Method and device for predicting safety state of pressure swing adsorption device in combination with digital twinning

Abstract

The invention discloses a method and device for predicting the safety state of a pressure swing adsorption device in combination with digital twinning, and relates to the related field of equipment safety state monitoring, and the method comprises the steps: carrying out the operation collection of the pressure swing adsorption device according to a target working condition, carrying out the detection of defects, carrying out the three-dimensional construction, obtaining a damage three-dimensional model, and generating a dynamic damage quantification information set; evaluating the pressure swing adsorption device, and establishing a multi-modal nondestructive testing database; processing according to the damage three-dimensional model in combination with a plurality of quantitative evaluation standards to obtain a digital twin module; performing classified learning training on the dynamic damage quantitative information set, and constructing a service life prediction model; and performing real-time evaluation on the pressure swing adsorption device, and completing state prediction of the pressure swing adsorption device based on the device operation prediction state information set. The technical problem of insufficient real-time performance and accuracy of safety state prediction of the existing pressure swing adsorption device is solved, and the technical effects of real-time monitoring and accurate prediction of the running state of the pressure swing adsorption device are achieved.

Description

Technical Field

[0001] The present application relates to fields related to equipment status monitoring, and in particular to a method and device for predicting the safety status of a pressure swing adsorption device in combination with digital twins. Background Art

[0002] With the rapid development of the coal chemical industry, pressure swing adsorption devices, as core equipment for gas separation and purification, play a vital role in industrial production. However, under complex working conditions, pressure swing adsorption devices often face unexpected damage problems such as cracking and bulging. These damages not only shorten the service life of the device, but also seriously threaten production safety. Traditional safety status monitoring methods for pressure swing adsorption devices mainly rely on regular offline detection and manual judgment. This method is not only time-consuming and labor-intensive, but also difficult to capture the dynamic damage changes of the device during operation. The accumulation of these damages and failure to detect them in time may eventually lead to serious safety accidents.

[0003] In the current related technologies, the safety status prediction of pressure swing adsorption devices has technical problems such as insufficient real-time performance and accuracy. Summary of the invention

[0004] The present application provides a method and device for predicting the safety status of a pressure swing adsorption device in combination with a digital twin. The operation of the pressure swing adsorption device is collected according to the target operating conditions to generate a dynamic damage quantification information set. Then, based on these information sets, a three-dimensional construction is performed to obtain a three-dimensional damage model to intuitively display the damage situation inside the device. Next, the three-dimensional damage model is evaluated to establish a multimodal non-destructive testing database containing multiple quantitative evaluation standards. Finally, the digital twin technology is used to process the three-dimensional damage model and the quantitative evaluation standards to obtain a digital twin module. Through this module, the dynamic damage quantification information set is classified and learned, and a service life prediction model is constructed. The operating status of the pressure swing adsorption device is evaluated in real time and the intelligent safety status prediction is predicted. Technical means such as these achieve the technical effect of realizing real-time monitoring and accurate prediction of the operating status of the pressure swing adsorption device.

[0005] The present application provides a method for predicting the safety status of a pressure swing adsorption device in combination with a digital twin, including:

[0006] The operation of the pressure swing adsorption device is collected according to the target operating conditions to generate a dynamic damage quantification information set, and a three-dimensional construction is performed based on the dynamic damage quantification information set to obtain a damage three-dimensional model; the pressure swing adsorption device is evaluated based on the three-dimensional damage model, and a multimodal non-destructive testing database is established, and the multimodal non-destructive testing database includes multiple quantitative evaluation criteria; the digital twin technology is used to process the three-dimensional damage model in combination with the multiple quantitative evaluation criteria to obtain a digital twin module; the dynamic damage quantification information set is classified and learned through the digital twin module to construct a service life prediction model; the operating status of the pressure swing adsorption device is evaluated in real time through the service life prediction model to generate a device operation prediction status information set, and the intelligent safety status prediction of the pressure swing adsorption device is completed based on the device operation prediction status information set.

[0007] In a possible implementation, the operation of the pressure swing adsorption device is collected according to the target operating conditions, a dynamic damage quantification information set is generated, and the following processing is performed:

[0008] Traversing multiple operating conditions of the pressure swing adsorption device for analysis, the target condition is determined; based on the target condition, multi-dimensional damage acquisition is performed on the pressure swing adsorption device to obtain a multimodal signal; based on the multimodal signal, a comprehensive inspection of the pressure swing adsorption device is performed to obtain a device inspection result, damage is located according to the device inspection result, and multiple damage locations are determined; data is quantified according to the multiple damage locations to obtain the dynamic damage quantification information set.

[0009] In a possible implementation, a three-dimensional construction is performed based on the dynamic damage quantification information set to obtain a three-dimensional damage model, and the following processing is performed:

[0010] Based on the dynamic damage quantification information set and the multiple damage positions, simulation verification is performed to obtain a damage verification result; based on the damage verification result, the dynamic damage quantification information set is stereo matched to obtain multiple point cloud data; based on the multimodal signal, damage evolution is performed on the multiple point cloud data to determine damage impact transfer data; using deep learning, the multimodal signal is fused according to the damage impact transfer data to generate signal fusion data; according to the signal fusion data, the multiple point cloud data are three-dimensionally combined to construct the damage three-dimensional model.

[0011] In a possible implementation, the pressure swing adsorption device is evaluated based on the three-dimensional damage model, a multimodal nondestructive testing database is established, and the following processing is performed:

[0012] The three-dimensional damage model is used to traverse and trace the pressure swing adsorption device to generate damage source data; the damage impact is quantified according to the damage source data, and the multiple quantitative evaluation standards are set; damage evaluation is performed according to the multiple quantitative evaluation standards in combination with the three-dimensional damage model to obtain multiple damage scores; cluster analysis is performed according to the damage source data to determine multiple damage types, and feature analysis is performed according to the multiple damage types in combination with the three-dimensional damage model to obtain multiple damage features; the multimodal signals are entered in descending order according to the multiple damage scores in combination with the multiple damage features to construct the multimodal nondestructive testing database.

[0013] In a possible implementation, the digital twin technology is used to process the three-dimensional damage model in combination with the multiple quantitative evaluation criteria to obtain a digital twin module, and the following processing is performed:

[0014] The digital twin technology is used in combination with the three-dimensional damage model for matching to construct a digital twin model; the multimodal signal is synchronized to the digital twin model for data-driven according to the multiple quantitative evaluation standards to obtain multimodal damage change data; dynamic damage is mapped to the three-dimensional damage model based on the multimodal damage change data to obtain dynamic damage visualization data; multi-standard evaluation is performed according to the multiple quantitative evaluation standards based on the dynamic damage visualization data to obtain a multi-standard comprehensive score, the digital twin model is evaluated according to the multi-standard comprehensive score, and the digital twin model is added to the digital twin module according to the evaluation result.

[0015] In a possible implementation, the dynamic damage quantification information set is classified and trained through the digital twin module to construct a service life prediction model, and the following processing is performed:

[0016] The dynamic damage quantification information set is classified based on the multiple damage characteristics to determine multiple damage labels; classification learning is performed through the digital twin module according to the multiple damage labels combined with multiple damage characteristics to construct a gradient boosting decision tree; data training is performed on the dynamic damage quantification information set based on the gradient boosting decision tree, and the training results are cross-validated to obtain the service life prediction model.

[0017] In a possible implementation, data training is performed on the dynamic damage quantification information set based on the gradient boosting decision tree, the training results are cross-validated, the service life prediction model is obtained, and the following processing is performed:

[0018] A time series analysis algorithm is used to perform trend analysis on the dynamic damage quantification information set to determine damage evolution trend data; a regularity analysis is performed based on the gradient boosting decision tree in combination with the damage evolution trend data to draw a damage evolution curve chart; a regression calculation is performed on the pressure swing adsorption device according to the damage evolution curve chart in combination with the multiple damage characteristics to generate multiple data distribution information; supervised training is performed on the dynamic damage quantification information set according to the multiple data distribution information in combination with the multiple damage labels to generate the training result; based on the training result, first training data, second training data...Nth training data are randomly extracted, and the first training data, the second training data...The Nth training data are all different training data; the first training data, the second training data...The Nth training data are cross-iteratively validated according to the error index until the training result converges, and the service life prediction model is constructed.

[0019] The present application also provides a safety state prediction device for a pressure swing adsorption device combined with a digital twin, including:

[0020] A device operation acquisition module, the device operation acquisition module is used to perform operation acquisition of the pressure swing adsorption device according to the target operating conditions, generate a dynamic damage quantification information set, perform three-dimensional construction based on the dynamic damage quantification information set, and obtain a damage three-dimensional model; a damage evaluation module, the damage evaluation module is used to evaluate the pressure swing adsorption device based on the damage three-dimensional model, and establish a multimodal non-destructive testing database, and the multimodal non-destructive testing database includes multiple quantitative evaluation criteria; a digital twin processing module, the digital twin processing module is used to use digital twin technology to process according to the three-dimensional damage model in combination with the multiple quantitative evaluation criteria to obtain a digital twin module; a service life prediction model construction module, the service life prediction model construction module is used to perform classification learning and training on the dynamic damage quantification information set through the digital twin module to construct a service life prediction model; a state prediction module, the state prediction module is used to perform real-time evaluation of the operating state of the pressure swing adsorption device through the service life prediction model, generate a device operation prediction state information set, and complete the intelligent safety state prediction of the pressure swing adsorption device based on the device operation prediction state information set.

[0021] It is intended to propose a method and device for predicting the safety status of a pressure swing adsorption device combined with a digital twin in this application. First, the operation of the pressure swing adsorption device is collected according to the target operating conditions to generate a dynamic damage quantification information set. A three-dimensional construction is performed based on the dynamic damage quantification information set to obtain a damage three-dimensional model. Then, the pressure swing adsorption device is evaluated based on the damage three-dimensional model, and a multimodal non-destructive testing database is established. The multimodal non-destructive testing database includes multiple quantitative evaluation criteria. The digital twin technology is then used to process the damage three-dimensional model in combination with multiple quantitative evaluation criteria to obtain a digital twin module. Then, the dynamic damage quantification information set is classified and learned and trained through the digital twin module to construct a service life prediction model. Finally, the operating status of the pressure swing adsorption device is evaluated in real time through the service life prediction model to generate a device operation prediction status information set. Based on the device operation prediction status information set, the intelligent safety status prediction of the pressure swing adsorption device is completed, thereby achieving the technical effect of realizing real-time monitoring and accurate prediction of the operating status of the pressure swing adsorption device. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the device according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0023] Figure 1 A schematic flow chart of a method for predicting the safety state of a pressure swing adsorption device in combination with a digital twin provided in an embodiment of the present application.

[0024] Figure 2 A schematic diagram of the structure of a safety state prediction device for a pressure swing adsorption device combined with a digital twin provided in an embodiment of the present application.

[0025] Explanation of the accompanying drawings: device operation acquisition module 10, damage assessment module 20, digital twin processing module 30, service life prediction model construction module 40, state prediction module 50. DETAILED DESCRIPTION

[0026] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0027] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0028] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0029] The present application embodiment provides a method for predicting the safety status of a pressure swing adsorption device in combination with a digital twin, such as Figure 1 As shown, the method includes:

[0030] Step S100, collect the operation data of the pressure swing adsorption device according to the target working condition, generate a dynamic damage quantification information set, perform three-dimensional construction based on the dynamic damage quantification information set, and obtain a three-dimensional damage model. Specifically, the target working condition refers to the working condition that has a significant impact on the damage of the pressure swing adsorption device, such as the H2-rich alternating load working condition. Based on the target working condition, multi-modal signal collection is performed on the pressure swing adsorption device, including collecting laser, electromagnetic, ultrasonic and X-ray signals, etc., to capture defects inside the pressure swing adsorption device (such as cracks, bulging, wall thickness thinning, etc.). The dynamic damage quantification information set is constructed using the collected multi-modal signals. The dynamic damage quantification information set contains the quantitative damage data of each damage position of the pressure swing adsorption device during operation. Based on the dynamic damage quantification information set, a three-dimensional model of the damaged part is generated using three-dimensional reconstruction technology. This model intuitively displays information such as the location, shape and size of the damage.

[0031] In a possible implementation, the operation of the pressure swing adsorption device is collected according to the target working condition to generate a dynamic damage quantification information set, and step S100 further includes step S110, traversing multiple operating conditions of the pressure swing adsorption device for analysis to determine the target working condition. Specifically, the operating data of the pressure swing adsorption device under different working conditions are collected, including key parameters such as pressure, temperature, flow, and adsorption time. These data are deeply analyzed by methods such as data statistical analysis, trend analysis, and correlation analysis to identify which working conditions have a significant impact on the damage of the device. Based on the analysis results, a target working condition that is most critical to the damage of the device is determined. Step S120, based on the target working condition, multi-dimensional damage collection is performed on the pressure swing adsorption device to obtain multi-modal signals. Specifically, non-destructive testing methods (laser, electromagnetic, ultrasonic, and X-ray, etc.) are used to collect multi-modal signals of the damage of the pressure swing adsorption device under the target working condition, and multi-dimensional information of damage characteristics such as cracks, stress, and bulging is obtained, and this information has different physical properties and forms of expression. Step S130, based on the multimodal signal, a comprehensive detection of the pressure swing adsorption device is performed to obtain the device detection result, and damage is located according to the device detection result to determine multiple damage positions. Specifically, the collected multimodal signal is preprocessed, such as filtering, denoising, feature extraction, etc., to improve the signal quality. Using the signal processing results, a comprehensive detection of the pressure swing adsorption device is performed, and according to the detection results, the damage position in the device is determined, including different types of damage such as cracks and bulging. Step S140, data is quantified according to the multiple damage positions to obtain the dynamic damage quantification information set. Specifically, for each determined damage position, quantification is performed according to its size, shape, depth and other characteristics to obtain the specific value of the damage. The damage quantification results are sorted into a data set, including information such as damage location, damage type, and damage degree, to form a dynamic damage quantification information set. This implementation method performs multi-dimensional damage collection and detection based on the target working condition, comprehensively understands the damage condition of the pressure swing adsorption device, and improves the accuracy of state prediction.

[0032] In a possible implementation, a three-dimensional construction is performed based on the dynamic damage quantification information set to obtain a three-dimensional damage model, and step S100 further includes step S150, performing simulation verification based on the dynamic damage quantification information set in combination with the multiple damage locations to obtain a damage verification result. Specifically, a dynamic damage quantification information set (including quantitative data such as damage type, damage degree, and damage location) and specific information of multiple damage locations are input, and the pressure swing adsorption device is modeled using simulation software (such as ANSYS, etc.), and corresponding simulation parameters are set according to the dynamic damage quantification information set and the damage location. Through simulation operation, the actual impact of damage in the pressure swing adsorption device is simulated, including stress distribution, deformation, etc., and the damage verification result, that is, the damage impact data and damage expansion trend obtained by simulation. Step S160, stereo matching is performed on the dynamic damage quantification information set according to the damage verification result to obtain multiple point cloud data. Specifically, the damage verification result and the dynamic damage quantification information set are input, and the damage impact data in the damage verification result is matched with the data in the dynamic damage quantification information set by using a stereo matching algorithm (such as an ICP algorithm, etc.), thereby generating three-dimensional point cloud data representing the shape, position and size of the damage, and these point cloud data constitute a three-dimensional representation of the damage. Step S170, based on the multimodal signal, the damage evolution of the multiple point cloud data is performed to determine the damage impact transfer data. Specifically, multiple point cloud data and multimodal signals are input, and the damage evolution model (such as a fracture mechanics model, a fatigue damage model, etc.) is used to process the point cloud data in combination with the multimodal signal to analyze the expansion and transfer process of the damage in the pressure swing adsorption device. The damage impact transfer data is determined by calculating parameters such as the damage rate, the damage area, and the damage transfer path. Step S180, deep learning is used to fuse the multimodal signals according to the damage impact transfer data to generate signal fusion data. Specifically, a deep learning algorithm is used to extract features and perform fusion processing on multimodal signals. By training the model, the mapping relationship between damage impact transmission data and multimodal signals is learned, thereby generating signal fusion data, that is, data that fuses the features of multimodal signals. Step S190, three-dimensionally combine the multiple point cloud data according to the signal fusion data to construct the three-dimensional damage model. Specifically, three-dimensionally combine and optimize the multiple point cloud data according to the feature information in the signal fusion data. By adjusting parameters such as the position, shape and size of the point cloud data, the three-dimensional model can more accurately reflect the actual situation of the damage, that is, the three-dimensional damage model is a three-dimensional representation that combines the features of multimodal signals and point cloud data. This implementation method obtains richer damage information through multimodal signal fusion processing, and improves the accuracy and reliability of the prediction.

[0033] Step S200, based on the three-dimensional damage model, the pressure swing adsorption device is evaluated and a multi-modal non-destructive testing database is established, wherein the multi-modal non-destructive testing database includes multiple quantitative evaluation criteria. Specifically, the three-dimensional damage model is used to trace the source of the damage, and the impact of the damage is quantitatively analyzed according to the source of the damage, and multiple quantitative evaluation criteria are set. The degree of damage, type of damage, and location of damage of the pressure swing adsorption device are comprehensively evaluated by combining the quantitative evaluation criteria with the three-dimensional damage model, and a multi-modal non-destructive testing database is established. The multi-modal non-destructive testing database is a comprehensive database including multiple non-destructive testing data, damage evaluation results, quantitative evaluation criteria, and other information.

[0034] In a possible implementation, the pressure swing adsorption device is evaluated based on the three-dimensional damage model, and a multimodal nondestructive testing database is established. Step S200 further includes step S210, using the three-dimensional damage model to traverse and trace the pressure swing adsorption device to generate damage source data. Specifically, the detailed damage information in the three-dimensional damage model is used to traverse and inspect each part of the pressure swing adsorption device. By analyzing the morphology, location, size and other characteristics of the damage, combined with the operating history, working environment and other factors of the pressure swing adsorption device, the formation mechanism and source of the damage are inferred to obtain damage source data, which refers to information about how the damage is generated and where it comes from. Step S220, quantify the damage impact according to the damage source data, and set the multiple quantitative evaluation criteria. Specifically, according to the damage source data, analyze the impact of the damage on the performance, safety and other aspects of the pressure swing adsorption device. Quantify these impacts and set multiple quantitative evaluation criteria, such as the degree of damage, the scope of impact, the difficulty of repair, etc. Step S230, perform damage evaluation according to the multiple quantitative evaluation criteria combined with the three-dimensional damage model to obtain multiple damage scores. Specifically, a comprehensive evaluation is performed on each damage of the pressure swing adsorption device in combination with the three-dimensional damage model and the set quantitative evaluation criteria. According to the evaluation results, a damage score is assigned to each damage, which reflects the severity and potential impact of the damage. Step S240, cluster analysis is performed based on the damage source data to determine multiple damage types, and feature analysis is performed based on the multiple damage types in combination with the three-dimensional damage model to obtain multiple damage features. Specifically, cluster analysis is performed on the damage source data, similar damages are classified into one category, and multiple damage types are determined. In combination with the three-dimensional damage model, feature analysis is performed on each damage type to extract typical features of each type. Step S250, the multimodal signals are entered in descending order according to the multiple damage scores combined with the multiple damage features to construct the multimodal non-destructive testing database. Specifically, the multimodal signals are sorted in descending order according to the damage scores and damage features, that is, they are arranged according to importance or priority. The sorted multimodal signals are entered into the multimodal non-destructive testing database in sequence to construct a complete database system. This implementation method utilizes the three-dimensional damage model to traverse and trace the source, quantify damage impacts, evaluate damage, determine damage types, perform feature analysis, and enter multimodal signals in descending order, which not only ensures the accuracy and completeness of the data, but also improves the effectiveness and practicality of the database.

[0035] Step S300, using digital twin technology, processing according to the three-dimensional damage model in combination with the multiple quantitative evaluation criteria to obtain a digital twin module. Specifically, the digital twin technology is used in combination with the three-dimensional damage model to construct a digital twin model, the multimodal signal is synchronized to the digital twin model for data driving, and multiple quantitative evaluation criteria are used for evaluation to obtain a digital twin module.

[0036] In a possible implementation, the digital twin technology is used to process the three-dimensional damage model in combination with the multiple quantitative evaluation criteria to obtain a digital twin module, and step S300 further includes step S310, using the digital twin technology in combination with the three-dimensional damage model for matching to construct a digital twin model. Specifically, the digital twin technology is used to match the three-dimensional damage model with the digital twin framework, that is, the pressure swing adsorption device in the physical world is associated with its counterpart in the virtual world (i.e., the digital twin model). Based on the matching result, a digital twin model is constructed, which can reflect the actual operating state and damage of the pressure swing adsorption device. Step S320, according to the multiple quantitative evaluation criteria, the multimodal signal is synchronized to the digital twin model for data driving to obtain multimodal damage change data. Specifically, according to the quantitative evaluation criteria, the multimodal signal is synchronized to the digital twin model, and the synchronized multimodal signal is used to drive the digital twin model for simulation and analysis, including simulating the operating state and damage of the pressure swing adsorption device under the target working condition. Based on the simulation results, multimodal damage change data are obtained, which reflect the changes of the pressure swing adsorption device during the damage process. Step S330, based on the multimodal damage change data, dynamic damage mapping is performed to the damage three-dimensional model to obtain dynamic damage visualization data. Specifically, the multimodal damage change data is mapped to the damage three-dimensional model, including marking the location, size, shape and other information of the damage in the model. Based on the mapping results, dynamic damage visualization data are obtained, which show the damage of the pressure swing adsorption device in an intuitive way. Step S340, a multi-standard evaluation is performed according to the multiple quantitative evaluation criteria based on the dynamic damage visualization data to obtain a multi-standard comprehensive score, the digital twin model is evaluated according to the multi-standard comprehensive score, and the digital twin model is added to the digital twin module according to the evaluation results. Specifically, the dynamic damage visualization data is used in combination with the quantitative evaluation criteria to evaluate the current health status of the pressure swing adsorption device. According to the results of the health status assessment, the digital twin model is updated and adjusted as necessary. If the assessment results show that the pressure swing adsorption device has new damage or performance changes, these changes will be reflected in the digital twin model to ensure that the model is consistent with the status of the physical device. The updated digital twin model will contain more accurate damage information and health status assessment results. Add the updated digital twin model to the corresponding digital twin module. By continuously updating and adding new digital twin models, the digital twin module can continue to provide accurate health status assessment capabilities. This implementation ensures that the digital twin model is always consistent with the status of the physical device, providing accurate data support for intelligent safety status prediction.

[0037] Step S400, classify and learn the dynamic damage quantification information set through the digital twin module to build a service life prediction model. In a possible implementation, step S400 further includes step S410, classify the dynamic damage quantification information set based on the multiple damage features to determine multiple damage labels. Specifically, extract features directly related to damage from the dynamic damage quantification information set, including the size, shape, location, evolution speed, etc. of the damage. Based on the extracted features, assign a damage label to each dynamic damage quantification information set, and these labels represent different damage types or damage degrees. Step S420, classify and learn according to the multiple damage labels combined with multiple damage features through the digital twin module to build a gradient boosting decision tree. Specifically, the dynamic damage quantification information set and the corresponding damage label are input into the gradient boosting decision tree, and training is performed according to multiple damage features and damage labels, so that the model can learn the mapping relationship between features and labels. During the training process, the parameters and structure of the model are continuously adjusted to improve the classification accuracy and generalization ability of the model. Step S430, data training is performed on the dynamic damage quantification information set based on the gradient boosting decision tree, and the training results are cross-validated to obtain the service life prediction model. Specifically, the dynamic damage quantification information set is trained using the gradient boosting decision tree. During the training process, the model will continuously and iteratively optimize its parameters to improve classification accuracy. The trained model is evaluated using a cross-validation method to check its generalization ability. Cross-validation includes dividing the data into multiple subsets, and then using one part as a training set and the other part as a test set for verification in turn. Based on the results of cross-validation, the optimal model parameters and structure are selected, and the trained model is output as a service life prediction model for subsequent real-time evaluation. Among them, the service life prediction model is a model that predicts the service life of a pressure swing adsorption device based on the input dynamic damage quantification information set and damage characteristics.

[0038] In a possible implementation, data training is performed on the dynamic damage quantification information set based on the gradient boosting decision tree, and the training results are cross-validated to obtain the service life prediction model. Step S430 further includes step S431, using a time series analysis algorithm to perform trend analysis on the dynamic damage quantification information set to determine damage evolution trend data. Specifically, a time series analysis algorithm, such as ARIMA (autoregressive integrated moving average model), is used to predict the dynamic damage quantification information set to obtain damage evolution trend data, and the prediction results are analyzed to determine the development trend of damage and the possible range of change. Step S432, based on the gradient boosting decision tree and the damage evolution trend data, a regularity analysis is performed to draw a damage evolution curve. Specifically, the damage evolution trend data is fused with the input features of the gradient boosting decision tree to form a new training data set. The gradient boosting decision tree is trained using the fused data set so that it can learn the mapping relationship between damage evolution and features. By analyzing the decision path and node splitting of the gradient boosting decision tree, the law of damage evolution is extracted, and a damage evolution curve is drawn to intuitively display the change trend and law of damage over time. Step S433, performing regression calculation on the pressure swing adsorption device according to the damage evolution curve diagram combined with the multiple damage features to generate multiple data distribution information. Specifically, a feature with a strong correlation with the damage evolution trend is selected from the multiple damage features as an input for the regression calculation, and a regression model is constructed based on the input features and the damage evolution trend data. The regression model is used to predict the damage of the pressure swing adsorption device to generate multiple data distribution information, which reflects the distribution and change trend of the damage under different conditions.

[0039] Step S434, supervised training is performed on the dynamic damage quantification information set according to the multiple data distribution information combined with the multiple damage labels to generate the training result. Specifically, a label is assigned to each dynamic damage quantification information set according to the multiple damage labels and data distribution information. The dynamic damage quantification information set with labels is used as a training set for training the machine learning model. The machine learning model is trained using the training set so that it can learn the mapping relationship between features and labels. Step S435, based on the training result, the first training data, the second training data...the Nth training data are randomly extracted, and the first training data, the second training data...the Nth training data are all different training data. Specifically, the training set is divided into multiple subsets, each subset containing a certain amount of training data. A portion of the data is randomly extracted from each subset as a validation set or a test set to evaluate the performance of the model. Ensure that the extracted data is different to avoid data duplication and overfitting. Step S436, cross-iteration validation is performed on the first training data, the second training data...the Nth training data according to the error index until the training result converges, and the service life prediction model is constructed. Specifically, the mean square error is used to calculate the prediction error of the model on each training data. The training data is divided into multiple folds (such as 5 folds, 10 folds, etc.), and a different fold is used as the validation set each time, and the remaining folds are used as the training set for training. Train and validate each fold, and record the prediction error of each fold. Adjust the parameters and structure of the model according to the results of cross-validation to reduce the prediction error. Repeat cross-validation and iterative optimization until the prediction error converges. Use the final optimized model parameters and structure to build a service life prediction model.

[0040] Step S500, the operating status of the pressure swing adsorption device is evaluated in real time through the service life prediction model, a device operation prediction status information set is generated, and the intelligent safety status prediction of the pressure swing adsorption device is completed based on the device operation prediction status information set. Specifically, data is obtained from the pressure swing adsorption device in real time, and the data is input into the constructed service life prediction model. The service life prediction model performs calculations based on the input data, evaluates the current operating status of the pressure swing adsorption device, and predicts its future service life. After the model evaluation, a series of prediction information about the operating status of the pressure swing adsorption device is generated, including possible damage locations, damage levels, remaining life, etc. This information is integrated into a device operation prediction status information set. Based on the device operation prediction status information set, the pressure swing adsorption device is predicted to be intelligently safe, to determine whether it is in a normal, abnormal or dangerous state, and to give corresponding maintenance suggestions or warning information to ensure the safety of production operations and prevent possible failures or accidents. The embodiment of the present application collects operation data of the pressure swing adsorption device according to the target operating conditions to generate a dynamic damage quantification information set. Then, based on these information sets, a three-dimensional construction is performed to obtain a three-dimensional damage model to intuitively display the damage situation inside the device. Next, the three-dimensional damage model is evaluated to establish a multimodal non-destructive testing database containing multiple quantitative evaluation standards. Finally, the digital twin technology is used to process the three-dimensional damage model and the quantitative evaluation standards to obtain a digital twin module. Through this module, the dynamic damage quantification information set is classified and learned, and a service life prediction model is constructed. Technical means such as real-time evaluation of the operating status of the pressure swing adsorption device and intelligent safety status prediction are performed to achieve the technical effect of realizing real-time monitoring and accurate prediction of the operating status of the pressure swing adsorption device.

[0041] In the above, refer to Figure 1 The present invention describes in detail a method for predicting the safety state of a pressure swing adsorption device combined with a digital twin according to an embodiment of the present invention. Figure 2 A safety state prediction device for a pressure swing adsorption device combined with digital twin according to an embodiment of the present invention is described.

[0042] The safety state prediction device of a pressure swing adsorption device combined with a digital twin according to an embodiment of the present invention is used to solve the technical problems of insufficient real-time performance and accuracy in the safety state prediction of an existing pressure swing adsorption device, and achieve the technical effect of realizing real-time monitoring and accurate prediction of the operating state of the pressure swing adsorption device. The safety state prediction device of a pressure swing adsorption device combined with a digital twin includes: a device operation acquisition module 10, a damage assessment module 20, a digital twin processing module 30, a service life prediction model construction module 40, and a state prediction module 50.

[0043] The device operation acquisition module 10 is used to collect the operation of the pressure swing adsorption device according to the target operating conditions, generate a dynamic damage quantification information set, perform three-dimensional construction based on the dynamic damage quantification information set, and obtain a three-dimensional damage model; the damage evaluation module 20 is used to evaluate the pressure swing adsorption device based on the three-dimensional damage model, and establish a multimodal non-destructive testing database, and the multimodal non-destructive testing database includes multiple quantitative evaluation standards; the digital twin processing module 30 is used to use the digital twin technology to process according to the three-dimensional damage model in combination with the multiple quantitative evaluation standards to obtain a digital twin module; the service life prediction model construction module 40 is used to classify and learn the dynamic damage quantification information set through the digital twin module to construct a service life prediction model; the state prediction module 50 is used to perform real-time evaluation of the operating state of the pressure swing adsorption device through the service life prediction model, generate a device operation prediction state information set, and complete the intelligent safety state prediction of the pressure swing adsorption device based on the device operation prediction state information set.

[0044] The specific configuration of the device operation acquisition module 10 will be described in detail below. As described above, the operation acquisition of the pressure swing adsorption device is performed according to the target working condition to generate a dynamic damage quantification information set. The device operation acquisition module 10 may further include: a target working condition determination unit for traversing multiple operating conditions of the pressure swing adsorption device for analysis to determine the target working condition; a multi-dimensional damage acquisition unit for performing multi-dimensional damage acquisition on the pressure swing adsorption device based on the target working condition to obtain a multi-modal signal; a damage location unit for performing a comprehensive detection of the pressure swing adsorption device based on the multi-modal signal to obtain a device detection result, performing damage location according to the device detection result, and determining multiple damage locations; a data quantization unit for performing data quantification according to the multiple damage locations to obtain the dynamic damage quantification information set.

[0045] Among them, three-dimensional construction is performed based on the dynamic damage quantification information set to obtain a three-dimensional damage model, and the device operation acquisition module 10 may further include: a simulation verification unit is used to perform simulation verification based on the dynamic damage quantification information set in combination with the multiple damage positions to obtain a damage verification result; a stereo matching unit is used to perform stereo matching on the dynamic damage quantification information set according to the damage verification result to obtain multiple point cloud data; a damage evolution unit is used to perform damage evolution on the multiple point cloud data based on the multimodal signal to determine the damage impact transfer data; a multimodal signal fusion unit is used to adopt deep learning to fuse the multimodal signal according to the damage impact transfer data to generate signal fusion data; a point cloud data three-dimensional combination unit is used to perform three-dimensional combination of the multiple point cloud data according to the signal fusion data to construct the three-dimensional damage model.

[0046] The specific configuration of the damage assessment module 20 will be described in detail below. As described above, based on the three-dimensional damage model, the pressure swing adsorption device is evaluated and a multimodal nondestructive testing database is established. The damage assessment module 20 may further include: a traversal and tracing unit for traversing and tracing the pressure swing adsorption device using the three-dimensional damage model to generate damage source data; a damage impact quantification unit for quantifying damage impact according to the damage source data and setting the multiple quantitative evaluation criteria; a damage assessment unit for performing damage assessment in accordance with the multiple quantitative evaluation criteria combined with the three-dimensional damage model to obtain multiple damage scores; a damage feature acquisition unit for performing cluster analysis based on the damage source data to determine multiple damage types, performing feature analysis based on the multiple damage types combined with the three-dimensional damage model to obtain multiple damage features; a multimodal nondestructive testing database construction unit for entering the multimodal signals in descending order according to the multiple damage scores combined with the multiple damage features to construct the multimodal nondestructive testing database.

[0047] The specific configuration of the digital twin processing module 30 will be described in detail below. As described above, the digital twin technology is used to process the three-dimensional damage model in combination with the multiple quantitative evaluation criteria to obtain a digital twin module. The digital twin processing module 30 may further include: a digital twin model construction unit for matching the three-dimensional damage model using the digital twin technology to construct a digital twin model; a data driving unit for synchronizing the multimodal signal to the digital twin model for data driving according to the multiple quantitative evaluation criteria to obtain multimodal damage change data; a dynamic damage mapping unit for dynamically mapping damage to the three-dimensional damage model based on the multimodal damage change data to obtain dynamic damage visualization data; a multi-standard evaluation unit for performing multi-standard evaluation according to the dynamic damage visualization data according to the multiple quantitative evaluation criteria to obtain a multi-standard comprehensive score, evaluating the digital twin model according to the multi-standard comprehensive score, and adding the digital twin model to the digital twin module according to the evaluation result.

[0048] The specific configuration of the service life prediction model construction module 40 will be described in detail below. As described above, the dynamic damage quantification information set is classified and learned through the digital twin module to construct a service life prediction model. The service life prediction model construction module 40 may further include: a damage label determination unit is used to classify the dynamic damage quantification information set based on the multiple damage features and determine multiple damage labels; a gradient boosting decision tree construction unit is used to classify and learn according to the multiple damage labels combined with multiple damage features through the digital twin module to construct a gradient boosting decision tree; a service life prediction model acquisition unit is used to perform data training on the dynamic damage quantification information set based on the gradient boosting decision tree, cross-validate the training results, and obtain the service life prediction model.

[0049] Among them, based on the gradient boosting decision tree, the dynamic damage quantification information set is trained with data, the training results are cross-validated, and the service life prediction model is obtained. The service life prediction model acquisition unit may further include: a trend analysis subunit is used to use a time series analysis algorithm to perform trend analysis on the dynamic damage quantification information set to determine the damage evolution trend data; a rule analysis subunit is used to perform rule analysis based on the gradient boosting decision tree combined with the damage evolution trend data to draw a damage evolution curve; a regression calculation subunit is used to perform regression calculation on the pressure swing adsorption device according to the damage evolution curve combined with the multiple damage characteristics to generate multiple data distribution information; a supervised training subunit is used to perform supervised training on the dynamic damage quantification information set according to the multiple data distribution information combined with the multiple damage labels to generate the training result; a training data random extraction subunit is used to randomly extract the first training data, the second training data...the Nth training data based on the training result, and the first training data, the second training data...the Nth training data are all different training data; a cross-iteration validation subunit is used to perform cross-iteration validation on the first training data, the second training data...the Nth training data according to an error indicator until the training result converges, so as to construct the service life prediction model.

[0050] The safety state prediction device for a pressure swing adsorption device combined with a digital twin provided in an embodiment of the present invention can execute the safety state prediction method for a pressure swing adsorption device combined with a digital twin provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0051] Although the present application makes various references to certain modules in the device according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0052] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A safety status prediction method for a pressure swing adsorption device combined with digital twins, characterized in that: The method comprises: Performing operation collection of the pressure swing adsorption device according to the target operating conditions to generate a dynamic damage quantification information set, and performing three-dimensional construction based on the dynamic damage quantification information set to obtain a three-dimensional damage model; The pressure swing adsorption device is evaluated based on the three-dimensional damage model, and a multi-modal non-destructive testing database is established, wherein the multi-modal non-destructive testing database includes multiple quantitative evaluation criteria; Using digital twin technology, processing is performed according to the three-dimensional damage model in combination with the multiple quantitative evaluation criteria to obtain a digital twin module; The dynamic damage quantification information set is subjected to classification learning training through the digital twin module to construct a service life prediction model; The service life prediction model is used to evaluate the operating status of the pressure swing adsorption device in real time, generate a device operation prediction status information set, and complete the intelligent safety status prediction of the pressure swing adsorption device based on the device operation prediction status information set.

2. The method for predicting the safety status of a pressure swing adsorption device combined with digital twin according to claim 1, characterized in that: The pressure swing adsorption device is operated and collected according to the target operating conditions to generate a dynamic damage quantification information set, and the method includes: Traversing multiple operating conditions of the pressure swing adsorption device for analysis to determine the target operating condition; Based on the target working condition, multi-dimensional damage acquisition is performed on the pressure swing adsorption device to obtain a multi-modal signal; Performing a comprehensive inspection of the pressure swing adsorption device based on the multimodal signal to obtain a device inspection result, locating damage according to the device inspection result, and determining multiple damage locations; Data quantification is performed according to the multiple damage positions to obtain the dynamic damage quantification information set.

3. The method for predicting the safety status of a pressure swing adsorption device combined with digital twin according to claim 2, characterized in that: Based on the dynamic damage quantification information set, a three-dimensional damage model is obtained by performing three-dimensional construction, and the method includes: Performing simulation verification based on the dynamic damage quantification information set and the multiple damage locations to obtain a damage verification result; Performing stereo matching on the dynamic damage quantification information set according to the damage verification result to obtain a plurality of point cloud data; Performing damage evolution on the plurality of point cloud data based on the multimodal signal to determine damage impact transfer data; Using deep learning, the multimodal signals are fused according to the damage impact transfer data to generate signal fusion data; The plurality of point cloud data are three-dimensionally combined according to the signal fusion data to construct the three-dimensional damage model.

4. The method for predicting the safety status of a pressure swing adsorption device combined with digital twin according to claim 2, characterized in that: The pressure swing adsorption device is evaluated based on the three-dimensional damage model, and a multi-modal non-destructive testing database is established. The method includes: Using the three-dimensional damage model to traverse and trace the source of the pressure swing adsorption device to generate damage source data; quantifying the damage impact according to the damage source data, and setting the multiple quantitative evaluation criteria; Perform damage evaluation according to the multiple quantitative evaluation criteria combined with the three-dimensional damage model to obtain multiple damage scores; Performing cluster analysis based on the damage source data to determine multiple damage types, and performing feature analysis based on the multiple damage types combined with the damage three-dimensional model to obtain multiple damage features; The multimodal signals are entered in descending order according to the multiple damage scores combined with the multiple damage features to construct the multimodal nondestructive testing database.

5. The method for predicting the safety status of a pressure swing adsorption device combined with digital twin according to claim 2, characterized in that: Using digital twin technology, processing is performed according to the three-dimensional damage model in combination with the multiple quantitative evaluation criteria to obtain a digital twin module, the method comprising: The digital twin technology is used in combination with the three-dimensional damage model for matching to construct a digital twin model; Synchronizing the multimodal signal to the digital twin model for data driving according to the multiple quantitative evaluation criteria to obtain multimodal damage change data; Based on the multimodal damage change data, dynamic damage mapping is performed to the damage three-dimensional model to obtain dynamic damage visualization data; A multi-standard evaluation is performed according to the dynamic damage visualization data in accordance with the multiple quantitative evaluation criteria to obtain a multi-standard comprehensive score, the digital twin model is evaluated according to the multi-standard comprehensive score, and the digital twin model is added to the digital twin module according to the evaluation result.

6. The method for predicting the safety status of a pressure swing adsorption device combined with digital twin according to claim 4, characterized in that: The dynamic damage quantification information set is subjected to classification learning training through the digital twin module to construct a service life prediction model, and the method includes: Classifying the dynamic damage quantification information set based on the multiple damage features to determine multiple damage labels; The digital twin module performs classification learning according to the multiple damage labels combined with multiple damage features to construct a gradient boosting decision tree; The dynamic damage quantification information set is trained with data based on the gradient boosting decision tree, and the training results are cross-validated to obtain the service life prediction model.

7. The method for predicting the safety status of a pressure swing adsorption device combined with digital twin according to claim 6, characterized in that: Based on the gradient boosting decision tree, data training is performed on the dynamic damage quantification information set, and the training results are cross-validated to obtain the service life prediction model. The method includes: Using a time series analysis algorithm to perform trend analysis on the dynamic damage quantification information set to determine damage evolution trend data; Based on the gradient boosting decision tree and the damage evolution trend data, a regularity analysis is performed to draw a damage evolution curve graph; Perform regression calculation on the pressure swing adsorption device according to the damage evolution curve diagram and the multiple damage characteristics to generate multiple data distribution information; Performing supervised training on the dynamic damage quantification information set according to the multiple data distribution information combined with the multiple damage labels to generate the training result; Randomly extracting first training data, second training data, ... Nth training data based on the training result, wherein the first training data, the second training data, ... the Nth training data are all different training data; The first training data, the second training data, ... the Nth training data are cross-iteratively validated according to an error index until the training results converge, thereby constructing the service life prediction model.

8. A safety status prediction device for a pressure swing adsorption device combined with digital twins, characterized in that: The device is used to implement the safety state prediction method of a pressure swing adsorption device combined with digital twin according to any one of claims 1 to 7, and the device includes: A device operation acquisition module, wherein the device operation acquisition module is used to perform operation acquisition of the pressure swing adsorption device according to the target working condition, generate a dynamic damage quantification information set, perform three-dimensional construction based on the dynamic damage quantification information set, and obtain a three-dimensional damage model; A damage assessment module, the damage assessment module is used to evaluate the pressure swing adsorption device based on the three-dimensional damage model and establish a multi-modal non-destructive testing database, the multi-modal non-destructive testing database includes multiple quantitative evaluation criteria; A digital twin processing module, wherein the digital twin processing module is used to utilize the digital twin technology to process the three-dimensional damage model in combination with the multiple quantitative evaluation criteria to obtain a digital twin module; A service life prediction model construction module, wherein the service life prediction model construction module is used to perform classification learning training on the dynamic damage quantization information set through the digital twin module to construct a service life prediction model; A state prediction module is used to evaluate the operating state of the pressure swing adsorption device in real time through the service life prediction model, generate a device operation prediction state information set, and complete the intelligent safety state prediction of the pressure swing adsorption device based on the device operation prediction state information set.

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