Crack expansion trend prediction and active repair method and device based on causal reasoning, storage medium and electronic equipment

Abstract

The embodiment of the invention provides a crack propagation trend prediction and active repair method and device based on causal reasoning, a storage medium and electronic equipment, and relates to the technical field of crack intelligent detection and repair, and the method comprises the steps: collecting field crack data and environment data, and constructing a knowledge graph; constructing a causal reasoning model for predicting crack propagation based on the knowledge graph; speculating a future expansion trend of the crack based on the causal reasoning model; generating an optimal restoration scheme based on the speculation result; and performing crack repair according to the optimal repair scheme. According to the technical scheme of the invention, the causal reasoning model is constructed, the crack development trend can be predicted in advance, and an active repair suggestion is given, so that the passive maintenance problem of crack expansion is effectively solved, and the overall safety and operation efficiency of a project are improved.

Description

Technical Field

[0001] This application relates to the technical field of intelligent crack detection and repair. Specifically, it relates to a method, device, storage medium, and electronic device for predicting crack propagation trends and active repair based on causal reasoning. Background Art

[0002] In the field of water conservancy and hydropower, the detection and repair of cracks play a crucial role in ensuring the safety of engineering structures, especially in the long-term health maintenance of concrete structures such as dams and channels. Currently, widely used deep learning methods, such as convolutional neural networks (CNNs), although have made some progress in crack detection, still have several major problems in practical applications: 1. High data dependence: Deep learning models usually require a large amount of labeled data for training. In the field of water conservancy and hydropower, it is very difficult and costly to obtain high-quality crack-labeled data.

[0003] 2. Poor model adaptability in dynamic environments: The crack propagation in hydraulic concrete structures is usually affected by multiple factors, such as environmental humidity, temperature, pressure, etc. These environmental variables change over time, making it difficult for the model to adapt to dynamic conditions.

[0004] 3. Passive maintenance mode: Existing methods usually repair cracks only after they are discovered, and cannot predict the crack propagation trend, resulting in the failure to handle crack problems in a timely manner in some cases, increasing the difficulty and cost of maintenance.

[0005] Considering that the prediction of crack propagation is extremely important in the maintenance of hydraulic structures, existing statistical-based prediction models, although can speculate the crack propagation trend through historical data, are often limited to linear or surface-related factors and cannot reveal the deep causes and complex relationships of crack propagation. Summary of the Invention

[0006] Embodiments of this application provide a method, device, storage medium, and electronic device for predicting crack propagation trends and active repair based on causal reasoning to solve problems such as difficult prediction of crack propagation trends and active repair.

[0007] Other features and advantages of this application will become apparent through the following detailed description, or be learned in part through the practice of this application.

[0008] According to the first aspect of the embodiments of this application, a method for predicting crack propagation trends and active repair based on causal reasoning is provided, including: Collect local crack feature data and environmental data; Combined with expert experience, extract key variables related to crack propagation based on the local crack feature data and the environmental data, and construct a fine-grained crack domain knowledge graph, where the key variables include: structural variables, environmental variables, material variables, and external event variables; Construct a fine-grained dynamic causal inference model based on the key variables in the knowledge graph and the dynamic evolution relationships of the key variables; Speculate on the future local propagation trend of the crack based on the causal inference model; Generate an optimal local repair plan according to the speculation result; Implement active repair on the crack area according to the optimal local repair plan.

[0009] In some embodiments of the present application, based on the foregoing solution, it further includes: After the crack is repaired, monitor the crack change situation in the repaired area in real time; Dynamically adjust the entity attributes in the knowledge graph and the causal link weights in the causal inference model based on the feedback data after repair.

[0010] In some embodiments of the present application, based on the foregoing solution, the acquisition of the local crack feature data and the environmental data includes: Obtain the local crack feature data through an automated acquisition device, including crack width, depth, length, position, and propagation speed; Collect the environmental data of the corresponding local area through an environmental monitoring system, including temperature, humidity, pressure, and vibration data.

[0011] In some embodiments of the present application, based on the foregoing solution, the combination of expert experience, extraction of key variables related to crack propagation based on the local crack feature data and the environmental data, and construction of a fine-grained crack domain knowledge graph include: Take the local crack unit as the modeling unit; Dynamically associate the local crack feature data with the environmental data; Extract the causal links of crack propagation based on historical data and expert experience to form a multi-level knowledge graph structure including entity nodes, attribute nodes, and dynamic relationship edges; Construct a fine-grained crack domain knowledge graph based on the dynamically associated data and the multi-level knowledge graph structure.

[0012] In some embodiments of the present application, based on the foregoing solution, the construction of a fine-grained dynamic causal inference model based on the key variables in the knowledge graph and the dynamic evolution relationships of the key variables includes: Determine the causal relationship according to the key variables in the knowledge graph and the dynamic evolution relationships of the key variables; Extract the change trend of key variables using a sliding time window as causal nodes; Based on the causal relationship and the causal nodes, a fine-grained dynamic causal inference model is constructed through a fine-grained causal Bayesian network.

[0013] In some embodiments of the present application, based on the foregoing solution, the speculation of the future local expansion trend of the crack based on the causal inference model includes: Input the current local crack characteristics and environmental conditions into the causal inference model; Use the causal inference model to infer the expansion rate risk level of the local crack within a set future time period; Mark the area where the expansion rate is higher than the preset threshold as high risk, and preferentially generate repair suggestions.

[0014] In some embodiments of the present application, based on the foregoing solution, the generation of the optimal local repair plan according to the speculation result includes: Determine the specific repair materials and construction methods according to the speculation result.

[0015] According to the second aspect of the embodiments of the present application, there is provided a crack expansion trend prediction and active repair device based on causal inference, including: An acquisition unit for acquiring local crack feature data and environmental data; A first construction unit for combining expert experience, extracting key variables related to crack expansion based on the local crack feature data and the environmental data, and constructing a fine-grained crack domain knowledge graph, where the key variables include: structural variables, environmental variables, material variables, and external event variables; A second construction unit for constructing a fine-grained dynamic causal inference model based on the key variables in the knowledge graph and the dynamic evolution relationship of the key variables; A speculation unit for speculating the future local expansion trend of the crack based on the causal inference model; A generation unit for generating an optimal local repair plan according to the speculation result; A repair unit for actively repairing the crack area according to the optimal local repair plan.

[0016] According to the third aspect of the embodiments of the present application, there is provided a computer-readable storage medium, in which computer instructions are stored, and when the computer instructions are run on a computer, the computer is caused to execute the method described in the first aspect above.

[0017] According to the fourth aspect of the embodiments of the present application, there is provided an electronic device, including: a memory and a processor; The memory is used for storing computer instructions; The processor is configured to call the computer instructions stored in the memory, so that the electronic device executes the method described in the first aspect above.

[0018] Through the technical solution of this application, by constructing a causal inference model, the development trend of cracks can be predicted in advance and proactive repair suggestions can be given, thus effectively solving the problem of passive maintenance of crack expansion and improving the overall safety and operation efficiency of the project.

[0019] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with this application, and together with the specification are used to explain the principles of this application. Obviously, the drawings in the following description are only some embodiments of this application, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts. In the drawings: Figure 1 A flowchart showing a method for predicting crack propagation trend and proactive repair based on causal inference according to an embodiment of this application; Figure 2 A block diagram showing an apparatus for predicting crack propagation trend and proactive repair based on causal inference according to an embodiment of this application; Figure 3 A block diagram showing an electronic device according to an embodiment of this application; Figure 4 A schematic structural diagram of a computer system of an electronic device suitable for implementing the embodiments of this application is shown. DETAILED DESCRIPTION

[0021] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art.

[0022] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of this application. However, those skilled in the art will realize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of this application.

[0023] The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0024] The flowcharts shown in the drawings are only illustrative and not necessarily include all the content and operations / steps, nor are they necessarily executed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0025] It should be noted that the terms "first", "second", etc. in the specification, claims and above-mentioned drawings of this application are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the objects so used can be interchanged under appropriate circumstances so that the embodiments of this application described herein can be implemented in an order other than those illustrated or described.

[0026] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0027] The following will describe in detail some embodiments of this application in conjunction with the drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0028] See Figure 1 , which shows a schematic flowchart of a method for predicting crack propagation trend and active repair based on causal reasoning according to an embodiment of this application.

[0029] As Figure 1 shown, a method for predicting crack propagation trend and active repair based on causal reasoning is shown, which specifically includes steps S100 to S600.

[0030] Refer to Figure 1 , step S100, collect local crack feature data and environmental data.

[0031] In some feasible embodiments, based on the foregoing solution, step S100 includes: Step S101: Obtain local crack feature data through an automated acquisition device, including crack width, depth, length, location, and propagation speed. Step S102: Collect environmental data of the corresponding local area through an environmental monitoring system, including temperature, humidity, pressure, and vibration data.

[0032] Continue to refer to Figure 1 , Step S200: Combining expert experience, extract key variables related to crack propagation based on the local crack feature data and the environmental data, and construct a fine-grained crack domain knowledge graph. Among them, the key variables include: structural variables, environmental variables, material variables, and external event variables.

[0033] It should be noted that in this embodiment, the specific explanations of the structural variables, environmental variables, material variables, and external event variables are as follows: Structural variables: such as crack length, width, depth, location, quantity, etc.

[0034] Environmental variables: such as temperature, humidity, pressure, environmental vibration, etc.

[0035] Material variables: such as concrete quality, aging degree, stress state, etc.

[0036] External event variables: such as changes in water flow pressure, large rainfall events, etc.

[0037] It should be noted that these variables can be constructed into a knowledge graph in the field of crack propagation, where each variable entity (such as cracks, materials, environment) has attributes and relationships, and these relationships reflect the potential impacts between crack propagation and factors such as the environment and materials.

[0038] In some feasible embodiments, based on the foregoing solution, the step S200 includes: Step S201: Use the local crack unit as the modeling unit; Step S202: Dynamically associate the local crack feature data with the environmental data; Step S203: Extract the causal link of crack propagation based on historical data and expert experience, and form a multi-level knowledge graph structure including entity nodes, attribute nodes, and dynamic relationship edges; Step S204: Construct a fine-grained crack domain knowledge graph based on the data after dynamic association and the multi-level knowledge graph structure.

[0039] Continue to refer to Figure 1 , Step S300: Based on the key variables in the knowledge graph and the dynamic evolution relationship of the key variables, construct a fine-grained dynamic causal reasoning model.

[0040] In some feasible embodiments, based on the foregoing solution, step S300 includes: Step S301: Determine the causal relationship according to the key variables in the knowledge graph and the dynamic evolution relationship of the key variables; Step S302: Use a sliding time window to extract the change trend of the key variables as causal nodes; Step S303: Based on the causal relationship and the causal nodes, construct a fine-grained dynamic causal inference model through a fine-grained causal Bayesian network.

[0041] It should be noted that the node granularity of the causal model constructed in this embodiment can be refined to the local crack unit described in step S201.

[0042] Continue to refer to Figure 1 , step S400: Infer the future local expansion trend of the crack based on the causal inference model.

[0043] It should be noted that the future expansion trend of the crack includes the crack expansion speed, development direction, and possible risks.

[0044] In some feasible embodiments, based on the foregoing solution, step S400 includes: Step S401: Input the current local crack characteristics and environmental conditions into the causal inference model; Step S402: Use the causal inference model to infer the expansion rate risk level of the local crack within a future set time period; Step S403: Mark the local crack area with an expansion rate higher than the preset threshold as a high risk, and preferentially generate a repair suggestion.

[0045] Exemplarily, the reasoning process is as follows: Input the crack data (such as crack width, depth, etc.) and environmental data (temperature, humidity, stress, etc.) collected on-site as input data into the causal inference model, and infer the expansion trend of the current crack through the causal model.

[0046] For example, if the temperature continues to rise, the model can predict the probability that the crack may accelerate its expansion within a future period of time.

[0047] Continue to refer to Figure 1 , step S500: Generate an optimal local repair plan according to the inference result.

[0048] In some feasible embodiments, based on the foregoing solution, step S500 includes: Determine the specific repair materials and construction methods according to the inference result.

[0049] Exemplarily, determine the repair materials to be selected, such as concrete, polymers, etc., and determine the repair methods to be selected, such as physical repair, chemical repair, etc.

[0050] Continue to refer to Figure 1 , step S600, actively repair the crack area according to the optimal local repair plan.

[0051] In some feasible embodiments, based on the foregoing solution, the method further includes: After crack repair, monitor the crack change situation in the repair area in real time; Dynamically adjust the entity attributes in the knowledge graph and the causal link weights in the causal inference model based on the feedback data after repair.

[0052] It can be understood that through this feedback mechanism, the causal inference model can be continuously optimized. For example, when it is found that a certain repair material has poor effect in a specific environment, the causal inference model will recalibrate the causal relationship between the material and crack propagation.

[0053] In summary, the method provided by this application has the following advantages: 1. By introducing a causal inference model and constructing the causal relationship between crack propagation and environmental factors, the development trend of cracks can be predicted in real time.

[0054] 2. Combining real-time data and causal inference results, actively recommend repair plans, avoid passive maintenance, and improve the timeliness of repair.

[0055] 3. Dynamically adjust the causal model through a feedback mechanism to form a closed-loop optimization, enhancing the adaptive ability of the system.

[0056] 4. By constructing a causal relationship network, the deep-seated reasons for crack propagation can be mined, rather than relying solely on surface correlations, so as to achieve more accurate crack propagation prediction.

[0057] 5. Through the combination of the knowledge graph and the causal inference model, the prediction results can be updated in real time, and the repair plan can be optimized according to the feedback, improving the self-adaptability and long-term efficiency of the model.

[0058] The following introduces the device embodiments of this application, which can be used to execute a method for predicting crack propagation trend and active repair based on causal inference in the above embodiments of this application. For details not disclosed in the device embodiments of this application, please refer to the method embodiments of this application above.

[0059] Refer to Figure 2 As shown in The acquisition unit 201 is configured to acquire local crack feature data and environmental data; The first construction unit 202 is configured to combine expert experience, extract key variables related to crack propagation based on the local crack feature data and the environmental data, and construct a fine-grained crack domain knowledge graph, where the key variables include: structural variables, environmental variables, material variables, and external event variables; The second construction unit 203 is configured to construct a fine-grained dynamic causal inference model based on the key variables in the knowledge graph and the dynamic evolution relationships of the key variables; The speculation unit 204 is configured to speculate on the future local propagation trend of the crack based on the causal inference model; The generation unit 205 is configured to generate an optimal local repair plan according to the speculation result; The repair unit 206 is configured to perform active repair on the crack area according to the optimal local repair plan.

[0060] In some feasible embodiments, based on the foregoing solution, the device further includes: The feedback and optimization unit is configured to monitor the change of the crack after the crack is repaired, obtain the feedback data after the crack is repaired, and optimize the knowledge graph and the causal inference model based on the feedback data.

[0061] As Figure 3 shown, an embodiment of the present application further provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored on the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, the steps of the foregoing method for predicting the crack propagation trend and active repair based on causal inference are implemented.

[0062] Since the electronic device introduced in this embodiment is the device adopted for implementing a device for predicting the crack propagation trend and active repair based on causal inference in an embodiment of the present application, based on the method introduced in the embodiment of the present application, those skilled in the art can understand the specific implementation manners and various change forms of the electronic device in this embodiment. Therefore, the specific implementation of how this electronic device implements the method in the embodiment of the present application will not be described in detail here. As long as the device adopted by those skilled in the art to implement the method in the embodiment of the present application belongs to the scope to be protected by the present application.

[0063] In the specific implementation process, when the computer program 311 is executed by the processor, any implementation manner in the corresponding embodiment of the first aspect can be implemented.

[0064] Figure 4 shows a schematic structural diagram of a computer system of an electronic device suitable for implementing an embodiment of the present application.

[0065] It should be noted that Figure 4 The computer system 400 of the illustrated electronic device is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0066] As Figure 4 shown, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage section 408 into the random access memory (RAM) 403, such as executing the method described in the above embodiments. In the RAM 403, various programs and data required for system operation are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0067] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that the computer program read from it can be installed into the storage section 408 as needed.

[0068] Specifically, according to the embodiments of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments of the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program codes for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 409, and / or installed from the removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, various functions defined in the system of the present application are executed.

[0069] It should be noted that the computer-readable medium shown in the embodiments of the present application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present application, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0070] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Among them, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0071] The units involved in the embodiments described in this application can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not constitute a limitation to the unit itself in some cases.

[0072] As another aspect, the present application also provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes a method for predicting crack propagation trend and active repair based on causal reasoning described in the above embodiments.

[0073] As another aspect, the present application also provides a computer-readable medium. The computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist separately without being assembled into the electronic device. The above computer-readable medium carries one or more programs. When the above one or more programs are executed by an electronic device, the electronic device implements a method for predicting crack propagation trend and active repair based on causal reasoning described in the above embodiments.

[0074] It should be noted that although several modules or units of a device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0075] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, and the software product can be stored in a non-volatile storage medium (which may be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which may be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0076] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include well-known common general knowledge or conventional technical means in the technical field not disclosed in the present application. It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A method for predicting the crack propagation trend and active repair based on causal inference, characterized in that Including: Collect local crack feature data and environmental data; Combined with expert experience, extract key variables related to crack propagation based on the local crack feature data and the environmental data, and construct a fine-grained crack domain knowledge graph, where the key variables include: structural variables, environmental variables, material variables, and external event variables; Based on the key variables in the knowledge graph and the dynamic evolution relationships of the key variables, construct a fine-grained dynamic causal inference model; Based on the causal inference model, speculate on the future local expansion trend of the crack; Generate an optimal local repair plan according to the speculation result; Implement active repair on the crack area according to the optimal local repair plan.

2. The method according to claim 1, wherein Also including: After crack repair, monitor the crack change situation in the repair area in real time; Based on the feedback data after repair, dynamically adjust the entity attributes in the knowledge graph and the causal link weights in the causal inference model.

3. The method according to claim 1, wherein The collecting local crack feature data and environmental data includes: Obtain local crack feature data through an automated acquisition device, including crack width, depth, length, position, and propagation speed; Collect environmental data of the corresponding local area through an environmental monitoring system, including temperature, humidity, pressure, and vibration data.

4. The method according to claim 1, characterized in that The combining expert experience, extracting key variables related to crack propagation based on the local crack feature data and the environmental data, and constructing a fine-grained crack domain knowledge graph includes: Taking the local crack unit as the modeling unit; Dynamically associate the local crack feature data with the environmental data; Extract the causal links of crack propagation based on historical data and expert experience to form a multi-level knowledge graph structure including entity nodes, attribute nodes, and dynamic relationship edges; Construct a fine-grained crack domain knowledge graph based on the dynamically associated data and the multi-level knowledge graph structure.

5. The method according to any one of claims 1-4, characterized in that, The constructing a fine-grained dynamic causal inference model based on the key variables in the knowledge graph and the dynamic evolution relationships of the key variables includes: Determine the causal relationship according to the key variables in the knowledge graph and the dynamic evolution relationships of the key variables; Use a sliding time window to extract the change trend of the key variables as causal nodes; Based on the causal relationship and the causal nodes, construct a fine-grained dynamic causal inference model through a fine-grained causal Bayesian network.

6. The method according to claim 5, wherein The speculating on the future local expansion trend of the crack based on the causal inference model includes: Input the current local crack features and environmental conditions into the causal inference model; Use the causal inference model to infer the expansion rate risk level of the local crack within a future set time period; Mark the local crack area with an expansion rate higher than the preset threshold as high risk, and preferentially generate repair suggestions.

7. The method according to claim 6, wherein The generating an optimal local repair plan according to the speculation result includes: Determine the specific repair materials and construction methods according to the speculation result.

8. A crack propagation trend prediction and active repair device based on causal reasoning, characterized in that Including: A collection unit for collecting local crack feature data and environmental data; A first construction unit for combining expert experience, extracting key variables related to crack propagation based on the local crack feature data and the environmental data, and constructing a fine-grained crack domain knowledge graph, where the key variables include: structural variables, environmental variables, material variables, and external event variables; A second construction unit, configured to construct a fine-grained dynamic causal inference model based on the key variables and the dynamic evolution relationships of the key variables in the knowledge graph; A speculation unit, configured to speculate on the future local expansion trend of the crack based on the causal inference model; A generation unit, configured to generate an optimal local repair plan according to the speculation result; A repair unit, configured to perform active repair on the crack area according to the optimal local repair plan.

9. A computer-readable storage medium, characterized in that, The computer instructions are stored in the storage medium, and when the computer instructions run on the computer, the computer is caused to execute the method according to any one of claims 1-7.

10. An electronic device, characterized in that, Comprising: A memory and a processor; The memory is configured to store computer instructions; The processor is configured to call the computer instructions stored in the memory, so that the electronic device executes the method according to any one of claims 1-7.

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