Autoclave temperature offline simulation and online prediction method and system based on Hybrid KAN feature fusion

Abstract

The invention discloses an autoclave temperature offline simulation and online prediction method based on Hybrid KAN feature fusion. The autoclave temperature offline simulation and online prediction method comprises the steps that historical temperature time sequence data and static process parameter data of different batches are collected; performing feature screening and preprocessing, and constructing a time sequence data input sequence and a static process parameter input sequence; designing a Hybrid KAN neural network model, taking the time sequence data input sequence and the static process parameter input sequence as input, and outputting a predicted value of the temperature of the autoclave at the next moment through feature extraction and fusion; taking minimization of a loss function as a target, training the model, and obtaining an optimal model by using a structured pruning strategy; and deploying an optimal model, and realizing off-line simulation and on-line prediction of the temperature of the autoclave through rolling time window iteration multi-step prediction. The method is high in accuracy, wide in universality, small in model parameter scale, high in reasoning efficiency and suitable for autoclave temperature prediction in complex scenes.

Description

Technical Field

[0001] This invention relates to the field of time series data prediction technology, specifically to a method and system for offline simulation and online prediction of autoclave temperature based on HybridKAN feature fusion. Background Technology

[0002] Temperature simulation and prediction in composite curing processes are crucial for ensuring product quality, and the accuracy of temperature prediction directly impacts product yield and production capacity. While traditional CAE-based temperature simulation offers high prediction accuracy and can simulate the entire temperature field distribution, it suffers from challenges such as difficult multi-field coupling modeling, high computational load, and low simulation efficiency. Furthermore, because it involves offline modeling and simulation, it cannot integrate measured data to improve prediction accuracy.

[0003] Data-driven methods such as ARIMA and LSTM algorithms perform autoregressive modeling of thermocouple temperature time-series data, achieving relatively high prediction accuracy through iterative prediction. However, due to the lack of integration of static pre-set process parameters (such as tooling dimensions and thermocouple positions), their generalization ability across multiple parts is insufficient. Data-driven methods such as random forests construct multiple decision trees for ensemble learning, demonstrating some ability to process static features. However, they are inferior to LSTM in capturing dynamic changes in temperature sequences and have lower efficiency in processing high-dimensional data. Faced with the large amounts of static process parameters and temperature data from autoclaves, both computational efficiency and prediction accuracy need improvement. Static process parameters reflect the inherent properties of the process system (such as tooling dimensions), while dynamic time-series data reflects the dynamic evolution of temperature (such as heating rate). Simple fusion of these two types of features is prone to information loss, leading to prediction bias. Therefore, there is an urgent need for a method for autoclave temperature prediction that can handle complex prediction scenarios where static process parameters and time-series data are highly coupled. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention proposes a method and system for offline simulation and online prediction of autoclave temperature based on HybridKAN feature fusion. Based on the HybridKAN neural network architecture, it deeply mines the coupling characteristics of static process parameters and time series data, aiming to improve the accuracy of autoclave temperature prediction and its generalization across multiple parts. Furthermore, relying on the characteristics of HybridKAN model parameters being small in scale and having high inference efficiency, it enables the deployment of offline temperature simulation and real-time prediction on resource-constrained equipment.

[0005] To achieve the above technical objectives, this invention provides the following technical solution: a method for offline simulation and online prediction of autoclave temperature based on HybridKAN feature fusion, which specifically includes: The curing and molding data of different batches of parts are collected from the historical database of autoclaves. The curing and molding data of the parts includes historical temperature time series data and static process parameter data. Feature filtering and preprocessing are performed on the solidification and molding data of the parts. Then, the time-series data input sequence and the static process parameter input sequence are constructed respectively using the historical temperature time-series data and static process parameter data after feature filtering and preprocessing. A HybridKAN neural network model is designed, using time-series data input sequences and static process parameter input sequences as model inputs, to extract dynamic time-series features and static process features respectively; the two types of features are fused and connected with the current autoclave temperature jump to obtain the output features of the HybridKAN neural network model; the output features are used to predict the autoclave temperature at the next time step; The training hyperparameters of the HybridKAN neural network model are set to minimize the loss function. The HybridKAN neural network model is trained and a structured pruning strategy is used to lightweight the model. The optimal HybridKAN neural network model is obtained after training and pruning. Deploy the optimal HybridKAN neural network model and use rolling time windows for iterative multi-step prediction to achieve offline simulation and online prediction of autoclave temperature.

[0006] Furthermore, the historical temperature time-series data and static process parameter data specifically include: The historical temperature time series data includes: each thermocouple ID, historical time index, and the temperature value of each thermocouple at each historical time. The static process parameter data includes: tooling heat capacity, part heat capacity, tooling length / width / height, part length / width / height, distance of thermocouple from tooling front end, distance of tooling from symmetry plane, distance of tooling from tank door, distance of tooling from tank wall, and part heat exchange area.

[0007] Furthermore, the step of performing feature filtering and preprocessing on the part curing data, and then constructing the time-series data input sequence and the static process parameter input sequence using the historical temperature time-series data and static process parameter data after feature filtering and preprocessing, specifically includes: First, the static process parameter data are initially screened through the heat conduction mechanism. Then, based on the heat transfer principle in the autoclave, parameters that are strongly correlated with heat distribution are retained first, while secondary parameters are eliminated. Then, the static process parameter data is finally filtered using a data-driven method to construct a random forest model containing all static features. Based on the parameter importance ranking results, the top N static process parameters are selected to obtain an N-dimensional static process parameter input sequence. Data cleaning and anomaly handling were carried out. Outliers were removed from different batches of historical temperature time series data using the 3σ principle, and then linear interpolation was used to fill in the missing values. The historical temperature time series data, after data cleaning and anomaly processing, is processed by a sliding window of size L. The temperature values ​​of the L historical moments and the index i of the last historical moment are combined into a sequence to obtain an L+1 dimensional time series data input sequence. The negative historical temperature data before the initial zero moment of the sequence are filled with the initial ambient temperature of the autoclave. The static process parameter input sequence and timing data input sequence are used as inputs for subsequent networks.

[0008] Furthermore, the HybridKAN neural network model is built based on the KAN network and includes a time-series data processing module, a static process parameter processing module, a feature fusion module, and a linear output layer, wherein: The time-series data processing module and the static process parameter processing module perform feature extraction in parallel. The time-series data processing module takes the time-series data input sequence as input, passes it through a three-layer KAN network, and outputs dynamic time-series features. The static process parameter processing module takes the static process parameter input sequence as input, passes it through a three-layer KAN network, and outputs static process features. The dynamic time-series features and the static process features have the same feature dimension. Then, the dynamic time-series features and static process features are first spliced ​​and fused in the feature fusion module to obtain fused features, which are then input into a four-layer KAN network; the dynamic time-series features and static process features are deeply coupled to obtain deeply coupled features; The deeply coupled features output by the feature fusion module are connected to the time series data input sequence through a residual network to obtain the current temperature value at the next time step. The result is then passed through a linear output layer to obtain the predicted temperature value at the next time step.

[0009] Furthermore, the loss function adopts the mean squared error loss (MSE).

[0010] Furthermore, the lightweighting of the model using a structured pruning strategy specifically involves: Neuron importance is determined by ranking based on the absolute value of weights, and low-importance neuron connections are removed according to a preset pruning rate. The pruned network is then fine-tuned and trained, and a learning rate decay strategy is used to restore model accuracy.

[0011] The deployment of the optimal HybridKAN neural network model, through rolling time window iterative multi-step prediction, achieves offline simulation and online prediction of autoclave temperature specifically as follows: The optimal HybridKAN neural network model is deployed to a computing device that interfaces with a database for offline simulation and online prediction. The offline simulation is as follows: In the absence of complete measured temperature data, the static process parameters and tank temperature are used as the initial inputs of the optimal HybridKAN neural network model. The tank temperature at each moment is recursively calculated to simulate the temperature evolution of the curing and molding process of the part under the current process scheme. The evolution results are presented in the form of temperature curves. The online prediction is as follows: when the computing device can obtain the measured temperature data collected by thermocouples from the database, the historical temperature time series data from the current moment backward, the static process parameter data, and the current moment index are used as the input of the optimal HybridKAN neural network model. The model outputs the temperature prediction value for the next moment, and generates a full-process temperature process curve that evolves over time by iterating through multiple steps in a rolling time window. This curve is used to guide the adjustment of the heating rate and holding time of the autoclave.

[0012] This invention also discloses an application system for the above-mentioned offline simulation and online prediction method for autoclave temperature based on HybridKAN feature fusion, which specifically includes: The data acquisition unit is used to solidify molding data of different batches of parts; The data processing unit is used to perform feature filtering and preprocessing on the solidified molding data of the parts; The model building unit is used to build the HybridKAN neural network model. The training optimization unit is used to train and prune the HybridKAN neural network model. The prediction application unit is used to deploy models and perform offline simulations and online predictions.

[0013] Furthermore, the present invention also proposes an electronic device comprising a memory and a processor, wherein: Memory is used to store computer programs that can run on a processor; The processor is configured to execute, while running the computer program, a method for offline simulation and online prediction of autoclave temperature based on HybridKAN feature fusion as described above.

[0014] A computer-readable storage medium is also disclosed, which stores computer instructions for causing a processor to execute an offline simulation and online prediction method for autoclave temperature based on HybridKAN feature fusion as described above.

[0015] Based on the above technical solution, the present invention has at least the following beneficial effects: The method proposed in this invention achieves high accuracy in predicting autoclave temperature while having a small number of model parameters, high inference efficiency, and good adaptability and generalization on various composite material components. The HybridKAN neural network architecture and input data format are compatible with both offline and online modes, supporting both offline temperature simulation and online prediction, facilitating flexible deployment and switching in practical engineering systems. Offline simulation can predict the heating curves of specific process parameters in advance before production scheduling, providing support for process and scheduling optimization; online prediction can guide the adjustment of parameters such as the heating rate and holding time of the autoclave, avoiding the impact of uneven temperature distribution on product quality; and it enables autoclave temperature prediction in complex prediction scenarios where static process parameters and time-series data are highly coupled. Attached Figure Description

[0016] Figure 1 This is a flowchart of an offline simulation and online prediction method for autoclave temperature based on HybridKAN feature fusion proposed in this invention; Figure 2 This is a framework diagram of the HybridKAN network autoclave temperature prediction method of the present invention. Figure 3 This is a diagram of the HybridKAN network architecture of the present invention; Figure 4 This is a graph showing the loss function during the training process of the HybridKAN network in this invention. Figure 5 A comparison curve of offline simulation and actual temperature measurement of a batch of parts in an autoclave; Figure 6 This is a graph showing the online temperature prediction for the autoclave. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the following description is provided in conjunction with the accompanying drawings. Figure 1-6 The present invention will be further described in detail with reference to the embodiments. This will allow for a full understanding of how the present application uses technical means to solve technical problems and achieve technical effects, and to facilitate its implementation.

[0018] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0019] To improve the accuracy of temperature prediction in composite material manufacturing and its generalizability across multiple parts, in this embodiment, such as Figure 1As shown, this invention proposes an offline simulation and online prediction method for autoclave temperature based on HybridKAN feature fusion, which specifically includes the following steps: The curing and molding data of parts from different batches (at least 30 batches in this embodiment) are collected from the autoclave historical database. The curing and molding data of parts includes historical temperature time series data and static process parameter data. In a preferred embodiment, the historical temperature time series data and static process parameter data specifically include: The historical temperature time series data includes: each thermocouple ID, historical time index, and the temperature value of each thermocouple at each historical time. The static process parameter data includes more than twenty items, including: tooling heat capacity, part heat capacity, tooling length / width / height, part length / width / height, distance of thermocouple from tooling front end, distance of tooling from symmetry plane, distance of tooling from tank door, distance of tooling from tank wall, and part heat exchange area.

[0020] In this embodiment, historical temperature time series data and static process parameters are collected for each thermocouple in the autoclave. Subsequently, the HybridKAN neural network model will be used to predict the temperature value of each thermocouple, and the temperature of the entire autoclave will be reflected based on the local temperature reflected by the temperature value of each thermocouple.

[0021] Feature filtering and preprocessing are performed on the solidification and molding data of the parts. Then, the time-series data input sequence and the static process parameter input sequence are constructed respectively using the historical temperature time-series data and static process parameter data after feature filtering and preprocessing. In a preferred embodiment, the step of performing feature filtering and preprocessing on the curing data of the parts, and then constructing a time-series data input sequence and a static process parameter input sequence using the historical temperature time-series data and static process parameter data after feature filtering and preprocessing, specifically includes: First, the static process parameter data are initially screened through the heat conduction mechanism. Then, based on the heat transfer principle in the autoclave, parameters that are strongly correlated with heat distribution are retained first, while secondary parameters are eliminated. Then, the static process parameter data is finally filtered using a data-driven method to construct a random forest model containing all static features. Based on the parameter importance ranking results, the top N static process parameters are selected to obtain an N-dimensional static process parameter input sequence. In this embodiment, six static process parameters were ultimately selected: the distance between the thermocouple and the front end of the tooling, the width of the tooling, the distance between the tooling and the plane of symmetry, the length of the tooling, the distance between the thermocouple and the door of the autoclave, and the distance between the thermocouple and the wall of the autoclave, to obtain a six-dimensional static process parameter input sequence. , is represented as: ; in, This indicates the six selected static process parameters; Data cleaning and anomaly handling were carried out. Outliers were removed from different batches of historical temperature time series data using the 3σ principle, and then linear interpolation was used to fill in the missing values. The historical temperature time series data, after data cleaning and anomaly processing, is processed by a sliding window of size L. The temperature values ​​of the L historical moments and the index i of the last historical moment (i.e. the current moment) are combined into a sequence to obtain an L+1 dimensional time series data input sequence. The negative historical temperature data before the initial zero moment of the sequence are filled with the initial ambient temperature of the autoclave. In this embodiment, temperature values ​​from 10 historical moments and the index of the current moment i are selected to obtain an 11-dimensional time-series data input sequence. , represented as ; in, The temperature values ​​are from 10 historical moments (the initial zero moment in this sequence is...). time, The time before a given time is considered a negative time. Static process parameter input sequence and time series data input sequence Used as input for subsequent networks.

[0022] Design a HybridKAN neural network model, such as Figure 2 As shown, using time-series data input sequences and static process parameter input sequences as model inputs, dynamic time-series features and static process features are extracted respectively; the two types of features are fused and connected with the current autoclave temperature jump to obtain the output features of the HybridKAN neural network model; the output features are used to predict the autoclave temperature at the next moment. In a preferred embodiment, the HybridKAN neural network model is constructed based on the KAN network, which is based on the Kolmogorov-Arnold network. For a multivariate input sequence... The KAN network represents it as the following multivariate function: ; in, It is a single-variable function. and The parameters of a univariate function For the number of univariate functions, For single-variable function indexing, Given the length of the input sequence, The index is used as the element index in the sequence; in this embodiment, the KAN network takes the sequence as input from static process parameters. and time series data input sequence In this process, the complex nonlinear relationships between tooling dimensions, distances, and other process parameters are captured.

[0023] The HybridKAN neural network model built based on the KAN network is as follows: Figure 3 As shown, it includes a time-series data processing module, a static process parameter processing module, a feature fusion module, and a linear output layer, wherein: The time-series data processing module and the static process parameter processing module perform feature extraction in parallel. The time-series data processing module takes an 11-dimensional time-series data input sequence as input, passes it through a three-layer KAN network, and outputs 16-dimensional dynamic time-series features, capturing nonlinear relationships such as time dependence and trend changes in temperature time-series data. The static process parameter processing module takes a 6-dimensional static process parameter input sequence as input, and also passes through a three-layer KAN network to output a 16-dimensional static process feature (although the time series data input sequence and the static process parameter input sequence have different dimensions, they can be upgraded by setting different univariate functions in each KAN layer, and the last layer uses a univariate function of the same dimension to reduce the dimension to 16). This captures the complex nonlinear spatial relationship between process parameters (such as the relationship between tooling size and heat distribution). It should also be noted that in the three-layer KAN network of the time series data processing module and the static process parameter processing module of this application, the first and second layers of KAN are used to process the original input features and extract basic local features (such as size-position correlation in static parameters and adjacent temperature dependence in time series data). The second and third layers of KAN are responsible for combining and transforming the features of the current layer, capturing more complex correlations and generating the final abstract features. Finally, the third layer of KAN integrates the local / middle-layer features of the first two layers into high-dimensional, globally representative features, providing input for the subsequent fusion module.

[0024] Dynamic time-series features and static process features are first concatenated and fused in the feature fusion module to obtain 32-dimensional fused features. The fused features are then input into a four-layer KAN network (the input dimension of each KAN layer is determined by the output of the previous layer; overall, the dimensionality is first increased and then reduced to fit the feature dimension of the linear output layer), deeply coupling the dynamic time-series features and static process features to obtain deeply coupled features. These deeply coupled features better reflect the combined effect of process parameters and time-series temperature on autoclave temperature prediction. Additionally, it should be noted that in the four-layer KAN network of the feature fusion module: the first and second layers of KAN are used to capture the interaction between static process parameter features and dynamic time series features; the second and third layers of KAN are used to generate higher-order comprehensive features to abstractly describe the global correlation; and finally, the fourth layer of KAN outputs the feature dimension adapted to the linear output layer to prepare for the final temperature prediction.

[0025] The deeply coupled features output by the feature fusion module are connected to the time series data input sequence through a residual network to obtain the current temperature value at the next time step. The result is then passed through a linear output layer to obtain the predicted temperature value at the next time step.

[0026] The training hyperparameters of the HybridKAN neural network model are set to minimize the loss function. In this embodiment, the loss function is the mean squared error loss (MSE), which is expressed as follows: ; Where n is the number of samples, , These are the actual value and the network's predicted value, respectively. The optimizer was chosen as Adam (with a learning rate of 0.001 and a decay rate of 0.9). During training, early stopping was used to prevent overfitting, ensuring the model's MSE value on the input dataset was close to 0, thus completing the initial training. The specific loss function curve during training is shown below. Figure 4 As shown; After initial training, a structured pruning strategy is used to lightweight the model. As a preferred implementation, in this embodiment, the lightweighting of the model using the structured pruning strategy specifically involves: Neuron importance is determined by ranking based on the absolute value of weights, and low-importance neuron connections are removed according to a preset pruning rate. The pruned network is then fine-tuned and trained, and a learning rate decay strategy is used to restore model accuracy.

[0027] The optimal HybridKAN neural network model was obtained through training and pruning. Compared with the initial model, the optimal model obtained in this embodiment has 35% fewer parameters, an inference speed that is about 1.2 times faster, and a cross-tank batch test accuracy of over 95%.

[0028] Deploy the optimal HybridKAN neural network model and use rolling time windows for iterative multi-step prediction to achieve offline simulation and online prediction of autoclave temperature; In a preferred embodiment, the deployment of the optimal HybridKAN neural network model, through iterative multi-step prediction via a rolling time window, to achieve offline simulation and online prediction of the autoclave temperature specifically involves: The optimal HybridKAN neural network model is deployed to a computing device that interfaces with a database for offline simulation and online prediction. The offline simulation involves using static process parameters and tank temperature as initial inputs to the optimal HybridKAN neural network model in the absence of complete measured temperature data. The tank temperature is then recursively calculated at each moment to simulate the temperature evolution of the part during the curing process under the current process scheme. The evolution results are presented as temperature curves, such as... Figure 5 As shown in the figure, the offline simulation and actual measurement curves of a batch of parts are compared. It can be seen that the offline simulation and actual measurement results of this method are extremely consistent. The online prediction is as follows: When the computing device can obtain measured temperature data collected by thermocouples from the database, historical temperature time-series data from the current moment backwards, static process parameter data, and the current moment index are used as inputs to the optimal HybridKAN neural network model. The model outputs the predicted temperature value for the next moment, and through iterative multi-step prediction via a rolling time window, a full-process temperature process curve evolving over time is generated. The online predicted temperature curve is shown below. Figure 6 As shown, the online prediction results can guide the adjustment of parameters such as the heating rate and holding time of the autoclave.

[0029] This concludes the description of the method proposed in this invention. This embodiment also describes the application system of the above method, which specifically includes: The data acquisition unit is used to solidify molding data of different batches of parts; The data processing unit is used to perform feature filtering and preprocessing on the solidified molding data of the parts; The model building unit is used to build the HybridKAN neural network model. The training optimization unit is used to train and prune the HybridKAN neural network model. The prediction application unit is used to deploy models and perform offline simulations and online predictions.

[0030] In addition, an electronic device is disclosed, which includes a memory and a processor, wherein: Memory is used to store computer programs that can run on a processor; The processor is configured to execute, while running the computer program, a method for offline simulation and online prediction of autoclave temperature based on HybridKAN feature fusion as described above.

[0031] A computer-readable storage medium is also disclosed, which stores computer instructions for causing a processor to execute an offline simulation and online prediction method for autoclave temperature based on HybridKAN feature fusion as described above.

[0032] In summary, the method proposed in this invention achieves high accuracy in predicting autoclave temperature while having a small number of model parameters, high inference efficiency, and good adaptability and generalization on various composite material components; it realizes autoclave temperature prediction in complex prediction scenarios where static process parameters and time-series data are highly coupled.

[0033] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0034] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for offline simulation and online prediction of autoclave temperature based on HybridKAN feature fusion, characterized in that, Specifically, the following steps are included: The curing and molding data of different batches of parts are collected from the historical database of autoclaves. The curing and molding data of the parts includes historical temperature time series data and static process parameter data. Feature filtering and preprocessing are performed on the solidification and molding data of the parts. Then, the time-series data input sequence and the static process parameter input sequence are constructed respectively using the historical temperature time-series data and static process parameter data after feature filtering and preprocessing. A HybridKAN neural network model is designed, using time-series data input sequences and static process parameter input sequences as model inputs, to extract dynamic time-series features and static process features respectively; the two types of features are fused and connected with the current autoclave temperature jump to obtain the output features of the HybridKAN neural network model; the output features are used to predict the autoclave temperature at the next time step; The training hyperparameters of the HybridKAN neural network model are set to minimize the loss function. The HybridKAN neural network model is trained and a structured pruning strategy is used to lightweight the model. The optimal HybridKAN neural network model is obtained after training and pruning. Deploy the optimal HybridKAN neural network model and use rolling time windows for iterative multi-step prediction to achieve offline simulation and online prediction of autoclave temperature.

2. The method for offline simulation and online prediction of autoclave temperature based on HybridKAN feature fusion as described in claim 1, characterized in that, The historical temperature time-series data and static process parameter data specifically include: The historical temperature time series data includes: each thermocouple ID, historical time index, and the temperature value of each thermocouple at each historical time. The static process parameter data includes: tooling heat capacity, part heat capacity, tooling length / width / height, part length / width / height, distance of thermocouple from tooling front end, distance of tooling from symmetry plane, distance of tooling from tank door, distance of tooling from tank wall, and part heat exchange area.

3. The method for offline simulation and online prediction of autoclave temperature based on HybridKAN feature fusion according to claim 1, characterized in that, The process of feature filtering and preprocessing the solidification and molding data of the parts, and then constructing the time-series data input sequence and the static process parameter input sequence using the historical temperature time-series data and static process parameter data after feature filtering and preprocessing, specifically includes: First, the static process parameter data are initially screened through the heat conduction mechanism. Then, based on the heat transfer principle in the autoclave, parameters that are strongly related to heat distribution are retained first, while secondary parameters are eliminated. Then, the static process parameter data is finally filtered using a data-driven method to construct a random forest model containing all static features. Based on the parameter importance ranking results, the top N static process parameters are selected to obtain an N-dimensional static process parameter input sequence. Data cleaning and anomaly handling were carried out. Outliers were removed from different batches of historical temperature time series data using the 3σ principle, and then linear interpolation was used to fill in the missing values. The historical temperature time series data, after data cleaning and anomaly processing, is processed by a sliding window of size L. The temperature values ​​of the L historical moments and the index i of the last historical moment are combined into a sequence to obtain an L+1 dimensional time series data input sequence. The negative historical temperature data before the initial zero moment of the sequence are filled with the initial ambient temperature of the autoclave. The static process parameter input sequence and timing data input sequence are used as inputs for subsequent networks.

4. The method for offline simulation and online prediction of autoclave temperature based on HybridKAN feature fusion as described in claim 1, characterized in that, The HybridKAN neural network model is built based on the KAN network and includes a time-series data processing module, a static process parameter processing module, a feature fusion module, and a linear output layer, wherein: The time-series data processing module and the static process parameter processing module perform feature extraction in parallel. The time-series data processing module takes the time-series data input sequence as input, passes it through a three-layer KAN network, and outputs dynamic time-series features. The static process parameter processing module takes the static process parameter input sequence as input, passes it through a three-layer KAN network, and outputs static process features. The dynamic time-series features and the static process features have the same feature dimension. Then, the dynamic time-series features and static process features are first spliced ​​and fused in the feature fusion module to obtain fused features. The fused features are then input into a four-layer KAN network to deeply couple the dynamic time-series features and static process features to obtain deeply coupled features. The deeply coupled features output by the feature fusion module are connected to the time series data input sequence through a residual network to obtain the current temperature value at the next time step. The result is then passed through a linear output layer to obtain the predicted temperature value at the next time step.

5. The method for offline simulation and online prediction of autoclave temperature based on HybridKAN feature fusion according to claim 1, characterized in that, The loss function used is the mean squared error loss (MSE).

6. The method for offline simulation and online prediction of autoclave temperature based on HybridKAN feature fusion according to claim 1, characterized in that, The specific steps of using a structured pruning strategy to lightweight the model are as follows: Neuron importance is determined by ranking based on the absolute value of weights, and low-importance neuron connections are removed according to a preset pruning rate. The pruned network is then fine-tuned and trained, and a learning rate decay strategy is used to restore model accuracy.

7. The method for offline simulation and online prediction of autoclave temperature based on HybridKAN feature fusion according to claim 1, characterized in that, The deployment of the optimal HybridKAN neural network model, through rolling time window iterative multi-step prediction, achieves offline simulation and online prediction of autoclave temperature specifically as follows: The optimal HybridKAN neural network model is deployed to a computing device that interfaces with a database for offline simulation and online prediction. The offline simulation is as follows: In the absence of complete measured temperature data, the static process parameters and tank temperature are used as the initial inputs of the optimal HybridKAN neural network model. The tank temperature at each moment is recursively calculated to simulate the temperature evolution of the curing and molding process of the part under the current process scheme. The evolution results are presented in the form of temperature curves. The online prediction is as follows: when the computing device can obtain the measured temperature data collected by thermocouples from the database, the historical temperature time series data from the current moment backward, the static process parameter data, and the current moment index are used as the input of the optimal HybridKAN neural network model. The model outputs the temperature prediction value for the next moment, and generates a full-process temperature process curve that evolves over time by iterating through multiple steps in a rolling time window. This curve is used to guide the adjustment of the heating rate and holding time of the autoclave.

8. An application system for an offline simulation and online prediction method of autoclave temperature based on HybridKAN feature fusion according to any one of claims 1-7, characterized in that, Specifically, it includes: The data acquisition unit is used to acquire curing data of different batches of parts; The data processing unit is used to perform feature filtering and preprocessing on the solidified molding data of the parts; The model building unit is used to build the HybridKAN neural network model. The training optimization unit is used to train and prune the HybridKAN neural network model. The prediction application unit is used to deploy models and perform offline simulations and online predictions.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, wherein: Memory is used to store computer programs that can run on a processor; A processor is configured to, while running the computer program, execute the offline simulation and online prediction method for autoclave temperature based on HybridKAN feature fusion as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the offline simulation and online prediction method for autoclave temperature based on HybridKAN feature fusion as described in any one of claims 1-7.

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