographic monitoring and repair method and system. The aim is to achieve high-precision acquisition of microscopic bridge defect data through a quantum entangled sensor array, overcoming the accuracy and sensitivity bottlenecks of traditional sensors. It exhibits significant advantages, particularly in the detection of microcracks, hidden corrosion, and other early-stage defects. Furthermore, by combining neuromorphic computing technology with multi-source data fusion, it achieves automated defect identification, classification, location, and severity assessment. This enables comprehensive monitoring, dynamic repair, and long-term health assessment of bridge structures, enhancing the intelligence and automation level of bridge management, significantly reducing maintenance costs and repair risks, and overcoming the shortcomings of traditional systems in terms of intelligence and automation.

[0011] To achieve the above objectives, the specific solution of the present invention is as follows:

[0012] A holographic monitoring and repair method for quantum entangled neuromorphic bridge defects includes the following steps:

[0013] Step 1: Deploy a quantum entangled sensor array on the bridge structure to obtain signal intensity and phase information of microscopic defects through quantum interference effect; fuse the signal intensity and phase information with data output from infrared sensors, lidar, and industrial cameras to obtain fused data; encrypt and transmit the fused data through a quantum communication network; store the encrypted data in a distributed cloud system and preprocess it to obtain preprocessed multi-source fused data.

[0014] Step 2: After spatiotemporal alignment, the preprocessed multi-source fusion data obtained in Step 1 is used to generate fusion feature vectors, confidence scores, disease category probabilities, severity scores, and comprehensive risk indices on a discrete spatial point set.

[0015] Step 3: Based on the preprocessed multi-source fusion data obtained in Step 1, establish a three-dimensional coordinate system for the bridge and reconstruct its three-dimensional geometric model; map the probability of disease categories, severity scores, and comprehensive risk index generated in Step 2 onto the point cloud or mesh in the bridge's three-dimensional coordinate system to form a three-dimensional disease model with disease attributes; perform spatial clustering on the three-dimensional disease model with disease attributes to extract several disease clusters; extract geometric parameters for each disease cluster and define a structured disease object for each cluster. The structured disease object includes the spatial range of the disease area, disease type, average severity, comprehensive risk index, and set of geometric parameters; collect all structured disease objects to form a three-dimensional holographic model of the bridge's disease.

[0016] Step 4: For each structured defect object in the set of structured defects obtained in Step 3, a comprehensive repair priority score is obtained by weighting and summing the comprehensive risk index, average severity, structural importance weight, and traffic importance weight, and then sorting them. Under the constraints that the total cost does not exceed the budget and the total construction period does not exceed the allowable time window, the optimal repair scheme is determined for each structured defect object in sequence, with the comprehensive optimization objective being a weighted combination of repair cost, repair time, risk reduction, and life increment. For each structured defect object, the construction control parameter trajectory that minimizes the weighted integral of repair error and control energy consumption is solved, with the state equation and control input range as constraints. During the repair process, the repair quality score and residual risk index are dynamically evaluated based on real-time monitoring data. When the repair quality score is not lower than the final repair quality judgment threshold and the residual risk index is not higher than the allowable residual risk upper limit, the repair of the structured defect object is determined to be completed, and a structured repair record containing the original structured defect object, the final repair scheme, key control parameters, start and end times, and its quality and risk curves is generated.

[0017] Step 5 involves continuously collecting bridge monitoring data based on the structured repair records generated in Step 4, calculating component-level health indicators and the overall bridge health index, extracting environmental action vectors and load action vectors, establishing a degradation state, building a degradation model and correcting the degradation model parameters, and using the updated component-level health indicators and degradation state to determine the failure probability and remaining life; performing multi-cycle cost discounting optimization on the maintenance action sequence at discrete decision moments to form a maintenance plan; and continuously updating the degradation model parameters, health indicator weights, risk thresholds, and maintenance strategy weights, and outputting the updated maintenance plan to the asset management platform.

[0018] Furthermore, the quantum interference effect described in step 1 employs quantum state and interferometry measurements, as shown in the following formula:

[0019] (1),

[0020] In the formula: Represents the quantum state of a quantum entangled sensor; and The probability amplitude of a qubit characterizes the performance of a quantum sensor under different measurement conditions;

[0021] The formula for measuring signal strength is as follows:

[0022] (2),

[0023] In the formula: For quantum entanglement sensors at time The intensity of the acquired image; For strength measurement operators; For the evolution of quantum states over time;

[0024] The formula for obtaining the phase information is as follows:

[0025] (3),

[0026] In the formula: For quantum phase; For phase measurement operators; Let be the quantum state of the system at time t;

[0027] The formula for data fusion is as follows:

[0028] (4),

[0029] In the formula: The merged data; For the first Data from one sensor; These are weighting coefficients, dynamically adjusted based on the accuracy and signal-to-noise ratio of the quantum entanglement sensor.

[0030] The formula for encrypted transmission is as follows:

[0031] (5),

[0032] In the formula: For transmitted quantum data; This refers to the quality factor of the transmission channel; Data collected by the sensor.

[0033] Furthermore, the formula for fusing the feature vectors in step 2 is as follows:

[0034] (6),

[0035] In the formula: , , , These are transformation functions for feature extraction and dimension unification of data from quantum entangled sensors, lidar, infrared sensors, and industrial cameras, respectively. The transformation functions are normalization, filtering, or principal component extraction, and the output is a vector with unified dimension. , , , The weighting coefficients for each sensor channel satisfy the following conditions: Each weight is dynamically updated based on the sensor's signal-to-noise ratio, reliability under current operating conditions, and historical performance evaluation.

[0036] The formula for the confidence level is as follows:

[0037] (7),

[0038] In the formula: Indicates confidence level; For each sensor at point ,time The signal-to-noise ratio estimate; It is a monotonically increasing normalization function used to map the multi-channel signal-to-noise ratio to a comprehensive confidence value between 0 and 1;

[0039] The formula for the probability of the disease category is as follows:

[0040] (11),

[0041] In the formula: For point This belongs to the category of disease The predicted probability; For category The corresponding output neuron at time 1 The pulse; This is a normalization factor used to normalize all category outputs to... The interval, and satisfying ;

[0042] The formula for the severity score is as follows:

[0043] (12),

[0044] In the formula: For point The overall severity score of the location; For the first The disease characteristics include estimated crack width, corrosion area ratio, texture roughness, and abnormal temperature gradient values. The weight of each disease characteristic's contribution to severity is determined by training or engineering experience;

[0045] The formula for the comprehensive risk index is as follows:

[0046] (13)

[0047] In the formula: For point The comprehensive risk index, also known as the priority index; It is a monotonically increasing function used to combine disease probability, severity, and confidence into a single risk score.

[0048] Furthermore, step 3, which involves establishing a three-dimensional coordinate system for the bridge and reconstructing its three-dimensional geometric model from the preprocessed multi-source fusion data, includes the following steps:

[0049] Step 301: Establish the world coordinate system of the bridge structure as the global three-dimensional reference coordinate system;

[0050] Step 302: Perform extrinsic parameter calibration on the quantum entangled sensor, industrial camera, infrared camera, and lidar to obtain the rigid body transformation matrices from the coordinate system of the quantum entangled sensor, industrial camera, infrared camera, and lidar to the world coordinate system as follows;

[0051] (14)

[0052] In the formula: for The rotation matrix represents the attitude of the sensor coordinate system relative to the world coordinate system; for The translation vector represents the position of the sensor origin in the world coordinate system; for The homogeneous transformation matrix;

[0053] Step 303, for camera-type sensors, there is also an intrinsic parameter matrix K:

[0054] (15)

[0055] In the formula: , These are the equivalent focal lengths in the horizontal and vertical directions, respectively. , These are the coordinates of the principal point on the imaging plane;

[0056] Step 304: Based on multi-view images, sparse point clouds of the bridge surface are reconstructed using structure-from-motion techniques, and dense point clouds are constructed through multi-view stereo modeling. For a 3D point with homogeneous coordinates in the world coordinate system, its homogeneous pixel coordinates projected onto the camera image plane are:

[0057] (16)

[0058] In the formula: It is a scaling factor; , This refers to the rotation and translation from the world coordinate system to the camera coordinate system; This is the intrinsic parameter matrix of the industrial camera;

[0059] Introducing a lidar point cloud, we align it to the world coordinate system of the moving point cloud of the structure through rigid body registration. The objective function is then optimized as follows:

[0060] (17)

[0061] In the formula: , This refers to the rotation and translation from the lidar coordinate system to the world coordinate system; To and The corresponding SfM point cloud points.

[0062] Further, step 3, which maps the probability of disease categories, severity scores, and comprehensive risk indices onto point clouds or meshes mapped to the bridge's three-dimensional coordinate system to form a three-dimensional disease model with disease attributes, includes the following steps:

[0063] Step 305, let the defective pixels on a certain camera image be... The corresponding camera extrinsic parameters are The projection matrix is:

[0064] (18)

[0065] In the formula, P represents the projection matrix, which contains the camera's intrinsic and extrinsic parameters; K represents the camera's intrinsic parameter matrix, which contains parameters such as the camera's focal length and principal point coordinates, and is usually a 3×3 matrix.

[0066] The spatial ray corresponding to the defective pixel is:

[0067] , (19)

[0068] In the formula: It is a point on the spatial ray, representing the spatial location of the defective pixel; This represents the camera's position in the world coordinate system. It is obtained by back-projection of pixel coordinates through an intrinsic rotation matrix; As a scaling factor or scale factor, it determines the distance from the camera's optical center along the ray direction, controlling the depth of the defective pixels.

[0069] Step 306, in the 3D point cloud or mesh model or In the given information, find the intersection point of the ray and the surface of the bridge. The intersection point is defined as the spatial location of the disease in the world coordinate system, and the corresponding disease category probability is assigned. Disease severity Comprehensive Risk Index Assign the value to this point:

[0070] (20)

[0071] Step 307: For the data from the lidar or quantum entangled sensor array described in Step 1, the points in the lidar or quantum entangled sensor array coordinate system are directly mapped to the world coordinate system through extrinsic parameter transformation:

[0072] (twenty two),

[0073] In the formula, X w Indicates the position of a point in the world coordinate system; T ws The transformation matrix from the world coordinate system to the sensor coordinate system; X s Indicates the position of the point in the sensor coordinate system;

[0074] The defect attributes of spatial points in the lidar or quantum entanglement sensor array are then assigned to the corresponding world coordinate system points:

[0075] (twenty three),

[0076] Obtain a 3D point set with disease attributes:

[0077] (twenty four),

[0078] In the formula, D w Represents a three-dimensional point set with disease attributes; This indicates the position of the k-th point in the world coordinate system; This indicates the disease category at the k-th point; This represents the severity score for the k-th point; This represents the risk index at point k;

[0079] Furthermore, step 3, which involves spatial clustering of the three-dimensional disease model with disease attributes, extracting geometric parameters, and constructing a three-dimensional holographic model of the disease, includes:

[0080] Step 308: Use a spatial clustering method based on distance and risk threshold to set the spatial neighborhood radius. and minimum points These points are clustered into several disease clusters. ;

[0081] Step 309: For crack-type defects, estimate the principal direction and length using principal component analysis:

[0082] (25)

[0083] For morphological diseases, project them onto a locally fitted plane and calculate the area:

[0084] (26)

[0085] In the formula, A i a represents the local area of ​​the i-th disease cluster; k This represents the local area contribution of the k-th point;

[0086] Step 310: Calculate the average severity and overall risk index for each disease cluster:

[0087] (27)

[0088] (28)

[0089] In the formula, This represents the average severity of the i-th disease cluster; Indicates disease clusters The number of midpoints; This indicates the severity of the k-th point; This represents the comprehensive risk index of the i-th disease cluster; This represents the risk index at point k;

[0090] For each disease cluster, construct a spatially labeled unit with attributes and define a structured disease object:

[0091] (29)

[0092] In the formula: This represents the structured disease object of the i-th disease cluster; The disease cluster represents the set of points or the range of regions in three-dimensional space. The types of damage include cracks, corrosion, and peeling, caused by... The largest category has been determined; This represents the average severity of the disease cluster; This represents the comprehensive risk index for this disease cluster; This is a set of geometric parameters for the defect, including length, area, volume, main direction, and component number.

[0093] Step 311: Gather all the damaged objects to form a three-dimensional holographic model of the bridge's damage:

[0094] (30)

[0095] In the formula, H represents the three-dimensional holographic model of the bridge's defects, which is a set containing structured defect objects of all defect clusters; B1 represents the first defect object in the set of structured defect objects; B2 represents the second defect pair in the set of structured defect objects; B M This represents the Mth disease pair in the structured disease object set;

[0096] Furthermore, the formula for the comprehensive repair priority scoring described in step 4 is as follows:

[0097] (33),

[0098] In the formula: Indicates disease clusters The repair priority score is set, with higher scores indicating higher priority for repair. This represents the comprehensive disease risk index obtained in step three; Indicates the average severity of the disease; This indicates the importance weight of the component where the defect is located to the overall structural safety; for example, the main beam and main tower can be assigned a higher value. This indicates the importance weight of the location of the defect to traffic function, such as assigning higher values ​​to lanes and key connecting parts; , , , These are weighting coefficients used to balance risk, severity, structural importance, and traffic importance.

[0099] The function of the comprehensive optimization objective is as follows:

[0100] (42),

[0101] In the formula, To comprehensively optimize the target value, a smaller value indicates a better overall effect; , , , The importance weights are respectively: cost, construction period, risk reduction, and lifespan increase; This represents the set of all disease clusters i and their corresponding remediation schemes. Perform summation; C ij Indicates the cost of repairing disease cluster i using repair scheme j; x ij Represents a binary variable, indicating whether repair scheme j corresponds to disease cluster i; T ij Indicates the repair time for disease cluster i under repair scheme j; △R ij Indicates the reduction in risk of disease cluster i by repair scheme j; △L ij This represents the lifespan increment of repair scheme j on disease cluster i;

[0102] The formula for determining the optimal repair scheme for each structured disease object in sequence is as follows:

[0103] (44),

[0104] In the formula, The indicator variable represents the optimal repair scheme for the i-th structured disease object. A value of 1 indicates that the repair scheme is selected, and a value of 0 indicates that the scheme is not selected. arg max represents the maximization operation, that is, selecting the j value that maximizes the objective function. This represents the set of repair solutions belonging to the i-th structured disease object. ; This represents the optimized value of the i-th structured defect object under the j-th repair scheme;

[0105] The constraints based on the state equation and control input range include: describing the dynamics of the repair process using the following state equation:

[0106] (45)

[0107] In the formula: The derivative of the state with respect to time; A kinetic function describing the repair process; This indicates external disturbances, such as ambient temperature and humidity, or on-site construction disturbances.

[0108] The solution for the construction control parameter trajectory that minimizes the weighted integral of repair error and control energy consumption includes:

[0109] In the time window Internal structural local performance indicators:

[0110] (46)

[0111] In the formula: Indicates disease clusters The control optimization objective; This indicates the correction of errors, such as the target state. Compared with the current state difference; This represents a quadratic term that controls the input energy or intensity. , Weighting coefficients used to balance repair accuracy and control energy consumption / construction intensity;

[0112] The dynamic evaluation of the repair quality score based on real-time monitoring data includes: calculating the repair quality score using a repair quality evaluation function, which is as follows:

[0113] (49)

[0114] In the formula: Indicates time Repair quality score; This represents the m-th quality index function, such as residual crack width, surface smoothness, material hardness, and bond strength. Indicates the importance weight of each quality indicator; This represents the repair status of the i-th structured defect object at time t;

[0115] The criteria for determining whether the repair of the structured defect object is complete are as follows:

[0116] (51),

[0117] In the formula: Indicates the time when the repair was completed; This represents the final repair quality assessment threshold, which is typically higher than the process threshold. ; This indicates the maximum permissible residual risk. This indicates the time when the i-th structured defect object is repaired. The residual risk index represents the degree of risk that remains after repair; the lower the value, the lower the risk after repair.

[0118] When all of the above conditions are met, the disease cluster is determined. Repair complete;

[0119] The formula for generating the structured repair record is as follows:

[0120] (52),

[0121] In the formula: Represents the structured repair record for the i-th structured defect object; Represents the original structured disease object; This indicates the final repair solution adopted; This indicates the trajectory or set of key parameters used during the repair process; , This represents the quality and risk curves of the repair process and its results. , Indicates the start and end times of the repair process.

[0122] Furthermore, the function of the component-level health indicator mentioned in step 5 is as follows:

[0123] (54),

[0124] In the formula: Representing components The health index is such that the higher the value, the healthier the person is. Represents the k-th component's... Individual health sub-indicator functions, such as features obtained based on damage indicators, frequency changes, and modal stiffness changes; y represents the weight of the m-th health sub-indicator of the k-th component; k (t) represents the health status value of the k-th component at time t, which represents the specific health parameters of the component at a certain time.

[0125] The formula for the overall health index of the bridge is as follows:

[0126] (55),

[0127] In the formula: The overall health index of the bridge represents the overall health status of the bridge at time t. For the first The weight of each component in relation to the overall structure; Indicates the first The health index of each component;

[0128] The formula for establishing the degradation model is as follows:

[0129] (56),

[0130] In the formula, Indicates the time of the i-th component The amount of degradation or disease status at any given time; It indicates the extent of damage or degradation, including average crack width, corrosion depth, and stiffness damage factor; The environmental impact vector, including temperature, humidity, chloride ion concentration, and number of freeze-thaw cycles, is obtained from sensor data. This represents the load vector, including traffic volume levels, daily average axle load, etc., obtained from monitoring or statistical data; The degradation evolution function is calibrated using a combination of mechanistic model and data-driven approach. This represents the random disturbance term, used to characterize model errors and unmeasurable uncertainties;

[0131] The formula for correcting the parameters of the degradation model is as follows:

[0132] (57),

[0133] In the formula: This represents the updated value of the degradation model parameter vector for the i-th component; This represents the old value of the degradation model parameter vector for the i-th component; This represents the actual measured degradation of the i-th component at time t, which is usually the measured value obtained by the sensor; This represents the predicted degradation amount of the i-th component at time t; This represents the gain matrix of the i-th component, which is a dynamically adjusted coefficient used to control the update speed and range of the degradation model;

[0134] The formula for the failure probability is as follows:

[0135] (59),

[0136] In the formula, This represents the failure probability of the k-th component at time t; The failure function represents the failure function of the k-th component; This represents the amount of degradation of the k-th component at time t; This represents the risk factor of the k-th component;

[0137] The formula for remaining lifetime is as follows:

[0138] (60)

[0139] In the formula, Indicates the time of the k-th component The remaining lifespan at any given moment; Indicates the predetermined failure time of the component; This indicates the current point in time and is typically the time stamp used when calculating remaining lifetime.

[0140] The multi-period cost discounting optimization of the maintenance action sequence at discrete decision moments includes the following steps:

[0141] Viewing long-term maintenance as occurring at discrete decision moments Sequential decision-making problems performed on top of each other;

[0142] Define period The status is:

[0143] (61),

[0144] In the formula, This represents the state vector of the nth cycle; This indicates the amount of degradation or the state of damage of the i-th component in the n-th cycle; This represents the health index of the i-th component in the n-th cycle; This represents the failure probability of the i-th component in the n-th cycle;

[0145] Define a single-cycle cost function:

[0146] (62),

[0147] In the formula: This represents the total cost of the nth cycle, which is the sum of all maintenance and risk-related costs within that cycle. Indicates the first The maintenance cost per cycle is usually related to the specific maintenance actions or strategies taken, such as repair, inspection, and replacement. It is typically related to the number and complexity of the actions performed. Indicates the first The risk cost per cycle is typically due to the expected losses that may result from component degradation or failure.

[0148] Long-run total cost written in discounted form:

[0149] (63),

[0150] In the formula: The long-term expected total cost is represented by E, which represents the expected value, typically calculated by weighting all possible scenarios (through a probability distribution) to determine the expected value of a cost. This represents the cycle number, where n ranges from 0 to N, traversing the entire maintenance decision cycle. This represents the discount factor, a coefficient less than 1 used to discount future costs; This represents the total cost of the nth cycle; This indicates the length of the planning period, which is the maximum value of the entire decision-making cycle;

[0151] Maintaining action sequences minimize on : A maintenance plan was developed.

[0152] A bridge defect holographic monitoring and adaptive repair system for implementing the method, comprising:

[0153] The quantum entanglement sensing unit is used to install a quantum entanglement sensor array on and inside the bridge surface. It obtains the signal strength and phase information of microscopic defects through quantum interference effect. The signal strength and phase information, infrared sensor data, lidar data, and industrial camera data are encrypted and transmitted to a distributed cloud system via a quantum communication network for storage and preprocessing. The preprocessed multi-source fusion data is then output.

[0154] The multi-source data fusion and neuromorphic computation analysis unit, connected to the quantum entanglement sensing unit, receives preprocessed multi-source fusion data, constructs a fusion feature vector through spatiotemporal alignment and feature-level fusion, and calculates the confidence level; it then transforms the fusion feature vector into a temporal impulse event sequence through neuromorphic encoding, generates disease category probabilities through forward inference of the neuromorphic disease identification network; it quantitatively assesses the severity of the disease to generate a severity score, and combines the disease category probability, severity score, and confidence level to generate a comprehensive risk index; finally, it outputs a structured disease diagnosis result containing the fusion feature vector, confidence level, disease category probability, severity score, and comprehensive risk index on a discrete point set.

[0155] A three-dimensional defect localization and spatial reconstruction unit is connected to a quantum entanglement sensing unit and a multi-source data fusion and neuromorphic computing analysis unit, respectively. It receives preprocessed multi-source fusion data from the quantum entanglement sensing unit to establish a three-dimensional coordinate system for the bridge and reconstruct its three-dimensional geometric model. It receives defect category probabilities, severity scores, and comprehensive risk indices from the multi-source data fusion and neuromorphic computing analysis unit and maps them to the bridge's three-dimensional geometric model to form a three-dimensional defect model with defect attributes. Spatial clustering is performed on the three-dimensional defect model with defect attributes to extract several defect clusters. Geometric parameters are extracted for each defect cluster, and a structured defect object is defined for each cluster. The structured defect object includes the spatial range of the defect area, defect type, average severity, comprehensive risk index, and a set of geometric parameters. All structured defect objects are combined to form a three-dimensional holographic model of the bridge's defects.

[0156] The adaptive repair unit, connected to the 3D defect localization and spatial restoration unit, receives each structured defect object from the structured defect object set. It then calculates and ranks the comprehensive repair priority scores based on a weighted sum of the comprehensive risk index, average severity, structural importance weight, and traffic importance weight. Under the constraints of total cost not exceeding the budget and total construction period not exceeding the allowable time window, it determines the optimal repair scheme for each structured defect object sequentially, using a weighted combination of cost, construction period, risk reduction, and lifespan increment as the comprehensive optimization objective. Using the state equation and control input range as constraints, it solves for the construction control parameter trajectory that minimizes the weighted integral of repair error and control energy consumption. During the repair process, it dynamically evaluates the repair quality score and residual risk index based on real-time monitoring data. When the repair quality score is not lower than the final repair quality judgment threshold and the residual risk index is not higher than the allowable residual risk upper limit, the structured defect object is deemed to have been repaired, generating a structured repair record containing the original structured defect object, the final repair scheme, key control parameters, start and end times, and its quality and risk curves.

[0157] The long-term monitoring and maintenance decision-making unit connects with the adaptive repair unit, receives structured repair records, continuously collects bridge monitoring data, calculates component-level health indicators and the overall bridge health index, extracts environmental action vectors and load action vectors, establishes the degradation state of the bridge, establishes a degradation model and corrects the degradation model parameters, and uses the updated component-level health indicators and degradation state to determine the failure probability and remaining life; at discrete decision moments, it performs multi-cycle cost discounting optimization on the maintenance action sequence to form a maintenance plan; it continuously updates the degradation model parameters, health indicator weights, risk thresholds and maintenance strategy weights, and outputs the updated maintenance plan to the asset management platform.

[0158] Advantages of the present invention

[0159] 1. The quantum entanglement neuromorphic holographic monitoring and repair method for bridge defects of this invention uses a quantum entanglement sensor array as the core of bridge defect monitoring, achieving high-sensitivity detection at the microscopic level. This overcomes the accuracy bottleneck of traditional sensors and two-dimensional image recognition, enabling early detection of defects and significantly improving the comprehensiveness and accuracy of monitoring. It also integrates data from multiple sensors such as lidar, infrared imaging, and industrial cameras, using a weighted fusion algorithm and neuromorphic computational neural network for automatic identification, classification, and severity assessment. Compared with existing systems that rely on only a single sensor or ordinary image recognition, this invention has a higher depth of data fusion and a stronger level of intelligent identification.

[0160] 2. The method of this invention, based on multi-source fusion data, combines structural motion from motion (SfM), lidar point cloud registration, and triangular mesh reconstruction technologies to achieve precise localization and spatial reconstruction of defects in the three-dimensional coordinate system of the bridge structure. This three-dimensional holographic defect model significantly outperforms existing two-dimensional detection or fuzzy localization methods, providing precise application locations and geometric parameters for subsequent repairs.

[0161] 3. The method of this invention achieves precise location and spatial reconstruction of defects through 3D modeling and defect localization technology. Based on the 3D localization results, a defect priority ranking model, a repair method library, and a multi-objective optimization model are established. Adaptive control (such as grouting pressure, spraying rate, and temperature and humidity control) and a real-time monitoring and feedback mechanism are introduced during implementation, forming a closed-loop mechanism of "detection—location—decision—execution—feedback—adjustment." This achieves intelligent and dynamic optimization of the repair process, and optimizes the repair strategy based on real-time defect feedback to ensure maximum repair effectiveness. By introducing adaptive repair algorithms and real-time monitoring mechanisms, this invention breaks through the limitations of traditional static repair methods, enabling dynamic adjustment of repair plans according to the actual condition of the bridge, improving repair effectiveness. It also possesses long-term health monitoring and predictive maintenance capabilities, optimizing maintenance strategies based on long-term monitoring data and historical repair records, realizing a shift from passive repair to proactive prevention and predictive maintenance.

[0162] 4. The method of this invention can also continue long-term monitoring after repair, defining component health indices, degradation models, remaining life estimates, risk predictions, and maintenance decision optimization. Through self-optimizing model parameter updates and maintenance strategy updates, it achieves a shift from "passive repair" to "proactive / predictive maintenance." Compared with traditional methods that only perform single inspections and repairs, this invention can extend the service life of bridges, reduce maintenance frequency, and optimize the allocation of maintenance resources.

[0163] 5. This invention's quantum entangled neuromorphic bridge defect holographic monitoring and repair system integrates quantum sensing, neuromorphic computing, multi-sensor fusion, automatic control, and long-term data feedback to form a highly intelligent and automated maintenance management system. Furthermore, the system maintains strong adaptability in complex environments (such as under bridges, tunnels, and areas without GNSS signals). This significantly improves operability and reliability in practical engineering scenarios.

[0164] Through high-precision detection and intelligent repair strategies, missed detections and misjudgments are reduced, and unnecessary or inefficient repairs are minimized, thereby effectively reducing maintenance costs. At the same time, timely detection and repair of structural hazards improve the safety and reliability of bridges during operation. The high degree of system automation also significantly improves management efficiency.

[0165] 6. The system of this invention acquires microscopic defect data of bridges with high precision through a quantum entangled sensor array, breaking through the precision and sensitivity bottlenecks of traditional sensors. It has significant advantages, especially in the detection of micro-cracks, hidden corrosion and other early defects. Combined with neuromorphic computing technology and multi-source data fusion, it can realize automated defect identification, classification, location and severity assessment, solving the shortcomings of traditional systems in terms of intelligence and automation.

[0166] In summary, this invention, through the organic integration of key technologies such as quantum entanglement sensing, multi-source fusion, three-dimensional positioning, intelligent repair, and full life-cycle management, comprehensively improves the accuracy, intelligence, and reliability of bridge defect monitoring, repair, and long-term maintenance. It fills the gaps in traditional technologies regarding low detection accuracy, ambiguous positioning, static repair, and lack of long-term maintenance, demonstrating significant technological advancement and practical engineering application value. This invention offers significant advantages in defect detection accuracy, three-dimensional positioning, intelligent repair, and long-term monitoring and maintenance, overcoming the shortcomings of existing technologies in terms of accuracy, automation, intelligence, and adaptability, and possesses broad application prospects and practical engineering value. Attached Figure Description

[0167] Figure 1 This is a flowchart illustrating the workflow of the quantum entangled neuromorphic bridge holographic monitoring and repair method of the present invention.

[0168] Figure 2 This is a schematic diagram illustrating the working principle of the quantum entangled neuromorphic bridge holographic monitoring and repair system of the present invention. Detailed Implementation

[0169] The present invention will be further explained and described below with reference to the accompanying drawings and specific embodiments. It should be noted that the specific embodiments are not intended to limit the scope of the present invention.

[0170] like Figure 1 As shown in the figure, this specific embodiment provides a method for holographic monitoring and repair of defects in quantum entangled neuromorphic bridges, including the following steps:

[0171] Step 1: Quantum entanglement sensing data acquisition and microscopic disease monitoring

[0172] Objective: To improve the accuracy of bridge defect detection, particularly in monitoring microscopic cracks, hidden corrosion, and other microscopic defects, by using a quantum entanglement sensor array to acquire high-precision data on bridge micro-defects. This step aims to overcome the limitations of traditional sensors in terms of sensitivity and accuracy, providing a data foundation for subsequent defect localization and repair. The specific implementation steps are as follows:

[0173] 1. Design and Deployment of Quantum Entangled Sensor Arrays

[0174] Deploying a quantum entanglement sensor array based on the quantum entanglement effect in bridge structures leverages the sensitivity and accuracy of quantum entanglement sensors to meet monitoring needs across various wavelengths, including infrared, ultrasonic, and visible light images from industrial cameras. Quantum entanglement sensors are used to detect microscopic defects such as tiny cracks, surface corrosion, and hidden structural damage, overcoming the resolution limitations of existing sensors.

[0175] Applications of quantum entanglement effect: Quantum entanglement sensors enhance signal sensitivity through the quantum entanglement effect, showing significant advantages, especially in detecting minute defects, such as cracks smaller than 0.1 mm and early corrosion.

[0176] 2. Microscopic disease detection and signal enhancement

[0177] Obtaining signal intensity and phase information of microscopic defects through quantum interference: Quantum interference is used to precisely capture signals of microscopic defects in bridge structures. Quantum entanglement sensors amplify the impact signal of defects using quantum interference phenomena, and enhance signal intensity and phase information to detect minute structural changes such as cracks, corrosion, and surface deformation. The quantum interference effect employs quantum state and interferometry measurements, as shown in the following formula:

[0178] (1),

[0179] In the formula: Represents the quantum state of a quantum entangled sensor; and The probability amplitude of a qubit characterizes the performance of a quantum sensor under different measurement conditions;

[0180] The formula for measuring signal strength is as follows:

[0181] (2),

[0182] In the formula: For quantum entanglement sensors at time The intensity of the acquired image; For strength measurement operators; For the evolution of quantum states over time;

[0183] The formula for obtaining the phase information is as follows:

[0184] (3),

[0185] In the formula: For quantum phase; For phase measurement operators; This represents the quantum state of the system at time t; quantum phase measurement improves the accuracy of disease detection, and is especially suitable for the early diagnosis of micro-cracks and initial corrosion.

[0186] 3. Multi-sensor collaborative operation and data fusion

[0187] To comprehensively improve data acquisition accuracy, quantum entangled sensors, infrared sensors, lidar, and industrial cameras work collaboratively using multi-sensor data fusion technology. Through wireless communication, data from each sensor is transmitted in real-time to a computing center for data integration and optimization analysis. Signal strength and phase information are fused with data output from the infrared sensor, lidar, and industrial camera to obtain the fused data. This data fusion employs advanced techniques such as Kalman filtering and weighted averaging, combining the advantages of various sensors to eliminate the limitations of single sensors and provide more comprehensive and accurate monitoring data.

[0188] The formula for data fusion is as follows:

[0189] (4),

[0190] In the formula: The merged data; For the first Data from one sensor; These are weighting coefficients, dynamically adjusted based on the accuracy and signal-to-noise ratio of the quantum entanglement sensor.

[0191] 4. Real-time transmission and security of quantum data

[0192] The fused data is transmitted encrypted via a quantum communication network, ensuring its security and integrity during transmission. Quantum encryption technology not only guarantees the quality of data transmission but also effectively prevents information leakage or tampering.

[0193] The formula for encrypted transmission is as follows:

[0194] (5),

[0195] In the formula: For transmitted quantum data; This refers to the quality factor of the transmission channel; The data is collected by sensors. Quantum encryption ensures the real-time nature and security of the data, reducing signal loss and interference during transmission.

[0196] 5. Real-time data storage and preprocessing

[0197] Encrypted data is stored in a distributed cloud system to ensure secure storage and efficient retrieval. Based on the characteristics of the quantum entanglement sensor data, real-time preprocessing, including noise removal and data smoothing, is performed to obtain preprocessed multi-source fused data, providing higher-quality data support for subsequent analysis and disease identification.

[0198] As can be seen, step 1, through the coordinated operation of a quantum entangled sensor array, infrared sensors, lidar, and industrial cameras, accurately collects microscopic damage data of the bridge, demonstrating significant advantages, particularly in the early identification and precise monitoring of damage such as micro-cracks and hidden corrosion. Quantum interference and phase measurement technologies greatly enhance the sensitivity and accuracy of damage signals, providing a reliable data foundation for subsequent damage localization and repair strategies. Furthermore, the multi-sensor data fusion and quantum communication technologies employed ensure the efficiency, security, and accuracy of the data, guaranteeing stable system operation even in complex environments.

[0199] Step 2: Multi-source data fusion and neuromorphic computational analysis

[0200] Objective: To construct a unified representation of bridge defects by spatiotemporal alignment and feature-level fusion of the preprocessed multi-source fusion data obtained in step 1, and to use a neuromorphic computing architecture to achieve automatic identification, classification and severity assessment of bridge defects, providing high-confidence diagnostic results for subsequent three-dimensional localization and repair decisions.

[0201] 1. Spatiotemporal alignment and normalization of multi-source sensor data

[0202] The preprocessed multi-source fusion data obtained in step 1 is uniformly aligned in time and space, and fusion feature vectors, confidence scores, disease category probabilities, severity scores, and comprehensive risk indices are generated sequentially on the discrete spatial point set; the specific contents are as follows:

[0203] Let a certain spatial point on the surface of the bridge be denoted as . The time is .

[0204] (1) The observations of the quantum entanglement sensor at this point and at this time are denoted as ,in It includes microscopic disease characteristic components such as intensity and phase.

[0205] (2) The reflection characteristics of the lidar at this point and at this time are denoted as: It includes information such as point cloud density and reflection intensity.

[0206] (3) The temperature / thermal radiation characteristics of the infrared sensor at this point and at this time are denoted as: .

[0207] (4) The texture and grayscale features of the visible light image from the industrial camera at that point and time are denoted as: .

[0208] By using the calibrated extrinsic and intrinsic parameter matrices, the coordinate systems of the quantum entangled sensor, lidar, infrared sensor, and industrial camera are mapped to the global three-dimensional coordinate system of the bridge, enabling... There is a one-to-one correspondence between the various data sources; through the timestamp synchronization mechanism, the observations of each sensor are unified on a unified timeline.

[0209] 2. Weighted fusion and confidence modeling of multi-source features

[0210] After completing spatiotemporal alignment, the quantum entangled sensor, lidar, infrared sensor, and industrial camera will be placed at a point. ,time The observed features are fused to obtain a fused feature vector. The fused feature vector The multi-source feature weighted fusion is adopted, and the formula is as follows:

[0211] (6),

[0212] In the formula: , , , These are transformation functions for feature extraction and dimension unification of data from quantum entangled sensors, lidar, infrared sensors, and industrial cameras, respectively. The transformation functions are normalization, filtering, or principal component extraction, and the output is a vector with unified dimension. , , , The weighting coefficients for each sensor channel satisfy the following conditions: Each weight is dynamically updated based on the sensor's signal-to-noise ratio, reliability under current operating conditions, and historical performance evaluation.

[0213] To describe the diagnostic reliability of the fused features, a confidence index is introduced, with the following formula:

[0214] (7),

[0215] In the formula: For each sensor at point ,time The signal-to-noise ratio estimate; It is a monotonically increasing normalization function used to map the multi-channel signal-to-noise ratio to a comprehensive confidence value between 0 and 1.

[0216] 3. Neuromorphic coding: From continuous features to impulse events

[0217] To adapt to neuromorphic computing architectures, continuous features are... It is encoded as a time-domain pulse event sequence.

[0218] For each spatial point Construct the corresponding input neurons, whose membrane potential It evolved over time as follows:

[0219] (8),

[0220] In the formula: For the first The input neurons at time 1 The membrane potential; The membrane potential time constant; This is the resting potential; To fuse feature vectors The One component; This is the coding gain coefficient corresponding to this component.

[0221] When the membrane potential exceeds the threshold At that time, the input neuron generates a pulse (i.e., an event):

[0222] (9),

[0223] In the formula: For the first The input neurons at time 1 An indicator of whether a discharge has occurred (1 indicates a pulse has been generated, 0 indicates no pulse has been generated); after discharge... Reset to .

[0224] Through the above mechanism, the disease characteristics of multiple sources are integrated. Transformed into a set of pulse sequences suitable for event-driven computation on neuromorphic chips. .

[0225] 4. Forward inference of neuromorphic disease recognition networks

[0226] On a neuromorphic hardware or equivalent software simulation platform, a multilayer spiking neural network (SNN) or brain-like network is constructed to perform disease identification and feature extraction on the pulse input.

[0227] A hidden layer neuron in the network The membrane potential evolves as follows:

[0228] (10)

[0229] when Exceeding the threshold At that time, the neuron generates a pulse. And transmit the pulse to the next layer, where: Hidden layer neurons The membrane potential; This is the time constant of the neurons in this layer; This represents the resting potential of neurons in this layer; To from neurons To neurons Synaptic weights; For neurons The discharge threshold.

[0230] In the output layer, for each disease category (For example: cracks, corrosion, peeling, water seepage, etc.), statistics are compiled over a period of time. The number of pulses emitted within the range is calculated and normalized to a disease category probability, and the formula for the disease category probability is as follows:

[0231] (11),

[0232] In the formula: For point This belongs to the category of disease The predicted probability; For category The corresponding output neuron at time 1 The pulse; This is a normalization factor used to normalize all category outputs to... The interval, and satisfying .

[0233] 5. Quantification of disease severity and intelligent prioritization assessment

[0234] After identifying the disease categories, the severity of the diseases is further quantitatively assessed.

[0235] For each spatial point Define a disease severity index, and the formula for the severity score is as follows:

[0236] (12)

[0237] In the formula: For point The overall severity score of the location; For the first Individual disease characteristics, such as estimated crack width, corrosion area ratio, texture roughness, and abnormal temperature gradient values, are output by the intermediate layer or post-processing module of the neuromorphic network. The weights for each feature's contribution to severity are determined by training or engineering experience.

[0238] Combined with recognition probability Severity With confidence level A comprehensive diagnostic index is generated, and the formula for the comprehensive risk index is as follows:

[0239] (13)

[0240] In the formula: For point The overall risk / priority index; It is a monotonically increasing function used to combine the probability, severity, and confidence of disease into a single risk score, which is then used for priority ranking and resource scheduling in subsequent steps.

[0241] 6. Structured output of disease diagnosis results

[0242] Finally, step 2 outputs structured disease diagnosis results, including but not limited to:

[0243] In a discrete space point set Probability distribution of disease categories ;

[0244] Corresponding severity score ;

[0245] Overall Risk / Priority Index .

[0246] The above results will serve as the input basis for the three-dimensional defect location and spatial restoration modeling in step 3, and will be used for accurate projection and spatial annotation in the three-dimensional bridge model.

[0247] Step 3: 3D Defect Location and Spatial Reconstruction Modeling

[0248] Based on the preprocessed multi-source fusion data obtained in step 1, a three-dimensional coordinate system for the bridge is established and a three-dimensional geometric model of the bridge is reconstructed. The specific steps are as follows:

[0249] Step 301: Establish the world coordinate system of the bridge structure as the global three-dimensional reference coordinate system, denoted as... (World coordinate system) is used to uniformly represent the bridge geometry and the location of all sensor data.

[0250] Step 302: Perform extrinsic parameter calibration on the quantum entanglement sensor, industrial camera, infrared sensor, and lidar to obtain the coordinate systems of the quantum entanglement sensor, industrial camera, infrared camera, and lidar. To the world coordinate system Rigid body transformation matrix:

[0251] (14)

[0252] In the formula: for The rotation matrix represents the attitude of the sensor coordinate system relative to the world coordinate system; for The translation vector represents the position of the sensor origin in the world coordinate system; for The homogeneous transformation matrix.

[0253] Step 303: For camera-type sensors, there is also an intrinsic parameter matrix. Used to describe imaging parameters such as focal length and principal point coordinates:

[0254] (15)

[0255] In the formula: , This refers to the equivalent focal length in the horizontal and vertical directions. , These are the coordinates of the principal point on the imaging plane.

[0256] Step 304: Based on multi-view images (including high-precision images acquired by quantum entanglement sensors and images from ordinary cameras), sparse point clouds on the bridge surface are reconstructed using structure from motion (SfM) technology, and dense point clouds are constructed using multi-view stereo (MVS).

[0257] For a three-dimensional point Homogeneous coordinates in the world coordinate system, and their homogeneous pixel coordinates projected onto a camera image plane. for:

[0258] (16)

[0259] In the formula: It is a scaling factor; , For rotation and translation from the world coordinate system to the camera coordinate system (as mentioned above) One-to-one correspondence); This is the intrinsic parameter matrix of the camera.

[0260] By matching corresponding points from multiple images and performing nonlinear optimization (bundling adjustment), a globally consistent camera pose is obtained. and a set of three-dimensional points This forms a sparse point cloud called SfM.

[0261] To improve geometric accuracy and integrity, lidar point clouds are introduced. The point cloud is aligned to the world coordinate system of the SfM point cloud using rigid body registration (e.g., ICP algorithm). The optimization objective function is in the form of:

[0262] (17)

[0263] In the formula: , This refers to the rotation and translation from the lidar coordinate system to the world coordinate system; To and The corresponding SfM point cloud points.

[0264] By minimizing By fusing lidar point clouds with SfM point clouds, a high-precision, globally consistent 3D point cloud model of the bridge is obtained. .

[0265] If necessary, a triangular mesh model of the bridge surface can be constructed based on the point cloud using a triangular mesh generation algorithm (such as Poisson Surface Reconstruction). .

[0266] In step 2, the disease diagnosis result has been obtained in the sensor / image domain. The disease diagnosis result includes the probability of disease category corresponding to a certain image pixel or sensor sampling point. Disease severity score and comprehensive risk index These results are then precisely mapped onto a point cloud or mesh in the bridge's three-dimensional coordinate system. This creates a three-dimensional defect model with defect attributes, providing a spatial basis for subsequent repair decisions and adaptive repair. The specific steps are as follows:

[0267] Step 305, Mapping of 2D defects to 3D surfaces based on the projection matrix: Let the defect pixels on a certain camera image be... The corresponding camera extrinsic parameters are The projection matrix is:

[0268] (18)

[0269] In the formula, P represents the projection matrix, which contains the camera's intrinsic and extrinsic parameters; K represents the camera's intrinsic parameter matrix, which contains parameters such as the camera's focal length and principal point coordinates, and is usually a 3×3 matrix.

[0270] The origin of the corresponding ray is known to be the optical center of the camera. The direction of the ray is The spatial ray corresponding to the defective pixel is:

[0271] , (19)

[0272] In the formula: It is a point on the spatial ray, representing the spatial location of the defective pixel; This represents the camera's position in the world coordinate system. It is obtained by back-projection of pixel coordinates through an intrinsic rotation matrix; As a scaling factor or scale factor, it determines the distance from the camera's optical center along the ray direction, controlling the depth of the defective pixels.

[0273] Step 306, in the 3D point cloud or mesh model or In the given information, find the intersection point of the ray and the surface of the bridge. Or the nearest point cloud point near the ray. Define this intersection point as the spatial location of the disease in the world coordinate system, and assign the corresponding... , , Assign the value to this point:

[0274] (20);

[0275] Step 307, 3D reconstruction based on direct depth information: For the data from the lidar or quantum entangled sensor array described in Step 1, the data is directly derived from the points in the lidar or quantum entangled sensor array coordinate system:

[0276] (twenty one),

[0277] In the formula, X s The X coordinate represents the coordinate system of a lidar or quantum entangled sensor array; the Y coordinate represents the coordinate system of the lidar or quantum entangled sensor array. s This represents the Y-coordinate in the coordinate system of a lidar or quantum entangled sensor array; Z represents the Y-coordinate. s This represents the Z-coordinate in the coordinate system of a lidar or quantum entangled sensor array; T represents the transpose symbol.

[0278] Mapping to the world coordinate system via extrinsic parameter transformation:

[0279] (twenty two),

[0280] In the formula, X w Indicates the position of a point in the world coordinate system; T ws The transformation matrix from the world coordinate system to the sensor coordinate system; X s Indicates the position of the point in the sensor coordinate system;

[0281] The defect attributes of spatial points in the lidar or quantum entanglement sensor array are then assigned to the corresponding world coordinate system points:

[0282] (twenty three),

[0283] Therefore, in a unified three-dimensional bridge coordinate system Below, we obtain a three-dimensional point set with disease attributes:

[0284] (twenty four),

[0285] In the formula, D w Represents a three-dimensional point set with disease attributes; This indicates the position of the k-th point in the world coordinate system; This indicates the disease category at the k-th point; This represents the severity score for the k-th point; This represents the risk index at point k;

[0286] Spatial clustering and geometric parameter extraction of the diseased area: Spatial clustering is performed on the 3D disease model with disease attributes to extract several disease clusters; geometric parameters are extracted for each disease cluster, and a structured disease object is defined for each disease cluster. The structured disease object includes the spatial range of the diseased area, disease type, average severity, comprehensive risk index, and set of geometric parameters; all structured disease objects are collected to form a 3D holographic model of the bridge's disease. The specific steps are as follows:

[0287] Step 308 involves extracting connected disease regions (e.g., a crack, a corroded area) from discrete disease points, and then performing this extraction in three-dimensional space. Clustering is performed. Spatial clustering methods based on distance and risk thresholds are employed, such as density clustering similar to DBSCAN, with a defined spatial neighborhood radius. and minimum points Find points in the point cloud that satisfy spatial distance constraints and have a risk index Exceeding the threshold The disease point set; these points are clustered into several disease clusters. .

[0288] Step 309, for each disease cluster Geometric parameter extraction: For crack-type defects, principal component analysis (PCA) is used to estimate the principal directions and lengths.

[0289] (25)

[0290] In the formula, It represents the largest eigenvalue of the covariance matrix of the disease cluster, corresponding to the spatial scale along the principal axis.

[0291] For surface-like defects (corrosion, peeling, etc.), the area is calculated by projecting it onto a locally fitted plane. :

[0292] (26)

[0293] In the formula, A i a represents the local area of ​​the i-th disease cluster; k This represents the local area contribution of the k-th point.

[0294] Step 310: Simultaneously, calculate the average severity and risk index for each disease cluster:

[0295] (27)

[0296] (28)

[0297] In the formula, This represents the average severity of the i-th disease cluster; Indicates disease clusters The number of midpoints; This indicates the severity of the k-th point; This represents the comprehensive risk index of the i-th disease cluster; This represents the risk index at point k;

[0298] Step 311, Construction of a 3D Holographic Model of Bridge Defects: In the 3D geometric model of the bridge or Above, for each disease cluster Construct spatially labeled cells with attributes. Define structured disease objects for each disease cluster:

[0299] (29)

[0300] In the formula: This represents the structured disease object of the i-th disease cluster; The disease cluster represents the set of points or the range of regions in three-dimensional space. The types of defects (such as cracks, corrosion, peeling, etc.) are determined by... The largest category has been determined; This represents the average severity of the disease cluster; This represents the comprehensive risk index for this disease cluster; This is a set of geometric parameters for the defect (such as length, area, volume, main direction, component number, etc.).

[0301] Step 312: Gather all structured defect objects to form a three-dimensional holographic model of the bridge's defects:

[0302] (30)

[0303] In the formula, H represents the three-dimensional holographic model of the bridge's defects, which is a set containing structured defect objects of all defect clusters; B1 represents the first structured defect object in the set of structured defect objects; B2 represents the second defect pair in the set of structured defect objects; B M This represents the Mth disease pair in the structured disease object set;

[0304] The bridge's three-dimensional holographic model of its defects can be visualized in a three-dimensional environment: the type, risk level, and severity of defects are expressed through color, transparency, size, and other means, enabling intuitive spatial perception.

[0305] The output interface connects to subsequent steps:

[0306] The output of step 3 mainly includes:

[0307] 1. 3D point cloud / mesh dataset with disease attributes

[0308] or Each point or surface element carries the disease category, severity, and risk attributes.

[0309] 2. Structured collection of disease objects This includes the spatial boundaries, geometric parameters, and risk indicators of the affected area. These outputs will directly serve as the input basis for the next step, "Intelligent Adaptive Repair Strategy Optimization and Execution," and will be used to determine the appropriate action based on the specific circumstances. Prioritize the repair of affected areas; [and] classify the affected areas... By incorporating finite element models or mechanism analysis modules, the impact on the overall performance of the structure can be assessed; precise three-dimensional coordinates and spatial shape information can be provided for planning the path and working area of ​​the repair robot / equipment.

[0310] Step 4: Optimization and Execution Control of Intelligent Adaptive Repair Strategy

[0311] Objective: Based on the three-dimensional holographic model of the disease obtained in step 3, prioritize each diseased area, select the optimal repair method, solve the adaptive repair parameters, and make closed-loop adjustments based on real-time monitoring during the construction process to achieve intelligent repair execution that optimizes cost, safety, and lifespan.

[0312] 1. Prioritization of diseased areas and generation of repair tasks

[0313] For the structured disease object set obtained in step 3: (31),

[0314] Each structured disease object: (32),

[0315] The comprehensive repair priority score is obtained by weighting and summing the comprehensive risk index, average severity, structural importance weight, and traffic importance weight, and then ranking them. The formula is as follows:

[0316] (33),

[0317] In the formula: Indicates disease clusters The repair priority score is set, with higher scores indicating higher priority for repair. This represents the comprehensive disease risk index obtained in step 3; Indicates the average severity of the disease; This indicates the importance weight of the component where the defect is located to the overall structural safety; for example, the main beam and main tower can be assigned a higher value. This indicates the importance weight of the location of the defect to traffic function, such as assigning higher values ​​to lanes and key connecting parts; , , , These are weighting coefficients used to balance risk, severity, structural importance, and traffic importance.

[0318] according to Sort all disease clusters and generate a sequence of tasks to be repaired:

[0319] (34),

[0320] in Corresponding to one or several The combined tasks serve as the basic unit for subsequent repair and optimization.

[0321] 2. Repair Method Library and Strategy Space Modeling: Under the constraints of total cost not exceeding the budget and total duration not exceeding the allowable time window, the optimal repair scheme is determined for each structured defect object by using a weighted combination of cost, duration, risk reduction, and lifespan increment as the comprehensive optimization objective; the specific steps are as follows:

[0322] For different types of diseases Pre-build a repair method library:

[0323] (35),

[0324] Examples include: crack grouting, surface sealing, steel plate reinforcement, fiber composite material reinforcement, rust removal and recoating, and partial plate replacement.

[0325] For each disease cluster Define its subset of optional repair schemes:

[0326] (36)

[0327] For each "disease cluster - remediation plan" combination Define the following performance metrics:

[0328] (1) Repair cost: Showing the cluster of diseases Adopted scheme The direct economic costs (comprehensive calculation of materials, labor, equipment, etc.).

[0329] (2) Repair time: This indicates the time required to complete the repair of the disease.

[0330] (3) Risk reduction amount: (37), of which To adopt the scheme After the repair is completed, the diseased clusters The predicted residual risk index.

[0331] (4) Lifetime increment: This indicates that the proposed solution has been adopted. Subsequently, the remaining life increment of the structure in the diseased area is predicted (based on fatigue assessment, aging model, etc.).

[0332] 3. Multi-objective repair strategy optimization model:

[0333] (1) Introducing decision variables:

[0334] (38)

[0335] The constraints are:

[0336] A. Select one remediation plan for each disease cluster:

[0337] (39)

[0338] (2) The total cost does not exceed the budget. :

[0339] (40)

[0340] (3) The total construction period shall not exceed the allowable time window. (Optional):

[0341] (41),

[0342] Cost, time, and risk reduction are unified into a multi-objective optimization function, and a comprehensive objective is constructed through a weighted summation:

[0343] (42),

[0344] In the formula, To comprehensively optimize the target value, a smaller value indicates a better overall effect; , , , The importance weights are respectively: cost, construction period, risk reduction, and lifespan increase; This represents the set of all disease clusters i and their corresponding remediation schemes. Perform summation; C ij Indicates the cost of repairing disease cluster i using repair scheme j; x ij Represents a binary variable, indicating whether repair scheme j corresponds to disease cluster i; T ij Indicates the repair time for disease cluster i under repair scheme j; △R ij Indicates the reduction in risk of disease cluster i by repair scheme j; △L ij This represents the lifespan increment of repair scheme j on disease cluster i;

[0345] The optimization problem format is as follows:

[0346] (43),

[0347] The solution is obtained using methods such as integer programming, genetic algorithm, and particle swarm optimization. The formula for determining the optimal repair scheme for each structured disease object in sequence is as follows:

[0348] (44),

[0349] In the formula, The indicator variable represents the optimal repair scheme for the i-th structured disease object. A value of 1 indicates that the repair scheme is selected, and a value of 0 indicates that the scheme is not selected. arg max represents the maximization operation, that is, selecting the j value that maximizes the objective function. This represents the set of repair solutions belonging to the i-th structured disease object. ; This represents the optimized value of the i-th structured defect object under the j-th repair scheme;

[0350] 4. Adaptive Repair Control Parameter Solution (Continuous Control Layer)

[0351] For each structured defect, using the state equation and control input range as constraints, we solve for the construction control parameter trajectory that minimizes the weighted integral of repair error and control energy consumption; the specific details are as follows:

[0352] After determining the remediation plan for each disease cluster. Afterwards, adaptive optimization of specific construction parameters is required, for example:

[0353] The injection pressure, flow rate, and time of the grouting material;

[0354] The thickness and application speed of the surface repair material;

[0355] Temperature and humidity control in the construction area, etc.

[0356] Set up disease clusters During the repair process, the state vector is: Examples include crack closure, material curing degree, and surface smoothness. Control inputs are... For example, injection pressure, spraying rate, hot air temperature, etc.

[0357] The constraints, including the state equations and control input range, include the following state equations describing the dynamics of the repair process:

[0358] (45)

[0359] In the formula: The derivative of the state with respect to time; A kinetic function describing the repair process (which can be obtained from empirical models, data-driven models, or simplified mechanistic models); This indicates external disturbances, such as ambient temperature and humidity, or on-site construction disturbances.

[0360] The solution for the construction control parameter trajectory that minimizes the weighted integral of repair error and control energy consumption includes:

[0361] To achieve the optimal balance between repair quality and cost, within the time window Internal structural local performance indicators:

[0362] (46)

[0363] In the formula: Indicates disease clusters The control optimization objective; This indicates the correction of errors, such as the target state. Compared with the current state difference; This represents a quadratic term that controls the input energy or intensity. , A weighting factor used to balance repair accuracy with control of energy consumption / construction intensity.

[0364] Under constraints

[0365] (47)

[0366] and control of the application range

[0367] (48)

[0368] Next, solve for the optimal control trajectory. .

[0369] The solution can be obtained online using methods such as Model Predictive Control (MPC), gradient method, or reinforcement learning.

[0370] 5. Online monitoring and closed-loop adaptive adjustment of the repair process

[0371] During the repair process, quantum entanglement sensors, infrared sensors, lidar, and industrial cameras are used to monitor the status of the repaired area in real time, updating the disease risk and repair quality indicators. Based on this real-time monitoring data, the repair quality score and residual risk index are dynamically evaluated. When the repair quality score is not lower than the final repair quality judgment threshold and the residual risk index is not higher than the allowable residual risk upper limit, the structured disease object is deemed to have been repaired, and a structured repair record is generated, containing the original structured disease object, the final repair plan, key control parameters, start and end times, and its quality and risk curves. The specific content is as follows:

[0372] The dynamic evaluation of repair quality scores based on real-time monitoring data includes: calculating repair quality scores through a repair quality evaluation function, and evaluating disease clusters. The repair quality evaluation function is defined as follows:

[0373] (49)

[0374] In the formula: Indicates time Repair quality score; This represents the m-th quality index function, such as residual crack width, surface smoothness, material hardness, and bond strength. Indicates the importance weight of each quality indicator; This represents the repair status of the i-th structured defect object at time t;

[0375] Meanwhile, the risk index is updated and repaired based on real-time monitoring. :

[0376] Set adaptive adjustment trigger conditions:

[0377] or → Trigger repair parameter adjustment (50).

[0378] In the formula: Indicates the minimum acceptable threshold for repair quality; This indicates the maximum allowable residual risk target value for the affected area.

[0379] Once the triggering condition is met, the control system re-optimizes the local control problem based on the current error and updates it. Alternatively, local repair process parameters (such as extended local pressure time, repeated grouting, and recoating the surface layer) can be used to achieve closed-loop adaptive repair.

[0380] 6. Structured archiving of repair completion criteria and repair records.

[0381] For each disease cluster The criteria for determining whether the repair of the structured defect object is complete are as follows:

[0382] (51),

[0383] In the formula: Indicates the time when the repair was completed; This represents the final repair quality assessment threshold, which is typically higher than the process threshold. ; This indicates the maximum permissible residual risk. This indicates the time when the i-th structured defect object is repaired. The residual risk index represents the degree of risk that remains after repair; the lower the value, the lower the risk after repair.

[0384] When all of the above conditions are met, the disease cluster is determined. Repair complete;

[0385] The formula for automatically generating structured repair records by the system is as follows:

[0386] (52),

[0387] In the formula: This indicates a structured repair record; Represents the original structured disease object; This indicates the final repair solution adopted; This indicates the trajectory or set of key parameters used during the repair process; , This represents the quality and risk curves of the repair process and its results. , Indicates the start and end times of the repair process.

[0388] all The data will be compiled into the bridge's "Repair-Health" digital archive, providing a historical data foundation for long-term monitoring and predictive maintenance in Step 5.

[0389] Step 5: Long-term health monitoring, life-cycle prediction, and self-optimizing maintenance decision-making

[0390] Objective: To build a bridge health monitoring and predictive maintenance mechanism for the entire life cycle, by continuously collecting quantum entanglement sensing data and conventional multi-source sensing data, dynamically updating the structural health status, disease evolution model and risk assessment results, forming a maintenance strategy that can be continuously optimized, and realizing the transformation from "passive repair" to "proactive prevention" and "predictive maintenance".

[0391] The specific implementation steps are as follows:

[0392] 1. Configuration of long-term monitoring system and definition of structural health indicators

[0393] After the repairs are completed, a long-term monitoring sensor network will be deployed for the main beams, main towers, main cables, supports, and key connection nodes of the bridge, including but not limited to:

[0394] Quantum entanglement sensor (for high-sensitivity strain / microcrack monitoring);

[0395] Fiber optic strain gauges, accelerometers, and displacement gauges;

[0396] Temperature, humidity, and corrosive environment sensors;

[0397] Visual / infrared monitoring nodes.

[0398] For the Each component, at time The monitoring vector is denoted as

[0399] (53),

[0400] In the formula: Indicates strain or deformation parameters, including high-precision strain results from quantum entanglement sensors; Indicates acceleration / vibration response characteristics; , Indicates temperature and relative humidity; This indicates a corrosive environment indicator, specifically the chloride ion concentration.

[0401] Based on the structured repair records generated in step 4, bridge monitoring data will be continuously collected to calculate component-level health indicators and the overall bridge health index. The component-level health indicator function will be defined as follows:

[0402] (54),

[0403] In the formula: Representation of components The health index is such that the higher the value, the healthier the person is. Represents the k-th component's... Individual health sub-indicator functions, such as features obtained based on damage indicators, frequency changes, and modal stiffness changes; y represents the weight of the m-th health sub-indicator of the k-th component; k (t) represents the health status value of the k-th component at time t, which represents the specific health parameters of the component at a certain time.

[0404] The formula for redefining the overall health index of bridges is as follows:

[0405] (55),

[0406] In the formula: The overall health index of the bridge represents the overall health status of the bridge at time t. For the first The weight of each component in relation to the overall structure; Indicates the first The health index of each component;

[0407] In the formula: For components Weights related to the importance of the overall structure;

[0408] 2. Modeling of disease evolution and degradation processes

[0409] Environmental and load vectors are extracted to establish the disease degradation state, a degradation model is built, and the model parameters are corrected. The specific details are as follows:

[0410] For each disease cluster (or key components) Establish time-evolving degenerate state variables Examples include crack width, corrosion depth, and stiffness degradation rate. This can be simplified to a discrete-time degradation model:

[0411] (56),

[0412] In the formula, Indicates the time of the i-th component The amount of degradation or disease status at any given time; This represents the amount of degradation or the state of damage of the i-th component at time t. The environmental impact vector, including temperature, humidity, chloride ion concentration, and number of freeze-thaw cycles, is obtained from sensor data. This represents the load vector, including traffic volume levels, daily average axle load, etc., obtained from monitoring or statistical data; The degradation evolution function is calibrated using a combination of mechanistic model and data-driven approach. This represents the random disturbance term, used to characterize model errors and unmeasurable uncertainties.

[0413] Utilizing long-term monitoring data and recorded disease development history (including remediation records from step 4) The model parameters are updated using either recursive least squares or Bayesian methods. The formula for continuously correcting the parameters of the degenerate model is as follows:

[0414] (57),

[0415] In the formula: This represents the updated value of the degradation model parameter vector for the i-th component; This represents the old value of the degradation model parameter vector for the i-th component; This represents the actual measured degradation of the i-th component at time t, which is usually the measured value obtained by the sensor; This represents the predicted degradation amount of the i-th component at time t; This represents the gain matrix of the i-th component, which is a dynamically adjusted coefficient used to control the update speed and range of the degradation model;

[0416] This allows the disease evolution model to "self-evolve" over time, gradually conforming to the actual service behavior of bridges.

[0417] 3. Structural risk prediction and remaining life estimation

[0418] The failure probability and remaining lifetime are determined using updated component-level health indicators and degradation status, as detailed below:

[0419] Based on degradation state and health indicators Establish the limit state function for a component or disease cluster. Taking a specific component as an example:

[0420] (58),

[0421] In the formula: This indicates the component's resistance or safety margin; Indicates a state of degradation The corresponding load effect or equivalent demand (e.g., stress amplification caused by stiffness reduction).

[0422] when The component is considered to have reached a failure or unacceptable state at a certain point. Based on the uncertainty of random variables, the time can be obtained through reliability analysis. The failure probability at time The formula for the failure probability is as follows:

[0423] (59),

[0424] In the formula, This represents the failure probability of the k-th component at time t; The failure function represents the failure function of the k-th component; This represents the amount of degradation of the k-th component at time t; This represents the risk factor of the k-th component;

[0425] The reliability index is calculated using the first-order reliability method (FORM). .

[0426] For remaining lifetime (RUL) estimation, the degradation process at future time steps is extrapolated using a prediction formula. (Maximum allowable failure probability) or Define the time to reach the limit state when (minimum reliability index is allowed). The formula for remaining lifetime is as follows:

[0427] (60)

[0428] In the formula, Indicates the time of the k-th component The remaining lifespan at any given moment; Indicates the predetermined failure time of the component; This indicates the current point in time and is typically the time stamp used when calculating remaining lifetime.

[0429] The remaining life estimate will be directly used for subsequent maintenance planning and preventative repair decisions.

[0430] 4. Predictive Maintenance Planning and Multi-Period Optimization Decision: At discrete decision points, the maintenance action sequence is optimized using multi-period cost discounting to form a maintenance plan; details are as follows:

[0431] Viewing long-term maintenance as occurring at discrete decision moments This is a sequential decision problem. At each decision time... For components or clusters of defects Select maintenance action ,For example:

[0432] 0: No maintenance;

[0433] 1: Minor repair (surface treatment, sealing);

[0434] 2: Intermediate repair (local reinforcement and grouting);

[0435] 3: Overhaul or replace components.

[0436] The multi-period cost discounting optimization of the maintenance action sequence at discrete decision moments includes the following steps:

[0437] Viewing long-term maintenance as occurring at discrete decision moments Sequential decision-making problems performed on top of each other;

[0438] Define period The status is:

[0439] (61),

[0440] In the formula, This represents the state vector of the nth cycle; This indicates the amount of degradation or the state of damage of the i-th component in the n-th cycle; This represents the health index of the i-th component in the n-th cycle; This represents the failure probability of the i-th component in the n-th cycle;

[0441] Define a single-cycle cost function:

[0442] (62),

[0443] In the formula: This represents the total cost of the nth cycle, which is the sum of all maintenance and risk-related costs within that cycle. Indicates the first The maintenance cost per cycle is usually related to the specific maintenance actions or strategies taken, such as repair, inspection, and replacement. It is typically related to the number and complexity of the actions performed. Indicates the first The risk cost per cycle is typically due to the expected losses that may result from component degradation or failure.

[0444] Long-run total cost can be written in discounted form:

[0445] (63),

[0446] In the formula: The long-term expected total cost is represented by E, which represents the expected value, typically calculated by weighting all possible scenarios (through a probability distribution) to determine the expected value of a cost. This represents the cycle number, where n ranges from 0 to N, traversing the entire maintenance decision cycle. This represents the discount factor, a coefficient less than 1 used to discount future costs; This represents the total cost of the nth period; This indicates the length of the planning period, which is the maximum value of the entire decision-making cycle;

[0447] The goal is to maintain the action sequence. minimize on : A maintenance plan was developed.

[0448] In practical implementation, approximate dynamic programming or reinforcement learning methods are used, based on real-time observations. The strategy automatically adjusts the maintenance strategy based on the historical maintenance results, enabling the maintenance strategy to adapt and optimize according to the bridge's service status, achieving "repairing when it is due, not over-repairing, and not missing any repairs".

[0449] 5. Self-optimization and update mechanism for models and thresholds: The parameters of the degraded model, weights of health indicators, risk thresholds, and weights of maintenance strategies are updated on a rolling basis, and the updated maintenance plan is output to the asset management platform. Details are as follows:

[0450] To avoid rigid monitoring and maintenance strategies, a "self-optimization" mechanism is introduced to continuously update the following elements:

[0451] (1) Degradation model parameters Dynamic correction: Through continuous correction using long-term data, the model gradually approaches the actual bridge.

[0452] (2) Health indicators and weights , Adjustments: By combining actual fault / risk data, the sensitivity of each indicator is analyzed, and the weights are dynamically adjusted to make health indicators more sensitive to real risks.

[0453] (3) Risk threshold , Optimization: Based on the operating unit's risk appetite, regulatory changes, historical risk statistics, etc., gradually adjust the permissible risk level and transform the "experience threshold" into a "data-supported threshold".

[0454] (4) Maintenance strategy parameters , , , , Adjustment: By comparing the actual cost and risk changes brought about by different strategies, the multi-objective optimization weights are automatically adjusted to make the balance between long-term maintenance cost, security and lifespan more in line with actual needs.

[0455] The above steps are updated regularly each year or quarter, resulting in a continuously evolving set of rules for the digital twin and maintenance of the bridge.

[0456] 6. Integration with asset management platforms and information output

[0457] The long-term monitoring and forecasting results are output in a structured format, mainly including:

[0458] Component health curve: , Changes over time;

[0459] Failure probability of each key component With reliability indicators ;

[0460] Remaining life estimation Compared to the recommended maintenance time window;

[0461] Maintenance cost forecasts and risk distribution for the next few years (e.g., 5 years / 10 years);

[0462] Recommended annual / quarterly maintenance plans and priority list.

[0463] These results are integrated with the bridge asset management system through standardized interfaces (such as database tables, APIs, or report files), allowing management units to directly use them for medium- and long-term investment planning, maintenance fund allocation, and risk management.

[0464] The bridge defect holographic monitoring and adaptive repair system that implements the above methods includes:

[0465] The quantum entanglement sensing unit is used to install multiple quantum entanglement sensor arrays on and inside the bridge surface. Employing quantum dot technology, it provides high-precision defect detection, particularly suitable for microscopic defects such as tiny cracks and hidden corrosion. It acquires signal strength and phase information of microscopic defects through quantum interference effects; the signal strength and phase information, infrared sensor data, lidar data, and industrial camera data are encrypted and transmitted via a quantum communication network to a distributed cloud system for storage and preprocessing, outputting preprocessed multi-source fused data.

[0466] The multi-source data fusion and neuromorphic computation analysis unit, connected to the quantum entanglement sensing unit, receives preprocessed multi-source fusion data, constructs a fusion feature vector through spatiotemporal alignment and feature-level fusion, and calculates the confidence level; it then transforms the fusion feature vector into a temporal impulse event sequence through neuromorphic encoding, generates disease category probabilities through forward inference of the neuromorphic disease identification network; it quantitatively assesses the severity of the disease to generate a severity score, and combines the disease category probability, severity score, and confidence level to generate a comprehensive risk index; finally, it outputs a structured disease diagnosis result containing the fusion feature vector, confidence level, disease category probability, severity score, and comprehensive risk index on a discrete point set.

[0467] In the multi-source data fusion and neuromorphic computation analysis unit, Kalman filtering or weighted averaging is used to fuse data from different sensors, enhancing the accuracy and reliability of the data. Deep neural networks (DNNs) or spiking neural networks (SNNs) are employed to achieve automatic disease identification, classification, and severity assessment.

[0468] The three-dimensional disease localization and spatial reconstruction unit is connected to the quantum entanglement sensing unit and the multi-source data fusion and neuromorphic computing analysis unit, respectively. It receives the preprocessed multi-source fusion data from the quantum entanglement sensing unit to establish the bridge's three-dimensional coordinate system and reconstruct the bridge's three-dimensional geometric model. It receives disease category probability, severity score and comprehensive risk index from the multi-source data fusion and neuromorphic computing analysis unit and maps them to the bridge's three-dimensional geometric model to form a three-dimensional disease model with disease attributes. It performs spatial clustering and geometric parameter extraction on the three-dimensional disease model with disease attributes and outputs a structured disease object set containing the spatial location, attributes and geometric parameters of the disease.

[0469] The three-dimensional disease location and spatial restoration unit combines structural motion (SfM) technology and lidar data to construct an accurate three-dimensional bridge model through point cloud reconstruction and geometric fitting, ensuring the spatial location accuracy of the disease.

[0470] The adaptive repair unit, connected to the 3D defect localization and spatial restoration unit, receives a set of structured defect objects. It then calculates and ranks the comprehensive repair priority scores by weighting the scores according to the comprehensive risk index, average severity, structural importance weight, and traffic importance weight. Under the constraints of total cost not exceeding the budget and total construction period not exceeding the allowable time window, it determines the optimal repair scheme for each defect object sequentially, using a weighted combination of cost, construction period, risk reduction, and lifespan increment as the comprehensive optimization objective. Using the state equation and control input range as constraints, it solves for the construction control parameter trajectory that minimizes the weighted integral of repair error and control energy consumption. During the repair process, it dynamically evaluates the repair quality score and residual risk index based on real-time monitoring data. When the repair quality score is not lower than the final repair quality judgment threshold and the residual risk index is not higher than the allowable residual risk upper limit, the defect object is deemed to have been repaired, generating a structured repair record containing the original structured defect object, the final repair scheme, key control parameters, start and end times, and its quality and risk curves.

[0471] The adaptive repair unit prioritizes repair schemes based on disease severity and risk index, automatically adjusts repair parameters based on real-time feedback during the repair process, and automatically adjusts the use of repair materials, repair intensity, and repair techniques based on repair quality feedback and real-time data analysis to ensure maximum repair effectiveness.

[0472] The long-term monitoring and maintenance decision-making unit connects with the adaptive repair unit, receives structured repair records, continuously collects bridge monitoring data, calculates component-level health indicators and the overall bridge health index, extracts environmental action vectors and load action vectors, establishes the degradation state of the bridge, establishes a degradation model and corrects the degradation model parameters, and uses the updated component-level health indicators and degradation state to determine the failure probability and remaining life; at discrete decision moments, it performs multi-cycle cost discounting optimization on the maintenance action sequence to form a maintenance plan; it continuously updates the degradation model parameters, health indicator weights, risk thresholds and maintenance strategy weights, and outputs the updated maintenance plan to the asset management platform.

[0473] The long-term monitoring and maintenance decision-making unit employs a degradation model-based predictive method, combined with long-term monitoring data, to dynamically assess the bridge's health status and optimize maintenance strategies in real time based on repair effectiveness. Machine learning methods, combined with historical repair data and disease evolution models, are used to predict future disease development trends and provide regular preventative maintenance plans.

[0474] The machine learning method shall be any one of the following:

[0475] (1) Deep Neural Network (DNN):

[0476] In disease identification and classification, deep neural networks (DNNs) are used to analyze sensor data, enabling automatic learning of disease patterns from large amounts of data. During training, DNN models can automatically extract features and identify potential diseases, such as microcracks, hidden corrosion, and other complex diseases.

[0477] Training data includes historical repair data, sensor data, and other external environmental data (such as climate, traffic load, etc.), which are used to generate classification models for disease detection and classification.

[0478] (2) Convolutional Neural Network (CNN):

[0479] CNNs are particularly well-suited for processing image data. In bridge defect diagnosis, image data is acquired using devices such as cameras and infrared sensors, and then processed by CNNs to identify surface defects such as cracks and corrosion.

[0480] After image data undergoes feature extraction and classification processing using CNN, it can effectively provide disease category probability and severity scores.

[0481] (3) Spiking Neural Network (SNN):

[0482] SNNs can better simulate biological nervous systems and have unique advantages in processing time-series data for bridge defect identification, making them suitable for processing real-time monitoring data.

[0483] For time-series data, such as sensor data during disease development, SNN models can be used to process time-domain pulse event sequences, helping to improve the timeliness and accuracy of disease detection.

[0484] (4) Support Vector Machine (SVM) and Random Forest (RF):

[0485] In the assessment of disease classification and severity scoring, Support Vector Machine (SVM) and Random Forest (RF) can be used to build classifiers and perform accurate classification based on the feature vectors of disease data.

[0486] These machine learning models are robust to data noise and can extract useful information from complex environments.

[0487] (5) Kalman Filter and Weighted Average Method:

[0488] In multi-source data fusion, Kalman filtering is used to perform spatiotemporal alignment of quantum entangled sensor data, lidar data, etc., to remove noise and enhance data accuracy.

[0489] Kalman filtering can model uncertainties in sensor data and estimate the true state of damage.

[0490] (6) Reinforcement Learning (RL):

[0491] For optimizing adaptive repair strategies, reinforcement learning can evaluate repair quality and risk indices in real time during the repair process and adjust the repair strategy based on environmental feedback. Through experimentation and optimization, the optimal repair solution can be found.

[0492] (7) Long Short-Term Memory Network (LSTM):

[0493] For predicting the development trend of bridge defects, time-series neural network models such as LSTM can learn the dynamic characteristics of bridge defects changing over time, predict the defect trend in the next few years, and provide a basis for long-term maintenance decisions.

[0494] The above machine learning methods will help improve the accuracy, timeliness and efficiency of bridge defect detection and repair. Furthermore, different algorithms can be used for optimization based on actual application needs, different data sources and problems.

Claims

1. A method for holographic monitoring and repair of defects in quantum entangled neuromorphic bridges, characterized in that, Includes the following steps: Step 1: Deploy a quantum entangled sensor array on the bridge structure to obtain signal intensity and phase information of microscopic defects through quantum interference effect; fuse the signal intensity and phase information with data output from infrared sensors, lidar, and industrial cameras to obtain fused data; encrypt and transmit the fused data through a quantum communication network; store the encrypted data in a distributed cloud system and preprocess it to obtain preprocessed multi-source fused data. Step 2: After spatiotemporal alignment, the preprocessed multi-source fusion data obtained in Step 1 is used to generate fusion feature vectors, confidence scores, disease category probabilities, severity scores, and comprehensive risk indices on a discrete spatial point set. Step 3: Based on the preprocessed multi-source fusion data obtained in Step 1, establish a three-dimensional coordinate system for the bridge and reconstruct its three-dimensional geometric model; map the probability of disease categories, severity scores, and comprehensive risk index generated in Step 2 onto the point cloud or mesh in the bridge's three-dimensional coordinate system to form a three-dimensional disease model with disease attributes; perform spatial clustering on the three-dimensional disease model with disease attributes to extract several disease clusters; extract geometric parameters for each disease cluster and define a structured disease object for each cluster. The structured disease object includes the spatial range of the disease area, disease type, average severity, comprehensive risk index, and set of geometric parameters; collect all structured disease objects to form a three-dimensional holographic model of the bridge's disease. Step 4: For each structured disease object in the set of structured disease objects obtained in Step 3, calculate the comprehensive repair priority score by weighting the comprehensive risk index, average severity, structural importance weight, and traffic importance weight, and then sort them. Under the constraints of total cost not exceeding the budget and total construction period not exceeding the allowable time window, the optimal repair scheme is determined for each structured defect object by taking a weighted combination of repair cost, repair time, risk reduction, and life increment as the comprehensive optimization objective. For each structured defect object, the construction control parameter trajectory that minimizes the weighted integral of repair error and control energy consumption is solved by using the state equation and control input range as constraints. During the repair process, the repair quality score and residual risk index are dynamically evaluated based on real-time monitoring data. When the repair quality score is not lower than the final repair quality judgment threshold and the residual risk index is not higher than the allowable residual risk upper limit, the repair of the structured defect object is determined to be completed, and a structured repair record containing the original structured defect object, the final repair scheme, key control parameters, start and end times, and its quality and risk curves is generated. Step 5: Based on the structured repair record generated in Step 4, continuously collect bridge monitoring data, calculate component-level health indicators and overall bridge health index, extract environmental action vector and load action vector, establish disease degradation state, establish degradation model and correct degradation model parameters, and use the updated component-level health indicators and degradation state to determine failure probability and remaining life. The maintenance action sequence is optimized by multi-cycle cost discounting at discrete decision moments to form a maintenance plan; The parameters of the degradation model, the weights of health indicators, the risk thresholds, and the weights of maintenance strategies are updated on a rolling basis, and the updated maintenance plan is output to the asset management platform.

2. The method according to claim 1, characterized in that, The quantum interference effect described in step 1 employs quantum states and interferometry, as shown in the following formula: （1）， In the formula: Represents the quantum state of a quantum entangled sensor; and The probability amplitude of a qubit characterizes the performance of a quantum sensor under different measurement conditions; The formula for measuring signal strength is as follows: （2）， In the formula: For quantum entanglement sensors at time The intensity of the acquired image; For strength measurement operators; For the evolution of quantum states over time; The formula for obtaining the phase information is as follows: （3）， In the formula: For quantum phase; For phase measurement operators; Let be the quantum state at time t; The formula for data fusion is as follows: （4）， In the formula: The merged data; For the first Data from one sensor; These are weighting coefficients, dynamically adjusted based on the accuracy and signal-to-noise ratio of the quantum entanglement sensor. The formula for encrypted transmission is as follows: （5）， In the formula: For transmitted quantum data; This refers to the quality factor of the transmission channel; Data collected by the sensor.

3. The method according to claim 1, characterized in that, The formula for fusing feature vectors in step 2 is as follows: （6）， In the formula: , , , These are transformation functions for feature extraction and dimension unification of data from quantum entangled sensors, lidar, infrared sensors, and industrial cameras, respectively. The transformation functions are normalization, filtering, or principal component extraction, and the output is a vector with unified dimension. , , , The weighting coefficients for each sensor channel satisfy the following conditions: Each weight is dynamically updated based on the sensor's signal-to-noise ratio, reliability under current operating conditions, and historical performance evaluation. The formula for the confidence level is as follows: （7）， In the formula: Indicates confidence level; For each sensor at point ,time The signal-to-noise ratio estimate; It is a monotonically increasing normalization function used to map the multi-channel signal-to-noise ratio to a comprehensive confidence value between 0 and 1; The formula for the probability of the disease category is as follows: （11）， In the formula: For point This belongs to the category of disease The predicted probability; For category The corresponding output neuron at time 1 The pulse; This is a normalization factor used to normalize all category outputs to... The interval, and satisfying ; The formula for the severity score is as follows: （12）， In the formula: For point The overall severity score of the location; For the first The disease characteristics include estimated crack width, corrosion area ratio, texture roughness, and abnormal temperature gradient values. The weight of each disease characteristic's contribution to severity is determined by training or engineering experience; The formula for the comprehensive risk index is as follows: （13）， In the formula: For point The comprehensive risk index, also known as the priority index; It is a monotonically increasing function used to combine disease probability, severity, and confidence into a single risk score.

4. The method according to claim 1, characterized in that, Step 3, which involves establishing a three-dimensional coordinate system for the bridge and reconstructing its three-dimensional geometric model using the preprocessed multi-source fusion data, includes the following steps: Step 301: Establish the world coordinate system of the bridge structure as the global three-dimensional reference coordinate system; Step 302: Perform extrinsic parameter calibration on the quantum entangled sensor, industrial camera, infrared camera, and lidar to obtain the rigid body transformation matrices from the coordinate system of the quantum entangled sensor, industrial camera, infrared camera, and lidar to the world coordinate system as follows; （14）， In the formula: for The rotation matrix represents the attitude of the sensor coordinate system relative to the world coordinate system; for The translation vector represents the position of the sensor origin in the world coordinate system; for The homogeneous transformation matrix; Step 303, for camera-type sensors, there is also an intrinsic parameter matrix K: （15）， In the formula: , These are the equivalent focal lengths in the horizontal and vertical directions, respectively. , These are the coordinates of the principal point on the imaging plane; Step 304: Based on multi-view images, sparse point clouds of the bridge surface are reconstructed using structure-from-motion techniques, and dense point clouds are constructed through multi-view stereo modeling. For a 3D point with homogeneous coordinates in the world coordinate system, its homogeneous pixel coordinates projected onto the camera image plane are: （16）， In the formula: It is a scaling factor; , This refers to the rotation and translation from the world coordinate system to the camera coordinate system; This is the intrinsic parameter matrix of the industrial camera; Introducing a lidar point cloud, we align it to the world coordinate system of the moving point cloud of the structure through rigid body registration. The objective function is then optimized as follows: （17）， In the formula: , This refers to the rotation and translation from the lidar coordinate system to the world coordinate system; To and The corresponding SfM point cloud points.

5. The method according to claim 1, characterized in that, Step 3, which maps the probability of disease categories, severity scores, and comprehensive risk indices onto point clouds or meshes mapped to the bridge's three-dimensional coordinate system to form a three-dimensional disease model with disease attributes, includes the following steps: Step 305, let the defective pixels on a certain camera image be... The corresponding camera extrinsic parameters are The projection matrix is: （18）， In the formula, P represents the projection matrix, which contains the camera's intrinsic and extrinsic parameters; K represents the camera's intrinsic parameter matrix, which contains parameters such as the camera's focal length and principal point coordinates, and is usually a 3×3 matrix. The spatial ray corresponding to the defective pixel is: ， （19）， In the formula: It is a point on the spatial ray, representing the spatial location of the defective pixel; This represents the camera's position in the world coordinate system. It is obtained by back-projection of pixel coordinates through an intrinsic rotation matrix; As a scaling factor or scale factor, it determines the distance from the camera's optical center along the ray direction, controlling the depth of the defective pixels. Step 306, in the 3D point cloud or mesh model or In the given information, find the intersection point of the ray and the surface of the bridge. The intersection point is defined as the spatial location of the disease in the world coordinate system, and the corresponding disease category probability is assigned. Disease severity Comprehensive Risk Index Assign the value to this point: （20）， Step 307: For the data from the lidar or quantum entangled sensor array described in Step 1, the points in the lidar or quantum entangled sensor array coordinate system are directly mapped to the world coordinate system through extrinsic parameter transformation: （22）， In the formula, X w Indicates the position of a point in the world coordinate system; T ws The transformation matrix from the world coordinate system to the sensor coordinate system; X s Indicates the position of the point in the sensor coordinate system; The defect attributes of spatial points in the lidar or quantum entanglement sensor array are then assigned to the corresponding world coordinate system points: （23）， Obtain a 3D point set with disease attributes: （24）， In the formula, D w Represents a three-dimensional point set with disease attributes; This indicates the position of the k-th point in the world coordinate system; This indicates the disease category at the k-th point; This represents the severity score for the k-th point; This represents the risk index at point k.

6. The method according to claim 1, characterized in that, Step 3, which constitutes the three-dimensional holographic model of bridge defects, includes the following steps: Step 308: Use a spatial clustering method based on distance and risk threshold to set the spatial neighborhood radius. and minimum points These points are clustered into several disease clusters. ; Step 309: For crack-type defects, estimate the principal direction and length using principal component analysis: （25）， For morphological diseases, the area is calculated by projecting the morphology onto a locally fitted plane: （26）， In the formula, A i a represents the local area of ​​the i-th disease cluster; k This represents the local area contribution of the k-th point; Step 310: Calculate the average severity and overall risk index for each disease cluster: （27）， （28）， In the formula, This represents the average severity of the i-th disease cluster; Indicates disease clusters The number of midpoints; This indicates the severity of the k-th point; This represents the comprehensive risk index of the i-th disease cluster; This represents the risk index at point k; For each disease cluster, construct a spatially labeled unit with attributes and define a structured disease object: （29）， In the formula: This represents the structured disease object of the i-th disease cluster; The disease cluster represents the set of points or the range of regions in three-dimensional space. The types of damage include cracks, corrosion, and peeling, caused by... The largest category has been determined; This represents the average severity of the disease cluster; This represents the comprehensive risk index for this disease cluster; This is a set of geometric parameters for the defect, including length, area, volume, main direction, and component number. Step 311: Gather all structured defect objects to form a three-dimensional holographic model of the bridge's defects: （30）， In the formula, H represents the three-dimensional holographic model of the bridge's defects, which is a set containing structured defect objects of all defect clusters; B1 represents the first defect object in the set of structured defect objects; B2 represents the second defect pair in the set of structured defect objects; B M This represents the Mth disease pair in the structured disease object set.

7. The method according to claim 1, characterized in that, The formula for the comprehensive repair priority scoring in step 4 is as follows: （33）， In the formula: Indicates disease clusters The repair priority score is set, with higher scores indicating higher priority for repair. This represents the comprehensive disease risk index obtained in step three; Indicates the average severity of the disease; This indicates the importance weight of the component where the defect is located to the overall structural safety; for example, the main beam and main tower can be assigned a higher value. This indicates the importance weight of the location of the defect to traffic function, such as assigning higher values ​​to lanes and key connecting parts; , , , These are weighting coefficients used to balance risk, severity, structural importance, and traffic importance. The function of the comprehensive optimization objective is as follows: （42）， In the formula, To comprehensively optimize the target value, a smaller value indicates a better overall effect; , , , The importance weights are respectively: cost, construction period, risk reduction, and lifespan increase; This represents the set of all disease clusters i and their corresponding remediation schemes. Perform summation; C ij Indicates the cost of repairing disease cluster i using repair scheme j; x ij Represents a binary variable, indicating whether repair scheme j corresponds to disease cluster i; T ij This indicates the repair time for disease cluster i under repair scheme j; △R ij Indicates the reduction in risk of disease cluster i by repair scheme j; △L ij This represents the lifespan increment of repair scheme j on disease cluster i; The formula for determining the optimal repair scheme for each structured disease object in sequence is as follows: （44）， In the formula, The indicator variable represents the optimal repair scheme for the i-th structured disease object. A value of 1 indicates that the repair scheme is selected, and a value of 0 indicates that the scheme is not selected. arg max represents the maximization operation, that is, selecting the j value that maximizes the objective function. This represents the set of repair solutions belonging to the i-th structured disease object. ; This represents the optimized value of the i-th structured defect object under the j-th repair scheme; The constraints based on the state equation and control input range include: describing the dynamics of the repair process using the following state equation: （45）， In the formula: The derivative of the state with respect to time; A kinetic function describing the repair process; This indicates external disturbances, such as ambient temperature and humidity, or on-site construction disturbances. The solution for the construction control parameter trajectory that minimizes the weighted integral of repair error and control energy consumption includes: In the time window Internal structural local performance indicators: （46）， In the formula: Indicates disease clusters The control optimization objective; This indicates the correction of errors, such as the target state. Compared with the current state difference; This represents a quadratic term that controls the input energy or intensity. , Weighting coefficients used to balance repair accuracy and control energy consumption / construction intensity; The dynamic evaluation of the repair quality score based on real-time monitoring data includes: calculating the repair quality score using a repair quality evaluation function, which is as follows: （49）， In the formula: Indicates time Repair quality score; This represents the m-th quality index function, such as residual crack width, surface smoothness, material hardness, and bond strength. Indicates the importance weight of each quality indicator; This represents the repair status of the i-th structured defect object at time t; The criteria for determining whether the repair of the structured defect object is complete are as follows: （51）， In the formula: Indicates the time when the repair was completed; This represents the final repair quality assessment threshold, which is typically higher than the process threshold. ; This indicates the maximum permissible residual risk. This indicates the time when the i-th structured defect object is repaired. The residual risk index represents the degree of risk that remains after repair; the lower the value, the lower the risk after repair. When all of the above conditions are met, the disease cluster is determined. Repair complete; The formula for generating the structured repair record is as follows: （52）， In the formula: Represents the structured repair record for the i-th structured defect object; Represents the original structured disease object; This indicates the final repair solution adopted; This indicates the trajectory or set of key parameters used during the repair process; , This represents the quality and risk curves of the repair process and its results. , Indicates the start and end times of the repair process.

8. The method according to claim 1, characterized in that, The function for the component-level health indicator mentioned in step 5 is as follows: （54）， In the formula: Representing components The health index is such that the higher the value, the healthier the person is. Represents the k-th component's... Individual health sub-indicator functions, such as features obtained based on damage indicators, frequency changes, and modal stiffness changes; y represents the weight of the m-th health sub-indicator of the k-th component; k (t) represents the health status value of the k-th component at time t, which represents the specific health parameters of the component at a certain time. The formula for the overall health index of the bridge is as follows: （55）， In the formula: The overall health index of the bridge represents the overall health status of the bridge at time t. For the first The weight of each component in relation to the overall structure; Indicates the first The health index of each component; The formula for establishing the degradation model is as follows: （56）， In the formula, Indicates the time of the i-th component The amount of degradation or disease status at any given time; This represents the amount of degradation or the state of damage of the i-th component at time t. The environmental impact vector, including temperature, humidity, chloride ion concentration, and number of freeze-thaw cycles, is obtained from sensor data. This represents the load vector, including traffic volume levels, daily average axle load, etc., obtained from monitoring or statistical data; The degradation evolution function is calibrated using a combination of mechanistic model and data-driven approach. This represents the random disturbance term, used to characterize model errors and unmeasurable uncertainties; The formula for correcting the parameters of the degradation model is as follows: （57）， In the formula: This represents the updated value of the degradation model parameter vector for the i-th component; This represents the old value of the degradation model parameter vector for the i-th component; This represents the actual measured degradation of the i-th component at time t, which is usually the measured value obtained by the sensor; This represents the predicted degradation amount of the i-th component at time t; This represents the gain matrix of the i-th component, which is a dynamically adjusted coefficient used to control the update speed and range of the degradation model; The formula for the failure probability is as follows: （59）， In the formula, This represents the failure probability of the k-th component at time t; The failure function represents the failure function of the k-th component; This represents the amount of degradation of the k-th component at time t; This represents the risk factor of the k-th component; The formula for remaining lifetime is as follows: （60）， In the formula, Indicates the time of the k-th component The remaining lifespan at any given moment; Indicates the predetermined failure time of the component; This indicates the current point in time and is typically the time stamp used when calculating remaining lifetime. The multi-period cost discounting optimization of the maintenance action sequence at discrete decision moments includes the following steps: Viewing long-term maintenance as occurring at discrete decision moments Sequential decision-making problems performed on top of each other; Define period The status is: （61）， In the formula, This represents the state vector of the nth cycle; This indicates the amount of degradation or the state of damage of the i-th component in the n-th cycle; This represents the health index of the i-th component in the n-th cycle; This represents the failure probability of the i-th component in the n-th cycle; Define a single-cycle cost function: （62）， In the formula: This represents the total cost of the nth cycle, which is the sum of all maintenance and risk-related costs within that cycle. Indicates the first The maintenance cost per cycle is usually related to the specific maintenance actions or strategies taken, such as repair, inspection, and replacement. It is typically related to the number and complexity of the actions performed. Indicates the first The risk cost per cycle is typically due to the expected loss that may result from component degradation or failure; Long-run total cost written in discounted form: （63）， In the formula: The long-term expected total cost is represented by E, which represents the expected value, typically calculated by weighting all possible scenarios (through a probability distribution) to determine the expected value of a cost. This represents the cycle number, where n ranges from 0 to N, traversing the entire maintenance decision cycle. This represents the discount factor, a coefficient less than 1 used to discount future costs; This represents the total cost of the nth cycle; This indicates the length of the planning period, which is the maximum value of the entire decision-making cycle; Maintaining action sequences minimize on : A maintenance plan was developed.

9. A bridge defect holographic monitoring and adaptive repair system implementing the method of any one of claims 1 to 8, characterized in that, include: The quantum entanglement sensing unit is used to install a quantum entanglement sensor array on and inside the bridge surface. It obtains the signal strength and phase information of microscopic defects through quantum interference effect. The signal strength and phase information, infrared sensor data, lidar data, and industrial camera data are encrypted and transmitted to a distributed cloud system via a quantum communication network for storage and preprocessing. The preprocessed multi-source fusion data is then output. The multi-source data fusion and neuromorphic computation analysis unit, connected to the quantum entanglement sensing unit, receives preprocessed multi-source fusion data, constructs a fusion feature vector through spatiotemporal alignment and feature-level fusion, and calculates the confidence level; it then transforms the fusion feature vector into a temporal impulse event sequence through neuromorphic encoding, generates disease category probabilities through forward inference of the neuromorphic disease identification network; it quantitatively assesses the severity of the disease to generate a severity score, and combines the disease category probability, severity score, and confidence level to generate a comprehensive risk index; finally, it outputs a structured disease diagnosis result containing the fusion feature vector, confidence level, disease category probability, severity score, and comprehensive risk index on a discrete point set. A three-dimensional defect localization and spatial reconstruction unit is connected to a quantum entanglement sensing unit and a multi-source data fusion and neuromorphic computing analysis unit, respectively. It receives preprocessed multi-source fusion data from the quantum entanglement sensing unit to establish a three-dimensional coordinate system for the bridge and reconstruct its three-dimensional geometric model. It receives defect category probabilities, severity scores, and comprehensive risk indices from the multi-source data fusion and neuromorphic computing analysis unit and maps them to the bridge's three-dimensional geometric model to form a three-dimensional defect model with defect attributes. Spatial clustering is performed on the three-dimensional defect model with defect attributes to extract several defect clusters. Geometric parameters are extracted for each defect cluster, and a structured defect object is defined for each cluster. The structured defect object includes the spatial range of the defect area, defect type, average severity, comprehensive risk index, and a set of geometric parameters. All structured defect objects are combined to form a three-dimensional holographic model of the bridge's defects. The adaptive repair unit, connected to the three-dimensional disease location and spatial restoration unit, receives each structured disease object from the structured disease object set, and obtains a comprehensive repair priority score and sorts them by weighted summation according to the comprehensive risk index, average severity, structural importance weight and traffic importance weight; Under the constraints of total cost not exceeding the budget and total construction period not exceeding the allowable time window, the optimal repair scheme is determined for each structured defect object in sequence, with a weighted combination of cost, construction period, risk reduction, and life increment as the comprehensive optimization objective. The construction control parameter trajectory that minimizes the weighted integral of repair error and control energy consumption is solved using the state equation and control input range as constraints. During the repair process, the repair quality score and residual risk index are dynamically evaluated based on real-time monitoring data. When the repair quality score is not lower than the final repair quality judgment threshold and the residual risk index is not higher than the allowable residual risk upper limit, the repair of the structured defect object is deemed complete, generating a structured repair record containing the original structured defect object, the final repair scheme, key control parameters, start and end times, and its quality and risk curves. The long-term monitoring and maintenance decision unit is connected to the adaptive repair unit, receives structured repair records, continuously collects bridge monitoring data, calculates component-level health indicators and overall bridge health index, extracts environmental action vectors and load action vectors, establishes disease degradation status, establishes degradation model and corrects degradation model parameters, and uses updated component-level health indicators and degradation status to determine failure probability and remaining life. The maintenance action sequence is optimized by multi-cycle cost discounting at discrete decision moments to form a maintenance plan; The parameters of the degradation model, the weights of health indicators, the risk thresholds, and the weights of maintenance strategies are updated on a rolling basis, and the updated maintenance plan is output to the asset management platform.

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