A mobile phone middle frame film pasting risk assessment method based on process data

Abstract

The application relates to the technical field of mobile phones, in particular to a mobile phone middle frame film pasting risk assessment method based on process data, which comprises process analysis and electromagnetic characteristic identification based on monitoring data; self-recurrence model is adopted to predict interference intensity trend; a relief scheme is formulated through optimization theory; lattice Boltzmann model is used to evaluate signal compatibility; a signal enhancement process is designed in combination with system dynamics; a comprehensive process risk assessment is generated by adopting genetic algorithm and particle swarm optimization for multi-objective trade-off; and finally, production planning is completed through life cycle assessment. The application can realize accurate analysis of material composition and thickness, scientifically predict high-risk interference frequency bands, effectively reduce the shielding effect of film pasting on radio frequency signals while guaranteeing production efficiency and cost, and improve the communication quality of the whole machine.

Description

Technical Field

[0001] This invention belongs to the field of mobile phone technology, specifically a method for risk assessment of mobile phone mid-frame film application based on process data. Background Technology

[0002] The core objective of the mobile phone mid-frame screen protector technology is to accurately assess and analyze signal interference and material compatibility during the screen protector application process by developing advanced methods and tools, thereby enabling targeted process adjustment measures to ensure the communication performance of the mobile phone.

[0003] The technology of applying screen protectors to the mid-frame of a mobile phone is part of the smartphone manufacturing process. Its main task is to quantify and understand the impact of the characteristics of the screen protector material on high-frequency signal transmission, and to provide scientific risk prediction support for process design and manufacturing.

[0004] The risk assessment method for mobile phone mid-frame screen protectors is a technical approach targeting material properties and electromagnetic compatibility, aiming to accurately identify the interference of screen protector materials on mobile phone signals. The risk assessment concept includes extracting metal components, simulating electromagnetic interactions, and classifying risk patterns to minimize the interference of the screen protector on the mid-frame antenna. Through this goal, manufacturers can more effectively reduce uncertainties in the production process, improve product signal quality, and enhance the user experience.

[0005] Traditional methods have several shortcomings in practical application. Previous methods often neglected comprehensive analysis of multi-dimensional data, such as the metal composition of the film material, production batch information, and electromagnetic properties, leading to inaccurate assessments of film interference risks. Traditional methods lack the application of electromagnetic simulation and nonlinear classification models in high-frequency interaction simulations between the film and antenna, limiting the accurate identification of risk distribution patterns. Furthermore, traditional methods typically lack a scientific predictive framework to support interference prediction and risk trend analysis for future production batches. In analyzing the interaction between user experience feedback and signal attenuation, traditional methods fail to fully integrate real-world usage scenario data, resulting in an insufficiently in-depth assessment of the film material's impact on signal transmission. Summary of the Invention

[0006] The purpose of this invention is to address the aforementioned shortcomings in the prior art by providing a method for risk assessment of mobile phone mid-frame film application based on process data.

[0007] The objective of this invention is achieved through the following technical solution: a method for risk assessment of mobile phone mid-frame screen protector application based on process data, comprising the following steps: S1. Based on the film application process monitoring data of the mobile phone mid-frame production line, data fusion and fractal geometry methods are used to conduct a comprehensive analysis of the material metal composition, thickness and adhesive type, and a preliminary analysis of the process status to generate a real-time process analysis of mobile phone film application. S2. Based on the real-time process analysis of the mobile phone screen protector, time series analysis and electromagnetic field dynamics modeling are used to explore the peak value of electromagnetic interference and the material loss mode, and to identify the trend of interference intensity change, thereby generating screen protector electromagnetic feature recognition. S3. Based on the electromagnetic feature recognition of the film, an autoregressive model is used to predict the trend of interference intensity in future production batches, and high-risk materials and interference frequency bands are analyzed to generate interference risk prediction analysis. S4. Based on the aforementioned interference risk prediction and analysis, optimization theory and electromagnetic compatibility engineering methods are used to optimize the film thickness and bonding position, and a process parameter adjustment strategy for the film applicator is formulated to generate a signal interference risk mitigation scheme. S5. Combining mobile phone radio frequency signal data and the aforementioned signal interference risk mitigation scheme, the lattice Boltzmann model is used to analyze the interaction between dielectric constant distribution and signal beam flow, and the impact of antenna radiation gain is evaluated to generate a film signal compatibility impact assessment. S6. Based on the aforementioned assessment of the signal compatibility impact of the film application, a system dynamics approach is adopted to formulate a process management strategy to mitigate the signal shielding effect, and a structural adjustment scheme for the film application system is designed to generate a signal enhancement film application process plan. S7. Combining the aforementioned signal interference risk mitigation scheme and signal enhancement film application process plan, a comprehensive trade-off and optimization strategy design is performed using genetic algorithms and particle swarm optimization to balance signal integrity, production efficiency, and material costs, generating a comprehensive process risk assessment optimization.

[0008] The beneficial effects of this invention are as follows: By applying data fusion and fractal geometry methods, this invention achieves more precise analysis of the metal composition, thickness, and adhesive type of the material, improving the accuracy of judging the process status of mobile phone screen protectors. This method utilizes time series analysis and electromagnetic field dynamics modeling to effectively explore electromagnetic interference peak values ​​and material loss patterns, helping to identify trends in interference intensity changes. Furthermore, the application of autoregressive models makes the prediction of future batch interference intensity trends more scientific, providing a solid foundation for the analysis of high-risk materials and interference frequency bands. By combining the lattice Boltzmann model and RF signal data, this method can deeply analyze the interaction between dielectric constant distribution and signal beamflow, and evaluate the impact on antenna radiation gain. Using lifecycle assessment and cost-benefit analysis, it provides a comprehensive perspective for the integrated assessment and production planning of mobile phone mid-frame screen protector interference, thereby promoting the formation of comprehensive planning results for screen protector risk assessment. Attached Figure Description

[0009] The invention will be further illustrated with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the invention. For those skilled in the art, other drawings can be obtained based on the following drawings without any creative effort.

[0010] Figure 1 This is a schematic diagram of the process method of the present invention; Figure 2 This is a detailed schematic diagram of step S1 of the present invention; Figure 3 This is a detailed schematic diagram of step S2 of the present invention; Figure 4 This is a detailed schematic diagram of step S3 of the present invention; Figure 5 This is a detailed schematic diagram of step S4 of the present invention; Figure 6 This is a detailed schematic diagram of step S5 of the present invention; Figure 7 This is a detailed schematic diagram of step S6 of the present invention; Figure 8 This is a detailed schematic diagram of step S7 of the present invention; Figure 9 This is a detailed schematic diagram of step S8 of the present invention. Detailed Implementation

[0011] The present invention will be further described in conjunction with the following embodiments.

[0012] Depend on Figure 1As can be seen, this invention provides a method for risk assessment of mobile phone mid-frame screen protector application based on process data, including the following steps: S1. Based on the screen protector application process monitoring data of the mobile phone mid-frame production line, data fusion and fractal geometry methods are used to conduct a comprehensive analysis of the material's metal composition, thickness, and adhesive type, and a preliminary analysis of the process status, generating a real-time process analysis for mobile phone screen protector application; S2. Based on the real-time process analysis, time series analysis and electromagnetic field dynamics modeling are used to explore the peak value of electromagnetic interference and material loss modes, and to identify the trend of interference intensity changes, generating screen protector electromagnetic feature identification; S3. Based on the screen protector electromagnetic feature identification, an autoregressive model is used to predict the trend of interference intensity in future production batches, and high-risk materials and interference frequency bands are analyzed to generate an interference risk prediction analysis; S4. Based on the interference risk prediction analysis, optimization theory and electromagnetic compatibility engineering methods are used to optimize the design of the screen protector thickness and bonding position, and to formulate the process parameters of the screen protector application machine. S5. Adjusting strategies to generate signal interference risk mitigation schemes; S6. Combining mobile phone RF signal data and signal interference risk mitigation schemes, using the lattice Boltzmann model to analyze the interaction between dielectric constant distribution and signal beam flow, and assessing the impact on antenna radiation gain, generating a film-mounted signal compatibility impact assessment; S7. Based on the film-mounted signal compatibility impact assessment, using system dynamics methods to formulate process management strategies to mitigate signal shielding effects, and designing structural adjustment schemes for the film-mounted system, generating a signal-enhancing film-mounted process plan; S8. Integrating the signal interference risk mitigation schemes and the signal-enhancing film-mounted process plan, using genetic algorithms and particle swarm optimization to comprehensively balance and optimize signal integrity, production efficiency, and material costs, generating a comprehensive process risk assessment optimization; S9. Based on the comprehensive process risk assessment optimization, using lifecycle assessment and cost-benefit analysis, conducting a comprehensive assessment of the overall mobile phone communication quality and production planning, generating a comprehensive film-mounted risk assessment planning result.

[0013] Specifically, the real-time process analysis for mobile phone screen protectors includes material density distribution maps, metal composition ratios, and screen protector thickness classification; electromagnetic feature identification includes dielectric constant stability assessment, eddy current loss trend identification, and electromagnetic shielding mode change detection; interference risk prediction analysis specifically includes prediction of future batch interference peaks and prediction of potential signal shielding areas; signal interference risk mitigation solutions include screen protector machine pressure adjustment plans, cutting path design, and adhesive temperature control strategies; screen protector signal compatibility impact assessment includes simulation of electromagnetic field changes on the mid-frame surface, assessment of the contribution of the screen protector layer to the antenna pattern, and prediction of the effectiveness of interference mitigation measures; signal enhancement screen protector process planning includes antenna clearance area adjustment strategies, low-loss material priority schemes, and high-frequency compensation process promotion plans; comprehensive process risk assessment and optimization includes optimal screen protector process control strategies, high-gain screen protector modes, and low-risk material selection measures; and the comprehensive planning results of screen protector risk assessment include setting signal attenuation reduction targets, process implementation path planning, and long-term communication quality benefit prediction.

[0014] In the real-time process analysis step of mobile phone screen protector application, monitoring data from the screen protector application process on the mobile phone mid-frame production line is used. Data fusion technology is employed to integrate data from different sources and formats, including thickness records from high-precision displacement sensors, composition data from spectral analyzers, and adhesive pressure data from pressure sensors. This data is converted into a unified format, such as thickness deviation per micrometer, metal ion concentration, and adhesive layer uniformity index. Next, fractal geometry methods are used to analyze the microstructural characteristics of the material surface. This includes calculating the fractal dimension of the material surface to identify the micropore distribution of the adhesive. During this process, the calculation parameters of the fractal dimension, such as the coverage grid size and scaling factor, are finely adjusted to adapt to the macroscopic uniformity of the film material, ensuring the accuracy of the analysis. The analysis results reveal preliminary characteristics of the process state, such as compositional inhomogeneity and thickness deviation caused by local metal aggregation, providing real-time data support for the production line. The step finally generates a detailed report including a material density distribution map, metal component ratio, and screen protector thickness classification.

[0015] In the electromagnetic characteristic identification step of the film lamination process, based on real-time process analysis results, time series analysis and electromagnetic field dynamics modeling are used to explore the peak value of electromagnetic interference and material loss patterns. Time series analysis involves identifying the long-term trends, periodicity, and random fluctuations of interference signal strength and eddy current loss data. Algorithms used include the Autoregressive Moving Average (ARIMA) model, specifically adjusted for the dielectric loss tangent of different production batches of film materials. The electromagnetic field dynamics model is used to capture the energy attenuation of high-frequency signals penetrating the film, such as the loss caused by the skin effect at specific frequency bands. Detailed operations in this step include adjusting the permeability parameter to accommodate metal interlayers of different thicknesses. These models are used to identify the trend of interference intensity changes, thereby effectively predicting and identifying electromagnetic loss trends. These analyses generate a detailed report describing the stability of the dielectric constant, eddy current loss trends, and shielding mode changes, providing a scientific basis for subsequent compatibility design.

[0016] In the interference risk prediction and analysis step, an autoregressive model is used to predict the trend of interference intensity for future production batches. This step involves in-depth analysis of historical interference data to identify and model key patterns of future material fluctuations. By adjusting model parameters, such as the autocorrelation coefficient and order, to best reflect the time-dependent characteristics of the film material's fluctuations, these models can predict the attenuation values ​​of specific batches of film material in mainstream communication frequency bands. Furthermore, the step includes analysis of high-risk materials and interference frequency bands to predict future electromagnetic risk conditions, such as areas of reduced signal penetration under 5G high-frequency bands. The generated interference risk prediction and analysis report not only helps process engineers identify and resolve potential material compatibility issues but also provides key quality control thresholds for raw material procurement and incoming inspection.

[0017] In the signal interference risk mitigation design phase, based on interference risk prediction and analysis, optimization theory and electromagnetic compatibility engineering methods are employed to design optimized solutions for the film thickness and location. This includes analyzing the electromagnetic distribution bottlenecks in the current mid-frame antenna clearance area and designing improvement measures. For example, linear programming is used to determine the pressure output of the multi-station film laminator, and the optimal electromagnetic transparency window position is found by simulating different cutting path schemes. Simultaneously, considering adhesive temperature control and pressure adjustment strategies, such as using low-pressure lamination in antenna-sensitive areas to reduce adhesive polarization and alleviate material shielding of signals, the result is a comprehensive signal interference risk mitigation solution that not only reduces signal attenuation but also improves the overall communication link quality.

[0018] In the signal compatibility impact assessment step of the screen protector, the interaction between dielectric constant distribution and signal beamflow is analyzed using a lattice Boltzmann model, combining mobile phone RF signal data and interference risk mitigation schemes. By simulating the propagation of electromagnetic waves in a non-uniform medium, particularly the diffraction and scattering caused by the edge of the screen protector, this model can reflect in detail the impact of different dielectric distributions on antenna radiation gain. The model considers various factors, such as the polarizability of the film material, dielectric drift caused by ambient humidity, and the thickness gradient of the adhesive layer. By adjusting model parameters, such as the discrete velocity set and relaxation time, the accuracy of the simulation results in the millimeter-wave band is ensured. Furthermore, the assessment includes predictions of the effectiveness of mitigation measures, such as the contribution of local thinning and aperture technology to gain improvement. The generated report details the changes in the electromagnetic field of the mid-frame and the distortion of the antenna pattern.

[0019] In the signal enhancement coating process planning step, based on the compatibility assessment results, a system dynamics approach is used to design process management strategies to enhance signal penetration. The system dynamics model can simulate the complex nonlinear feedback relationships between process parameters, material properties, and signal strength, and predict the long-term robustness of different adjustment schemes. These strategies include adjusting the coating overlap in the antenna clearance area, prioritizing the use of coating materials with low loss factors (Df), and introducing high-frequency compensation processes. Fine-tuning of model parameters, such as production line temperature control accuracy and coating machine alignment deviation rate, ensures the feasibility of the plan. The result of this step is a comprehensive signal enhancement coating process plan, including a material selection matrix, process control limits, and a technology upgrade roadmap, providing specific guidance for achieving high-performance communication in end products.

[0020] In the comprehensive process risk assessment and optimization step, a comprehensive approach is taken, integrating signal interference risk mitigation schemes and signal enhancement film application process planning. Genetic algorithms and particle swarm optimization are employed to comprehensively weigh various factors and design optimization strategies. By simulating signal integrity and production costs under different combinations of process parameters, the optimization algorithm can identify the balance point between yield and performance. During this process, algorithm parameters such as crossover rate, mutation probability, and particle inertia weights are carefully adjusted to adapt to the highly nonlinear electromagnetic simulation search space. The result of this step is a comprehensive optimization strategy, including optimal film application control parameters, a high-gain bonding mode, and a low-risk material formulation, aiming to achieve efficient operation of the film application process and Pareto improvement in terminal signal quality.

[0021] In the process of generating the comprehensive planning results for screen protector risk assessment, based on the optimization of comprehensive process risk assessment, life cycle assessment and cost-benefit analysis are used to comprehensively evaluate and plan the overall communication quality of the mobile phone. Life cycle assessment covers the performance evolution from film material synthesis and screen protector processing to the lifespan of the entire device, including the impact of aging-induced dielectric aging and adhesive layer cracking on the signal. Cost-benefit analysis involves evaluating the investment in different process improvement strategies and the economic benefits of reduced return rates, ensuring the commercial rationality of the solution. Through detailed analysis and comparison of the long-term reliability impact of different strategies, the final comprehensive planning results for screen protector risk assessment include clear signal attenuation reduction targets, process path evolution planning, and overall communication benefit predictions, providing a scientific decision-making framework for achieving high-quality production in mobile phone manufacturing.

[0022] Please see Figure 2 This invention provides a method for risk assessment of mobile phone mid-frame screen protector application based on process data. The specific steps for generating real-time process analysis of mobile phone screen protector application are as follows: S101. Based on the screen protector application process monitoring data of the mobile phone mid-frame production line, a data fusion algorithm is used to synchronize the thickness data collected by multiple displacement sensors. Then, the geometric position of the mid-frame corresponding to the data is calibrated using a visual coordinate system. A weighted average is then applied, assigning differentiated weights based on the measurement accuracy of each sensor, integrating process parameters from multiple sources to generate a comprehensive screen protector application data analysis set. S102. Based on the comprehensive screen protector application data analysis set, a time series analysis algorithm is used to analyze historical batch data using an autoregressive model to identify the long-term trend and periodic deviation of the screen protector thickness. Spectral analysis is used to distinguish process data. The frequency components in the image reveal the periodic fluctuations and trend changes of the adhesive coating pressure, generating dynamic material analysis results; S103, based on the dynamic material analysis results, a machine learning classification algorithm is used to extract the conductivity and magnetic susceptibility features of the material through a support vector machine, and the boundary of the feature space is divided. Then, a decision tree algorithm is used to classify the film material type according to the extracted features, generating a film material classification overview; S104, based on the film material classification overview, a process situation analysis method is used to conduct probabilistic analysis of multiple risk levels of the production state through a Markov chain model, predict the conversion path of process deviation, use a stochastic process model to quantify the randomness of film quality, assess future changes in interference risk, and generate real-time process analysis for mobile phone film application.

[0023] In sub-step S101, heterogeneous data is fused using real-time data streams from the mobile phone mid-frame film application production line. First, micron-level thickness data collected by multiple displacement sensors is synchronized using a timestamp correction algorithm to ensure spatial alignment. Next, the coordinates of the film application on the mid-frame are reconstructed using an industrial camera from a vision system. Data from each sensor is weighted according to its static repeatability and dynamic stability. For example, the laser displacement sensor's weight is set to... The weight of the stylus sensor is set to The comprehensive film application data analysis set generated in this process has high confidence, providing a high-quality foundation for subsequent fractal structure analysis.

[0024] In sub-step S102, the comprehensive film extrusion data analysis set is fed into the time-frequency domain analysis unit. First, an autoregressive (AR) model is applied to identify long-term drift in thickness fluctuations, which are typically associated with roller wear. Next, spectral analysis is performed using Fast Fourier Transform (FFT) to identify pressure pulsations at specific frequencies. These analyses reveal non-random patterns in the process, and the resulting material dynamic analysis results reflect the hydrodynamic characteristics of the film extrusion process, providing an early warning system for preventing batch quality incidents.

[0025] In substep S103, the electromagnetic properties of the material are processed. Support Vector Machine (SVM) projects multidimensional property data (conductivity, magnetic susceptibility, dielectric loss) into a high-dimensional feature space using a kernel function to find the optimal classification hyperplane. This enables the system to distinguish subtle differences between surface coatings containing metal particles and pure polymer materials. Subsequently, the decision tree generates logical discriminative chains based on this boundary information. The final coating material classification overview includes not only the material name but also its electromagnetic field sensitivity rating, providing parameter input for radio frequency compensation.

[0026] In sub-step S104, a stochastic process model is used to evaluate the dynamic stability of the process. Markov chains are used to define three process states: normal, warning, and high-risk, and the state transition probabilities are calculated based on current process parameters (such as tension, temperature, and velocity). For example, when the ambient humidity exceeds... At this time, the probability of the process shifting to a "high-risk" state increases. This probabilistic analysis can effectively capture the risk of occasional interference in production, and the generated real-time process analysis report for mobile phone screen protector application is the core input for subsequent risk mitigation strategies. Assuming a screen protector application station on a smartphone assembly line, the data collected by the sensors includes the film thickness... The detected diameter distribution of metal particles. After data synchronization, the laser sensor weights are integrated through weighted averaging. In time series analysis, the autoregressive model found that thickness per production The item will appear after The periodic increase. When using SVM classification, the film material was identified as aluminum-coated PET. Finally, the Markov chain predicts that under continuous high-speed operation, the probability of bonding bubble generation will increase from... Rise to These analytical results together constitute a high-precision monitoring platform for mobile phone screen protector application processes.

[0027] Please see Figure 3This invention provides a method for risk assessment of mobile phone mid-frame screen protector application based on process data. The specific steps for generating electromagnetic feature recognition of the screen protector are as follows: S201, based on the real-time process analysis of the mobile phone screen protector application, an autoregressive moving average model is used. By statistically modeling the time series data, the autocorrelation and moving average characteristics in historical interference data are analyzed to reveal the periodic changes in signal attenuation, generating a periodic analysis of electromagnetic attenuation; S202, based on the periodic analysis of electromagnetic attenuation, a system dynamics model is used. By establishing and analyzing the differential equation describing the penetration rate of electromagnetic waves in multi-layer materials, the dynamic changes of the electromagnetic field are simulated, including reflection loss and absorption loss modes, generating electromagnetic interference and loss mode analysis. Analysis; S203. Based on the electromagnetic interference and loss mode analysis, network analysis methods are applied to calculate the impedance connectivity and coupling coefficient of multiple nodes in the equivalent circuit of the film surface, analyze the mutual electromagnetic influence between differentiated bonding areas, identify the electromagnetic weak points of the film layer, and generate film electromagnetic interaction analysis; S204. Based on the film electromagnetic interaction analysis, multiple regression analysis is adopted, and through statistical modeling, combined with historical test and real-time sensing data, the interference change trend of future production batches is quantitatively predicted, including potential signal attenuation points and interference concentration areas, and film electromagnetic feature identification is generated; the analysis of autocorrelation and moving average characteristics in historical interference data adopts the autoregressive integral moving average model formula; in, This is the electromagnetic attenuation observation value at the current production time point. It is the time lag operator. These are autoregressive parameters. It is a moving average parameter. It is the difference order. It is white noise measurement error. It is a constant term. It is the autoregressive order. It is a difference operator. It is a moving average order index. It is the order of the moving average.

[0028] In sub-step S201, the signal strength attenuation after film application is modeled using an ARIMA model. First, raw RF detection data from continuous production is collected and converted into a time series with a fixed sampling frequency. An autoregressive component is used to capture systematic signal deviations caused by batch fluctuations in the film roll raw material, while a moving average component is used to filter out electromagnetic background noise from the production line. Parameters and The choice is made by minimizing The criteria were established. The generated attenuation periodicity analysis revealed a deep correlation between the film thickness uniformity and the signal fluctuation frequency.

[0029] In substep S202, the electromagnetic field dynamics modeling employs the discrete form of Maxwell's equations. By establishing partial differential equations describing the electromagnetic wave passing through the three-layer structure of the film-adhesive-middle frame, different frequencies (such as...) are simulated. and Loss distribution under ( ). Reflection loss and absorption loss The generated interference and loss modes were analyzed as key variables. The analysis revealed that, under a specific metal composition ratio, heat loss caused by the eddy current effect was the dominant factor in signal attenuation, providing a theoretical basis for improving the membrane material formulation.

[0030] In substep S203, the physical layer of the film is abstracted as an impedance network. By calculating the equivalent capacitance and mutual inductance (edges) between different bonding points (nodes), the distribution of electromagnetic energy on the film surface is analyzed. High connectivity areas correspond to charge accumulation areas, which are usually ghosting areas induced by antennas. The electromagnetic weak points identified by network analysis are the physical locations where signals are most susceptible to interference, providing a geographical reference for optimizing and avoiding subsequent cutting paths.

[0031] In sub-step S204, the multivariate regression model integrates multiple independent variables such as material thickness, metal conductivity, and ambient temperature and humidity. By fitting a large sample size to the regression model, a predictive equation for signal attenuation is obtained. The model's output prediction of the interference concentration area can indicate which mid-frame areas will experience the first drop in communication quality below a threshold if environmental parameters fluctuate in future production. The generated electromagnetic feature recognition results for the film achieve a cross-dimensional mapping from physical parameters to communication performance. Assuming that in the testing of a certain type of mobile phone, the collected electromagnetic attenuation data sequence is processed using ARIMA(1,1,1) to identify each... The roll film material exhibits a sinusoidal oscillation trend. In S202, simulations using differential equations revealed that as the thickness increases... At that time, the absorption loss increased. Impedance network analysis revealed that the electromagnetic coupling coefficient near the antenna in the upper left corner of the frame was as high as [missing information]. This area is designated as a high-risk zone. Finally, the multiple regression model predicts that the signal strength in this area will decrease further under the high humidity conditions of summer. These in-depth electromagnetic identification data greatly enhance the predictability of process design.

[0032] Please see Figure 4This invention provides a method for risk assessment of mobile phone frame screen protector application based on process data. The specific steps for generating interference risk prediction analysis are as follows: S301, based on the electromagnetic feature recognition of the screen protector, an autoregressive model is used to predict the change in signal attenuation values ​​in future batches by calculating the autocorrelation in the time series of historical process data, analyzing and simulating the time dependence of interference data, and generating an interference intensity trend prediction; S302, based on the interference intensity trend prediction, an exponential smoothing method is used to perform weighted averaging on the predicted data, highlighting the impact of recent process changes on future signal quality and reducing the impact of single measurement errors on the prediction. S303. Based on the short-term interference risk prediction, a seasonal autoregressive moving average model is used to analyze the seasonal quality changes in raw material batch supply and predict the interference probability in a specific production cycle in the future. With reference to raw material environmental factors, a batch-based interference prediction analysis is generated. S304. Based on the batch-based interference prediction analysis, cluster analysis is used to divide the film-applying area into differentiated risk groups according to the similarity of electromagnetic feature data, identify high interference frequency bands and key interference sections, and highlight key risk characteristics by quantifying the distribution differences of process parameters, thereby generating an interference risk prediction analysis.

[0033] In substep S301, the evolution logic of material properties is quantified using an autoregressive model. Focusing on the key characteristic of metal content, its historical evolution is analyzed. The autocorrelation function in each batch. By establishing an order of... The model extrapolates the future. The electromagnetic response of each batch is analyzed. The generated trend forecast can anticipate signal consistency risks caused by fluctuations in supplier raw material prices, providing the purchasing department with recommendations for adjusting quality benchmarks.

[0034] In substep S302, the exponential smoothing method employs a triple smoothing strategy (Holt-Winters). This is achieved by setting smoothing coefficients... Focus on process feedback from recent batches, while eliminating noise interference during equipment startup. The generated short-term forecasts can accurately guide fine-tuning of process parameters for the next shift, such as adjusting the laminator pressure in advance based on the slight thickening trend of the film material to ensure signal gain stability.

[0035] In sub-step S303, a seasonal model is used to analyze the impact of ambient temperature and humidity on membrane material storage. Since the dielectric constant of the adhesive fluctuates seasonally with temperature, the SARIMA model incorporates a periodic term. Monthly forecasts predict peak shifts in disturbances caused by climate change. This batch analysis effectively identifies production risks under special conditions such as the rainy season, guiding parameter settings for workshop environmental control systems.

[0036] In substep S304, the cluster analysis employs the K-means++ algorithm. Based on the center band attenuation value, eddy current loss rate, and dielectric constant deviation, different parts of the phone's frame are divided into low-sensitivity areas, conventional interference areas, and critical shielding areas. This spatial clustering hierarchy makes risk management more targeted, prioritizing the process accuracy of the antenna's main radiation area. The resulting interference risk prediction analysis report serves as direct input for developing mitigation solutions. Imagine a production base predicting, using an autoregressive model, that the electrical loss of the next batch of PET film will increase for a particular 5G phone. Exponential smoothing confirmed that the noise was a gradual process drift rather than a random error. The SARIMA model further indicated that with the arrival of the dry winter season, electrostatic electromagnetic noise would become dominant. Finally, cluster analysis identified the area of ​​the top antenna support as a "high-risk group" with an interference probability as high as [missing information]. These multi-dimensional predictive analyses provide a scientific basis for the dynamic scheduling of production lines.

[0037] Please see Figure 5 This invention provides a method for assessing the risk of applying a screen protector to a mobile phone frame based on process data. The specific steps for generating a signal interference risk mitigation scheme are as follows: S401. Based on the interference risk prediction analysis, a linear programming algorithm is used. The pressure and speed of multiple workstations in the process flow are set as decision variables, and material strength limitations and signal gain indicators are used as constraints. The parameters of each workstation are allocated by solving an optimization problem to reduce antenna shielding caused by the screen protector application, generating an optimized screen protector process design; S402. Based on the optimized screen protector process design, graph theory analysis is used, including constructing a charge distribution map of the screen protector surface, where feature points are nodes and current paths are edges. The equivalent impedance distribution between nodes is calculated, and the screen protector cutting scheme is adjusted according to the current distribution to optimize the overall electromagnetic transparency of the frame, generating an optimized cutting path configuration; S40 3. Based on the optimized cutting path configuration, a dynamic resource allocation model is applied. The downward pressure and dwell time of the laminating head are updated according to real-time sensor feedback. The laminating execution system is adjusted in real-time to match changes in production line speed, reducing electromagnetic scattering caused by lamination bubbles and wrinkles, and generating a process parameter optimization strategy. S404. Based on the process parameter optimization strategy, laminating process optimization design, and optimized cutting path configuration, a comprehensive risk management strategy is formulated. The production line layout and calibration frequency are adjusted, and compensation schemes are proposed to address material composition fluctuations, reduce signal attenuation risks, and generate a signal interference risk mitigation scheme. The pressure and speed of multiple stations in the process flow are set as decision variables, and material strength limitations and signal gain indicators are used as constraints. The parameters of each station are allocated by solving an optimization problem using a signal flow graph model formula. in, Surface charge density, representing coordinates Location and Time The charge distribution, is the flux function, representing the product of charge density and diffusion velocity.

[0038] In substep S401, a process-performance optimization model is established using linear programming. The objective function is set to maximize antenna gain. The decision variables include the downforce at the four workstations. and conveyor belt speed The constraints include the stress threshold preventing plastic deformation of the membrane material and the minimum pressure requirement for adhesive layer venting. The optimal parameter combination was obtained using the simplex method. The resulting optimized process design minimized the increase in dielectric layer density due to excessive pressure while ensuring bonding quality, thereby reducing the reflectivity of electromagnetic signals.

[0039] In the S402 sub-step, graph theory analysis focuses on the conductivity continuity of the film. By constructing a feature point impedance model, high-impedance nodes in the current return path are identified. For these nodes, asymmetric cutting paths are designed, introducing electromagnetic conduction windows or dielectric grids to break the eddy current loops formed by the large-area metal film layer. The optimized configuration of the generated cutting paths significantly improves the electromagnetic transparency of the midframe, increasing the antenna radiation efficiency by approximately [percentage missing]. .

[0040] In sub-step S403, the dynamic resource allocation model is based on control theory. When the upstream thickness sensor detects that the membrane material is too thick, the controller increases the dwell time of the application head in real time. This ensures the adhesive is fully wetted, reducing micron-level air gaps between interfaces. This closed-loop control effectively reduces electromagnetic wave reflection (scattering) caused by air gaps, ensuring electromagnetic consistency for every product.

[0041] In sub-step S404, a global risk management strategy is developed. This includes automatically adjusting equipment calibration cycles based on environmental changes and establishing a library of preset parameters for different supplier membrane materials. The solution incorporates "electromagnetic redundancy design," automatically activating a backup low-boost mode in batches with high signal attenuation risk. The resulting signal interference risk mitigation plan is a process guideline covering mechanical, electrical, and material aspects. Assuming a high-frequency communication product's film lamination process, linear programming calculates... At that time, the pressure distribution should be To achieve optimal gain, graph theory analysis suggests cutting three lines directly above the main antenna. The equipotential bonding joint. The dynamic control system automatically shortens the dwell time after detecting a rise in bonding temperature. The final mitigation solution enabled the entire system to... Reduced transmission loss in the frequency band This greatly improves network speed in weak signal environments.

[0042] Please see Figure 6 This invention provides a method for risk assessment of screen protector application on mobile phone frames based on process data. The specific steps for generating a screen protector signal compatibility impact assessment are as follows: S501, based on mobile phone radio frequency signal data and the aforementioned signal interference risk mitigation scheme, an electromagnetic wave propagation simulation is performed using a lattice Boltzmann model. This simulates the electromagnetic environment on the frame surface by simulating the oscillation and scattering of electromagnetic microparticles, achieving signal strength simulation for multiple areas of the screen protector, analyzing the potential improvement in signal gain from the implementation of the scheme, and generating an initial signal distribution simulation diagram; S502, based on the initial signal distribution simulation diagram and combined with material dielectric data, a cellular automata model is used to analyze the influence of screen protector particle arrangement on the electromagnetic field distribution. This is achieved by defining electromagnetic response rules for the microstructure to simulate equivalent electromagnetic field distribution. S503. Based on the thermal influence analysis diagram of the material, the signal strength and material stress data are combined using data fusion technology. By integrating multi-source parameters, the interaction between dielectric drift caused by mechanical stress and signal attenuation is analyzed to evaluate its comprehensive impact on communication reliability and generate a compatibility interaction comprehensive diagram. S504. Based on the compatibility interaction comprehensive diagram, the antenna gain and efficiency are comprehensively evaluated using a radiation assessment model. By quantitatively analyzing the impact of film loss and its interaction with beamforming on the terminal environment, a compensation scheme is provided to conduct a comprehensive evaluation of the mid-frame RF performance and generate a film signal compatibility impact assessment.

[0043] In the S501 substep, the Lattice Boltzmann Method (LBM) was used for high-fidelity electromagnetic simulation. The propagation of radio frequency waves in a non-uniform film layer was simulated by discretizing the electromagnetic evolution equations on a D3Q19 lattice. LBM can handle complex boundary conditions, such as stepped protrusions at the film edges. The initial plot generated from the simulation results shows the signal intensity contour map on the mid-frame surface, visually reflecting the mitigation scheme's ability to cover signal dead zones and assessing the expected gain improvement.

[0044] In the S502 sub-step, a cellular automata model is used to handle the microscopic heterogeneity within the membrane material. The film layer is divided into millions of cellular units, each of which executes a state update rule based on its local metal powder filling rate. This step is able to capture "micrometer-scale hotspots," i.e., localized charge enrichment points, that are undetectable by macroscopic simulations. The generated material thermal effect map reveals the microscopic roots of signal phase distortion.

[0045] In substep S503, data fusion technology superimposes electromagnetic field intensity with structural mechanical stress contour maps. Analysis shows that in regions of high tensile stress during film application, changes in polymer chain orientation lead to anisotropic dielectric responses. By establishing a stress-dielectric coupling matrix, signal performance fluctuations caused by stretching during production are assessed. The generated interactive synthesis map provides a quantitative basis for optimizing the tension system of the laminating machine.

[0046] In substep S504, radiative efficiency is quantitatively characterized. Using antenna pattern data, the total radiated power (TRP) and total received sensitivity (TIS) after applying the film are calculated. The evaluation model considers dynamic losses under beamforming technology. By comparing the gain curves before and after film application, a final compatibility evaluation report is generated, including compatibility scores for different mobile phone frequency bands, providing key RF performance references for product finalization. Assuming that in a 5G millimeter-wave test simulation, the LBM model shows that diffraction at the film edge causes a decrease in signal strength... Cellular automata analysis revealed that the directional arrangement of metal fibers within the membrane material caused lateral leakage current. Stress data fusion showed that compressive stress at the corners of the middle frame locally increased the dielectric constant at those locations. Ultimately, the radiation assessment model provides an overall evaluation: under the current scheme, the antenna efficiency remains... The above meets the mass production standards.

[0047] Please see Figure 7 This invention provides a method for risk assessment of mobile phone mid-frame screen protector application based on process data. The specific steps for generating a signal enhancement screen protector application process plan are as follows: S601, Based on the screen protector signal compatibility impact assessment diagram, a system dynamics method is used for analysis. By establishing a dynamic feedback model of process variables, electromagnetic losses, and their interactions, the impact of differentiated process adjustments on the signal shielding degree is simulated. Referring to factors such as feed rate, heating temperature, and pressure, dynamic analysis results of process adjustments are generated; S602, Based on the dynamic analysis results of process adjustments, multi-criteria decision analysis is used, combining screen protector accuracy data and signal test parameters, to evaluate and compare differentiated parameters. The effectiveness of the combination in improving signal penetration is used to generate an optimized process strategy to mitigate signal shielding; S603, based on the optimized process strategy to mitigate signal shielding, Monte Carlo simulation is used to simulate multiple production scenarios after the strategy is implemented through random sampling technology, predict the performance of process adjustment under various environmental humidity and temperature fluctuation conditions, evaluate the robustness of the scheme, and generate process implementation simulation prediction results; S604, based on the process implementation simulation prediction results, a process path optimization model is used to adjust and redesign each execution link of the film application system, and generate a signal enhancement film application process plan by referring to pressure gradient distribution and bonding angle optimization.

[0048] In substep S601, system dynamics modeling describes the process chain using a stock-flow diagram. Stocks include film quality stability and signal gain balance. Difference equations are established to simulate the effect of increasing heating temperature. The dynamic process of reduced signal scattering due to increased adhesive flowability was analyzed. The results show the contribution weights of various process factors to the signal shielding effect, establishing temperature control as a key factor in enhancing the signal.

[0049] In sub-step S602, Multi-Criterion Decision Analysis (MCDA) employs the Analytic Hierarchy Process (AHP). The criterion layer includes signal transmittance, process time, and membrane material utilization. By comparing expert scores and simulation data, the optimal path is selected from multiple alternatives. The generated strategy achieves synergistic optimization of communication performance and production efficiency.

[0050] In substep S603, the Monte Carlo simulation was performed. This was a series of independent randomized trials. Simulation parameters included random perturbations in feed tension and sensor measurement errors. Histograms generated from statistical analysis show that... Within the confidence interval, the signal gain fluctuation under the new process strategy is controlled within [the specified range]. Within this range. This verifies the strong robustness of the strategy in real-world, complex workshop environments.

[0051] In sub-step S604, a structural redesign of the equipment is performed. Based on the optimized pressure gradient distribution, the elastic support structure of the laminating head is rearranged, and the laminating trajectory angle of the robotic arm is adjusted from traditional vertical pressing to oblique sweeping pressing to facilitate air expulsion. The generated plan includes updated code for the laminating machine control software and a hardware fine-tuning list, aiming to enhance the signal friendliness of the laminating system from the source. In a specific signal enhancement project, the system dynamics model indicates that increasing the preheating temperature can reduce... Interface electromagnetic noise. MCDA evaluation showed that the segmented pressure control scheme received the highest overall score. Monte Carlo simulation predicted that even with fluctuations in the dielectric constant of the raw materials... In extreme cases, this scheme can still ensure that the antenna gain is not lower than the initial value. The final process plan was successfully implemented on the production line, improving the signal throughput of this phone model by approximately [percentage missing]. .

[0052] Please see Figure 8This invention provides a method for risk assessment of mobile phone mid-frame screen protector application based on process data. The specific steps for generating a comprehensive process risk assessment optimization are as follows: S701, based on the comprehensive signal interference risk mitigation scheme and signal enhancement screen protector application process plan, a genetic algorithm is used to optimize the screen protector material ratio and application position by simulating individual selection, gene crossover, and mutation, obtaining an optimized process system configuration; S702, based on the optimized process system configuration, a particle swarm optimization algorithm is used to simulate group cooperative search, optimizing the motion control curve and vacuum adsorption pressure of the screen protector application machine to improve production efficiency and reduce the parasitic capacitance effect caused by screen protector application, generating an optimized... S703. Based on the optimized motion control and process parameter scheme, a multi-objective genetic algorithm is adopted to capture the Pareto optimal solution by simulating the natural selection mechanism among multiple objectives, including reducing interference noise, reducing yield loss, and saving material costs, balancing multi-dimensional indicators, and generating a comprehensive optimized multi-objective process strategy; S704. Based on the comprehensive optimized multi-objective process strategy, a particle swarm optimization algorithm is adopted to iteratively update the velocity and position of particles representing potential process schemes, making them move closer to the individual and global optimal process points, finely adjusting the strategy, refining the parameter tolerance range, and generating a comprehensive process risk assessment optimization.

[0053] In the S701 sub-step, the chromosomes of the genetic algorithm encode the metal composition of the material and the coordinates of key locations. By defining the fitness function as a signal integrity metric, after... Generational evolution eliminates configurations that could cause antenna detuning. The resulting process configuration scheme, through genetic optimization, ensures low-interference characteristics at the material level.

[0054] In the S702 sub-step, Particle Swarm Optimization (PSO) fine-tunes the motion control. Each particle represents a set of velocity, acceleration, and adsorption pressure parameters. By searching for the global optimum, PSO discovers that the microscopic flatness of the membrane material is highest when the adsorption pressure pulsates at a specific frequency, thereby minimizing the parasitic capacitance between the film layer and the frame. The resulting motion control scheme takes into account... Fitting precision and The rhythm.

[0055] In the S703 sub-step, the multi-objective genetic algorithm (NSGA-II) addresses the trade-off between cost and performance. By constructing a non-dominated sort, the algorithm provides a set of Pareto optimal front solutions. For example, solution A has the lowest cost but only a moderate improvement in signal gain, while solution B has the highest gain but a slightly higher cost. This provides management with a flexible decision-making space, and the generated comprehensive strategy can be switched between models with different market positioning.

[0056] In the S704 sub-step, the selected process strategy undergoes final parameter fine-tuning. The particle swarm optimization rapidly converges within the narrowed feasible region, determining the gain coefficient and response threshold of each sensor feedback control loop. The generated comprehensive risk assessment optimization scheme is an intelligent production scheme containing a specific instruction set, marking a shift in process design from experience-driven to data-driven global optimization. Assuming that in the process optimization of a flagship model, the genetic algorithm determines the parameters containing... The composite film of aluminum powder is offset at the center of the antenna region. The signal is best at that time. The particle swarm optimization algorithm improves the efficiency of screen protector application. At the same time, it reduces parasitic capacitance. NSGA-II found that the cost only increased. However, the signal anti-interference capability has been improved. The final solution not only reduced yield losses but also achieved a significant leap in overall communication performance.

[0057] Please see Figure 9 This invention provides a method for risk assessment of mobile phone mid-frame screen protector application based on process data. The specific steps for generating a comprehensive planning result for screen protector risk assessment are as follows: S801. Based on the comprehensive process risk assessment optimization, statistical analysis methods are used to analyze the trend and fluctuation of yield data in batch production. Subsequently, a machine learning prediction model is used to evaluate the yield distribution through random forest and mine the distribution patterns of interference sources using neural networks to generate a current status analysis result for screen protector quality; S802. Based on the current status analysis result for screen protector quality, a lifecycle assessment model is used to analyze the energy consumption and material loss in multiple stages of screen protector production, and to analyze the deep correlation between these factors and the risk of user returns due to signal attenuation, thereby enabling the assessment of the risk assessment results. S803. Based on the life cycle quality assessment of the screen protector system, a cost-benefit analysis method is used to analyze the economic returns of differentiated improvement strategies through the present value method, and the financial feasibility of process upgrades is assessed using the internal rate of return method. The most efficient risk management strategy is selected, and an optimized risk management strategy analysis is generated. S804. Based on the optimized risk management strategy analysis, combined with the system dynamic simulation method and multi-objective optimization model, the evolutionary behavior of the screen protector process system is simulated through system dynamic theory, and the optimal quality balance point is searched using a genetic algorithm. A screen protector risk management plan matching the characteristics of a specific mobile phone model is formulated, and a comprehensive planning result for screen protector risk assessment is generated.

[0058] In the S801 sub-step, a random forest algorithm is used to evaluate the multi-feature importance of yield across thousands of batches. Analysis reveals that batch consistency among membrane material suppliers is the leading factor affecting signal quality fluctuations. The neural network further identifies a nonlinear coupling pattern between ambient humidity and high-frequency signal attenuation. The resulting status quo analysis not only reflects the current level but also provides a root cause map of quality fluctuations.

[0059] In substep S802, the life cycle assessment (LCA) is extended to the product's service life. Analysis shows that some inexpensive film materials, after one year of user use, experience a decrease in signal strength due to an increase in dielectric constant caused by aging. By establishing a long-term degradation model, the contribution of process improvements to reducing the risk of returns throughout the entire product lifecycle was evaluated. The resulting lifecycle assessment report extends the quality perspective from the point of manufacture to the entire user's usage period.

[0060] In substep S803, cost-benefit analysis (CBA) calculates input and output using present value (NPV). For example, while introducing a high-precision vision alignment system increases initial investment, its internal rate of return (IRR) is significantly higher by reducing after-sales costs due to poor signal performance. The analysis results support the rationality of the decision to upgrade the technology for the core frequency band.

[0061] In the S804 sub-step, analysis from all dimensions is integrated to form the final comprehensive plan. System dynamic simulation is used to predict the changing trend of process redundancy over the next year as production ramps up. A genetic algorithm ultimately identifies the optimal balance between quality and cost, resulting in a comprehensive white paper that includes signal reduction quantification targets, implementation paths for each stage, and expected communication benefits. This signifies that mobile phone mid-frame screen protector risk assessment has become a quantifiable, predictable, and optimizable scientific closed loop. For the production planning of a specific mobile phone model, statistical analysis confirms the current screen protector yield rate. LCA analysis shows that without optimization, the long-term signal risk cost can reach as high as 10,000 units. The cost was tens of thousands of yuan. CBA analysis proved that the newly developed low-loss film application process could recover the upgrade cost within three months. The final comprehensive planning results provided the company with a detailed implementation timeline and expected benefits, successfully reducing the overall communication failure rate. .

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A method for risk assessment of mobile phone mid-frame film application based on process data, characterized in that: Includes the following steps: S1. Based on the film application process monitoring data of the mobile phone mid-frame production line, data fusion and fractal geometry methods are used to conduct a comprehensive analysis of the material metal composition, thickness and adhesive type, and a preliminary analysis of the process status to generate a real-time process analysis of mobile phone film application. S2. Based on the real-time process analysis of the mobile phone screen protector, time series analysis and electromagnetic field dynamics modeling are used to explore the peak value of electromagnetic interference and the material loss mode, and to identify the trend of interference intensity change, thereby generating screen protector electromagnetic feature recognition. S3. Based on the electromagnetic feature recognition of the film, an autoregressive model is used to predict the trend of interference intensity in future production batches, and high-risk materials and interference frequency bands are analyzed to generate interference risk prediction analysis. S4. Based on the aforementioned interference risk prediction and analysis, optimization theory and electromagnetic compatibility engineering methods are used to optimize the film thickness and bonding position, and a process parameter adjustment strategy for the film applicator is formulated to generate a signal interference risk mitigation scheme. S5. Combining mobile phone radio frequency signal data and the aforementioned signal interference risk mitigation scheme, the lattice Boltzmann model is used to analyze the interaction between dielectric constant distribution and signal beam flow, and the impact of antenna radiation gain is evaluated to generate a film signal compatibility impact assessment. S6. Based on the aforementioned assessment of the signal compatibility impact of the film application, a system dynamics approach is adopted to formulate a process management strategy to mitigate the signal shielding effect, and a structural adjustment scheme for the film application system is designed to generate a signal enhancement film application process plan. S7. Combining the aforementioned signal interference risk mitigation scheme and signal enhancement film application process plan, a comprehensive trade-off and optimization strategy design is performed using genetic algorithms and particle swarm optimization to balance signal integrity, production efficiency, and material costs, generating a comprehensive process risk assessment optimization.

2. The method for risk assessment of mobile phone mid-frame film application based on process data according to claim 1, characterized in that: Step S1 is as follows: S101. Based on the film application process monitoring data of the mobile phone mid-frame production line, a data fusion algorithm is used to synchronize the thickness data collected by multiple displacement sensors in time. Then, the geometric position of the mid-frame corresponding to the data is calibrated through the visual coordinate system. Then, a weighted average is applied, and differentiated weights are assigned according to the measurement accuracy of each sensor. The process parameters from multiple sources are integrated to generate a comprehensive film application data analysis set. S102. Based on the comprehensive film application data analysis set, a time series analysis algorithm is used to analyze historical batch data through an autoregressive model to identify the long-term trend and periodic deviation of the film thickness. Spectral analysis is used to distinguish the frequency components in the process data, reveal the periodic fluctuations and trend changes of the adhesive pressure, and generate dynamic material analysis results. S103. Based on the material dynamic analysis results, a machine learning classification algorithm is used to extract the electrical conductivity and magnetic susceptibility features of the material through a support vector machine, and the boundary of the feature space is divided. Then, a decision tree algorithm is used to classify the film material type according to the extracted features, and an overview of the film material classification is generated. S104. Based on the overview of the film material classification, the process situation analysis method is used to perform probabilistic analysis of multiple risk levels of the production status through the Markov chain model, predict the transformation path of process deviation, use the stochastic process model to quantify the randomness of the film quality, assess future changes in interference risk, and generate real-time process analysis of mobile phone film application.

3. The method for risk assessment of mobile phone mid-frame film application based on process data according to claim 1, characterized in that: Step S2 is as follows: S201. Based on the real-time process analysis of the mobile phone screen protector, an autoregressive moving average model is adopted. By statistically modeling the time series data, the autocorrelation and moving average characteristics in the historical interference data are analyzed to reveal the periodic changes in signal attenuation and generate electromagnetic attenuation periodic analysis. S202. Based on the electromagnetic attenuation periodicity analysis, using the system dynamics model, by establishing and analyzing the differential equation describing the penetration rate of electromagnetic waves in multilayer materials, the dynamic changes of the electromagnetic field are simulated, including reflection loss and absorption loss modes, and electromagnetic interference and loss mode analysis are generated. S203. Based on the electromagnetic interference and loss mode analysis, network analysis method is applied to analyze the mutual electromagnetic influence between different bonding areas by calculating the impedance connectivity and coupling coefficient of multiple nodes in the equivalent circuit of the film surface, identifying the electromagnetic weak links of the film layer, and generating film electromagnetic interaction analysis. S204. Based on the electromagnetic interaction analysis of the film, multiple regression analysis is adopted. Through statistical modeling, combined with historical test and real-time sensing data, the interference change trend of future production batches is quantitatively predicted, including potential signal attenuation points and interference concentration areas, and the electromagnetic feature recognition of the film is generated.

4. The method for risk assessment of mobile phone mid-frame film application based on process data according to claim 1, characterized in that: Step S3 is as follows: S301. Based on the electromagnetic feature recognition of the film, an autoregressive model is adopted. By calculating the autocorrelation in the time series of historical process data, the signal attenuation value is predicted to change in multiple future batches. The time dependence of interference data is analyzed and simulated to generate an interference intensity trend prediction. S302. Based on the predicted interference intensity trend, the predicted data is weighted and averaged using the exponential smoothing method to highlight the impact of recent process changes on future signal quality and reduce the impact of single measurement errors on the prediction, thereby generating a short-term interference risk prediction. S303. Based on the aforementioned short-term interference risk prediction, a seasonal autoregressive moving average model is used to analyze the seasonal quality changes in raw material batch supply and predict the probability of interference in a specific production cycle in the future. A batch-specific interference prediction analysis is generated with reference to raw material environmental factors. S304. Based on the batch interference prediction analysis, the film-applied area is divided into differentiated risk groups according to the similarity of electromagnetic feature data through cluster analysis, high interference frequency bands and key interference sections are identified, and key risk characteristics are highlighted by quantifying the distribution differences of process parameters, thereby generating interference risk prediction analysis.

5. The method for risk assessment of mobile phone mid-frame film application based on process data according to claim 1, characterized in that: Step S4 is as follows: S401. Based on the interference risk prediction analysis, a linear programming algorithm is adopted to set the pressure and speed of multiple stations in the process flow as decision variables, and to use material strength limits and signal gain indicators as constraints. The parameters of each station are allocated by solving the optimization problem, thereby reducing antenna shielding caused by film application and generating an optimized design for the film application process. S402. Based on the optimized design of the film application process, graph theory analysis is used, including constructing a charge distribution map of the film surface, where feature points are used as nodes and current paths are used as edges, calculating the equivalent impedance distribution between nodes, adjusting the film cutting scheme according to the current distribution, optimizing the overall electromagnetic transparency of the middle frame, and generating an optimized configuration of the cutting path. S403. Based on the optimized configuration of the cutting path, apply a dynamic resource allocation model, update the downward pressure and dwell time of the film application head according to real-time sensor feedback, adjust the film application execution system in real time, match the changes in production line speed, reduce electromagnetic scattering caused by bonding bubbles and wrinkles, and generate process parameter optimization strategies. S404. Based on the process parameter optimization strategy, film application process optimization design, and cutting path optimization configuration, formulate a comprehensive risk management strategy, adjust the production line layout and calibration frequency, propose compensation schemes to cope with material composition fluctuations, reduce signal attenuation risks, and generate signal interference risk mitigation schemes.

6. The method for risk assessment of mobile phone mid-frame film application based on process data according to claim 1, characterized in that: Step S5 is as follows: S501. Based on mobile phone radio frequency signal data and the signal interference risk mitigation scheme, the lattice Boltzmann model is used to simulate electromagnetic wave propagation. The electromagnetic environment of the middle frame surface is simulated by simulating the oscillation and scattering of electromagnetic microparticles, thereby simulating the signal strength of multiple areas of the film, analyzing the potential improvement of signal gain by the implementation of the scheme, and generating an initial simulation diagram of signal distribution. S502. Based on the initial simulation diagram of the signal distribution, combined with the material dielectric data, the influence of the arrangement of the film particles on the electromagnetic field distribution is analyzed using a cellular automata model. The equivalent conductivity change is simulated by defining the electromagnetic response rules of the microstructure, the direct influence of the material microstructure on the signal wavefront distortion is evaluated, and a material thermal zone influence analysis diagram is generated. S503. Based on the thermal influence analysis diagram of the material, the signal strength and material stress data are combined using data fusion technology. By integrating multi-source parameters, the interaction between dielectric property drift caused by mechanical stress and signal attenuation is analyzed, the comprehensive impact on communication reliability is evaluated, and a compatibility interaction comprehensive diagram is generated. S504. Based on the aforementioned compatibility interaction comprehensive diagram, a radiation assessment model is applied to comprehensively evaluate the antenna gain and efficiency. By quantitatively analyzing the impact of film loss and its interaction with beamforming on the terminal environment, a compensation scheme is provided, a comprehensive evaluation of the mid-frame RF performance is conducted, and an assessment of the compatibility impact of film signals is generated.

7. The method for risk assessment of mobile phone mid-frame film application based on process data according to claim 1, characterized in that: Step S6 is as follows: S601. Based on the aforementioned film-applied signal compatibility impact assessment diagram, a system dynamics method is used for analysis. By establishing a dynamic feedback model of process variables, electromagnetic losses and their interactions, the impact of differentiated process adjustments on the degree of signal shielding is simulated. The dynamic analysis results of process adjustments are generated with reference to factors such as feeding speed, heating temperature and pressure. S602. Based on the dynamic analysis results of the process adjustment, a multi-criteria decision analysis is adopted. Combining film application accuracy data and signal test parameters, the effectiveness of differentiated parameter combinations in improving signal penetration is evaluated and compared, and an optimized process strategy for mitigating signal shielding is generated. S603. Based on the process strategy optimization scheme for mitigating signal shielding, Monte Carlo simulation is adopted. Multiple production scenarios after the implementation of the strategy are simulated through random sampling technology. The performance of process adjustment under various environmental humidity and temperature fluctuation conditions is predicted, the robustness of the scheme is evaluated, and the process implementation simulation prediction results are generated. S604. Based on the simulation and prediction results of the process implementation, the process path optimization model is adopted to adjust and redesign each execution link of the film application system, and the signal enhancement film application process plan is generated by referring to the pressure gradient distribution and bonding angle optimization.

8. The method for risk assessment of mobile phone mid-frame film application based on process data according to claim 1, characterized in that: Step S7 is as follows: S701. Based on the comprehensive signal interference risk mitigation scheme and signal enhancement film application process planning, a genetic algorithm is used to optimize the film material ratio and application position by simulating individual selection, gene crossover and mutation, so as to obtain the optimized process system configuration. S702. Based on the optimized process system configuration, the particle swarm optimization algorithm is used to simulate group cooperative search and optimize the motion control curve and vacuum adsorption pressure of the laminating machine to improve production efficiency and reduce the parasitic capacitance effect caused by lamination, thereby generating an optimized motion control and process parameter scheme. S703. Based on the optimized motion control and process parameter scheme, a multi-objective genetic algorithm is adopted to capture the Pareto optimal solution by simulating the natural selection mechanism among multiple objectives, including reducing interference noise, reducing yield loss and saving material costs, balancing multi-dimensional indicators, and generating a comprehensive optimized multi-objective process strategy. S704. Based on the comprehensive optimization of the multi-objective process strategy, a particle swarm optimization algorithm is adopted to iteratively update the velocity and position of particles representing potential process schemes, so that they move closer to the individual and global optimal process points, refine the strategy, refine the parameter tolerance range, and generate a comprehensive process risk assessment optimization.

9. The method for risk assessment of mobile phone mid-frame film application based on process data according to claim 1, characterized in that: The mobile phone mid-frame screen protector risk assessment method based on process data also includes the following steps: S8. Based on the comprehensive process risk assessment and optimization, life cycle assessment and cost-benefit analysis are adopted to conduct a comprehensive assessment and production planning of the overall communication quality of the mobile phone and generate a comprehensive planning result for screen protector risk assessment.

10. The method for risk assessment of mobile phone mid-frame film application based on process data according to claim 9, characterized in that: Step S8 is as follows: S801. Based on the comprehensive process risk assessment and optimization, statistical analysis methods are used to conduct trend analysis and fluctuation exploration of the yield data in batch production. Subsequently, a machine learning prediction model is used to evaluate the yield distribution through random forest, mine the distribution pattern of interference sources through neural network, and generate the current status analysis results of film application quality. S802. Based on the analysis results of the current status of the film quality, a life cycle assessment model is adopted to analyze the energy consumption and material loss in multiple stages of film production, and to analyze the deep correlation between them and the risk of user returns caused by signal attenuation. In this way, a reliability life cycle assessment is carried out to generate a life cycle quality assessment of the film system. S803. Based on the life cycle quality assessment of the film application system, the cost-benefit analysis method is used to analyze the economic returns of the differentiated improvement strategy through the present value method, the internal rate of return method is used to assess the financial feasibility of the process upgrade, the most efficient risk management strategy is selected, and an optimized risk management strategy analysis is generated. S804. Based on the analysis of the optimized risk management strategy, combined with the system dynamic simulation method and multi-objective optimization model, the evolutionary behavior of the screen protector application process system is simulated through system dynamic theory, the optimal quality balance point is searched using a genetic algorithm, a screen protector risk management plan matching the characteristics of a specific mobile phone model is formulated, and a comprehensive planning result for screen protector risk assessment is generated.

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