Large-area modular multilayer grid damage detection system and method

Abstract

The invention relates to the technical field of large-area damage detection, in particular to a large-area modular multilayer grid damage detection system and method.The large-area modular multilayer grid damage detection system comprises a central monitoring system and a detection unit subsystem, and the central monitoring system is connected with the detection unit subsystem through wireless / wired communication; the central monitoring system comprises a display and early warning module, a data analysis module and a data receiving module; the detection unit subsystem comprises a multi-layer cross grid, a signal acquisition module, a data processing module, a communication module and a self-diagnosis and fault processing module. According to the method, the surface and deep damage of the large-area plate-shaped object is monitored in real time by constructing a modular detection unit and a multi-layer crossed conductor network, and the depth and severity of the damage position are accurately evaluated by means of sparse matrix data processing and a damage positioning algorithm, so that online health monitoring of a large-area structure is realized, and the detection precision and efficiency are improved; and the operation safety and reliability of the equipment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of large-area damage detection, and in particular to a large-area modular multi-layer grid damage detection system and method. Background Art

[0002] In the fields of navigation, aerospace, and military, large equipment such as ship decks, spacecraft skins, and armor plates usually require large-area plate structures for load-bearing or protection. However, these plate structures may suffer from various forms of damage such as fatigue, cracks, corrosion, and impact during long-term service. If the damage accumulates to a certain extent but is not discovered in time, it is very likely to cause major safety hazards or even catastrophic failures. Therefore, rapid, real-time, and highly sensitive damage detection of such large-area plate structures has always been an important topic in the field of structural health monitoring and non-destructive testing. The health monitoring of large plate structures faces many limitations of traditional detection methods. For example, detection methods such as ultrasonic, X-ray, eddy current, and infrared thermal imaging detection are difficult to achieve large-area real-time monitoring. At the same time, they are also affected by high cost, strong environmental sensitivity, and insufficient deep defect detection capabilities. To meet the needs of fast and highly sensitive online monitoring, the industry has actively explored new technical paths, such as solutions based on fiber grating sensors (FBG), piezoelectric smart materials (PZT), and two-dimensional conductive ink grids. These methods perform well in local monitoring or specific material applications, but most of them have problems such as difficulty in simultaneously taking into account large-area coverage and deep monitoring, complex deployment processes, insufficient real-time performance, poor scalability, and limited versatility, and therefore need further improvement.

[0003] Currently, existing technologies for nondestructive testing of large-area plate-like structures suffer from several common challenges, including low detection efficiency, insufficient detection accuracy, poor environmental adaptability, and difficulty in achieving real-time monitoring. Traditional ultrasonic, radiographic, and eddy current testing methods mostly rely on point-to-point or small-area scanning, which limits their detection range and hinders online monitoring. Infrared thermal imaging is susceptible to interference from external temperature and environmental factors, limiting its ability to identify defects in deep or multi-layered structures. While fiber Bragg grating (FBG) and piezoelectric sensors can be embedded within materials, large-area applications require complex layout and demodulation equipment, resulting in relatively high deployment and maintenance costs. Existing resistive / conductive grid technologies are mostly deployed for single-layer deployments, making it difficult to obtain damage depth information. Their low modularity makes flexible expansion and unitized maintenance difficult. These limitations make these technologies inadequate for rapid, depth-controlled, and highly sensitive damage monitoring of large-area, complex, multi-layered structures. Therefore, a new solution with multi-layered detection capabilities, scalability, and real-time performance is urgently needed to address the shortcomings and drawbacks of existing technologies. Summary of the Invention

[0004] The purpose of the present invention is to address the shortcomings of the existing technology and propose a large-area modular multi-layer grid damage detection system and method. By constructing modular detection units and a multi-layer cross-conductor network, the surface and deep damage of large-area plate-like objects can be monitored in real time. With the help of sparse matrix data processing and damage localization algorithm, the depth and severity of the damage location can be accurately assessed, thereby realizing online health monitoring of large-area structures, improving detection accuracy and efficiency, and thereby enhancing the safety and reliability of equipment operation.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A large-area modular multi-layer grid damage detection system includes a central monitoring system and a detection unit subsystem, wherein the central monitoring system is connected to the detection unit subsystem via wireless / wired communication;

[0007] The central monitoring system includes a display and warning module, a data analysis module and a data receiving module;

[0008] The data receiving module receives sparse matrix data from each detection unit and ensures that the data transmission is accurate and timely;

[0009] The data analysis module analyzes the data using a specially designed algorithm to assess the extent, location, and depth of damage and generate a damage report;

[0010] The display and warning module: Visualizes the analysis results to show the specific location, depth and severity of the damage. When the damage exceeds the set threshold, the system will issue an early warning to remind maintenance personnel to take timely measures.

[0011] The detection unit subsystem includes a multi-layer cross grid, a signal acquisition module, a data processing module, a communication module and a self-diagnosis and fault processing module;

[0012] The multi-layer cross grid: Figure 3 The demonstration shows the arrangement of multiple layers of horizontal and vertical conductors within the plate-like object to be inspected. These conductors can be laid on the surface of the structure, between structural layers, or embedded within the composite material to form a two-dimensional or three-dimensional cross-grid. Each layer of the grid is used to monitor damage at different depths or areas, and the specific damage situation is reflected by the on-off status of the conductors.

[0013] The signal acquisition module scans each wire in the multi-layer cross-grid, obtains the on / off status (i.e., 0 or 1) of each intersection, and converts the signal into a digital signal;

[0014] The data processing module pre-processes the collected signals to generate row / column state matrices, and further converts the state matrix data into a sparse matrix format to reduce the amount of data stored and transmitted;

[0015] The communication module transmits data to the central monitoring system via wired or wireless means. The module supports standard communication protocols to ensure the reliability and compatibility of data transmission.

[0016] The self-diagnosis and fault handling module monitors the status of the detection unit in real time, checks the connection of the wires and the operation of the signal acquisition module and the communication module. If a fault is found, it will perform self-repair or alarm, and switch to the backup channel to ensure the continuous operation of the system.

[0017] A large-area modular multi-layer grid damage detection method is provided. The method is implemented based on the large-area modular multi-layer grid damage detection system described above and includes the following steps:

[0018] Step 1: After the system is started, it will perform a self-check to ensure that all hardware devices, including sensors and communication modules, are functioning properly. This check covers aspects such as device connection status, sensor status, and communication stability.

[0019] Step 2: Scan the multi-layer cross-conductor grid according to the set sampling period to obtain the on-off status of the horizontal and vertical conductors. The data of each layer is stored in the row state vector A. l and column state vector B l middle;

[0020] Step 3: Each detection unit module M converts the collected row / column conductor on / off status information of each layer into a state matrix C of each layer l M , and upload it to the central monitoring system;

[0021] Step 4: Each modular detection unit sends a data packet through the communication module. The data packet contains its unique module ID M, layer number l and corresponding sparse data D l M To the central monitoring system;

[0022] Step 5: The central monitoring system connects each layer of module M to D l M Compare with the initial reference state to obtain the local difference matrix S l M To identify possible injury sites;

[0023] Step 6: According to the local difference matrix S of each layer l MConduct damage quantification analysis, specifically the size of the damaged area and the depth of the damage;

[0024] Step 7: If the damage size and depth exceed the safety threshold, the system will immediately trigger an alarm and display the damage location and depth to remind maintenance personnel to repair it;

[0025] Step 8: The central monitoring system summarizes and analyzes the damage data from each floor, comprehensively displays the damage distribution of each floor, and provides real-time feedback and decision support for maintenance personnel;

[0026] Step 9: Generate and upload a detailed damage report to the database, recording the damage information of each layer for subsequent analysis and monitoring;

[0027] Step 10: Enter the cyclic monitoring mode and rescan and analyze regularly. The system performs self-diagnosis and updates the reference matrix as needed to ensure monitoring accuracy.

[0028] Preferably, in step 1, after the system is started, a comprehensive self-test procedure is first performed. This self-test process includes the following:

[0029] Hardware connection check: The central monitoring system sends query signals to each modular detection unit to confirm whether the power supply and data line (wired or wireless) connection status of all units are normal. If a connection failure or abnormality is detected, the system will record an error log and send a prompt to the user interface to check the physical connection or network configuration.

[0030] Sensor status check: The signal acquisition module inside each detection unit performs a preliminary continuity test on the multi-layer cross-grid wires connected to it (for example, applying a small current and checking the loop integrity) to confirm that the sensor itself is functioning properly and there is no initial open circuit or short circuit fault;

[0031] The communication module test verifies the stability of the communication link and the integrity of data transmission by sending and receiving test data packets between the central monitoring system and each detection unit, ensuring that there is no packet loss or significant delay in data transmission;

[0032] Sensor calibration executes the sensor calibration procedure to confirm that the readings are within the acceptable error range. Periodic calibration can also be performed using the built-in reference or an external calibration plate. After the self-test is completed, if all devices and links are in normal status, the system will record the normal status and prepare to enter the monitoring process. If any faults are found, the system will activate the fault diagnosis module to indicate the specific problem and may suspend subsequent operations until the problem is resolved.

[0033] Preferably, in step 2, the sampling period should be set appropriately based on the importance of the structure and environmental conditions. The sampling period can be dynamically adjusted to balance real-time performance and data volume according to actual needs. In certain specific situations, such as when damage in a certain area is detected to be likely to expand, the system will automatically shorten the sampling period and increase the data collection frequency.

[0034] For the grid of the first layer, the on-off status of the horizontal and vertical conductors is scanned, and the data is collected by sensors and recorded in the form of digital signals; the state of the horizontal conductor is stored in the row state vector A l =[a0,a1,...,a i ,...a m ] Τ , a i is the on / off state of the i+1th wire (0 represents on, 1 represents off); the state of the longitudinal wire is stored in the column state vector B l =[b0,b1,...,b j ,...b n ], each b j is the on / off state of the j+1th node (0 represents on, 1 represents off).

[0035] Preferably, in step 3: in the data processing module inside each modular detection unit ID M, a state matrix of each layer in the area responsible for the unit is generated according to the collected row and column state vectors, and a sparse processing is performed before uploading;

[0036] For the lth layer, based on the row state vector A l and column state vector B l Construct the two-dimensional state matrix C of this layer l , matrix element c l (i, j) reflects the status of the wires passing through the area near the intersection (i, j). The logical OR operation is used to determine the damage: when at least one of the i-th row wire or j-th column wire passing through the intersection area is broken (i.e., a l (i) = 1 or b l (i)=1), then the intersection position (i, j) is determined to be affected by damage, and C l (i, j) = 1; otherwise, if both wires are connected, then C l (i,j)=0;

[0037] The formula is as follows:

[0038] C l M =A l |B l

[0039] Then, the state matrix is converted to sparse format: the generated C l M The matrix (for each layer l) is converted to an efficient sparse matrix representation format, which is as follows:

[0040] Coordinate list (COO): Generate a D l M A list containing the coordinates (i, j) of all elements whose value is 1, that is, D l M ={(i,j)|C l (i,j)=1}.

[0041] Preferably, in step 5: the data receiving module of the central monitoring system receives and classifies the incoming sparse data packets according to the module ID, and determines the possible damage location by comparing the state matrix of each layer with the initial reference matrix, where the reference matrix is the normal state data obtained when the system is first set up;

[0042] For each unit module ID M and its layers l, the system uses the received D l M , and retrieve the pre-stored local reference state data D of this module and this layer l基 M , by comparing the current state, by D l M Directly infer and calculate the local difference matrix S l M :

[0043] S l M (i,j)=D l M (i,j)×(1-D l基 M (i,j))

[0044] S l M The non-zero element (i, j) in the matrix identifies the newly added damage point on the lth layer within the M region of the module.

[0045] Preferably, in step six: based on the local difference matrix S of each layer of each module M obtained in step five l M , to quantify the damage:

[0046] Calculate the local damage area: Statistical S l M The number of damage points (i.e., non-zero elements) in the

[0047] Determine the damage depth within the module: count the number of layers with a damage area greater than zero and the total number of layers.

[0048] Preferably, in step seven: after the system completes the damage assessment, if it determines that the damage level of a certain layer or area exceeds the set safety threshold, the system will immediately trigger the alarm mechanism. The damage assessment uses a pre-set threshold to judge the severity of the damage. When the damage level at a certain location is higher than the set threshold, the system will determine that the location is in a dangerous state. The system will visualize the damage location and severity. The damaged area will be marked in red or other conspicuous colors, and the damage depth and damage score will be displayed. The alarm information contains a detailed description of the damage, including the damage type, the physical location of the damage, i.e., the coordinate location, depth, damage severity, etc. Based on these detailed information, the system will give corresponding repair suggestions.

[0049] Preferably, in step eight: after receiving data from each detection unit, the system will summarize the damage information of each layer and conduct a comprehensive analysis, and the damage information includes damage type, depth, and location of the affected wire;

[0050] (1) In terms of damage location visualization, a graphical user interface will be used to display the damage location of each layer, and color coding (green represents no damage, yellow represents mild damage, and red represents severe damage) will be used to help operators quickly determine the extent of damage;

[0051] (2) In terms of three-dimensional graphic display, in order to more intuitively display the damaged area, the system can provide three-dimensional display, draw a 3D model of the damaged area and mark the damage position and depth of each layer. The user can rotate the view to observe the damage at different angles;

[0052] (3) Damage severity map: After comprehensively analyzing the data from each layer, the system will generate a damage score map for each area, showing the severity of the damage and helping engineers formulate maintenance plans.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. The present invention demonstrates significant technical benefits in terms of real-time monitoring of large-area plate-like structures, depth-controlled detection, modular and easy maintenance, and data processing efficiency. It employs a multi-module combination to flexibly cover large-scale or irregularly shaped plate-like objects, supports parallel detection, and transmits data back in real time. Compared with traditional point-to-point or small-scale scanning methods, this significantly improves efficiency and response speed. The use of a multi-layer cross-grid enables monitoring from the surface to deep layers, overcoming the lack of depth information perception associated with single-layer detection. This allows for a more comprehensive assessment of three-dimensional damage morphology. The modular detection units facilitate deployment, maintenance, and expansion, ensuring that local failures will not result in global failures, effectively reducing system downtime and operational costs.

[0055] 2. The present invention transmits data through sparse matrix conversion, which significantly reduces the amount of raw data, improves bandwidth utilization, and simplifies subsequent calculations and processing, allowing the system to operate stably even in harsh environments or bandwidth-limited scenarios. The cross-conductor continuity detection has relatively loose environmental requirements and does not rely on specific conditions such as coupling media or radiation sources. The real-time early warning mechanism of the central monitoring system can immediately alert maintenance personnel to intervene when crack propagation or sudden damage is detected. The interactive comparison of multi-layer grids can effectively reduce the probability of false detection and missed detection, achieving higher-precision and higher-reliability monitoring of key equipment, and providing a new structural health monitoring solution with high efficiency, high sensitivity, and easy maintenance and upgrade for application fields such as navigation, aerospace, and military. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a block diagram of the system of the present invention;

[0057] Figure 2 is a schematic diagram of the system of the present invention;

[0058] Figure 3 Schematic diagram of the wire grid structure in the present invention;

[0059] Figure 4 4 is a flowchart of the method of the present invention. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings so that those skilled in the art can better understand the advantages and features of the present invention and thus more clearly define the scope of protection of the present invention. The embodiments described in the present invention are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative work shall fall within the scope of protection of the present invention.

[0061] A large-area modular multi-layer grid damage detection system comprises a central monitoring system and a detection unit subsystem. The central monitoring system is connected to the detection unit subsystem via wireless / wired communication.

[0062] Among them, the detection system is Figure 1 As shown, it is composed of multiple modular detection units and a central monitoring system. Figure 2The paper presents the specific composition of the detection units and how they are connected to form a large-area monitoring network. Each modular detection unit contains a multi-layer cross-grid and is responsible for monitoring a specific area of the plate-like object. Multiple detection units can be combined into a whole to cover the entire surface of a large-area plate-like object or embedded in the material to form a fully covered monitoring system. Each detection unit transmits the collected data to the central computer system through wired or wireless communication.

[0063] The central monitoring system includes a display and warning module, a data analysis module and a data receiving module;

[0064] The data receiving module receives sparse matrix data from each detection unit and ensures that the data transmission is accurate and timely;

[0065] The data analysis module analyzes the data using a specially designed algorithm to assess the extent, location, and depth of damage and generate a damage report;

[0066] The display and warning module: Visualizes the analysis results to show the specific location, depth and severity of the damage. When the damage exceeds the set threshold, the system will issue an early warning to remind maintenance personnel to take timely measures.

[0067] The detection unit subsystem includes a multi-layer cross grid, a signal acquisition module, a data processing module, a communication module and a self-diagnosis and fault processing module;

[0068] The multi-layer cross grid: Figure 3 The demonstration shows the arrangement of multiple layers of horizontal and vertical conductors within the plate-like object to be inspected. These conductors can be laid on the surface of the structure, between structural layers, or embedded within the composite material to form a two-dimensional or three-dimensional cross-grid. Each layer of the grid is used to monitor damage at different depths or areas, and the specific damage situation is reflected by the on-off status of the conductors.

[0069] The signal acquisition module scans each wire in the multi-layer cross-grid, obtains the on / off status (i.e., 0 or 1) of each intersection, and converts the signal into a digital signal;

[0070] The data processing module pre-processes the collected signals to generate row / column state matrices, and further converts the state matrix data into a sparse matrix format to reduce the amount of data stored and transmitted;

[0071] The communication module transmits data to the central monitoring system via wired or wireless means. The module supports standard communication protocols to ensure the reliability and compatibility of data transmission.

[0072] The self-diagnosis and fault handling module monitors the status of the detection unit in real time, checks the connection of the wires and the operation of the signal acquisition module and the communication module. If a fault is found, it will perform self-repair or alarm, and switch to the backup channel to ensure the continuous operation of the system.

[0073] A large-area modular multi-layer grid damage detection method is provided. The method is implemented based on the large-area modular multi-layer grid damage detection system described above and includes the following steps:

[0074] Step 1: After the system is started, it will perform a self-check to ensure that all hardware devices, including sensors and communication modules, are functioning properly. This check covers aspects such as device connection status, sensor status, and communication stability.

[0075] Step 2: Scan the multi-layer cross-conductor grid according to the set sampling period to obtain the on-off status of the horizontal and vertical conductors. The data of each layer is stored in the row state vector A. l and column state vector B l middle;

[0076] Step 3: Each detection unit module M converts the collected row / column conductor on / off status information of each layer into a state matrix C of each layer l M , and upload it to the central monitoring system;

[0077] Step 4: Each modular detection unit sends a data packet through the communication module. The data packet contains its unique module ID M, layer number l and corresponding sparse data D l M To the central monitoring system;

[0078] Step 5: The central monitoring system connects each layer of module M to D l M Compare with the initial reference state to obtain the local difference matrix S l M To identify possible injury sites;

[0079] Step 6: According to the local difference matrix S of each layer l M Conduct damage quantification analysis, specifically the size of the damaged area and the depth of the damage;

[0080] Step 7: If the damage size and depth exceed the safety threshold, the system will immediately trigger an alarm and display the damage location and depth to remind maintenance personnel to repair it;

[0081] Step 8: The central monitoring system summarizes and analyzes the damage data from each floor, comprehensively displays the damage distribution of each floor, and provides real-time feedback and decision support for maintenance personnel;

[0082] Step 9: Generate and upload a detailed damage report to the database, recording the damage information of each layer for subsequent analysis and monitoring;

[0083] Step 10: Enter the cyclic monitoring mode and rescan and analyze regularly. The system performs self-diagnosis and updates the reference matrix as needed to ensure monitoring accuracy.

[0084] Specifically, in step 1, after the system is started, a comprehensive self-test procedure is first performed. This self-test process includes the following:

[0085] Hardware connection check: The central monitoring system sends query signals to each modular detection unit to confirm whether the power supply and data line (wired or wireless) connection status of all units are normal. If a connection failure or abnormality is detected, the system will record an error log and send a prompt to the user interface to check the physical connection or network configuration.

[0086] Sensor status check: The signal acquisition module inside each detection unit performs a preliminary continuity test on the multi-layer cross-grid wires connected to it (for example, applying a small current and checking the loop integrity) to confirm that the sensor itself is functioning properly and there is no initial open circuit or short circuit fault;

[0087] The communication module test verifies the stability of the communication link and the integrity of data transmission by sending and receiving test data packets between the central monitoring system and each detection unit, ensuring that there is no packet loss or significant delay in data transmission;

[0088] Sensor calibration executes the sensor calibration procedure to confirm that the readings are within the acceptable error range. Periodic calibration can also be performed using the built-in reference or an external calibration plate. After the self-test is completed, if all devices and links are in normal status, the system will record the normal status and prepare to enter the monitoring process. If any faults are found, the system will activate the fault diagnosis module to indicate the specific problem and may suspend subsequent operations until the problem is resolved.

[0089] Specifically, in step 2, the sampling period should be set appropriately based on the importance of the structure and environmental conditions. The sampling period can be dynamically adjusted to balance real-time performance and data volume based on actual needs. In certain situations, if damage in a certain area is detected to be likely to expand, the system will automatically shorten the sampling period and increase the data collection frequency.

[0090] For the grid of the first layer, the on-off status of the horizontal and vertical conductors is scanned, and the data is collected by sensors and recorded in the form of digital signals; the state of the horizontal conductor is stored in the row state vector

[0091] Al =[a0,a1,...,a i ,...a m ] Τ , a i is the on / off state of the i+1th wire (0 represents on, 1 represents off); the state of the longitudinal wire is stored in the column state vector B l =[b0,b1,...,b j ,...b n ], each b j is the on / off state of the j+1th wire (0 represents on, 1 represents off).

[0092] Specifically, in step 3: in the data processing module inside each modular detection unit (ID M), a state matrix for each layer in the area responsible for the unit is generated based on the collected row and column state vectors, and the state matrix is uploaded after sparse processing;

[0093] For the lth layer, based on the row state vector A l and column state vector B l Construct the two-dimensional state matrix C of this layer l , matrix element c l (i, j) reflects the status of the wires passing through the area near the intersection (i, j). The logical OR operation is used to determine the damage: when at least one of the i-th row wire or j-th column wire passing through the intersection area is broken (i.e., a l (i) = 1 or b l (i)=1), then the intersection position (i, j) is determined to be affected by damage, and C l (i, j) = 1; otherwise, if both wires are connected, then C l (i,j)=0;

[0094] The formula is as follows:

[0095] C l M =A l |B l

[0096] Then, the state matrix is converted to sparse format: the generated C l M The matrix (for each layer l) is converted to an efficient sparse matrix representation format, which is as follows:

[0097] Coordinate list (COO): Generate a D l M A list containing the coordinates (i, j) of all elements whose value is 1, that is, D l M ={(i,j)|C l(i,j)=1}.

[0098] Specifically, in step 5: the data receiving module of the central monitoring system receives and classifies the incoming sparse data packets according to the module ID, and determines the possible damage locations by comparing the state matrix of each layer with the initial reference matrix. The reference matrix is the normal state data obtained when the system is first set up;

[0099] For each unit module ID M and its layers l, the system uses the received D l M , and retrieve the pre-stored local reference state data D of this module and this layer l基 M , by comparing the current state, by D l M Directly infer and calculate the local difference matrix S l M :

[0100] S l M (i,j)=D l M (i,j)×(1-D l基 M (i,j))

[0101] S l M The non-zero element (i, j) in the matrix identifies the newly added damage point on the lth layer within the M region of the module.

[0102] Specifically, in step 6: based on the local difference matrix S of each layer of each module M obtained in step 5 l M , to quantify the damage:

[0103] Calculate the local damage area: Statistical S l M The number of damage points (i.e., non-zero elements) in the

[0104] Determine the damage depth within the module: count the number of layers with a damage area greater than zero and the total number of layers.

[0105] Specifically, in step seven: after the system completes the damage assessment, if it determines that the damage level of a certain layer or area exceeds the set safety threshold, the system will immediately trigger the alarm mechanism. The damage assessment uses a pre-set threshold to judge the severity of the damage. When the damage level at a certain location is higher than the set threshold, the system will determine that the location is in a dangerous state. The system will visualize the location and severity of the damage. The damaged area will be marked in red or other conspicuous colors, and the damage depth and damage score will be displayed. The alarm information contains a detailed description of the damage, including the damage type, the physical location of the damage, i.e. the coordinate location, depth, severity of the damage, etc. Based on these detailed information, the system will give corresponding repair suggestions.

[0106] Specifically, in step eight: after receiving data from each detection unit, the system will summarize the damage information of each layer and conduct a comprehensive analysis. The damage information includes damage type, depth, and location of affected wires;

[0107] (1) In terms of damage location visualization, a graphical user interface will be used to display the damage location of each layer, and color coding (green represents no damage, yellow represents mild damage, and red represents severe damage) will be used to help operators quickly determine the extent of damage;

[0108] (2) In terms of three-dimensional graphic display, in order to more intuitively display the damaged area, the system can provide three-dimensional display, draw a 3D model of the damaged area and mark the damage position and depth of each layer. The user can rotate the view to observe the damage at different angles;

[0109] (3) Damage severity map: After comprehensively analyzing the data from each layer, the system will generate a damage score map for each area, showing the severity of the damage and helping engineers formulate maintenance plans.

[0110] The present invention has outstanding features such as large-area coverage, modular combination, multi-layer depth detection, and high-sensitivity real-time monitoring, and has broad application potential, mainly including but not limited to the following fields:

[0111] (1) In the maritime field, when monitoring surface and deep damage of large-area metal or composite materials such as ship decks and hulls, harsh working conditions such as salt spray and high humidity in the marine environment will interfere with traditional detection methods or cause their efficiency to decrease. The present invention has good adaptability to such situations and can continuously monitor cracks, corrosion, impact damage and other conditions of key parts online.

[0112] (2) In the field of aerospace, health monitoring of plate structures such as aircraft wings and fuselage skins and spacecraft bulkheads is very important. For composite materials or multi-layer sandwich structures, the present invention can use multi-layer grid technology to identify deep and potential cracks and issue early warnings in a timely manner, thereby improving the safety and reliability of aircraft.

[0113] (3) Military protection and armor fields, such as armored vehicles, fighter aircraft protective plates and other large-area plate-shaped protective structures of military equipment. Due to the modular design, fault detection units can be quickly replaced or repaired on site, and the equipment can always be kept in a state of high-reliability monitoring, thereby meeting the strict requirements of the military field for safety and continuous availability.

[0114] (4) In marine engineering and offshore platforms, there are large metal or composite structures such as offshore wind power equipment, offshore drilling platforms and offshore aquaculture facilities. These structures can take advantage of real-time online detection in the high salt spray environment of the ocean, thereby timely discovering structural fatigue or corrosion damage and carrying out early warning work.

[0115] (5) In the fields of infrastructure and civil engineering, for large-area plate or plate-beam structures such as long-span bridges and tunnels, linings of high-rise buildings, and curtain walls, by attaching or pre-embedding detection units at key locations, long-term online monitoring of problems such as concrete cracks, steel plate corrosion, and interlayer debonding of composite materials can be achieved, thereby enhancing the safety and durability of infrastructure operations.

[0116] (6) There are many other occasions where large-area structural health monitoring is required, including but not limited to automobile bodies, large storage tanks, nuclear power plant shielding walls, rail transit vehicle bodies, etc. In such applications, the system can flexibly configure the number of monitoring layers and detection units based on the stress characteristics and material properties, thereby achieving targeted and efficient health monitoring.

[0117] In summary, the present invention can provide efficient and sensitive online monitoring capabilities when performing damage detection on large, complex, multi-layer or high-reliability plate structures. It has broad promotion and application prospects and has unique advantages in real-time, modular and multi-level detection. It can not only meet the current needs of key fields such as ocean, aerospace, and military equipment, but can also be applied to other potential industries, providing strong technical support for structural health monitoring and non-destructive testing.

[0118] The descriptions and practices disclosed in this invention are easy to understand and comprehend for those skilled in the art, and modifications and refinements may be made without departing from the principles of the invention. Therefore, modifications and improvements made without departing from the spirit of the invention should also be considered within the scope of protection of this invention.

Claims

1. A large-area modular multi-layer grid damage detection system, characterized in that: It includes a central monitoring system and a detection unit subsystem, wherein the central monitoring system is connected to the detection unit subsystem via wireless / wired communication; The central monitoring system includes a display and warning module, a data analysis module and a data receiving module; The data receiving module receives sparse matrix data from each detection unit and ensures that the data transmission is accurate and timely; The data analysis module analyzes the data through an algorithm to assess the extent, location, and depth of the damage and generates a damage report; The display and warning module: Visually displays the analysis results to show the specific location, depth and severity of the damage. When the damage exceeds the set threshold, the system issues an early warning to remind maintenance personnel to take timely measures. The detection unit subsystem includes a multi-layer cross grid, a signal acquisition module, a data processing module, a communication module and a self-diagnosis and fault processing module; The multi-layer cross grid: multiple layers of horizontal and vertical conductors are laid in the plate-like object to be inspected. These conductors are laid on the surface of the structure, between structural layers, or embedded in the composite material to form a two-dimensional or three-dimensional cross grid. Each layer of the grid is used to monitor damage at different depths or areas, and the specific damage situation is reflected by the on-off status of the conductors. The signal acquisition module scans each wire in the multi-layer cross grid, obtains the on / off status of each intersection, and converts the signal into a digital signal; The data processing module pre-processes the collected signals, generates a row / column state matrix, and converts the state matrix data into a sparse matrix format, thereby reducing the amount of data stored and transmitted; The communication module transmits data to the central monitoring system via wired or wireless means. The module supports standard communication protocols to ensure the reliability and compatibility of data transmission. The self-diagnosis and fault handling module monitors the status of the detection unit in real time, checks the connection of the wires and the operation of the signal acquisition module and the communication module. If a fault is found, it will perform self-repair or alarm, and switch to the backup channel to ensure the continuous operation of the system.

2. A large-area modular multi-layer grid damage detection method, characterized in that: The detection method is implemented based on the large-area modular multi-layer grid damage detection system as claimed in claim 1, and includes the following steps: Step 1: After the system is started, a self-check is performed to ensure that all hardware devices of the sensor and communication module are working properly, covering the device connection status, sensor status, and communication stability; Step 2: Scan the multi-layer cross-conductor grid according to the set sampling period to obtain the on-off status of the horizontal and vertical conductors. The data of each layer is stored in the row state vector A. l and column state vector B l middle; Step 3: Each detection unit module M converts the collected row / column conductor on / off status information of each layer into a state matrix C of each layer l M , and upload it to the central monitoring system; Step 4: Each modular detection unit sends a data packet through the communication module. The data packet contains its unique module ID M, layer number l and corresponding sparse data D l M To the central monitoring system; Step 5: The central monitoring system connects each layer of module M to D l M Compare with the initial reference state to obtain the local difference matrix S l M To identify possible injury sites; Step 6: According to the local difference matrix S of each layer l M Conduct damage quantification analysis, specifically the size of the damaged area and the depth of the damage; Step 7: If the damage size and depth exceed the safety threshold, the system will immediately trigger an alarm and display the damage location and depth to remind maintenance personnel to repair it; Step 8: The central monitoring system summarizes and analyzes the damage data from each floor, comprehensively displays the damage distribution of each floor, and provides real-time feedback and decision support for maintenance personnel; Step 9: Generate and upload a detailed damage report to the database, recording the damage information of each layer for subsequent analysis and monitoring; Step 10: Enter the cyclic monitoring mode and rescan and analyze regularly. The system performs self-diagnosis and updates the reference matrix as needed to ensure monitoring accuracy.

3. A large-area modular multi-layer grid damage detection method according to claim 2, characterized in that: In step 1, after the system is started, it first performs a comprehensive self-test procedure. This self-test process includes the following: Hardware connection check: The central monitoring system sends query signals to each modular detection unit to confirm whether the power supply and data line connection status of all units are normal. If a connection failure or abnormality is detected, the system records the error log and sends a prompt to the user interface to check the physical connection or network configuration. Sensor status check: The signal acquisition module inside each detection unit performs a preliminary continuity test on the multi-layer cross-grid wires connected to it to confirm that the sensor itself is functioning normally and there is no initial open circuit or short circuit fault; The communication module test verifies the stability of the communication link and the integrity of data transmission by sending and receiving test data packets between the central monitoring system and each detection unit, ensuring that there is no packet loss or significant delay in data transmission; Sensor calibration executes the sensor calibration procedure to confirm that the readings are within the acceptable error range. Periodic calibration is performed using the built-in reference or external calibration board. After the self-test is completed, if all devices and links are in normal status, the system records the normal status and prepares to enter the monitoring process. If any fault is found, the system will activate the fault diagnosis module, indicate the specific problem, and suspend subsequent operations until the problem is resolved.

4. A large-area modular multi-layer grid damage detection method according to claim 2, characterized in that: In step 2, the sampling period should be set appropriately based on the structural importance and environmental conditions. The sampling period can be dynamically adjusted to balance real-time performance and data volume based on actual needs. In certain situations, such as when damage in a certain area is detected to be potentially spreading, the system automatically shortens the sampling period and increases the data collection frequency. For the grid of the first layer, the on-off status of the horizontal and vertical conductors is scanned, and the data is collected by sensors and recorded in the form of digital signals; the status of the m+1 horizontal conductors is stored in the row state vector A. l =[a0,a1,...,a i ,...a m ] Τ , a i is the on / off state of the i+1th wire; The states of the n+1 conductors in the longitudinal direction are stored in the column state vector B l =[b0,b1,...,b j ,...b n ], each b j is the on-off state of the j+1th.

5. The large-area modular multi-layer grid damage detection method according to claim 2, characterized in that: In step 3: In the data processing module inside each modular detection unit (ID M), the state matrix of each layer in the unit's responsible area is generated based on the collected row and column state vectors, and the state matrix is uploaded after sparse processing; For the lth layer, based on the row state vector A l and column state vector B l Construct the two-dimensional state matrix C of this layer l , matrix element c l (i, j) reflects the status of the wires passing through the area near the intersection (i, j). The damage is determined by logical OR operation: when at least one of the wires in the i-th row or the j-th column passing through the intersection is broken, that is, a l (i) = 1 or b l (i) = 1, then the intersection position (i, j) is determined to be affected by damage, let C l (i, j) = 1; otherwise, if both wires are connected, then C l (i,j)=0; The formula is as follows: C l M =A l |B l Then, the state matrix is converted to sparse format: the generated C l M The matrix is converted to an efficient sparse matrix representation format, the format is as follows: Coordinate list (COO): Generate a D l M A list containing the coordinates (i, j) of all elements whose value is 1, that is, D l M ={(i,j)|C l (i,j)=1}.

6. A large-area modular multi-layer grid damage detection method according to claim 2, characterized in that: In step 5: the data receiving module of the central monitoring system receives and classifies the incoming sparse data packets according to the module ID, and determines the possible damage locations by comparing the state matrix of each layer with the initial reference matrix. The reference matrix is the normal state data obtained when the system is first set up; For each unit module ID M and its layers l, the system uses the received D l M , and retrieve the pre-stored local reference state data D of this module and this layer l基 M , by comparing the current state, by D l M Directly infer and calculate the local difference matrix S l M : S l M (i,j)=D l M (i,j)×(1-D l基 M (i,j)) S l M The non-zero element (i, j) in the matrix identifies the newly added damage point on the lth layer within the M region of the module.

7. A large-area modular multi-layer grid damage detection method according to claim 6, characterized in that: In step 6: Based on the local difference matrix S of each layer of each module M obtained in step 5 l M , to quantify the damage: Calculate the local damage area: Statistical S l M The number of damage points; Determine the damage depth within the module: count the number of layers with a damage area greater than zero and the total number of layers.

8. The large-area modular multi-layer grid damage detection method according to claim 2, characterized in that: In step seven: After the system completes the damage assessment, if it determines that the damage level of a certain layer or area exceeds the set safety threshold, the system immediately triggers the alarm mechanism. The damage assessment uses the pre-set threshold to determine the severity of the damage. When the damage level at a certain location exceeds the set threshold, the system determines that the location is in a dangerous state. The system visualizes the damage location and severity, marks the damaged area in red or other conspicuous colors, and displays the damage depth and damage score. The alarm information contains a detailed description of the damage, including the damage type, the physical location of the damage, i.e. the coordinate location, depth, and damage severity. Based on these detailed information, the system gives corresponding repair suggestions.

9. The large-area modular multi-layer grid damage detection method according to claim 2, characterized in that: In step eight, after receiving data from each detection unit, the system summarizes the damage information of each layer and conducts a comprehensive analysis. The damage information includes the damage type, depth, and location of the affected conductors. (1) In terms of damage location visualization, a graphical user interface is used to display the damage location of each layer, and color coding is used to help operators quickly determine the extent of damage; (2) In terms of three-dimensional graphic display, in order to more intuitively display the damaged area, the system can provide three-dimensional display, draw a 3D model of the damaged area and mark the damage position and depth of each layer. The user can rotate the view to observe the damage at different angles; (3) Damage severity map: After comprehensively analyzing the data from each layer, the system generates a damage score map for each area, showing the severity of the damage and helping engineers develop maintenance plans.

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