Product forging data intelligent management system and method based on 5G cloud computing

Abstract

The invention discloses a product forging data intelligent management system and method based on 5G cloud computing, and relates to the field of intelligent manufacturing. According to the system, multi-source data such as temperature, stress, load and images are collected through a process data module and weighted labeling is carried out; the multi-model reasoning module selects support vector regression, a random forest and a convolutional neural network for parallel prediction according to data features; the prediction fusion module generates a unified prediction result by adopting a nonlinear function based on confidence score; the cloud-edge collaborative scheduling module determines cloud or edge deployment in combination with task complexity, edge computing power and a network state; the parameter optimization module adopts an EPQ-Opt algorithm to optimize process parameters based on energy consumption prediction and residual score; and the security guarantee module adjusts the strategy through consistency detection and robustness evaluation and guarantees data security. The method significantly improves the prediction precision and response speed, reduces the energy consumption, enhances the system safety, and is suitable for the field of high-end forging and intelligent manufacturing.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing technology, and in particular to a product forging data intelligent management system and method based on 5G cloud computing. Background Art

[0002] Currently, traditional control systems for forging processes rely primarily on a single data source to adjust process parameters, lacking the comprehensive collection and effective integration of multi-source data. Existing systems have limited capabilities for collecting, processing, and analyzing process data, typically only providing simple parameter estimates or feedback. This results in insufficient process control accuracy, making it difficult for the system to quickly respond and adjust under complex production conditions, and unable to maintain production efficiency while meeting high precision requirements.

[0003] Furthermore, existing forging process systems generally lack intelligent analysis and optimization methods. Many rely on single predictive models or empirical rules for reasoning and optimization, failing to comprehensively consider the characteristics of diverse data types and lacking effective model fusion mechanisms. Consequently, the accuracy of prediction results and the system's adaptability are weak, failing to effectively improve the accuracy and efficiency of process optimization. Furthermore, traditional systems also have problems with the scheduling of computing resources. They are unable to dynamically optimize based on the complexity of production tasks and resource status, resulting in wasted computing resources or delayed responses.

[0004] Therefore, there is an urgent need for an intelligent management system that can overcome the shortcomings of existing technologies, provide comprehensive data collection, intelligent reasoning and multi-model fusion, and improve the precise control and optimization efficiency of the forging process through cloud-edge collaborative scheduling and security assurance mechanisms. Summary of the Invention

[0005] The purpose of the present invention is to provide a product forging data intelligent management system and method based on 5G cloud computing to solve the limitations of the existing technology such as inaccurate data collection, insufficient model reasoning and fusion capabilities, inflexible computing resource scheduling, and insufficient security. Through intelligent data processing, multi-model fusion and optimization, cloud-edge collaborative scheduling and security assurance mechanisms, the precise control capability and production efficiency of the forging process are significantly improved, while ensuring the high efficiency and data security of the system.

[0006] In order to achieve the above object, the present invention is implemented through the following technical solutions: In one aspect, the present invention provides a product forging data intelligent management system based on 5G cloud computing, comprising: The process data module is used to obtain multi-source process data during the forging process, evaluate the data credibility based on the operating status of the acquisition equipment and the network transmission status, and generate process feature data after weighted annotation; a multi-model inference module, configured to receive the process feature data, select a plurality of prediction sub-models with heterogeneous modeling methods according to the feature type identifier and the completeness score, including a support vector regression model, a random forest model, and a convolutional neural network model, call the prediction sub-models in parallel, and output prediction results of the plurality of sub-models; A prediction fusion module is configured to receive the prediction results of the multiple sub-models, calculate a confidence score based on the performance of each sub-model in a historical validation dataset, and dynamically assign fusion weights using a nonlinear normalization function based on the score results and a set fusion path control strategy to generate a unified prediction result; A cloud-edge collaborative scheduling module is used to generate deployment instructions based on the unified prediction results, edge node computing resource status, and 5G network transmission status, and determine the deployment path of the multi-model reasoning module based on the comparative analysis of the complexity level of the reasoning task, the processing capability of the edge node, and the network stability, and output the deployment type determination result; Parameter optimization module, used to construct an objective function based on the unified prediction result, the objective function integrates the energy consumption prediction value and the fusion model residual scoring mechanism, using The optimization algorithm performs process parameter combination optimization and outputs the optimization results to the industrial control system; The security assurance module is connected to the multi-model reasoning module, the prediction fusion module and the cloud-edge collaborative scheduling module respectively, and is used to perform robustness evaluation on the sub-model, conduct credibility detection on the fusion results based on the historical data consistency scoring mechanism, and automatically trigger the model fusion strategy adjustment when the score is lower than the preset threshold, while implementing access control and encryption protection for the data transmission process in the system.

[0007] Preferably, the process data module includes: The trusted acquisition unit is used to collect multi-source process data such as temperature, stress, load, and image during the forging process, and quantitatively evaluates the data credibility based on the operating status of the acquisition equipment and the network transmission status, and outputs weighted and annotated process data; The semantic preprocessing unit receives weighted annotated process data, performs data consistency analysis based on a stress-temperature prediction model constructed based on the thermal-mechanical coupling relationship, identifies abnormal samples, and interpolates missing data. It also performs time synchronization processing on image data and non-image data. The feedback optimization unit is used to receive the prediction error information output by the stress-temperature prediction model, dynamically adjust the feature screening rules and normalization parameter configuration, improve the consistency and effectiveness of subsequent modeling data, and output process feature data.

[0008] Preferably, the stress-temperature prediction model includes: A regression model constructed based on historical forging process data uses temperature, strain rate, and loading rate at multiple time points in the same process as input variables and material stress as output variable to describe the nonlinear relationship between thermal and mechanical processes; When the regression model is used, based on the feature similarity between the current process data and the historical sample data, the most matching sub-model is dynamically selected or the weighted fusion output result is adopted to identify abnormal data points and perform regression estimation to complete the missing values.

[0009] Preferably, the multi-model reasoning module includes: Multiple structurally heterogeneous sub-models, including vector regression models, random forest models, and convolutional neural network models, are suitable for processing subsets of time series sensor data, statistical feature data, and image-based process feature data, respectively; The model calling control unit is used to receive the process feature data, dynamically select matching sub-model combinations based on the type identification and integrity score results of each data feature, and execute prediction tasks in parallel to generate prediction output results of multiple sub-models.

[0010] Preferably, the prediction fusion module includes: The confidence evaluation unit is used to receive the prediction output results of multiple sub-models and generate a corresponding historical reliability score based on the performance of each sub-model in the historical validation dataset, indicating the prediction accuracy and stability of each sub-model in the past samples; The fusion weight calculation unit is used to calculate the fusion weight of each sub-model based on the following function: ; in: For the The reliability score obtained by each sub-model on the historical validation dataset indicates its prediction accuracy or stability in past samples; For the The historical reliability scores of each sub-model are used for normalized weight calculation; For the The fusion weight of each sub-model; is the total number of sub-models currently involved in the fusion; is the response adjustment coefficient, which is a positive real number used to control the sensitivity of the impact of score differences on weights; is the scoring benchmark value, which is used to set the central value of the scoring function; is the base of natural logarithms; A fusion output unit is used to output the fusion weights according to the fusion weights. Prediction output for each sub-model Perform weighted processing to generate a unified prediction result, the expression is: ; in: Output a unified prediction result after weighted fusion for multiple sub-models; For the The predicted output of each sub-model for the current input data.

[0011] Preferably, the cloud-edge collaborative scheduling module includes: The reasoning perception unit is used to receive the unified prediction results and analyze the complexity level of the reasoning task based on the number of sub-models involved in the prediction results, the call frequency, and the data feature dimensions; The path decision unit is used to compare the complexity level of the inference task with the preset edge node processing capability threshold and communication link stability threshold, select the edge deployment path or the cloud deployment path based on the comparison result, and output the deployment type determination result; The inference deployment generation unit is used to build deployment instructions based on the deployment type determination results. The deployment instructions include the target node identifier, model call sequence, and model loading priority, and are used to control the deployment and execution of multi-model inference modules on the edge or in the cloud.

[0012] Preferably, the parameter optimization module adopts The optimization algorithm performs process parameter combination optimization, the The optimization algorithm is built based on the following multi-objective fitness function: ; in: Indicates the combination of forging process parameters to be optimized; The energy consumption forecast value estimated based on the unified forecast results; The prediction quality score output by the residual scoring mechanism of the fusion model; It is the upper limit of the system reference energy consumption; 、 These are the minimum and maximum values ​​set for the system scoring mechanism; 、 is the target weight coefficient, which is used to adjust the weight relationship between the energy efficiency target and the prediction quality target; described The optimization algorithm adopts the particle swarm optimization method to search for the optimal parameter combination under the constraints of the fitness function, and outputs the optimal parameter combination to the industrial control system to achieve process control.

[0013] Preferably, the security assurance module includes: The credibility detection unit is used to compare the unified prediction results with historical data, specifically including the following steps: Obtain the distribution consistency score between the current prediction result and the historical sample data; If the consistency score is lower than a preset credibility threshold, an alarm signal is triggered and a fusion strategy adjustment mechanism is initiated; Start the model adaptive adjustment mechanism to dynamically adjust the weight of the fusion model based on historical data and current prediction errors to optimize subsequent prediction results.

[0014] In another aspect, the present invention provides a method for intelligent management of product forging data based on 5G cloud computing, which is applied to the above-mentioned intelligent management system for product forging data based on 5G cloud computing, and includes the following steps: Step 1: Acquire multi-source process data from the forging process, including temperature, stress, load, and image data; perform credibility assessment on the data based on the operating status of the acquisition equipment and the network transmission status, and generate weighted annotated process feature data; Step 2: Receive the process feature data, select multiple prediction sub-models with heterogeneous modeling methods based on the feature type identifier and the completeness score, the prediction sub-models including a support vector regression model, a random forest model, and a convolutional neural network model, and perform inference in parallel to generate prediction results for the multiple sub-models; Step 3: Receive the prediction results of the multiple sub-models, calculate the confidence score of each sub-model based on its performance in the historical validation dataset, and assign fusion weights using a nonlinear normalization function based on the score results and the set fusion path control strategy to generate a unified prediction result; Step 4: Generate deployment instructions based on the unified prediction results, edge node computing resource status, and 5G network transmission status, and determine the deployment path of the multi-model inference module based on the comparative analysis of the complexity level of the inference task, edge node processing capability, and network stability, and output the deployment type determination result; Step 5: Based on the unified prediction results, an objective function including energy consumption prediction value and fusion model residual scoring mechanism is constructed. The optimization algorithm performs process parameter combination optimization and outputs the optimization results to the industrial control system for process control; Step 6: Perform robustness evaluation on the sub-models and conduct credibility check on the fusion results based on the historical data consistency scoring mechanism; when the score is lower than the preset threshold, the model fusion strategy adjustment is automatically triggered, and access control and encryption protection are implemented for the data transmission process in the system.

[0015] The beneficial effects of the present invention are: the present invention uses the process data module to comprehensively collect multi-source data such as temperature, stress, load and image of the forging process, and combines the equipment operation status and network transmission status to perform credibility quantitative evaluation, generate weighted labeled high-reliability process feature data, and significantly improve the data quality. The multi-model reasoning module dynamically selects heterogeneous sub-models such as support vector regression, random forest and convolutional neural network for parallel prediction based on feature type identification and data integrity score, giving full play to the advantages of each model and improving the prediction accuracy under complex working conditions. The prediction fusion module is based on the confidence score of historical verification data, and uses a nonlinear normalization function to dynamically optimize the weight distribution to achieve highly reliable weighted fusion of prediction results, which is better than the traditional linear weighted method. The cloud-edge collaborative scheduling module combines the characteristics of the 5G network, and through comprehensive analysis of the complexity of the reasoning task, the computing power of the edge node and the network stability, it intelligently generates deployment instructions and flexibly selects cloud or edge paths, significantly reducing resource waste and response delay. Under the constraints of the energy consumption prediction value and the model residual scoring mechanism, the parameter optimization module adopts The optimization algorithm realizes the global optimization of process parameters, effectively reducing energy consumption and improving production efficiency while ensuring prediction accuracy. The security assurance module automatically triggers the adjustment of model fusion strategy through robustness evaluation and consistency detection, and performs access control and encryption protection on data transmission, thereby enhancing the stability and security of the system. Therefore, the present invention has made significant progress in process control accuracy, energy efficiency optimization and system security through the coordinated application of technologies such as multi-source data trusted collection, heterogeneous model intelligent reasoning, dynamic fusion optimization and cloud-edge collaborative scheduling, and has obvious technical advantages and industrial application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort. Among them: Figure 1 Schematic diagram of the overall structure of the system of the present invention; Figure 2 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of the present invention.

[0018] like Figure 1 FIG. 1 is an embodiment of the present invention, which provides a product forging data intelligent management system based on 5G cloud computing, including: (1) Process data module It is used to obtain multi-source process data during the forging process, evaluate the data credibility based on the operating status of the acquisition equipment and the network transmission status, and generate process feature data after weighted annotation; The process data module includes: The trusted acquisition unit is used to collect multi-source process data such as temperature, stress, load, and image during the forging process, and quantitatively evaluates the data credibility based on the operating status of the acquisition equipment and the network transmission status, and outputs weighted and annotated process data; The semantic preprocessing unit receives weighted annotated process data, performs data consistency analysis based on a stress-temperature prediction model constructed based on the thermal-mechanical coupling relationship, identifies abnormal samples, and interpolates missing data. It also performs time synchronization processing on image data and non-image data. The feedback optimization unit is used to receive the prediction error information output by the stress-temperature prediction model, dynamically adjust the feature screening rules and normalization parameter configuration, improve the consistency and effectiveness of subsequent modeling data, and output process feature data.

[0019] As a preferred implementation of this embodiment, the stress-temperature prediction model includes: A regression model constructed based on historical forging process data uses temperature, strain rate, and loading rate at multiple time points in the same process as input variables and material stress as output variable to describe the nonlinear relationship between thermal and mechanical processes; When the regression model is used, based on the feature similarity between the current process data and the historical sample data, the most matching sub-model is dynamically selected or the weighted fusion output result is adopted to identify abnormal data points and perform regression estimation to complete the missing values.

[0020] During the forging process, the stress distribution of the material is significantly coupled with temperature changes. To improve the reliability of the collected data, this embodiment constructs a physical model based on the thermal-mechanical coupling relationship to detect and repair missing values ​​and anomalies in multi-source sensor data. Specifically, the system constructs the following thermodynamic relationship model using historical forging data sets: ; in: is the stress of the material during the forging process, is the temperature, For strain, is the strain rate. This model is used to determine whether the data collected by the sensor exceeds the reasonable data range; if the data collected by some sensors within a certain period of time cannot meet the model, the system will automatically trigger the data interpolation and repair process.

[0021] The implementation of this model uses empirical formulas based on finite element calculation models, or models trained through machine learning to perform data repair. The specific process is as follows: by comparing the similarity between current process data and historical data, abnormal data points are identified; for identified missing or abnormal data points, reasonable data are estimated using interpolation or regression models.

[0022] This process ensures the accuracy of data repair and improves data quality by combining physical models and machine learning technology, thereby providing reliable data support for subsequent process optimization and predictive analysis.

[0023] (2) Multi-model inference module for receiving the process feature data, selecting a plurality of prediction sub-models with heterogeneous modeling methods according to the feature type identifier and the completeness score, including a support vector regression model, a random forest model, and a convolutional neural network model, calling the prediction sub-models in parallel, and outputting prediction results of the plurality of sub-models; The multi-model inference module includes: Multiple structurally heterogeneous sub-models, including at least a vector regression model, a random forest model, and a convolutional neural network model, which are respectively suitable for processing subsets of time series sensor data, statistical feature data, and image-based process feature data; The model calling control unit is used to receive the process feature data, dynamically select matching sub-model combinations based on the type identification and integrity score results of each data feature, and execute prediction tasks in parallel to generate prediction output results of multiple sub-models.

[0024] In this embodiment, through the design of the above-mentioned multi-model reasoning module, the advantages of different models in processing multi-source process data such as time series, statistics and images can be fully utilized, the comprehensiveness and accuracy of the prediction results can be improved, and good stability and adaptability can be maintained under complex working conditions, thereby providing reliable data support for subsequent prediction fusion and process optimization.

[0025] (3) Prediction Fusion Module Used to receive the prediction results of the multiple sub-models, calculate the confidence score based on the performance of each sub-model in the historical verification data set, and dynamically assign fusion weights using a nonlinear normalization function based on the score results and the set fusion path control strategy to generate a unified prediction result; The prediction fusion module includes: The confidence evaluation unit is used to receive the prediction output results of multiple sub-models and generate a corresponding historical reliability score based on the performance of each sub-model in the historical validation dataset, indicating the prediction accuracy and stability of each sub-model in the past samples; The fusion weight calculation unit is used to calculate the fusion weight of each sub-model based on the following function: ; in: For the The reliability score obtained by each sub-model on the historical validation dataset indicates its prediction accuracy or stability in past samples; For the The historical reliability scores of each sub-model are used for normalized weight calculation; For the The fusion weight of each sub-model; is the total number of sub-models currently involved in the fusion; is the response adjustment coefficient, which is a positive real number used to control the sensitivity of the impact of score differences on weights; is the scoring benchmark value, which is used to set the central value of the scoring function; is the base of natural logarithms; A fusion output unit is used to output the fusion weights according to the fusion weights. Prediction output for each sub-model Perform weighted processing to generate a unified prediction result, the expression is: ; in: Output a unified prediction result after weighted fusion for multiple sub-models; For the The predicted output of each sub-model for the current input data.

[0026] In this embodiment, through the design of the above-mentioned prediction fusion module, the advantages of each sub-model in different data characteristics can be fully utilized, and the reasonable weighting of the prediction results of each sub-model can be achieved based on the reliability score of historical verification data. This not only improves the overall accuracy and stability of the fusion results, but also effectively reduces the prediction deviation that may be caused by a single model under complex working conditions, thereby providing a more reliable basis for subsequent process optimization and parameter adjustment.

[0027] (4) Cloud-edge collaborative scheduling module Used to generate deployment instructions based on the unified prediction results, edge node computing resource status and 5G network transmission status, and determine the deployment path of the multi-model reasoning module based on the comparative analysis of the complexity level of the reasoning task, the edge node processing capability and the network stability, and output the deployment type determination result; The cloud-edge collaborative scheduling module includes: The reasoning perception unit is used to receive the unified prediction results and analyze the complexity level of the reasoning task based on the number of sub-models involved in the prediction results, the call frequency, and the data feature dimensions; The path decision unit is used to compare the complexity level of the inference task with the preset edge node processing capability threshold and communication link stability threshold, select the edge deployment path or the cloud deployment path based on the comparison result, and output the deployment type determination result; The inference deployment generation unit is used to build deployment instructions based on the deployment type determination results. The deployment instructions include the target node identifier, model call sequence, and model loading priority, and are used to control the deployment and execution of multi-model inference modules on the edge or in the cloud.

[0028] In this embodiment, the design of the cloud-edge collaborative scheduling module enables intelligent switching and optimal deployment of inference tasks between the cloud and edge based on a comprehensive analysis of inference task complexity, edge node computing resource status, and 5G network transmission stability. This design not only effectively improves the system's resource utilization and response speed, but also maintains high operational stability even in the face of network fluctuations or resource constraints, thereby ensuring the real-time and reliability of prediction and optimization tasks.

[0029] (V) Parameter Optimization Module It is used to construct an objective function based on the unified prediction result, and the objective function integrates the energy consumption prediction value and the fusion model residual scoring mechanism, using The optimization algorithm performs process parameter combination optimization and outputs the optimization results to the industrial control system; Furthermore, the parameter optimization module adopts The optimization algorithm performs process parameter combination optimization, the The optimization algorithm is built based on the following multi-objective fitness function: ; in: Indicates the combination of forging process parameters to be optimized; The energy consumption forecast value estimated based on the unified forecast results; The prediction quality score output by the residual scoring mechanism of the fusion model; It is the upper limit of the system reference energy consumption; 、 These are the minimum and maximum values ​​set for the system scoring mechanism; 、 is the target weight coefficient, which is used to adjust the weight relationship between the energy efficiency target and the prediction quality target; described The optimization algorithm adopts the particle swarm optimization method to search for the optimal parameter combination under the constraints of the fitness function, and outputs the optimal parameter combination to the industrial control system to achieve process control.

[0030] The following is The pseudo code of the optimization algorithm shows the basic implementation logic of particle swarm optimization: # Optimizing pseudocode Initialize swarm of particles with random @i For each @i: Compute E_pred(@i) Compute S_qual(@i) Evaluate fitness F(@i) Repeat until convergence: For each particle: Update velocity and position Evaluate new F(@i) Update personal and global best Return θ* with max F(θ) In this embodiment, by The application of optimization algorithms can significantly improve forging process efficiency and product quality. Specific benefits include: Improved energy efficiency: By optimizing the combination of process parameters, energy consumption is reduced. Improved prediction accuracy: Through multi-model fusion optimization, the prediction accuracy of process parameters is improved. Improved process quality stability: The optimized process parameters significantly improve the consistency and reliability of the forging process, ensuring stable product quality.

[0031] In this embodiment, The optimization algorithm has the following advantages: The algorithm comprehensively considers energy efficiency and prediction accuracy, and can achieve all-round optimization of the forging process; through global optimization, it avoids the problem of local optimal solutions and ensures the optimal selection of process parameters; it can flexibly adapt to different production environments and process conditions, and has high adaptability and scalability.

[0032] Through the parameter optimization module of this embodiment, using The optimization algorithm significantly improved the energy efficiency and prediction accuracy of the forging process. This method not only reduces energy consumption but also improves product quality, and has broad industrial application prospects.

[0033] (6) Security Assurance Module It is respectively connected to the multi-model reasoning module, prediction fusion module and cloud-edge collaborative scheduling module to perform robustness evaluation on the sub-model, conduct credibility detection on the fusion results based on the historical data consistency scoring mechanism, and automatically trigger the model fusion strategy adjustment when the score is lower than the preset threshold, while implementing access control and encryption protection for the data transmission process in the system.

[0034] The security modules include: The credibility detection unit is used to compare the unified prediction results with historical data, specifically including the following steps: Obtain the distribution consistency score between the current prediction result and the historical sample data; If the consistency score is lower than a preset credibility threshold, an alarm signal is triggered and a fusion strategy adjustment mechanism is initiated; Start the model adaptive adjustment mechanism to dynamically adjust the weight of the fusion model based on historical data and current prediction errors to optimize subsequent prediction results.

[0035] In this embodiment, the design of the aforementioned security assurance module enables real-time testing of prediction result credibility during the prediction process. It also triggers timely adaptive model adjustments when anomalies occur or confidence levels drop, effectively ensuring the stability and reliability of the prediction results. Furthermore, combined with access control and encryption protection during data transmission, the system's anti-interference capabilities and data security are significantly enhanced in complex industrial environments, providing a more reliable guarantee for overall process optimization.

[0036] like Figure 2 FIG. 1 is another embodiment of the present invention, which provides a method for intelligent management of product forging data based on 5G cloud computing, and is applied to the above-mentioned intelligent management system for product forging data based on 5G cloud computing, and includes the following steps: Step 1: Acquire multi-source process data from the forging process, including temperature, stress, load, and image data; perform credibility assessment on the data based on the operating status of the acquisition equipment and the network transmission status, and generate weighted annotated process feature data; Step 2: Receive the process feature data, select multiple prediction sub-models with heterogeneous modeling methods based on the feature type identifier and the completeness score, the prediction sub-models including a support vector regression model, a random forest model, and a convolutional neural network model, and perform inference in parallel to generate prediction results for the multiple sub-models; Step 3: Receive the prediction results of the multiple sub-models, calculate the confidence score of each sub-model based on its performance in the historical validation dataset, and assign fusion weights using a nonlinear normalization function based on the score results and the set fusion path control strategy to generate a unified prediction result; Step 4: Generate deployment instructions based on the unified prediction results, edge node computing resource status, and 5G network transmission status, and determine the deployment path of the multi-model inference module based on the comparative analysis of the complexity level of the inference task, edge node processing capability, and network stability, and output the deployment type determination result; Step 5: Based on the unified prediction results, an objective function including energy consumption prediction value and fusion model residual scoring mechanism is constructed. The optimization algorithm performs process parameter combination optimization and outputs the optimization results to the industrial control system for process control; Step 6: Perform robustness evaluation on the sub-models and conduct credibility check on the fusion results based on the historical data consistency scoring mechanism; when the score is lower than the preset threshold, the model fusion strategy adjustment is automatically triggered, and access control and encryption protection are implemented for the data transmission process in the system.

[0037] In summary, the present invention realizes intelligent, refined and efficient management of the entire forging process through multi-source trusted collection of process data, heterogeneous multi-model parallel reasoning based on feature identification, dynamic prediction fusion of nonlinear normalized weights, cloud-edge collaborative scheduling combining task complexity and network status, process parameter optimization of integrated energy consumption prediction and residual scoring mechanism, and safe and reliable robustness guarantee. Compared with the existing technology, the present invention significantly improves the accuracy of process parameter prediction and the real-time response capability of the system, effectively reduces production energy consumption, and maintains high data security and system stability in complex industrial environments. The technical solution has good versatility and scalability, and is particularly suitable for intelligent manufacturing, high-end forging and other industrial scenarios with high requirements for precision and real-time performance. It has obvious technological advancement, significant economic benefits and broad industrial application prospects.

[0038] In the description of this specification, the reference terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" mean that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless they are mutually inconsistent.

[0039] Any process or method description in the flowchart or otherwise described herein can be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process. The scope of the preferred embodiments of the present application includes additional implementations in which the functions may be performed in a different order than shown or discussed, including in a substantially simultaneous manner or in a reverse order depending on the functions involved.

[0040] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included within the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A product forging data intelligent management system based on 5G cloud computing, characterized in that: include: The process data module is used to obtain multi-source process data during the forging process, evaluate the data credibility based on the operating status of the acquisition equipment and the network transmission status, and generate process feature data after weighted annotation; a multi-model inference module, configured to receive the process feature data, select a plurality of prediction sub-models with heterogeneous modeling methods according to the feature type identifier and the completeness score, including a support vector regression model, a random forest model, and a convolutional neural network model, call the prediction sub-models in parallel, and output prediction results of the plurality of sub-models; A prediction fusion module is configured to receive the prediction results of the multiple sub-models, calculate a confidence score based on the performance of each sub-model in a historical validation dataset, and dynamically assign fusion weights using a nonlinear normalization function based on the score results and a set fusion path control strategy to generate a unified prediction result; A cloud-edge collaborative scheduling module is used to generate deployment instructions based on the unified prediction results, edge node computing resource status, and 5G network transmission status, and determine the deployment path of the multi-model reasoning module based on the comparative analysis of the complexity level of the reasoning task, the processing capability of the edge node, and the network stability, and output the deployment type determination result; Parameter optimization module, used to construct an objective function based on the unified prediction result, the objective function integrates the energy consumption prediction value and the fusion model residual scoring mechanism, using The optimization algorithm performs process parameter combination optimization and outputs the optimization results to the industrial control system; The security assurance module is connected to the multi-model reasoning module, the prediction fusion module and the cloud-edge collaborative scheduling module respectively, and is used to perform robustness evaluation on the sub-model, conduct credibility detection on the fusion results based on the historical data consistency scoring mechanism, and automatically trigger the model fusion strategy adjustment when the score is lower than the preset threshold, while implementing access control and encryption protection for the data transmission process in the system.

2. The product forging data intelligent management system based on 5G cloud computing according to claim 1 is characterized in that: The process data module includes: The trusted acquisition unit is used to collect multi-source process data such as temperature, stress, load, and image during the forging process, and quantitatively evaluates the data credibility based on the operating status of the acquisition equipment and the network transmission status, and outputs weighted and annotated process data; The semantic preprocessing unit receives weighted annotated process data, performs data consistency analysis based on a stress-temperature prediction model constructed based on the thermal-mechanical coupling relationship, identifies abnormal samples, and interpolates missing data. It also performs time synchronization processing on image data and non-image data. The feedback optimization unit is used to receive the prediction error information output by the stress-temperature prediction model, dynamically adjust the feature screening rules and normalization parameter configuration, improve the consistency and effectiveness of subsequent modeling data, and output process feature data.

3. The product forging data intelligent management system based on 5G cloud computing according to claim 2 is characterized in that: The stress-temperature prediction model includes: A regression model constructed based on historical forging process data uses temperature, strain rate, and loading rate at multiple time points in the same process as input variables and material stress as output variable to describe the nonlinear relationship between thermal and mechanical processes; When the regression model is used, based on the feature similarity between the current process data and the historical sample data, the most matching sub-model is dynamically selected or the weighted fusion output result is adopted to identify abnormal data points and perform regression estimation to complete the missing values.

4. The product forging data intelligent management system based on 5G cloud computing according to claim 1 is characterized in that: The multi-model reasoning module includes: Multiple structurally heterogeneous sub-models, including support vector regression models, random forest models, and convolutional neural network models, are suitable for processing subsets of time series sensor data, statistical feature data, and image-based process feature data, respectively; The model calling control unit is used to receive the process feature data, dynamically select matching sub-model combinations based on the type identification and integrity score results of each data feature, and execute prediction tasks in parallel to generate prediction output results of multiple sub-models.

5. The product forging data intelligent management system based on 5G cloud computing according to claim 1 is characterized in that: The prediction fusion module includes: The confidence evaluation unit is used to receive the prediction output results of multiple sub-models and generate a corresponding historical reliability score based on the performance of each sub-model in the historical validation dataset, indicating the prediction accuracy and stability of each sub-model in the past samples; The fusion weight calculation unit is used to calculate the fusion weight of each sub-model based on the following function: ； in: For the The reliability score of each sub-model obtained on the historical validation dataset; For the Historical reliability scores of each sub-model; For the The fusion weight of each sub-model; is the total number of sub-models currently involved in the fusion; is the response adjustment coefficient; is the benchmark value for the score; is the base of natural logarithms; A fusion output unit is used to output the fusion weights according to the fusion weights. Prediction output for each sub-model Perform weighted processing to generate a unified prediction result, the expression is: ； in: Output a unified prediction result after weighted fusion for multiple sub-models; For the The predicted output of each sub-model for the current input data.

6. According to the 5G cloud computing-based product forging data intelligent management system of claim 1, it is characterized in that: The cloud-edge collaborative scheduling module includes: The reasoning perception unit is used to receive the unified prediction results and analyze the complexity level of the reasoning task based on the number of sub-models involved in the prediction results, the call frequency, and the data feature dimensions; The path decision unit is used to compare the complexity level of the inference task with the preset edge node processing capability threshold and communication link stability threshold, select the edge deployment path or the cloud deployment path based on the comparison result, and output the deployment type determination result; The inference deployment generation unit is used to build deployment instructions based on the deployment type determination results. The deployment instructions include the target node identifier, model call sequence, and model loading priority, and are used to control the deployment and execution of multi-model inference modules on the edge or in the cloud.

7. The product forging data intelligent management system based on 5G cloud computing according to claim 1 is characterized in that: The parameter optimization module adopts The optimization algorithm performs process parameter combination optimization, the The optimization algorithm is constructed based on the following multi-objective fitness function, which is expressed as: ； in: Indicates the combination of forging process parameters to be optimized; The energy consumption forecast value estimated based on the unified forecast results; The prediction quality score output by the residual scoring mechanism of the fusion model; It is the reference energy consumption upper limit of the system; 、 These are the minimum and maximum values ​​set for the system scoring mechanism; 、 is the target weight coefficient, which is used to adjust the weight relationship between the energy efficiency target and the prediction quality target; described The optimization algorithm adopts the particle swarm optimization method to search for the optimal parameter combination under the constraints of the fitness function, and outputs the optimal parameter combination to the industrial control system to achieve process control.

8. The product forging data intelligent management system based on 5G cloud computing according to claim 1 is characterized in that: The security assurance module includes: The credibility detection unit is used to compare the unified prediction results with historical data, specifically including the following steps: Obtain the consistency score between the current prediction result and the distribution of historical sample data; If the consistency score is lower than a preset credibility threshold, an alarm signal is triggered and a fusion strategy adjustment mechanism is initiated; Start the model adaptive adjustment mechanism to dynamically adjust the weight of the fusion model based on historical data and current prediction errors to optimize subsequent prediction results.

9. A method for intelligent management of product forging data based on 5G cloud computing, applied to a product forging data intelligent management system based on 5G cloud computing as claimed in any one of claims 1 to 8, characterized in that: The following steps are involved: Step 1: Acquire multi-source process data during the forging process, wherein the process data includes temperature, stress, load, and image data; and performing a credibility assessment on the data based on the operating status of the acquisition equipment and the network transmission status, and generating weighted annotated process feature data; Step 2: Receive the process feature data, select multiple prediction sub-models with heterogeneous modeling methods based on the feature type identifier and the completeness score, the prediction sub-models including a support vector regression model, a random forest model, and a convolutional neural network model, and perform inference in parallel to generate prediction results for the multiple sub-models; Step 3: Receive the prediction results of the multiple sub-models, calculate the confidence score of each sub-model based on its performance in the historical validation dataset, and assign fusion weights using a nonlinear normalization function based on the score results and the set fusion path control strategy to generate a unified prediction result; Step 4: Generate deployment instructions based on the unified prediction results, edge node computing resource status, and 5G network transmission status. Determine the deployment path for the multi-model inference module based on a comparative analysis of the complexity level of the inference task, edge node processing capabilities, and network stability, and output a deployment type determination result. Step 5: Based on the unified prediction results, an objective function including energy consumption prediction value and fusion model residual scoring mechanism is constructed. The optimization algorithm performs process parameter combination optimization and outputs the optimization results to the industrial control system for process control; Step 6: Perform robustness evaluation on the sub-models and conduct credibility check on the fusion results based on the historical data consistency scoring mechanism; when the score is lower than the preset threshold, the model fusion strategy adjustment is automatically triggered, and access control and encryption protection are implemented for the data transmission process in the system.

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