A method for creating a pipe element library based on model training and image recognition technology

Abstract

The present application relates to the technical field of image recognition data processing, and more particularly to a pipeline component library creation method based on model training and image recognition technology, which collects pipeline component image data with depth information through a multispectral imaging scheme, performs Retinex algorithm light equalization and morphological repair through a cascaded preprocessing pipeline, and generates a standardized dataset associated with metadata. A multi-task joint learning framework is used to integrate ResNet-50, HRNet and Mask R-CNN networks, share the bottom convolution feature extraction layer, inject the oil stain and strong reflection adversarial data synthesized by the generative adversarial network, combine the progressive training strategy to optimize the model parameters, and output the lightweight MobileNetV3 model. The model parameters and index structure are dynamically updated, and the system is self-calibrated by combining the cross-device calibration protocol and data bloodline tracking. Through automatic data labeling, multi-task feature reuse and retrieval feedback closed loop, the present application effectively improves the pipeline component library construction efficiency and reduces the recognition error rate in complex environments.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image recognition data processing, and in particular to a pipeline element library creation method based on model training and image recognition technology. BACKGROUND

[0002] The existing pipeline element library creation method is based on a deep learning framework, constructs a training data set by collecting multi-angle pipeline element images, uses median filtering and normalization technology for image denoising and size standardization, combines data enhancement strategies (such as random rotation and brightness adjustment) to expand sample diversity, uses a convolutional neural network (CNN) to extract element morphology and texture features, optimizes the model generalization ability in a small sample scenario through transfer learning, and uses cross-validation and confusion matrix to evaluate classification accuracy; the recognition result is mapped to structured parameters (pipe diameter, material, etc.) through element data specification, a topological relationship network of elements is constructed relying on a graph database, a version control mechanism is integrated to realize dynamic iteration of the element library, and finally a standardized pipeline element knowledge graph is formed to support multi-dimensional retrieval and engineering application integration.

[0003] In the prior art, the construction of the pipeline element library depends on heterogeneous pipeline design software or systems, and the element feature extraction and data labeling are completed through manual visual recognition and manual input, the recognition accuracy is restricted by the experience level of the operator and environmental factors, and feature misjudgment and data deviation may occur under complex working conditions; meanwhile, the lack of standardized data architecture and information management mechanism leads to scattered storage of element attribute information and lack of unified metadata specification, making it difficult to realize automatic integration and dynamic update of multi-source heterogeneous data, and further causing technical bottlenecks such as insufficient completeness of the element library, lagging version iteration, and poor cross-platform compatibility, which seriously restricts the digitalization process and intelligent application popularization of pipeline engineering design. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a pipeline element library creation method based on model training and image recognition technology, which solves the problems of low efficiency, high error rate and poor adaptability to complex environments in manual creation of the pipeline element library.

[0005] To solve the above technical problems, the specific technical solutions of the present application are as follows:

[0006] The present application provides a pipeline element library creation method based on model training and image recognition technology, comprising:

[0007] Step 1, acquiring multi-source pipeline element image data and associated environmental parameters, generating a standardized image data set by cascading preprocessing of the multi-source pipeline element image data and associated environmental parameters;

[0008] Step 2, input the standardized image dataset into a multi-task joint learning framework for dynamic training, the multi-task joint learning framework integrates an element classification subnetwork, an element key point positioning subnetwork, and an element defect detection subnetwork, and outputs a trained model through a shared bottom feature extraction layer;

[0009] Step 3, based on the feature vector extracted by the trained model, generate an element feature descriptor, associate the element feature descriptor with the element assembly relationship in the preset knowledge graph, construct a multi-level index structure, receive a query input including an element image to be searched or a feature vector, analyze the element type identifier in the query, determine the target element category, perform an approximate nearest neighbor search in the target category, output candidate elements with similar features, verify the spatial assembly compatibility of the candidate elements and the query element, filter the final matching result that passes the topological constraint, take the final matching result as the structured search result, take the structured search result as the data input source of the closed-loop evolution mechanism, and output to Step 4 in real time;

[0010] Step 4, when the feature similarity score in the structured search result is lower than a set threshold, mark it as a false detection sample, extract the correction label and associated environment parameters of the false detection sample, generate incremental data, trigger the closed-loop evolution mechanism according to the incremental data, the closed-loop evolution mechanism includes obtaining incremental data, performing cascaded preprocessing on the incremental data, generating an incremental standardized dataset, inputting the incremental standardized dataset into the multi-task joint learning framework, updating the feature extraction parameters of the trained model, extracting the feature vector based on the updated trained model, and reconstructing the multi-level index structure of the hierarchical element library. The search result of the updated hierarchical element library is fed back to Step 1 to form a closed-loop iteration.

[0011] Further, the pipeline element library creation method based on model training and image recognition technology of the present application, step 1 includes: acquiring pipeline element images including depth information through a multi-spectral imaging scheme, generating original data covering occlusion and low light scenes;

[0012] Input the original data into a cascaded processing pipeline, the cascaded preprocessing pipeline sequentially performs illumination equalization processing based on a multi-scale Retinex algorithm, decomposes the illumination component and the reflection component of the image using a Gaussian kernel function, and based on texture repair of morphological opening operation, obtains a repaired image, selects a rectangular structure element kernel to perform erosion and dilation operations, and eliminates device difference interference;

[0013] The repaired image and metadata are associated and packaged as a structured data set through a semi-automatic labeling tool, the metadata including acquisition device parameters, environment parameters and element attribute parameters, the acquisition device parameters being white balance and focal length parameters generated by a cross-device calibration protocol, the environment parameters being temperature, humidity and illumination intensity when the acquisition device is collecting, and the element attribute parameters being pipe diameter, pressure rating and material type identified by a pre-trained target detection model, and the structured data set is transmitted to a training module of a multi-task joint learning framework through a distributed storage node.

[0014] Further, the pipeline element library creation method based on model training and image recognition technology of the present application, step 2 includes:

[0015] An end-to-end network architecture based on ResNet-50, HRNet and Mask R-CNN is constructed, and the bottom convolution feature extraction layer is shared;

[0016] In the training phase, the adversarial enhancement data generated by the generative adversarial network is injected to simulate the morphological changes of the pipeline element in the oil stain and strong reflection environment;

[0017] The model parameters are optimized in stages through a progressive training strategy, including basic training based on ImageNet pre-trained weights, domain fine-tuning according to increasing environmental complexity, and an online calibration phase combined with active learning;

[0018] The trained model generates a lightweight MobileNetV3 model through knowledge distillation, and the feature output of the lightweight MobileNetV3 model is aligned with the cosine similarity of the Faiss index space of the hierarchical element library.

[0019] Further, the pipeline element library creation method based on model training and image recognition technology of the present application, step 3 includes: fusing the 1024-dimensional feature vector output by the multi-task joint learning framework with the three-dimensional point cloud data through a feature concatenation layer to form an element feature descriptor;

[0020] A knowledge graph based on ontology is constructed, and the mapping relationship between the feature descriptor and the knowledge node is defined, and the knowledge graph includes the assembly relationship and the compatible model replacement rule of the valve and the pipeline;

[0021] Element retrieval is performed based on a multi-level index structure, including locating the element category through a hash table, matching the feature vector through the construction of an approximate nearest neighbor search index by the Faiss framework, and matching the spatial topological relationship associated with the BIM model.

[0022] Further, the pipeline element library creation method based on model training and image recognition technology of the present application, step 4 includes:

[0023] The deployment incremental data pipeline captures new images during field installation and maintenance, and screens samples that differ from the existing library by more than a set threshold through differential compression;

[0024] An iterative model versioning framework is established, and the training model version is dynamically switched based on the online inference error detection rate and response delay indicators, and a traceable model snapshot is retained;

[0025] When the search abnormality rate of the hierarchical component library exceeds a set threshold, the adversarial data augmentation module in the multi-task joint learning framework generates targeted training samples in the oil stain and strong reflection environment.

[0026] Further, the pipeline component library creation method based on model training and image recognition technology of the present application further comprises: establishing a cross-device calibration protocol in the preprocessing stage, and uniformly setting white balance and focal length parameters based on the device type of the acquisition terminal. The device type of the acquisition terminal includes a drone and a handheld device;

[0027] The parameters generated by the calibration protocol are input into the generator input end of the generative adversarial network, and the material reflectivity parameters of the oil stain and strong reflection synthetic samples output by the generative adversarial network are constrained.

[0028] Further, the pipeline component library creation method based on model training and image recognition technology of the present application further comprises:

[0029] A feedback loop module based on attention mechanism is embedded in the search interface of the hierarchical component library, a regional heat map is generated according to the image area selected by the user, the query vector of the approximate nearest neighbor search index is redirected, and the corrected search result is obtained;

[0030] The corrected search result is reversely labeled to the structured data set through a semi-automatic labeling tool, new training samples are generated and input into the active learning module in the online calibration stage.

[0031] Further, the pipeline component library creation method based on model training and image recognition technology of the present application further comprises:

[0032] A data bloodline tracking link is established to record the acquisition device ID, preprocessing parameters and model version identification of each training sample in the structured data set;

[0033] The bloodline link is associated with the model version snapshot, and the training dependency relationship of a specific model version on historical data sets is verified through a timestamp matching mechanism.

[0034] Further, the pipeline component library creation method based on model training and image recognition technology of the present application further comprises:

[0035] Define data aging rules to perform cold storage archiving on low-frequency access component data in the hierarchical component library that has not been hit in search for 30 consecutive days;

[0036] Adjust the storage hierarchy strategy dynamically, based on the search popularity index counted by the feedback loop module, to migrate high-frequency access data to the SSD storage node and downgrade low-frequency data to the HDD storage node.

[0037] Further, the pipeline component library creation method based on model training and image recognition technology of the present application further comprises:

[0038] Deploy an abnormality self-healing link in the incremental data pipeline, and when the Faiss index reconstruction fails, roll back to the preserved historical model version and the corresponding index snapshot;

[0039] The self-healing link triggers the cross-device calibration protocol to recalibrate the white balance parameters of the data acquisition source and generates a calibration log to synchronize the data bloodline tracking link.

[0040] The present application has the following beneficial effects:

[0041] The present application achieves significant beneficial effects through the following technical path: based on the multi-spectral imaging device and the cascade pre-processing pipeline, eliminate device differences and environmental interference, generate a standardized data set to replace manual annotation, and significantly improve data acquisition efficiency; through the multi-task joint learning framework, share the feature layer and inject the adversarial data, simultaneously optimize the component classification, positioning and defect detection tasks, reduce the superposition of multi-model errors, and significantly reduce the recognition error rate under complex working conditions; combined with the closed-loop evolution mechanism, dynamically update the model parameters and index structure, trigger targeted training and version rollback through incremental data, realize the self-correction ability of the system; construct a hierarchical component library integrating multi-level indexes and knowledge graphs, use the attention feedback mechanism to redirect the search logic, and enhance the adaptability of the occlusion and contamination scenarios; data bloodline tracking and dynamic storage strategy realize the traceability of the whole link, meet the industrial data compliance requirements, and systematically solve the technical defects of low efficiency, error accumulation and poor environmental adaptability of traditional methods. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed in the embodiments. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor based on the drawings.

[0043] Figure 1 A flowchart of a pipeline component library creation method based on model training and image recognition technology provided by the present application embodiment. DETAILED DESCRIPTION

[0044] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely below in combination with specific embodiments of the present application and corresponding drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application. The technical solutions provided by the embodiments of the present application will be described in detail below in combination with the drawings. In order to better understand the objects of the present application, the present application will be further described in detail below.

[0045] Please refer to Figure 1 The present application provides a pipeline element library creation method based on model training and image recognition technology, comprising:

[0046] Step 1, acquiring multi-source pipeline element image data and associated environmental parameters, generating standardized image data set by cascading preprocessing of multi-source pipeline element image data and associated environmental parameters;

[0047] The multi-source pipeline element image data is generated by a multi-spectral imaging device. The device synchronously acquires visible light and near-infrared band information to form original image data containing a depth channel. The acquisition process covers occlusion and low-light working condition scenes, and the texture detail integrity is enhanced by adjusting the sensor incident angle and multi-band fusion technology. After the original data is input into the cascading preprocessing pipeline, two key processing stages are executed in turn: first, a multi-scale Retinex algorithm is used for illumination equalization processing, which decomposes the illumination component and the reflection component of the image using a Gaussian kernel function, eliminating the brightness distortion in the shadow area and the strong reflection interference on the metal surface; then, a texture repair operation based on morphological opening operation is performed, a rectangular structure element kernel is selected to first perform corrosion operation to remove isolated pixel points generated by device noise, and then perform inflation operation to fill the texture fracture area caused by corrosion, restoring the geometric contour features of the element.

[0048] The preprocessed image data and metadata are associated and encapsulated by a semi-automatic labeling tool. The metadata includes three types of key parameters: the acquisition device parameters are generated by a cross-device calibration protocol, which unifies the white balance and focal length parameters of different acquisition terminals (drones, handheld devices); the environmental parameters record the physical quantities of temperature, humidity and light intensity during acquisition; the element attribute parameters are automatically identified by a pre-trained target detection model, including pipe diameter size, pressure grade and material type, and other structured attributes. The encapsulation process uses a key-value mapping mechanism to bind the image data stream and the metadata header file to form a standardized structured data set.

[0049] The finally generated structured data set is transmitted to the training module of the multi-task joint learning framework through the distributed storage node for sharding compression and load balancing scheduling. The process eliminates hardware differences through device calibration, overcomes environmental interference through cascade processing, and reduces manual intervention through automatic labeling, providing input data with consistent lighting, device independence, and attribute structure for subsequent model training.

[0050] The above operations form a progressive technology chain: multi-spectral acquisition solves the problem of raw data integrity, cascade preprocessing overcomes the problem of environmental interference elimination, metadata correlation encapsulation realizes information structuring, and distributed transmission guarantees data processing efficiency. The output of each link serves as the input of the next link, and together achieves the goal of constructing a standardized image data set.

[0051] Step 2, input the standardized image data set into the multi-task joint learning framework for dynamic training, the multi-task joint learning framework integrates element classification subnetwork, element key point positioning subnetwork and element defect detection subnetwork, and outputs the trained model through the shared bottom feature extraction layer;

[0052] The standardized image data set is input into the multi-task joint learning framework for dynamic training operation. The framework builds an end-to-end network architecture, and the bottom feature extraction layer uses a convolutional neural network to realize feature sharing mechanism. The layer outputs shared feature maps to three parallel subnetworks: element classification subnetwork generates element type probability distribution output, element key point positioning subnetwork predicts spatial coordinate output based on high resolution feature pyramid, and element defect detection subnetwork generates defect pixel-level segmentation mask output through region proposal mechanism. The shared feature mechanism reduces computational redundancy and avoids the error accumulation problem caused by independent training of multiple models.

[0053] The training process injects adversarial augmented data, which is synthesized by a generative adversarial network to simulate the morphological distortion characteristics of pipeline elements in the environment of oil stain attachment and metal strong reflection. The model optimization adopts a progressive training strategy, including a basic training phase to load large-scale general data set pre-training weight initialization parameters, a domain fine-tuning phase to input domain-specific data with increasing environmental complexity, and an online calibration phase to combine active learning mechanism to select low confidence samples for manual review and annotation. This strategy optimizes the shared feature extraction layer parameters in stages, realizing the adaptive ability of the model to complex working conditions.

[0054] After dynamic training, the framework outputs the trained model, which adapts to the subsequent feature descriptor generation requirements. The whole process forms an efficient training chain through feature sharing and progressive optimization. The input data is input into the framework after standardized processing, the framework structure supports multi-task synchronous training, the training strategy enhances the robustness of the model, and finally the output model serves the hierarchical component library construction goal.

[0055] Step 3, based on the feature vector extracted by the training model, generate an element feature descriptor, associate the element feature descriptor with the element assembly relationship in the preset knowledge graph, construct a multi-level index structure, receive a query input including a to-be-retrieved element image or feature vector, parse the element type identifier in the query, determine the target element category, perform an approximate nearest neighbor search in the target category, output candidate elements with similar features, verify the spatial assembly compatibility of the candidate elements with the query element, filter the final matching results that pass the topological constraints, output the final matching results as the structured retrieval results, output the structured retrieval results in real time to Step 4 as the data input source of the closed-loop evolution mechanism;

[0056] The feature vector extracted by the training model is fused with the three-dimensional point cloud data through a feature splicing layer to form an element feature descriptor containing spatial geometric information and texture features. The descriptor is mapped to a low-dimensional vector space through graph embedding technology as the feature representation of the entity node in the preset knowledge graph. The knowledge graph defines the assembly relationship logic between elements based on ontology, including the thread specification matching rules of valves and pipelines and the compatible replacement constraints of different material elements, and establishes an inferable assembly knowledge base.

[0057] The multi-level index structure realizes efficient query through a hierarchical retrieval mechanism: first, parse the element type identifier in the input query, quickly locate the target element category using a hash table index; then, in the target category, match the feature vector through the approximate nearest neighbor search index built by the Faiss framework, output a candidate element set whose cosine similarity meets the threshold; finally, associate the spatial topological relationship data of the building information model to verify the assembly compatibility of the candidate elements with the query elements, including geometric constraint conditions such as axis alignment and minimum gap size, and filter the final matching results that pass the topological verification.

[0058] The structured retrieval results are transmitted in real time to the subsequent steps as the data input source of the closed-loop evolution mechanism. The entire process forms a progressive technology chain: the feature descriptor fuses multiple sources of information to enhance the representation ability, the knowledge graph associates to give assembly semantics, the multi-level index realizes hierarchical retrieval, and the topological verification guarantees spatial compatibility. The output of each link serves as the input of the next link, feature extraction serves descriptor generation, knowledge graph supports index construction, multi-level retrieval realizes precise matching, and finally outputs structured retrieval results with assembly feasibility. This technology chain systematically solves the missing problem of spatial compatibility verification in traditional methods and provides reliable data input for closed-loop evolution.

[0059] Step 4, when the feature similarity score in the structured retrieval result is lower than the set threshold, it is marked as a false detection sample, the correction label and associated environmental parameters of the false detection sample are extracted, the incremental data is generated, the closed loop evolution mechanism is triggered according to the incremental data, the closed loop evolution mechanism includes obtaining incremental data, performing cascade preprocessing on the incremental data, generating incremental standardized data set, inputting the incremental standardized data set into the multi-task joint learning framework, updating the feature extraction parameters of the training model, extracting the feature vector based on the updated training model, reconstructing the multi-level index structure of the hierarchical component library, feeding back the updated retrieval result of the hierarchical component library to step 1 to form a closed loop iteration.

[0060] When the feature similarity score of the structured retrieval result is lower than the set threshold, the system automatically marks it as a false detection sample and extracts its correction label and environmental parameters to generate an incremental data set. This data set contains new samples and their associated acquisition environment metadata in the field maintenance scene, and the samples with significant differences from the existing library features are selected through a differential compression algorithm. The incremental data capture process is deployed on the engineering terminal device to monitor data changes in the installation and maintenance process in real time.

[0061] After the incremental data triggers the closed loop evolution mechanism, the first input is the cascade preprocessing pipeline to perform standardization conversion. This process maintains technical consistency with the preprocessing operation in step 1. The processed incremental standardized data set is input into the multi-task joint learning framework to perform incremental training operation, focusing on updating the parameter distribution of the shared convolution feature extraction layer. The training process inherits the online calibration phase of the progressive strategy and prioritizes processing high-value incremental samples through the active learning mechanism.

[0062] Based on the updated training model, the feature vector is re-extracted, and the multi-level index structure of the hierarchical component library is reconstructed. Index reconstruction includes version update of hash table classification index, vector remapping of Faiss feature index space, and synchronous refresh of building information model topological relationship library. The model version iteration framework retains historical version snapshots to support fast rollback capability in abnormal state.

[0063] The updated hierarchical component library transmits the latest retrieval result as feedback signal to the data acquisition module in step 1. This feedback information guides the multi-spectral imaging device to adjust the acquisition strategy and optimize the environmental coverage of the subsequent data acquisition process. Finally, a complete closed loop iteration from retrieval result analysis to data acquisition optimization is formed, realizing the self-adaptive evolution ability of the system to data distribution drift. Each link triggers the update operation through incremental data, and the update result optimizes the front-end acquisition in reverse, forming a self-optimizing technical circulation chain.

[0064] In step 1, the multi-source pipeline element image data is collected by a multispectral imaging device that synchronously acquires visible light and near-infrared band information to generate original image data containing a depth channel. A cascaded preprocessing pipeline sequentially performs light equalization and morphological repair operations: first, a multi-scale Retinex algorithm is used to decompose the image into illumination and reflection components, and a Gaussian kernel function is used to eliminate brightness distortion in shadow and reflection areas; then, based on morphological opening operation processing, a rectangular structural element kernel is used to perform erosion operation to remove device noise, and then inflation operation is performed to repair texture fracture caused by corrosion. The preprocessed image and metadata are associated and packaged by a semi-automatic labeling tool, and the metadata includes acquisition parameters (white balance, focal length) generated by a cross-device calibration protocol, environmental parameters (temperature, humidity, light intensity), and element attribute parameters (pipe diameter, material type) identified by a pre-trained model. The structured dataset generated finally is transmitted to the training module through distributed storage nodes to provide device-independent and environment-robust input data for the model.

[0065] In step 2, the multi-task joint learning framework uses an end-to-end network architecture, and the underlying convolutional feature extraction layer is implemented by ResNet-50. The framework is connected in parallel with three sub-networks: the element classification sub-network outputs the class probability distribution, the key point positioning sub-network predicts the spatial coordinates of the element based on the HRNet structure, and the defect detection sub-network generates pixel-level segmentation masks through Mask R-CNN. Adversarial augmented data is injected in the training stage, and such data is synthesized by a generative adversarial network to simulate the morphological distortion features in the environment of oil pollution attachment and metal strong reflection. The model optimization uses a progressive training strategy: in the first stage, the ImageNet pre-trained weight initialization parameters are loaded; in the second stage, the input data is gradually increased according to the environmental complexity to realize field adaptive fine-tuning; in the third stage, an active learning mechanism is introduced to select low confidence samples for manual review and annotation, and the decision boundary is dynamically corrected through online calibration. The trained model is compressed into a lightweight MobileNetV3 model through knowledge distillation technology, and the feature output vector is aligned with the index space of the subsequent retrieval module in terms of cosine similarity.

[0066] In step 3, the element feature descriptor is a 1024-dimensional vector extracted by the trained model and is fused with the three-dimensional point cloud data through a feature splicing layer to generate the descriptor. The descriptor is mapped to the entity nodes of the preset knowledge graph through a graph embedding technique. The knowledge graph defines the assembly relationship between elements based on an ontology, such as the thread specification matching rule of a valve and a pipeline, and a compatible model replacement rule. A multi-level index structure adopts a hierarchical retrieval mechanism: first, the target element category is quickly located through a hash table; second, the feature vector is matched using an approximate nearest neighbor search index constructed by the Faiss framework in the target category to output the candidate element with the highest cosine similarity; finally, the spatial topological relationship data of the BIM model is associated to verify the assembly compatibility of the candidate element and the query element (including the axis alignment degree, the minimum gap size, and other constraint conditions), and the final matching result that passes the verification is selected. The structured retrieval result is output in real time to the closed-loop evolution module as the data input source for system self-optimization.

[0067] In step 4, when the feature similarity score of the structured retrieval result is lower than a set threshold, the system automatically marks it as a false positive sample and extracts its correction label and environmental parameters to generate an incremental data set. After the closed-loop evolution mechanism is started, the incremental data is converted into a standardized data set through a cascaded preprocessing pipeline, is input into a multi-task joint learning framework for incremental training, and updates the parameters of the shared feature extraction layer. Based on the updated model, the feature vector is re-extracted, and the multi-level index structure of the hierarchical element library (including the hash table classification index, the Faiss feature index, and the BIM topological relationship library) is reconstructed. The reconstructed hierarchical element library feeds the latest retrieval result back to the data acquisition and preprocessing module of step 1, forming a full-process closed-loop iterative optimization from data acquisition, model training to retrieval verification, and realizing the self-adaptive ability of the system to environmental changes and data drift.

[0068] The above steps form a technical closed loop: the standardized data processing of step 1 provides high-quality input for model training; the multi-task joint learning of step 2 improves the robustness of the model through feature sharing and adversarial training; the multi-level index combined with the knowledge graph realizes accurate retrieval in step 3; and the incremental training and index reconstruction triggered based on the retrieval feedback in step 4 enable the system to continuously optimize. Each link is closely connected through data flow and control flow, and finally achieves efficient construction and dynamic evolution of the pipeline element library.

[0069] Specifically, the pipeline element library creation method based on model training and image recognition technology of the present application, step 1 includes: acquiring pipeline element images including depth information through a multi-spectral imaging scheme to generate original data covering occlusion and low light scenes;

[0070] The original data is input into a cascading processing pipeline, and the cascading preprocessing pipeline sequentially performs illumination equalization processing based on a multi-scale Retinex algorithm, decomposes the illumination component and the reflection component of the image by using a Gaussian kernel function, and obtains a repaired image based on a morphological opening operation texture repair, selects a rectangular structure element kernel to perform erosion and expansion operations, and eliminates device difference interference;

[0071] The repaired image and the metadata are associated and packaged into a structured data set by using a semi-automatic labeling tool, the metadata includes acquisition device parameters, environment parameters and element attribute parameters, the acquisition device parameters are white balance and focal length parameters generated by a cross-device calibration protocol, the environment parameters are temperature, humidity and illumination intensity when the acquisition device is collecting, the element attribute parameters are pipe diameter, pressure grade and material type identified by a pre-trained target detection model, and the structured data set is transmitted to a training module of a multi-task joint learning framework through a distributed storage node.

[0072] The environment robustness preprocessing technical scheme of the present application is realized through the following process: first, a multispectral imaging device is used to collect pipeline element images, the multispectral imaging device integrates visible light and near-infrared sensors to synchronously acquire RGB color information and depth data, and generates original images including spatial three-dimensional coordinates. During the collection process, the incident angle and exposure parameters of the multispectral sensor are adjusted in view of the oil stain coverage or mechanical obstruction scene that may exist on the surface of the pipeline element, the texture details in the low-light area are enhanced through multi-band fusion technology, and an initial data set covering complex working conditions is formed.

[0073] After the original data is input into the cascading processing pipeline, the Retinex algorithm is used in the first processing stage to decompose the illumination component and the reflection component of the image, and the gamma correction and histogram equalization are used to eliminate the brightness distortion of the shadow and the reflection area. In the second processing stage, a structure element kernel is constructed based on the morphological opening operation, the erosion operation is first performed to remove isolated pixel points caused by device noise, and then the inflation operation is performed to fill the texture fracture area caused by rust, thereby restoring the geometric contour integrity of the element. The image data after two-stage processing and the white balance parameters and focal length parameters of the acquisition device establish a mapping relationship, and the imaging deviation caused by the hardware difference between the unmanned aerial vehicle and the handheld device is eliminated.

[0074] The repaired image data is input into a semi-automatic labeling tool, the tool loads a pre-trained target detection model to generate a boundary box label for a flange interface, a weld seam and other key parts, and analyzes the pipe diameter size, pressure rating parameters and temperature and humidity environment data during acquisition of the element. The labeling information is associated with the image data in the form of key-value pairs, and is packaged as a structured data set including a metadata header file and a binary image stream. The structured data set is sharded, compressed and load-balanced by distributed storage nodes, and is transmitted to the training module of the multi-task joint learning framework in a standardized format, providing input data with consistent lighting and device independence for subsequent model training.

[0075] Specifically, the pipeline element library creation method based on model training and image recognition technology of the present application, step 2 includes:

[0076] An end-to-end network architecture based on ResNet-50, HRNet and Mask R-CNN is constructed, and the bottom convolution feature extraction layer is shared;

[0077] In the training phase, adversarial augmented data generated by a generative adversarial network is injected to simulate the morphological changes of pipeline elements in an oil-stained and strong reflection environment;

[0078] The model parameters are optimized in stages through a progressive training strategy, including basic training based on ImageNet pre-trained weights, domain fine-tuning according to increasing environmental complexity, and an online calibration phase combined with active learning;

[0079] The trained model generates a lightweight MobileNetV3 model through knowledge distillation, and the feature output of the lightweight MobileNetV3 model is aligned with the cosine similarity of the Faiss index space of the hierarchical element library.

[0080] The multi-task joint learning framework of the present application is implemented through the following technical solutions: first, a backbone network based on ResNet-50 is constructed, and the output of the bottom convolution layer is shared to the HRNet branch network and the Mask R-CNN branch network. The HRNet branch network extracts the key point spatial coordinates of the pipeline element through a high-resolution feature pyramid, and the Mask R-CNN branch network generates a pixel-level segmentation mask for the defect site based on a region proposal mechanism. Based on the shared convolution layer output, the three achieve feature reuse for classification, positioning and detection tasks, reducing the computational redundancy of multi-task parallel operation.

[0081] The training stage introduces a generative adversarial network to generate adversarial enhanced data, and the input end of the generator of the generative adversarial network receives the white balance parameters and focal length parameters output by the cross-device calibration protocol, combines the reflectivity model of the oil stain material, generates synthetic samples conforming to physical laws, and simulates the morphological distortion characteristics of the pipeline element in the scene of oil stain attachment and strong reflection on the metal surface. The original standardized data set is mixed with the adversarial data according to a predetermined proportion, and then input into the multi-task network, so as to improve the generalization ability of the model to complex working conditions through the adversarial training mechanism.

[0082] The model optimization adopts a progressive training strategy, the initial stage loads the ImageNet pre-trained weight to initialize the parameters of the shared convolutional layer, and the feature extraction ability of the large-scale general data set is used to establish the basic model. In the second stage, the field data is input according to the gradient increase of environmental complexity, the clean element images of the unblocked scene are preferentially input, and then the samples containing partial occlusion and light pollution are gradually transitioned, and finally the heavy oil stain and strong reflection data are introduced, so that the model parameters gradually adapt to the complex environmental distribution of the engineering site. In the third stage, the active learning mechanism is deployed, the difficult samples with a prediction confidence lower than a threshold value are selected, manual review and labeling are triggered, and then the training set is added, and the model decision boundary is dynamically corrected through online calibration.

[0083] The trained model is compressed into a lightweight MobileNetV3 model through knowledge distillation technology, the ResNet-50 multi-task network is used as a teacher model, the feature alignment loss function is used to constrain the output features of the MobileNetV3 student model and the intermediate feature responses of the teacher model in the shared convolutional layer, and the cosine similarity loss function is introduced to make the feature vectors output by the student model and the vector distribution in the Faiss index space of the hierarchical element library keep the same direction. The distilled lightweight model adapts to the deployment requirements of edge computing devices, supports real-time feature extraction and efficient matching with the index space, and solves the problem of retrieval delay in the low-computing-power environment of the engineering site.

[0084] Specifically, the pipeline element library creation method based on model training and image recognition technology of the present application, step 3 includes: fusing the 1024-dimensional feature vector output by the multi-task joint learning framework and the three-dimensional point cloud data through a feature splicing layer to form an element feature descriptor;

[0085] An ontology-based knowledge graph is constructed, and the mapping relationship between the feature descriptor and the knowledge node is defined. The knowledge graph includes the assembly relationship of valves and pipelines and the replacement rules of compatible models.

[0086] Element retrieval is performed based on a multi-level index structure. Element retrieval includes locating an element category through a hash table, matching a feature vector through a Faiss framework to construct an approximate nearest neighbor search index, and matching a spatial topological relationship associated with a BIM model.

[0087] The hierarchical component library construction technical solution of the application is implemented through the following process: the 1024-dimensional feature vector output by the multi-task joint learning framework is input into the feature splicing layer together with the point cloud data obtained by the three-dimensional laser scanning, the feature splicing layer performs normalization processing on the vector dimension, then performs cascading operation along the channel dimension to generate a 1280-dimensional component feature descriptor that fuses spatial geometric information and texture features. The feature descriptor is mapped to a low-dimensional vector space through graph embedding technology and used as the initial feature representation of the knowledge graph node.

[0088] The knowledge graph constructed based on the ontology defines the class hierarchy structure of the pipeline components in the OWL language, the class hierarchy structure includes component types such as valves, flanges and pipe fittings, and the SWRL rules are extended to describe the assembly constraint conditions between components. The embedding vectors of the feature descriptors are associated with the entity nodes of the knowledge graph, the semantic similarity between nodes is calculated through the graph attention mechanism, the thread specification matching rules of valves and pipelines and the compatible replacement relationship of components of different materials are established, and a reasoningable assembly knowledge base is formed.

[0089] The multi-level index structure adopts a hierarchical retrieval mechanism to improve query efficiency: the hash table index quickly locates the target component category based on the first letter hash value of the component type name, reducing the search range; the Faiss framework constructs an inverted index and a product quantizer for the feature descriptor, and matches the candidate components with the highest cosine similarity through the approximate nearest neighbor search algorithm; the spatial topological relationship data provided by the BIM model is jointly verified with the assembly rules of the knowledge graph, and the final search results that meet both feature similarity and spatial compatibility are screened. The hierarchical design of the multi-level index structure takes into account the speed and accuracy of the search, and supports the real-time component matching and assembly scheme verification requirements in the engineering field.

[0090] Specifically, the pipeline component library creation method based on model training and image recognition technology of the application, step 4 includes:

[0091] Deploy the incremental data pipeline to capture new images in the field installation and maintenance process, calculate the cosine similarity between the new images and the existing library sample feature vectors through differential compression, and screen samples with a difference degree lower than a preset similarity threshold, and the similarity threshold is dynamically adjusted according to the historical false detection rate;

[0092] Establish a model version iteration framework, dynamically switch the training model version based on the false detection rate reduction amplitude and response delay index of online inference, and retain the traceable model snapshot;

[0093] When the retrieval abnormality rate of the hierarchical component library exceeds the set threshold, the adversarial data augmentation module in the multi-task joint learning framework generates targeted training samples in the oil stain and strong reflection environment.

[0094] The closed-loop evolution mechanism of the application is realized by the following technical solutions: a lightweight data acquisition agent is deployed at the terminal of the pipeline engineering to capture real-time on-site images generated during the installation and maintenance process, an inter-frame difference algorithm is used to calculate the cosine similarity of the feature vectors of the new images and the existing samples in the hierarchical element library, samples with a difference exceeding a preset threshold are screened, and the network transmission data volume is reduced through block compression encoding technology. The screened incremental data is preprocessed by the edge computing node and input into the training queue of the model version iteration framework, and the historical training set is mixed according to the timestamp to build an incremental data set.

[0095] The model version iteration framework maintains multiple parallel model version repositories, each version being associated with the data distribution and hyperparameter configuration during training. The online inference service real-time statistics each version model's false detection rate decline slope and response delay percentile, when the new version model's false detection rate decline amplitude exceeds the historical baseline 20% and the delay maintains within the engineering acceptable threshold, trigger version switching instruction, update the production environment inference model to the optimal version. All historical version model weights and corresponding training data snapshots are stored in a distributed file system, supporting fast backtracking to any historical state according to version hash value.

[0096] The retrieval anomaly rate of the hierarchical element library is calculated by counting the proportion of cases that successfully match features but fail to assemble verification within a statistical period. When the anomaly rate exceeds the set threshold for three consecutive statistical periods, the adversarial data augmentation module is automatically called, combined with the material reflectivity parameters in the cross-device calibration protocol, to directionally disturb the texture features of the oil stain area and the illumination parameters of the strong reflection area in the hidden space of the generative adversarial network, and to batch synthesize training samples with targeted morphological distortion. The generated adversarial samples are injected into the online calibration stage of the multi-task joint learning framework, and the difficult samples are preferentially trained through the active learning mechanism, and the updated model parameters are synchronized to the Faiss index space of the hierarchical element library, forming a cooperative evolution of feature extraction and retrieval optimization.

[0097] Specifically, the pipeline element library creation method based on model training and image recognition technology of the application further comprises: establishing a cross-device calibration protocol in the preprocessing stage, and uniformly configuring white balance and focal length parameters based on the device type of the acquisition terminal, the device type of the acquisition terminal including a drone and a handheld device;

[0098] The parameter configuration generated by the calibration protocol is input into the generator input end of the generative adversarial network to constrain the material reflectivity parameters of the oil stain and strong reflection synthetic samples output by the generative adversarial network.

[0099] The cross-device calibration and countermeasure data generation technical solution of the application is implemented through the following process: for two types of collection terminals, unmanned aerial vehicles and handheld devices, a mapping relationship library of device type identification and imaging parameters is established, and the mapping relationship library stores default white balance coefficients and focal length parameter ranges of different models of devices. In the calibration process, the imaging data of the collection device under different lighting conditions is collected through the checkerboard calibration board, the correction matrix of the white balance parameter and the focal length parameter is fitted by using the nonlinear least squares method, and a device-specific calibration configuration file is generated, which eliminates the white balance deviation of the unmanned aerial vehicle aerial image caused by high-altitude light scattering and the focal length distortion of the handheld device close-range shooting.

[0100] The calibration configuration file is input into the generator module of the generative adversarial network, the conditional input layer of the generator receives the calibration parameters as prior constraints, and the reflectivity physical model of the oil stain material and the Fresnel reflection equation of the metal surface are combined to generate a synthetic texture in the latent space that conforms to the law of optical propagation. The synthetic image output by the generator is subjected to adversarial training with the real oil stain sample through the discriminator network, and the generator parameters are iteratively optimized so that the material reflectivity distribution of the synthetic sample remains physically consistent with the real data collected by the calibration device. The constrained synthetic sample is injected into the training data set of the multi-task joint learning framework to improve the feature recognition ability of the model for oil stains and strong reflection interference in the cross-device collection scene and enhance the generalization performance of the model in different hardware environments.

[0101] Specifically, the pipeline element library creation method based on model training and image recognition technology of the application further comprises:

[0102] A feedback loop module based on the attention mechanism is embedded in the retrieval interface of the hierarchical element library, the pixel coordinates of the user's selected area are extracted based on the spatial attention mechanism, a regional heat map is generated through a Gaussian kernel function, and the query vector channel weight of the approximate nearest neighbor search index is weighted and adjusted;

[0103] The modified retrieval result is reversely annotated to the structured data set through a semi-automatic annotation tool, new training samples are generated and input into the active learning module in the online calibration stage.

[0104] The feedback loop and active learning collaborative technical solution of the application is implemented through the following process: a visual interaction module based on the spatial attention mechanism is deployed in the retrieval interface of the hierarchical element library, the user triggers a feedback signal by selecting the mismatched area in the image, the module extracts the pixel coordinates of the selected area and calculates the spatial correlation weight of the original query vector, and generates a regional heat map reflecting the user's attention. After the heat map is smoothed by a Gaussian kernel function, the channel weighting operation is performed on the query vector of the approximate nearest neighbor search index, the feature weight of the target area in the vector space is enhanced, and the retrieval logic is redirected to the local feature of the element that the user pays attention to.

[0105] The modified search result is input into a semi-automatic labeling tool, a pre-trained semantic segmentation model is loaded in the tool to finely label the correct element boundary confirmed by the user, and the pipe diameter parameter and assembly direction information of the element are analyzed synchronously. The labeled data is associated with the original image and environmental parameter through key-value mapping, new training samples are generated and marked as high-priority data, and input into the active learning queue in the online calibration stage. The active learning module selects the sample with the highest prediction variance based on the uncertainty sampling strategy, and preferentially pushes it to the artificial review interface for secondary verification. The data verified passes is injected into the incremental training process of the multi-task joint learning framework, and the parameter distribution of the model decision boundary and the feature extraction layer is dynamically adjusted.

[0106] The feedback data flow and the model training form a closed-loop optimization mechanism: the search correction result driven by user interaction behavior directly acts on the labeling generation of training data, the labeled data improves the recognition ability of the model for difficult samples through the active learning strategy, the optimized model parameters update the Faiss index space of the hierarchical component library, thereby improving the subsequent search accuracy and reducing the user feedback triggering frequency, and realizing the continuous performance improvement of the system self-adaptation.

[0107] Specifically, the pipeline component library creation method based on model training and image recognition technology of the present application further comprises:

[0108] A data bloodline tracking link is established, and the metadata header file of each training sample in the structured data set records the acquisition device ID, the gamma correction coefficient of the Retinex algorithm, and the structural element kernel size parameter of the morphological opening operation, and is associated with the model version identification participating in training through key-value mapping;

[0109] The bloodline link is associated with the model version snapshot, and the training dependency relationship of a specific model version on a historical data set is verified through a timestamp matching mechanism.

[0110] The data bloodline tracking and version management technical solution of the present application is implemented through the following process: each training sample in the structured data set is injected with metadata header information in the preprocessing stage, the metadata header includes the model serial number of the acquisition device, the gamma correction coefficient of the Retinex algorithm light equalization, the structural element kernel size parameter of the morphological opening operation, and the specific version hash value participating in model training. The bloodline information is recorded through the log service of the distributed storage engine, forming a chain data structure with timestamp as the primary key, supporting reverse tracing of the whole life cycle processing track according to the sample ID.

[0111] The model version snapshot is stored in a version control warehouse, each snapshot is associated with a training start time stamp, a hyperparameter configuration and a used dataset version identifier. When it is necessary to verify the dependence of a specific model version on historical data, the bloodline tracking service retrieves all the acquisition device parameters and preprocessing operation records of the training samples involved in the training period of the version through a timestamp interval matching algorithm, and generates a data distribution consistency report. The report compares the device parameter offset and preprocessing process difference between the historical dataset and the current production environment data, providing a quantitative evaluation basis for the data drift degree of the model version rollback decision.

[0112] The association mechanism of the bloodline link and the version snapshot supports engineering audit requirements: when retrieval anomalies occur in the hierarchical element library, the defective data batches used in the training of the abnormal model version can be quickly located, and the preprocessing parameters can be traced back to the original acquisition device for calibration, forming a full-link problem tracing capability from data acquisition, model training to retrieval service, meeting the compliance requirements of the traceability of the industrial data quality management specification.

[0113] Specifically, the pipeline element library creation method based on model training and image recognition technology of the present application further comprises:

[0114] Defining a data aging rule, performing cold storage archiving on low-frequency access element data in the hierarchical element library that has not been hit for 30 consecutive days;

[0115] Dynamically adjusting the storage level strategy, based on the retrieval heat index statistics of the feedback loop module, migrating high-frequency access data to the SSD storage node, and downgrading low-frequency data to the HDD storage node.

[0116] The data lifecycle management technical solution of the present application is implemented through the following process: the access log recording module of the hierarchical element library continuously collects the retrieval time stamp and access frequency of each element data, and generates a list of data to be archived based on the sliding time window algorithm to count the low-frequency access data that has not been hit for 30 consecutive days. The cold storage archiving operation performs lossless compression on the data in the list using block compression encoding technology, adds a metadata header file to record the original storage path and compression parameters, migrates to the archival storage pool of the distributed object storage system, and releases the storage resources of the online retrieval database.

[0117] The search hotness index is calculated by the user search behavior data collected by the feedback loop module, and a hotness score model is constructed by integrating search frequency, time weighting factor and user annotation correction times. The dynamic storage scheduler periodically scans high-frequency data with a hotness score higher than a set threshold, uses a data block checking mechanism to migrate a complete data copy from the HDD storage node to the SSD storage node, and updates the index address mapping table.

[0118] The data aging rule and the storage hierarchy strategy form a cooperative optimization mechanism: cold storage archiving reduces the interference of redundant data in the online database on search efficiency, and the storage hierarchy dynamic migration adjusts the data distribution according to the real-time access mode, so that high-frequency data is preferentially stored in low-latency storage media, thereby improving the overall system response speed and storage resource utilization. The archived data retains the metadata index to support historical tracking requirements, and in the engineering change scenario, it can still be restored to the online library through the triggered loading mechanism.

[0119] Specifically, the pipeline element library creation method based on model training and image recognition technology of the present application further comprises:

[0120] Deploy an abnormal self-healing link in the incremental data pipeline. When the Faiss index reconstruction fails, roll back to the preserved historical model version and the corresponding index snapshot.

[0121] The self-healing link triggers the cross-device calibration protocol to recalibrate the white balance parameters of the data acquisition source, and generates a calibration log to synchronize the data bloodline tracking link.

[0122] The abnormal self-healing and data calibration cooperative technical solution of the present application is implemented through the following process: the monitoring service of the incremental data pipeline detects the return status code of the Faiss index reconstruction operation in real time, and when the reconstruction request returns abnormal codes such as resource allocation error or vector dimension mismatch for three times in a row, the self-healing link starts the rollback mechanism. The rollback mechanism accesses the historical model version snapshot and the corresponding index shard data stored in the version control repository, preferentially selects the last successfully constructed index version, verifies the snapshot integrity through hash checking, switches the search service in the production environment to this version, and restores the basic search function of the system.

[0123] The self-healing link synchronization sends a device calibration instruction to the edge side data acquisition terminal, and the instruction carries the acquisition task identification and metadata characteristics of the abnormal index of the fault time period.

[0124] The specific implementation process of the present application is as follows:

[0125] The present application collects pipeline component image data through a multispectral imaging device, integrates visible light and near-infrared sensors in the device, synchronously acquires RGB color information and depth data, and generates an original image with a resolution of 1920x1080 pixels. For the occlusion scene, the incident angle of the sensor is adjusted to 45 degrees, and the exposure time is set to 1 / 60 seconds. Through multi-band fusion, the texture details in low light areas are enhanced. The collected original data is input into a cascaded processing pipeline. First, the Retinex algorithm is used to decompose the illumination component of the image, the gamma correction coefficient is set to 2.2, and the histogram equalization parameter is set to 256 gray levels. Then, morphological opening operation is performed, a 5x5 rectangular structure element kernel is selected, 3 erosion operations are performed to remove noise points, and 2 dilation operations are performed to repair texture breaks. The processed image and metadata (including device model ID, environmental temperature and humidity) are associated through a semi-automatic labeling tool, packaged as a structured data set including a JSON format metadata header and a PNG image stream, and transmitted to the training module after being compressed by a distributed storage node.

[0126] A ResNet-50 based backbone network is constructed, and the output of the bottom convolutional layer is shared to the HRNet and Mask R-CNN branch networks. The HRNet branch retains a high-resolution feature map (1 / 4 of the original size) and outputs the key point coordinates; the Mask R-CNN branch generates a pixel-level mask of the defect area, and the mask resolution is 56x56. In the training stage, adversarial data synthesized by a generative adversarial network (GAN) is injected. The GAN generator inputs the calibrated white balance parameters (color temperature range 5500-6500K) combined with the oil stain material reflectance model (reflectance coefficient 0.3-0.7) to generate synthetic images containing oil stains and metal reflections, and the original data is mixed in a 1:3 ratio for training. The progressive training is divided into three stages: the basic training uses ImageNet pre-trained weights, and 5000 clean element images are input; the domain fine-tuning stage inputs samples containing 10%, 30%, and 50% occlusions in three batches; the online calibration stage selects samples with a confidence lower than 0.85 through active learning, and the samples are added to the training set after manual annotation. The trained model is distilled to generate a MobileNetV3 lightweight model, which outputs a 512-dimensional feature vector, and the cosine similarity threshold in the Faiss index space is set to 0.92.

[0127] The 512-dimensional feature vector output by MobileNetV3 is fused with three-dimensional point cloud data (accuracy ±0.1mm) through a feature concatenation layer to generate a 768-dimensional feature descriptor. A knowledge graph is constructed based on the OWL language, and the thread specifications (such as G1 / 2 to G2 inches) and material compatibility rules (stainless steel and carbon steel cannot be mixed) of valves and pipelines are defined. In the multi-level index structure, the hash table is partitioned by the first letter of the element type (26 buckets); the Faiss index uses the IVF-PQ algorithm, with an inverted list size of 100 and a quantization bit size of 8; the spatial topological relationship verification of the BIM model includes the minimum assembly gap (≥3mm) and the axis alignment error (≤1°).

[0128] The incremental data pipeline is deployed on the engineering terminal to capture real-time images on site, and samples with feature differences exceeding 15% are selected through inter-frame difference algorithm. The model version repository maintains 5 historical versions, and the online inference service statistics the false detection rate (target value ≤2%) and response delay (≤200ms) every hour. When the retrieval abnormality rate exceeds 5% for 3 consecutive days, 5000 targeted synthetic samples are generated by GAN, and injected into the online calibration module. When the Faiss index reconstruction fails (error code E1024) is detected by the abnormality self-healing link, the system rolls back to the optimal version in the last 3 days and recalibrates the white balance parameters of the acquisition device (error tolerance ±150K), and the calibration log is synchronized to the data bloodline link.

[0129] Data bloodline tracking records the collection device ID of each sample (such as DJI_Mavic3_No.01), preprocessing parameters (gamma value 2.2), and model version hash value (64 bits). Low-frequency data (no access for 30 consecutive days) is archived to cold storage after ZSTD compression (compression ratio 1:3); high-frequency data (daily access volume ≥100 times) is migrated to an SSD storage node. The feedback loop module generates a heat map (Gaussian kernel σ=5px) based on the user's selected area, redirects the search vector weight (local feature weight increased by 30%), and generates new training samples after correcting the result label. The priority is marked as P1, and the model iteration update is completed within 48 hours.

[0130] Explanation of technical features of the present application:

[0131] Multi-spectral imaging scheme: refers to an image acquisition technology that synchronously acquires RGB color and depth information through the integration of visible light and near-infrared sensors. Through multi-band fusion, the texture details in low-light areas are enhanced, and original data including three-dimensional spatial coordinates are generated, solving the data acquisition integrity requirement in complex occlusion scenarios.

[0132] Cascade preprocessing pipeline: includes a serialized processing module of Retinex light equalization and morphological opening operation: the former eliminates shadow and glare interference by decomposing the image's light / reflection components; the latter removes device noise and repairs rusted and broken textures using erosion and dilation operations, achieving standardization of cross-device imaging quality.

[0133] Multi-task joint learning framework: based on the ResNet-50 backbone network sharing low-level convolutional features, cooperates with HRNet high-resolution key point positioning and Mask R-CNN defect detection branches, reduces computational redundancy through feature reuse, and synchronously optimizes the model accuracy and efficiency of classification, positioning, and detection tasks.

[0134] Adversarial data augmentation: uses a generative adversarial network (GAN) to inject calibration parameter constraints at the input end, generating synthetic images with physically reasonable oil stain reflection and metal strong reflection, simulating morphological distortion under extreme working conditions, and improving the model's generalization ability in complex environments.

[0135] Progressive training strategy: optimizes parameters in three stages: the basic training stage loads ImageNet pre-trained weights to establish general feature extraction capability; the domain fine-tuning stage inputs data according to environmental complexity gradient; the online calibration stage selects low confidence samples for manual review through active learning, and dynamically corrects the decision boundary.

[0136] Knowledge distillation compression: transfer the feature response of the multi-task teacher model (ResNet-50) to the lightweight student model (MobileNetV3) through the cosine similarity loss function, compress the model size while keeping the Faiss index space feature alignment, and adapt to the edge computing deployment requirements.

[0137] Hierarchical component library construction: generate a composite descriptor by fusing image feature vectors and three-dimensional point cloud data through a feature splicing layer, and combine the knowledge graph node mapping relationship defined by the ontology (such as valve-pipeline thread specification matching) to construct a reasoning knowledge base including assembly rules and compatibility relationships.

[0138] Multi-level index structure: adopt a three-layer retrieval mechanism of hash table for fast positioning of component categories, Faiss framework for approximate nearest neighbor search, and BIM model space topology verification, which balances the retrieval speed (hash table O(1) time complexity) and accuracy (BIM constraint verification) to meet the real-time matching requirements of engineering scenarios.

[0139] Closed-loop evolution mechanism: the incremental data pipeline triggers model updating by filtering difference samples exceeding the threshold through inter-frame difference algorithm, the versioned iterative framework dynamically switches the optimal model based on the false detection rate / delay index, and the abnormal self-healing link rolls back to the historical version and recalibrates the device parameters when the index reconstruction fails, forming a self-optimizing system.

[0140] Data bloodline tracking: record the whole life cycle information of samples (collection device ID, preprocessing parameters, model version identifier), verify the data distribution consistency through timestamp matching, support quick tracing of abnormal model versions and quantitative evaluation of data drift, and meet the requirements of industrial data compliance audit.

[0141] The above features form a synergistic effect through the following technical chain: multi-spectral acquisition and preprocessing eliminate environmental interference to generate standardized data input into the multi-task model; adversarial training and progressive learning enhance the robustness of the model, and distillation compression adapts to edge deployment; hierarchical indexing and knowledge graph support efficient retrieval, and the closed-loop evolution mechanism realizes system self-optimization through incremental data and abnormal processing; data bloodline management ensures traceability throughout the chain. Each module is closely linked, and systematically solves the technical defects of low efficiency of manual library construction, cumulative recognition error in complex working conditions, and poor cross-platform compatibility.

[0142] Incremental training trigger mechanism:

[0143] Trigger condition: the feature similarity score of the structured search result is lower than the dynamic threshold (threshold range: 90%-95% of the historical false detection rate statistics).

[0144] Threshold setting basis: based on the similarity distribution of historical false detection samples, dynamically adjust the lower limit of the threshold. When the real-time similarity is lower than the threshold, it is determined as a high-value false detection sample.

[0145] The core design logic of this threshold range stems from the balance between optimizing model decision boundaries and resource efficiency. Historical misclassification rate statistics reflect the model's recognition ability shortcomings under the current data distribution, and the similarity score distribution exhibits typical skewness characteristics - most misclassified samples are concentrated in the low similarity interval, while a small number of boundary samples are distributed near the decision boundary. Setting the threshold in the 90%-95% quantile interval of the historical misclassification rate statistics means that the system can accurately capture the most valuable samples for training: samples at the 90% quantile represent the most difficult cases for the model to recognize (such as heavy oil coverage or strong reflection interference scenarios), and these samples contain key feature patterns that traditional training cannot cover; while the 95% quantile effectively filters out abnormal values caused by transient interference or data noise, avoiding wasting training resources on invalid data.

[0146] Engineering verification shows that this range maximizes the optimization effect. A threshold below 90% will miss about 40% of difficult samples that can significantly improve the model's robustness, limiting the incremental training effect; a threshold above 95% will trigger the training process frequently, increasing computing resource consumption by more than 30%, and may also harm the model's generalization ability due to over-training of individual abnormal samples. The dynamic adjustment mechanism further enhances the adaptability of this design - the threshold is automatically updated every 24 hours based on the latest misclassified data, compensating for the data distribution drift caused by model iteration with a decay factor of 0.98, ensuring that the system always focuses on the most critical optimization direction.

[0147] In typical pipeline detection scenarios, this range exhibits precise problem positioning capabilities. For example, the oil pipeline flange gasket detection case shows that when the threshold is set at the 90% quantile of the historical misclassified sample similarity score, the misclassification rate of similar working conditions decreases by more than 30% after incremental training, and the boundary sample recognition accuracy improves by 27%. This proves that the 90%-95% threshold interval can effectively distinguish between truly optimized feature ambiguity cases and temporary interference data, allowing incremental training resources to focus on key samples that can most improve system performance.

[0148] Execution logic: automatically extract the environmental parameters (temperature, humidity, light intensity) of this sample and manually correct the label to generate an incremental data set and trigger the closed-loop evolution process.

[0149] Adversarial data augmentation trigger mechanism:

[0150] Trigger condition: The retrieval abnormality rate of the hierarchical component library exceeds the set threshold (threshold range: 5%-8%) for three consecutive statistical periods.

[0151] Abnormality rate calculation: Abnormality rate = (number of feature matching successes but assembly verification failures) / total retrieval times × 100%.

[0152] Execution logic: When the abnormal rate exceeds the standard, the generative adversarial network is automatically called to synthesize targeted training samples combined with the current environmental parameters (oil pollution reflectivity 0.3-0.7, strong reflection area proportion ≥ 15%).

[0153] Model version switching trigger mechanism:

[0154] Trigger condition: Both indicators are met:

[0155] False detection rate decrease amplitude ≥ 20% of historical baseline

[0156] Response delay ≤ engineering acceptable threshold (threshold range: 200-300 ms).

[0157] Execution logic: dynamically switch the production environment model to the optimal version, and keep historical version snapshots (store the last 5 versions).

[0158] User feedback redirection trigger:

[0159] Trigger condition: the difference between the user's selected area and the original search result's visual features ≥ significant difference threshold (threshold range: 30%-40%).

[0160] Execution logic: generate a regional heat map based on the attention mechanism, and adjust the query vector channel weight (weight improvement amplitude: 25%-35%).

[0161] Index reconstruction self-healing trigger:

[0162] Trigger condition: Faiss index reconstruction fails continuously for 3 times (error type: resource allocation error / vector dimension mismatch).

[0163] Execution logic: roll back to the successfully built index version within the last 3 days, and trigger the acquisition device calibration process simultaneously (white balance parameter calibration tolerance: ±150K).

[0164] Data storage level migration trigger:

[0165] Trigger condition:

[0166] High-frequency data: search heat score ≥ dynamic threshold (threshold range: based on the 90th percentile of access volume in the last 7 days).

[0167] Low-frequency data: consecutive days of non-access > cold storage threshold (threshold range: 30±3 days).

[0168] Execution logic: migrate SSD / HDD storage nodes according to heat scores, and control the compression ratio of low-frequency data to 1:3 to 1:5.

[0169] Application of incremental training trigger mechanism: In the flange component detection scenario of oil pipeline, when the system identifies that the similarity score of a certain sealing ring component is 0.82 (lower than the current dynamic threshold 0.85), the system automatically marks it as a false detection sample. The sample's collection environment parameters show that there is heavy oil contamination (oil contamination area ratio ≥ 60%) and strong side light interference (light intensity > 100,000 lux). According to the threshold setting rule (90% quantile value of historical false detection rate), the system triggers the incremental training process: extracts the corrected label of the sample (artificially confirmed as DN100 carbon steel flange), generates standardized data after cascading preprocessing, and inputs the multi-task joint learning framework to update the shared convolutional layer parameters. After training, the model's recognition accuracy for similar working conditions improves by 32%, and the false detection rate decreases to one-fourth of the original level.

[0170] On-site adaptation of adversarial data augmentation: In a liquefied natural gas pipeline valve detection project, the system monitors that the retrieval abnormality rate reaches 7% for 3 consecutive days (exceeding the set threshold of 5%). Analysis shows that the recognition error of stainless steel valves under low-temperature condensation conditions is concentrated. The system immediately calls the generative adversarial network, combined with the current environment parameters (temperature -10℃, humidity 90%) to synthesize 5000 adversarial samples with condensation water film. After these samples are injected into the online calibration stage, the model's recognition accuracy for condensation valves improves from 68% to 92%, while the spatial assembly verification failure rate is controlled within 1.5%.

[0171] Real-time control of model version switching: In the cross-regional pipeline engineering collaborative work, the edge computing node monitors that the false detection rate of the new model version decreases by 25% (exceeding the 20% threshold), and the inference delay stabilizes at 180ms (lower than the 250ms threshold). The system automatically switches the production environment to this version, while synchronously retaining the historical version snapshot. When strong sand dust interference causes the false detection rate to rise again, the system rolls back to the previous stable version within 45 seconds, ensuring the continuity of on-site installation work. This mechanism enables the system to maintain an average service availability rate of 98.7% when the environment changes suddenly.

[0172] User feedback-driven retrieval optimization: In a subsea pipeline repair project, engineers circle the flange area partially covered by sand (visual difference degree reaches 35%, exceeding the 30% threshold). The feedback loop module generates a regional heat map, increasing the local feature weight of the query vector by 30%. After re-searching, it accurately matches the same type of flange fittings, taking only 0.8 seconds. The corrected result generates training samples through a semi-automatic labeling tool, and the model updated within 48 hours improves the recognition accuracy of occluded samples by 22%.

[0173] Implementation of hot and cold data tiered storage: Access analysis of a certain chemical industry park pipe element library shows that the access frequency of large-diameter pipe fittings above DN80 is 120 times per day (higher than the 90th percentile), and the system automatically migrates to the SSD node, and the retrieval delay is reduced to 50ms. Some special-shaped pipe fittings have not been accessed for 33 consecutive days (more than 30 days threshold), and after ZSTD compression, they are archived to cold storage, releasing 23% of online storage resources. When a certain maintenance needs to call the data of the expansion joint in the cold storage, the system completes the decompression and loading within 2.3 seconds.

[0174] Fault recovery of self-healing link: A certain Faiss index reconstruction failed continuously due to vector dimension mismatch (error code E1024), and the system rolled back to the index snapshot 3 days ago within 10 seconds. The self-healing link synchronization triggered the calibration protocol update of the unmanned aerial vehicle acquisition terminal, and the calibration log showed that the white balance parameter was corrected from 6500K to 6200K (error -300K). The success rate of index reconstruction after correction of the collected data increased to 99.9%, and the data bloodline tracking link recorded the abnormal event and the processing path.

[0175] Each trigger mechanism forms a tight technical closed loop through quantitative indicators: incremental training solves the bottleneck of specific working condition recognition, adversarial enhancement optimizes model environmental adaptability, version switching ensures service stability, user feedback improves local feature weight, data tiering optimizes resource utilization, and self-healing link realizes rapid fault isolation. This trigger system based on dynamic threshold enables the system to have continuous evolution capability in complex scenarios such as pipeline detection, maintenance, and installation, and systematically solves the performance degradation problem of traditional methods in environmental mutations.

[0176] Multispectral imaging scheme: input the pipe element entity in the environment, output the original image data containing depth information. The core parameters include the visible light and near-infrared waveband range. The processing process eliminates shadow interference through multi-band fusion, adjusts the sensor incident angle and exposure time, and enhances the integrity of texture details in low-light areas. Finally, stereoscopic image data covering occluded scenes are generated.

[0177] Cascade pre-processing pipeline: input the original multispectral image data, and perform two levels of operations in sequence. The first level uses a multi-scale Retinex algorithm to decompose the illumination component and the reflection component, with the core parameter being the size of the Gaussian kernel function. The processing eliminates the halation effect in the metal reflection area. The second level is based on morphological opening operation, and a rectangular structural element kernel is used to perform erosion and dilation operations, with the core parameter being the number of iterations. The processing repairs the broken rust texture and removes device noise points. The output is a standardized image with balanced lighting and complete geometry.

[0178] Multi-task joint learning framework: input standardized image dataset, construct shared convolutional feature extraction layer based on ResNet-50. The framework contains three parallel subnetworks: element classification subnetwork outputs class probability distribution; key point positioning subnetwork outputs spatial coordinates based on high-resolution feature map; defect detection subnetwork generates pixel-level mask through region proposal mechanism. Inject adversarial data synthesized by generative adversarial network during training phase, synthesis sample constraint parameters include oil stain material reflectivity and strong reflection area proportion. Output feature response map fused with multi-task features.

[0179] Progressive training strategy: optimize model parameters in three stages. Load large-scale dataset pre-training weights in basic training stage. Gradually input data according to environmental complexity in domain fine-tuning stage, from clean samples to heavily polluted samples. Select low confidence samples through active learning mechanism in online calibration stage, core parameter is confidence threshold. Add training set after manual annotation to dynamically correct decision boundary. Output robust model adapted to complex working conditions.

[0180] Knowledge distillation compression: input the trained multi-task model as the teacher model, constrain the lightweight student model through feature alignment loss. Core parameter is cosine similarity loss weight coefficient. Processing makes the student model output feature consistent with the intermediate layer response of the teacher model, and aligns with the index space vector direction. Output lightweight model feature vector adapted to edge devices.

[0181] Hierarchical component library construction: input 1024-dimensional feature vector extracted by training model and three-dimensional point cloud data, fuse through feature concatenation layer. Core parameter is normalization proportion coefficient. Output 1280-dimensional component feature descriptor. Knowledge graph defines assembly rules based on ontology, core parameters include thread specification tolerance and material compatibility table. Processing establishes the mapping relationship between feature descriptor and knowledge node. Output structured knowledge base supporting assembly reasoning.

[0182] Multi-level index retrieval mechanism: input component image or feature vector to be retrieved. First level uses hash table to locate target category by first letter of component type. Second level uses product quantizer to perform approximate nearest neighbor search within target category. Third level verifies assembly compatibility by associating building information model spatial topology data, core parameters include axis alignment tolerance and minimum gap size. Output matching results verified by geometric constraints.

[0183] Closed-loop evolution mechanism: trigger the process when feature similarity score is lower than 90%-95% quantile of historical false rejection rate. Input false rejection sample and its environmental parameters to generate incremental data. Cascade preprocessing generates standardized dataset. Update shared feature layer parameters through multi-task framework incremental training. Process reconstructs hash table index and feature vector space. Output optimized hierarchical component library index structure.

[0184] Abnormal self-healing link: index reconstruction failure signal in the input incremental data pipeline. The processing rolls back to the latest 3-day valid index version. The synchronization triggers the acquisition device calibration protocol, and the core parameter is the white balance calibration tolerance. The output calibration log and update the data bloodline tracking record.

[0185] Dynamic storage strategy: input element library access log data. Process based on sliding time window statistics retrieval hotness, core parameters for hotness score model coefficients. High frequency data migration to high speed storage node, low frequency data execution block compression archiving. Output resource optimization scheme of hierarchical storage.

[0186] Each model algorithm forms a collaborative chain through parameterized control: imaging and preprocessing ensure data quality; multi-task training generates discriminative features; knowledge graph provides assembly semantics; multi-level index enables efficient retrieval; closed-loop evolution continuously optimizes the system. The design of key technical parameters balances precision and efficiency, and systematically solves the defects of traditional pipeline element library construction.

[0187] The present application systematically solves the problems of low efficiency, high error rate and poor adaptability to complex environment of artificial creation of pipeline element library through the following technical means:

[0188] The present application collects multi-source pipeline element image data through a multispectral imaging device, and combines a cascading preprocessing pipeline (Retinex light balance and morphological opening operation) to eliminate device differences and environmental interference, generating standardized image data sets. A semi-automatic labeling tool automatically labels key components (such as flange interface, weld) based on a pre-trained model, and associates and encapsulates metadata (pipe diameter, material) with images, reducing the amount of manual labeling work. Distributed storage nodes enable fast data transmission and processing, avoiding the time-consuming aspects of traditional manual entry and significantly improving the efficiency of the data preparation stage.

[0189] A multi-task joint learning framework (ResNet-50, HRNet, Mask R-CNN) is used to share the bottom feature extraction layer, synchronize the optimization of element classification, key point positioning and defect detection tasks, and reduce the resource consumption and error accumulation of independent training of multiple models. A generative adversarial network (GAN) injects adversarial data in oily and strongly reflective environments, combined with a progressive training strategy (basic training, domain fine-tuning, online calibration) to enhance the model's robustness to complex working conditions. The closed-loop evolution mechanism dynamically updates the model parameters and index structure with incremental data, and when the retrieval abnormality rate exceeds the threshold, it triggers targeted data augmentation and model version rollback, forming an error self-correction capability, avoiding the lag of manual intervention.

[0190] A hierarchical component library is constructed by fusing feature vectors and three-dimensional point cloud data, and efficient retrieval is achieved through multi-level indexing (hash table, Faiss, BIM topology verification). The feedback loop module based on attention mechanism captures the user's selected area and redirects the retrieval logic to the local features, solving the problem of false matching in complex occlusion scenarios. The data blood relationship tracking link records the dependence relationship between device parameters and model version, combined with dynamic storage strategy (SSD / HDD migration, cold storage archiving), to ensure the data consistency of the system under the device iteration and environmental changes. The abnormal self-healing link automatically rolls back to the stable version when the index fails, synchronously calibrates the data collection source parameters, forms the full-link adaptive capability, and breaks through the limitations of traditional methods in cross-platform compatibility and dynamic updating.

Claims

1. A method for creating a pipe element library based on model training and image recognition technology, characterized in that, The method comprises the following steps: Step 1, acquiring multi-source pipeline element image data and associated environmental parameters, and generating a standardized image data set by cascading preprocessing of the multi-source pipeline element image data and the associated environmental parameters; Step 2, inputting the standardized image data set into a multi-task joint learning framework for dynamic training, the multi-task joint learning framework integrating an element classification subnetwork, an element key point positioning subnetwork and an element defect detection subnetwork, and outputting a training model through a shared bottom layer feature extraction layer; Step 3, generating an element feature descriptor based on a feature vector extracted by the training model, associating the element feature descriptor with an element assembly relationship in a preset knowledge graph, constructing a multi-level index structure, receiving a query input comprising an element image to be searched or a feature vector, analyzing an element type identifier in the query, determining a target element category, performing an approximate nearest neighbor search in the target category, outputting candidate elements similar in features, verifying spatial assembly compatibility of the candidate elements with the query element, screening a final matching result passing a topological constraint, taking the final matching result as a structured search result, and outputting the structured search result to Step 4 in real time; Step 4, when a feature similarity score in the structured search result is lower than a set threshold, marking the result as a false detection sample, extracting a correction label and associated environmental parameters of the false detection sample, generating incremental data, triggering a closed-loop evolution mechanism according to the incremental data, the closed-loop evolution mechanism comprising acquiring the incremental data, cascading preprocessing of the incremental data, generating an incremental standardized data set, inputting the incremental standardized data set into the multi-task joint learning framework, updating feature extraction parameters of the training model, extracting a feature vector based on the updated training model, reconstructing a multi-level index structure of the hierarchical element library, and feeding back a search result of the updated hierarchical element library to Step 1 to form a closed-loop iteration.

2. The method of claim 1, wherein the model-based training and image recognition technique-based pipe element library creation method is characterized by, Step 1 comprises: collecting pipeline element images including depth information to generate original data covering occlusion and low-light scenes; The original data is input into a cascading processing pipeline, and the cascading preprocessing pipeline sequentially performs illumination equalization processing based on a multi-scale Retinex algorithm, decomposes the illumination component and the reflection component of the image using a Gaussian kernel function, and obtains a repaired image based on texture repair of morphological opening operation, and performs erosion and dilation operation by selecting a rectangular structure element kernel; The repaired image and the metadata are associated and encapsulated as a structured data set by a semi-automatic labeling tool, the metadata comprising device parameters, environmental parameters and element attribute parameters, the device parameters being white balance and focal length parameters generated by a cross-device calibration protocol, the environmental parameters being temperature, humidity and illumination intensity when the device is collecting, and the element attribute parameters being pipe diameter, pressure rating and material type identified by a pre-trained object detection model, and the structured data set is transmitted to a training module of the multi-task joint learning framework through a distributed storage node.

3. The method of claim 2, wherein the model-based training and image recognition technique is used to create a library of pipe elements. Step 2 comprises: An end-to-end network architecture based on ResNet-50, HRNet and Mask R-CNN is constructed, and a bottom layer convolution feature extraction layer is shared; Injecting adversarial augmented data generated by the generative adversarial network in the training stage simulates the morphological changes of the pipeline elements in the oil pollution and strong reflection environment; Optimizing model parameters in stages through a progressive training strategy, including basic training based on ImageNet pre-trained weights, domain fine-tuning according to increasing environmental complexity, and an online calibration stage combined with active learning; The trained model generates a lightweight MobileNetV3 model through knowledge distillation, and the feature output of the lightweight MobileNetV3 model is aligned with the cosine similarity of the Faiss index space of the hierarchical element library.

4. The method of claim 3, wherein the model-based training and image recognition technique-based pipe element library creation method is characterized by, Step 3 includes: fusing the 1024-dimensional feature vector output by the multi-task joint learning framework with the three-dimensional point cloud data through a feature concatenation layer to form an element feature descriptor; An ontology-based knowledge graph is constructed to define the mapping relationship between the feature descriptor and the knowledge node, and the knowledge graph includes the assembly relationship and compatible model replacement rules of valves and pipelines; Based on the multi-level index structure, element retrieval is performed, which includes locating the element category through a hash table, matching the feature vector through the Faiss framework to construct an approximate nearest neighbor search index, and matching the spatial topology relationship associated with the BIM model.

5. The method of claim 4, wherein the model-based training and image recognition technique-based pipe element library creation method is characterized by, Step 4 includes: Deploying an incremental data pipeline to capture new images during on-site installation and maintenance, and screening samples that differ from the existing library by more than a certain threshold through differential compression; Establishing a model version iteration framework, dynamically switching the training model version based on the decrease in the false detection rate of online inference and the response delay index, and retaining traceable model snapshots; When the retrieval abnormality rate of the hierarchical element library exceeds a certain threshold, trigger the adversarial data augmentation module in the multi-task joint learning framework to generate targeted training samples in the oil pollution and strong reflection environment.

6. The method of claim 5, wherein the model-based training and image recognition technique is used to create a library of pipe elements. Also includes: Establishing a cross-device calibration protocol in the preprocessing stage, and uniformly setting white balance and focal length parameters based on the device type of the acquisition terminal, which includes drones and handheld devices; Input the parameters generated by the calibration protocol into the generator input end of the generative adversarial network to constrain the material reflectivity parameters of the oil pollution and strong reflection synthetic samples output by the generative adversarial network.

7. The method of claim 6, wherein the model-based training and image recognition technique is used to create a library of pipe elements. Also includes: Embedding a feedback loop module based on the attention mechanism in the retrieval interface of the hierarchical element library, generating a regional heat map based on the user's selected image area, redirecting the query vector of the approximate nearest neighbor search index, and obtaining the corrected retrieval result; Reverse labeling the corrected retrieval result to the structured data set through a semi-automatic labeling tool to generate new training samples and input them into the active learning module of the online calibration stage.

8. The method of claim 7, wherein the model-based training and image recognition technique-based pipe element library creation method is characterized by, Also includes: Establishing a data bloodline tracking link to record the acquisition device ID, preprocessing parameters, and model version identification of each training sample in the structured data set; Associate the bloodline link with the model version snapshot, and verify the training dependency relationship of a specific model version on historical data sets through a timestamp matching mechanism. 9.The method of creating a pipe element library based on model training and image recognition technology according to claim 8, wherein, Also includes: Define data aging rules, and perform cold storage archiving on low-frequency access element data in the hierarchical element library that have not been retrieved for 30 consecutive days; Dynamic adjustment of storage hierarchy strategy, based on the search frequency index statistics of the feedback loop module, migrate high-frequency access data to SSD storage nodes, and downgrade low-frequency data to HDD storage nodes. 10.The method of creating a pipe element library based on model training and image recognition technology according to claim 9, wherein, Also includes: Deploy an exception self-healing link in the incremental data pipeline. When the Faiss index reconstruction fails, roll back to the preserved historical model version and the corresponding index snapshot. The self-healing link triggers the cross-device calibration protocol to recalibrate the white balance parameters of the data acquisition source and generates a calibration log to synchronize the data bloodline tracking link.

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