Method and system for production data acquisition and monitoring of automotive interior injection molding equipment

Abstract

The present application relates to the technical field of equipment monitoring, and more particularly to a production data acquisition and monitoring method and system for automotive interior injection molding equipment. First, real-time operation state data is obtained from the injection molding equipment through a communication node, a device state data set is generated, and a parameter coupling model is constructed through time series analysis method; when the temperature parameter fluctuation exceeds the preset threshold, the optimized configuration is generated by fusing the pressure data, and is transmitted to the related equipment through the interactive network to determine the linkage parameter set; then, the linkage parameter set is processed by using the classification method to obtain the deviation index, and the synchronization instruction is generated according to the deviation to ensure the consistent beat between the devices and form a unified production cycle; finally, the production cycle data is extracted through the prediction model to predict the parameter fluctuation, adjust the equipment parameters, and determine the final process configuration. The present application solves the problems of beat deviation and parameter fluctuation between injection molding equipment in production, optimizes the production process, and improves the production efficiency and product quality.

Description

Technical Field

[0001] This invention relates to the field of equipment monitoring technology, and in particular to a method and system for collecting and monitoring production data in automotive interior injection molding equipment. Background Technology

[0002] In the injection molding production of automotive interior parts, production efficiency and product quality stability highly depend on the coordinated operation of multiple pieces of equipment, such as injection molding machines, mold temperature controllers, and dryers. However, there are complex dynamic coupling relationships between key process parameters such as barrel temperature, mold temperature, and injection pressure. Even slight fluctuations in these parameters can trigger abnormal changes in injection pressure, leading to either extended or shortened production cycles, decreased product dimensional accuracy, increased surface defects, and even higher defect rates. For example, minute fluctuations in barrel temperature around the set value can cause instability in injection pressure, directly affecting the molding consistency of the finished product.

[0003] Meanwhile, cycle time deviations between different devices on the production line can disrupt overall production synchronization. If the operating rhythms of injection molding machines, mold temperature controllers, and robotic arms differ, inconsistencies in production cycles can easily arise, increasing scheduling and control complexity and ultimately reducing overall production efficiency. Existing technologies typically rely on static control of single-machine parameters, which struggles to cope with the complexities of real-time interaction between multiple devices and parameters in actual production. Especially in the manufacturing of high-precision automotive interior parts, existing solutions cannot accurately model the dynamic correlation between minute fluctuations in barrel temperature and injection pressure, nor can they quantify cycle time deviations in real time and synchronize the production cycles of multiple devices. This leads to problems such as poor production consistency, difficulty in quality control, and high scrap rates.

[0004] Therefore, how to effectively collect and monitor key process parameters in the injection molding production of automotive interiors, establish a model that reflects the coupling relationship between temperature and pressure, and identify and correct cycle deviations in real time to achieve dynamic synchronization of production cycles of multiple equipment has become a core technical problem that urgently needs to be solved in the field of intelligent injection molding manufacturing. Summary of the Invention

[0005] The present invention proposes a method and system for data acquisition and monitoring in automotive interior injection molding equipment, which can realize the dynamic acquisition, fusion analysis and collaborative control of multi-source data from injection molding machines, mold temperature controllers, barrels and related equipment, thereby significantly improving the stability of the production process and the quality of products.

[0006] In a first aspect, the present invention provides a method for collecting and monitoring production data in automotive interior injection molding equipment, mainly comprising: Step S1: Obtain real-time operating status data from the injection molding equipment through the communication node to obtain the equipment status dataset, and use time series analysis method to process the equipment status dataset to construct a parameter coupling model; Step S2: Input the real-time collected barrel temperature sequence and injection pressure sequence into the parameter coupling model to determine whether the temperature parameter fluctuation exceeds the preset threshold. If it does, the injection pressure data is fused to generate mold temperature controller control instructions and temperature optimization configuration. The barrel temperature optimization configuration is obtained through the barrel fluctuation amplitude and transmitted to relevant devices through the interactive network to determine the linkage parameter set. Step S3: The set of linkage parameters is processed using a classification method to obtain a deviation index. If the deviation index exceeds the allowable range, a synchronization instruction sequence is generated and transmitted to the relevant equipment to obtain a unified production cycle. Step S4: Extract data from the unified production cycle using the prediction model to predict parameter fluctuations, adjust equipment parameters, and determine the final process configuration.

[0007] As a preferred embodiment of the present invention, in step S1, obtaining equipment status dataset by acquiring real-time operating status data from the injection molding equipment through a communication node includes: By collecting barrel temperature, mold temperature, and injection pressure data through communication nodes deployed in the injection molding machine, mold temperature controller, and dryer, an equipment status dataset containing the barrel temperature, mold temperature, and injection pressure is generated. Feature extraction is performed on the small fluctuation data of the barrel temperature to determine the influence characteristics of the small fluctuations of the barrel temperature on the injection pressure, and an equipment status dataset for subsequent analysis is generated.

[0008] As a preferred embodiment of the present invention, step S1 involves processing the equipment status dataset using a time series analysis method to determine the parameter coupling model, including: The dynamic change characteristics are obtained by performing time series decomposition on the equipment status dataset. The time series correlation between the micro-fluctuation of the barrel temperature and the injection pressure is analyzed. A coupling effect model including the micro-fluctuation of the barrel temperature and the instability of the injection pressure is constructed. Based on the coupling effect model, the correspondence between the barrel temperature fluctuation and the injection pressure is obtained. Based on the correspondence, a parameter coupling model is constructed. The barrel temperature micro-fluctuation data and the injection pressure data are used as training data, and the parameter coupling model is trained.

[0009] As a preferred embodiment of the present invention, in step S2, it is determined whether the temperature parameter fluctuation exceeds a preset threshold based on the parameter coupling model. If it does, the pressure data is fused to adjust the control command and generate an optimized configuration, including: The real-time acquired barrel temperature sequence and injection pressure sequence are input into the parameter coupling model, and it is determined whether the micro-fluctuation amplitude of the barrel temperature exceeds a preset threshold. If it does, the injection pressure data is fused to generate the control factor of the mold temperature controller. The mold temperature controller control command is obtained according to the control factor. The barrel temperature configuration is also optimized by the barrel fluctuation amplitude and the temperature of the mold temperature controller, generating a barrel temperature optimization configuration containing the optimized temperature value. This step is repeated to obtain the control command and temperature optimization configuration of the mold temperature controller in real time and transmit them to the mold temperature controller to achieve real-time adjustment cycle.

[0010] As a preferred embodiment of the present invention, step S2, which involves transmitting the optimized configuration to relevant devices via an interactive network to determine the linkage parameter set, includes: The adjusted mold temperature value is extracted from the temperature optimization configuration and transmitted to the injection molding machine and mold temperature controller through an interactive network to generate a linkage parameter set containing mold temperature and cycle time series. The cycle time difference between devices in the linkage parameter set is quantified using the parameter vector representation method to determine the linkage parameter set used for subsequent classification.

[0011] As a preferred embodiment of the present invention, in step S3, the set of linkage parameters is processed using a classification method to obtain a deviation index, including: The set of linkage parameters is classified using the support vector classification method to generate classification results that characterize the degree of rhythm difference. Based on the classification results, a rhythm deviation index is calculated. The range of the rhythm deviation index is defined by a preset classification boundary to determine the deviation index used to evaluate the synchronization of linkage equipment and generate a real-time judgment mechanism.

[0012] As a preferred embodiment of the present invention, in step S3, if the deviation index exceeds the allowable range, a synchronization instruction sequence is generated and transmitted to relevant equipment to obtain a unified production cycle, including: If the deviation index exceeds the preset allowable range, a synchronization instruction sequence is generated based on the deviation index. The synchronization instruction sequence is transmitted to the injection molding machine and the robot, and the equipment operation cycle is adjusted to generate a unified production cycle. The cycle difference is quantified by an optimization algorithm to correct minor deviations.

[0013] As a preferred embodiment of the present invention, step S4 includes: Cycle matching data is extracted from the unified production cycle, and the cycle matching data is processed using a neural network model to generate a predicted parameter fluctuation sequence. The dryer parameters are adjusted by integrating historical data and real-time adjustment cycle data, and the final process configuration is generated by integrating the predicted parameter fluctuation sequence and the adjusted parameters.

[0014] Secondly, the present invention also provides a production data acquisition and monitoring system for automotive interior injection molding equipment, used to implement the above-mentioned method, the system comprising: The model building unit is used to obtain real-time operating status data from the injection molding equipment through the communication node to obtain the equipment status dataset, and to process the equipment status dataset using time series analysis to construct a parameter-coupled model. The parameter determination unit is used to input the real-time collected barrel temperature sequence and injection pressure sequence into the parameter coupling model, determine whether the temperature parameter fluctuation exceeds the preset threshold, and if it does, it integrates the injection pressure data to generate mold temperature controller control instructions and temperature optimization configuration, and obtains the barrel temperature optimization configuration through the barrel fluctuation amplitude, and transmits the temperature optimization configuration to relevant devices through the interactive network to determine the linkage parameter set. The calculation unit is used to process the set of linkage parameters using a classification method to obtain a deviation index. If the deviation index exceeds the allowable range, a synchronization instruction sequence is generated and transmitted to the relevant equipment to obtain a unified production cycle. The equipment adjustment unit is used to extract data from the unified production cycle through a prediction model to predict parameter fluctuations, adjust equipment parameters, and determine the final process configuration.

[0015] Thirdly, the present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the above-described method.

[0016] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention, through time series analysis and parameter coupling model construction in step S1, enables the system to effectively capture the dynamic relationship between micro-fluctuations in barrel temperature and unstable injection pressure, avoiding the limitations of single-parameter control and providing a scientific basis for subsequent judgment. Step S2, based on the coupling model, determines whether the temperature fluctuation exceeds a threshold. If it does, it integrates injection pressure data to generate an adjustment factor, thereby forming optimized mold temperature controller control commands to achieve dynamic correction of the barrel temperature configuration and reduce pressure instability caused by temperature fluctuations. In step S3, by classifying the linkage parameter set and calculating deviation indices, it can promptly detect inconsistencies in cycle times between devices. When the deviation exceeds the allowable range, a synchronization command is automatically generated to ensure that the injection molding machine, mold temperature controller, and robotic arm maintain a unified production cycle, thus guaranteeing the time coordination of multiple devices. Further, step S4… By extracting characteristic data within a unified production cycle through predictive models and combining historical and real-time data to generate predictive fluctuation sequences, process configurations can be adjusted in advance to achieve predictive control of future operating conditions. Through the synergy of the above technical solutions, abnormal fluctuations in barrel temperature and injection pressure can be suppressed in real time, while ensuring the consistency of production cycles across multiple devices. It also has a forward-looking prediction function, thus effectively solving the problems of unstable production processes, large cycle deviations, and difficulty in guaranteeing product quality in existing technologies. Ultimately, it achieves intelligent, refined, and efficient injection molding production of automotive interior parts. Attached Figure Description

[0017] Figure 1 This is a flowchart of a production data acquisition and monitoring method for automotive interior injection molding equipment, as described in an embodiment of the present invention.

[0018] Figure 2 This is a structural diagram of a production data acquisition and monitoring system for automotive interior injection molding equipment, as described in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0020] like Figure 1 This embodiment provides a method for collecting and monitoring production data in automotive interior injection molding equipment, which may specifically include: Step S1: Obtain real-time operating status data from the injection molding equipment through the communication node to obtain the equipment status dataset, and use time series analysis method to process the equipment status dataset to determine the parameter coupling model; In step S1, the equipment status dataset is obtained by acquiring real-time operating status data from the injection molding equipment through the communication node, including: Injection pressure, mold temperature, and barrel temperature data are collected by communication nodes deployed in the injection molding machine, mold temperature controller, and barrel. A device status dataset containing the barrel temperature, mold temperature, and injection pressure is generated. Feature extraction is performed on the small fluctuation data of the barrel temperature to determine the influence characteristics of the small fluctuations of the barrel temperature on the injection pressure, and the device status dataset is generated.

[0021] Specifically, in the injection molding production of automotive interior parts, fluctuations and synchronization issues in equipment parameters often lead to low production efficiency and unstable product quality. This is especially true in the production of high-precision interior parts, where even minor parameter fluctuations can affect the dimensional accuracy and surface quality of the molded products. The injection molding machine is the core execution equipment in production, and the barrel, located in its heating and injection unit, is a crucial part for the melting and storage of plastic granules. Its temperature directly determines the viscosity of the melt and the stability of the injection pressure. The mold temperature controller, by adjusting the mold temperature, affects the cooling rate and thermal balance of the melt in the cavity. The heat exchange between the mold and the melt, in turn, affects the stability of the barrel temperature. Specifically, there are complex interrelationships between key parameters such as barrel temperature, mold temperature, and injection pressure. Fluctuations in these parameters directly impact equipment synchronization and production cycles. Therefore, by deploying communication nodes at the injection molding machine, mold temperature controller, and barrel, real-time operating status data, including parameters such as barrel temperature, mold temperature, and injection pressure, is collected to generate an equipment status dataset. To ensure the real-time nature of the data… The communication node collects data once per second via wireless sensors, storing this data in a time-series format, including timestamps and corresponding parameter values, to form an initial equipment status dataset. Next, for the minute fluctuations in barrel temperature, wavelet transform technology is used to extract features, decomposing the signal into different frequency components. By selecting an appropriate wavelet basis, such as the Daubechies wavelet, coefficients are calculated, extracting features representing minute fluctuations, such as the standard deviation of detail coefficients. This processing method can identify minute changes in barrel temperature less than 1 degree Celsius, i.e., the aforementioned minute fluctuation data, and then analyze the impact of temperature fluctuations on injection pressure. Specifically, the Pearson correlation coefficient method is used to calculate the linear relationship between temperature fluctuations and injection pressure. If the correlation coefficient is greater than 0.8, it indicates that minute temperature fluctuations lead to unstable pressure fluctuations, thus affecting molding accuracy and product quality during injection molding. By integrating the extracted fluctuation features and impact features into the initial dataset, an extended dataset is formed, providing data support for subsequent parameter coupling analysis.

[0022] In the implementation process, for example, in the scenario of ABS plastic injection molding, if the barrel temperature is set to 220 degrees Celsius and the fluctuation is less than 0.3 degrees Celsius, the average value of the detail coefficient extracted by wavelet transform is 0.1, indicating low-frequency micro-fluctuations. This helps to identify potential deviations early, improve the consistency of automotive interior parts molding, and reduce the scrap rate. Furthermore, in the PC material injection molding process, a temperature fluctuation of 0.4 degrees Celsius may cause an injection pressure deviation of 1.5 MPa, with a correlation coefficient of 0.85, indicating the strong influence of temperature fluctuations on pressure. The above data supports optimizing production efficiency and reducing micro-cycle deviations by adjusting control parameters in real time, ensuring equipment synchronization, and improving the stability of overall output. In addition, this embodiment is extended to other scenarios, such as automotive precision gear injection molding, where the temperature fluctuation threshold is set to 0.2 degrees Celsius to further reduce micro-cycle deviations and ensure the coordinated operation of production equipment. By comprehensively analyzing the equipment status dataset, using the fluctuation characteristics extracted by wavelet transform, the influence characteristics analyzed by Pearson correlation coefficient, and real-time data processing, the precise control of micro-deviations in the injection molding process is ultimately achieved, improving product quality and production efficiency.

[0023] Further, in step S1, the device state dataset is processed using time series analysis to determine the parameter coupling model, including: The barrel temperature sequence and injection pressure sequence in the equipment status dataset are decomposed into time series to obtain dynamic change characteristics. The time series correlation between the barrel temperature micro-fluctuation and the injection pressure is analyzed. A coupling effect model including the barrel temperature micro-fluctuation and the injection pressure instability is constructed. Based on the coupling effect model, the correspondence between the barrel temperature fluctuation and the injection pressure is obtained. Based on the correspondence, a parameter coupling model is constructed. The barrel temperature micro-fluctuation data, the barrel temperature sequence data, and the injection pressure data are used as training data, and the parameter coupling model is trained.

[0024] Specifically, in the production process of automotive interior injection molding equipment, the coordination between multiple devices such as the injection molding machine, mold temperature controller, and barrel plays a crucial role in production efficiency and product quality. However, due to the dynamic fluctuations and complex coupling relationships of equipment parameters such as barrel temperature and injection pressure, instability in the production cycle, deviations in cycle time between devices, and quality problems in the final product are often caused. Therefore, time series analysis is used to decompose the barrel temperature sequence and injection pressure sequence in the equipment status dataset, dividing the data into trend components, seasonal components, and residual components. Among them, the trend component captures long-term changes, the seasonal component reflects periodic patterns, and the residual component represents random fluctuations. Furthermore, the contribution of each component is quantified based on the decomposition results to extract dynamic change characteristics, paying particular attention to the impact of small fluctuations in barrel temperature on injection pressure, effectively avoiding interference from noise in the original data.

[0025] Next, the time-series correlation between barrel temperature fluctuations and injection pressure is analyzed. The correlation between the barrel temperature fluctuation series and the injection pressure series under different lag conditions is calculated using a cross-correlation function. The Pearson correlation coefficient is used to quantify the linear correlation strength between temperature fluctuations and pressure instability. Finally, a Granger causality test is used to determine whether one time series can be used to predict another. If the three analysis results are consistent—that is, temperature changes precede pressure changes in time, the two are significantly correlated, and temperature has Granger causality with pressure—it is determined that barrel temperature fluctuations directly cause injection pressure instability, and the degree of its impact is quantified; otherwise, it will not. The above analysis provides key data support for subsequent steps, helping to accurately identify the instability of pressure caused by temperature changes and ensuring the robustness of the model under different materials such as ABS plastic or POM material.

[0026] Based on this, a coupled effect model of barrel temperature fluctuation and injection pressure instability is constructed using a vector autoregression algorithm. The barrel temperature sequence and injection pressure sequence are used as endogenous variables, and the estimated coefficient matrix captures the dynamic interaction between them. For example, the temperature fluctuation term serves as a hysteresis predictor of pressure instability. By introducing a coupling coefficient, the sensitivity of the barrel temperature change rate to injection pressure deviation is quantified. The coupled effect model is fitted using the least squares method to simulate the coupling effect. For example, a 1% increase in temperature fluctuation leads to a 1.5-fold amplification of pressure instability, ensuring accurate capture of the impact of small deviations on production control. At the same time, the parametric coupled effect model can be adjusted according to the load of different equipment in actual production to ensure production consistency under different operating conditions.

[0027] Furthermore, barrel temperature and injection pressure data are fused using a Kalman filter. Iterative updates using state and observation equations reduce measurement noise and generate a smooth joint sequence. By assigning appropriate weights to temperature and pressure data, accurate data fusion is achieved, enhancing the adaptability of the parameter coupling model in complex production scenarios. This trains and optimizes the coupling effect model. Specifically, the barrel temperature and injection pressure sequences are used as input data, and the predicted relationship between the barrel temperature and injection pressure in the future time period (i.e., the coupling coefficient between them) and the predicted injection pressure deviation are used as outputs. Finally, based on the processing and training results in the preceding steps, a final parameter coupling model is generated, providing support for optimizing the injection molding process. This model not only ensures the coordination between multiple parameters such as temperature and pressure but also optimizes the production cycle by adjusting equipment parameters in real time, improving production efficiency and product quality stability. It effectively solves the technical challenges of cycle time deviation and parameter fluctuations between multiple devices, realizing the intelligent and efficient operation of the injection molding process.

[0028] Step S2: Input the real-time collected barrel temperature sequence and injection pressure sequence into the parameter coupling model to determine whether the temperature parameter fluctuation exceeds the preset threshold. If it does, the injection pressure data is fused to generate mold temperature controller control instructions and temperature optimization configuration. The barrel temperature optimization configuration is obtained through the barrel fluctuation amplitude and transmitted to relevant devices through the interactive network to determine the linkage parameter set. In step S2, the temperature parameter fluctuation is determined based on the parameter coupling model to see if it exceeds a preset threshold. If it does, the injection pressure data is fused to generate mold temperature controller control commands and temperature optimization configurations. The barrel temperature optimization configuration is obtained through the barrel fluctuation amplitude, including: The real-time acquired barrel temperature sequence and injection pressure sequence are input into the parameter coupling model, and it is determined whether the micro-fluctuation amplitude of the barrel temperature exceeds a preset threshold. If it does, the injection pressure data is fused to generate the control factor of the mold temperature controller. The mold temperature controller control command is obtained according to the control factor. The barrel temperature configuration is also optimized by the barrel fluctuation amplitude and the temperature of the mold temperature controller, generating a barrel temperature optimization configuration containing the optimized temperature value. This step is repeated to obtain the control command and temperature optimization configuration of the mold temperature controller in real time and transmit them to the mold temperature controller to achieve real-time adjustment cycle.

[0029] Specifically, in this embodiment, real-time operating status data is first acquired from the automotive interior injection molding equipment via a communication node to form an equipment status dataset. This dataset includes time-series data of key parameters such as barrel temperature, mold temperature, and injection pressure. Next, time series analysis is used to process the equipment status dataset and construct a parameter coupling model. Specifically, for the barrel temperature data, its micro-fluctuation amplitude is calculated, for example, by calculating the temperature difference sequence between adjacent time points and obtaining the standard deviation as the amplitude index. If the calculated amplitude exceeds a preset threshold (e.g., 0.5 degrees Celsius), the subsequent fusion step is triggered. In this process, the injection pressure data sequence is extracted, and the barrel temperature micro-fluctuation data is fused with the injection pressure data. To this end, the parameter coupling model is used to predict the impact of temperature fluctuations on pressure changes. The coupling coefficient is generated through data fusion, which is the intensity of the impact of the barrel temperature change on the injection pressure change. The corresponding predicted injection pressure deviation is also obtained. The mold temperature is controlled by a mold temperature controller. When the mold temperature controller temperature changes, the mold surface temperature change will change the heat dissipation rate of the melt, thereby affecting the overall temperature balance of the melt in the barrel. The mold temperature controller does not directly intervene in the barrel heating, but plays the role of a "thermal buffer" in the injection heating process. For example, when changing different types or batches of plastic granules, such as ABS... Switching to POM increases the difficulty of controlling the barrel temperature and causes greater fluctuations due to the different thermal sensitivity of the material. Adjusting the mold temperature controller indirectly alters the thermal balance of the barrel and melt. Therefore, based on the aforementioned coupling coefficient and predicted injection pressure deviation, an adjustment factor for the mold temperature controller is generated, representing the intensity of the additional injection pressure impact caused by the barrel temperature exceeding the threshold. Based on the correspondence between this adjustment factor and the mold temperature controller temperature, a mold temperature controller control command is generated. For example, if temperature fluctuations cause an increase of 5 bar in injection pressure, a control command to adjust the mold temperature controller temperature can be generated, such as lowering the mold temperature controller temperature by 1 degree Celsius to compensate for the impact of barrel temperature fluctuations. After the mold temperature controller executes the command, the barrel temperature sensor and injection pressure sensor will provide new data. According to the data, it is determined whether the barrel temperature fluctuation has fallen back to within the threshold. Based on the real-time acquisition of the barrel temperature fluctuation amplitude and the temperature of the mold temperature controller, the temperature optimization configuration of the barrel temperature is adjusted to reduce the temperature fluctuation of the barrel. This step is repeated to adjust the real-time temperature of the mold temperature controller. The optimization configuration will verify the change in fluctuation amplitude by simulating the adjusted temperature value, such as 199.5 degrees Celsius, to ensure that it falls below the threshold, thereby ensuring temperature stability and stability of melt viscosity and injection pressure. This improves production efficiency and reduces the accumulation of small deviations, ensuring product consistency and precision. Especially when producing high-precision automotive interior parts, it ensures precise control of various parameters, reduces part defects caused by barrel temperature fluctuations and unstable injection pressure, and optimizes the production cycle and process configuration.

[0030] Further, in step S2, the optimized configuration is transmitted to the relevant devices via the interactive network to determine the linkage parameter set, including: The adjusted mold temperature value is extracted from the temperature optimization configuration and transmitted to the injection molding machine and mold temperature controller through an interactive network to generate a linkage parameter set containing mold temperature and cycle time series. The cycle time difference between devices in the linkage parameter set is quantified by the parameter vector representation method to determine the linkage parameter set used for subsequent classification.

[0031] Specifically, in order to improve the efficiency of injection molding production and the stability of product quality, the system first acquires real-time operating status data from the injection molding equipment through communication nodes to generate an equipment status dataset. Then, it processes the dataset using time series analysis to construct a parameter coupling model. Based on this, when the temperature parameter fluctuation exceeds a preset threshold, it integrates pressure data to generate optimized control commands and transmits the optimized configuration to relevant equipment through an interactive network to determine the linkage parameter set.

[0032] In the specific implementation process, the adjusted mold temperature value is extracted from the above-mentioned temperature optimization configuration, which includes the barrel temperature optimization configuration and the mold temperature controller temperature optimization configuration. The optimized temperature configuration represents the corresponding optimized temperature value for the equipment. This value is transmitted to the injection molding machine and the mold temperature controller via an interactive network, generating a set of linkage parameters containing mold temperature and cycle time series. The cycle time series represents the duration data of the injection molding equipment (barrel, mold temperature controller, and injection molding machine) within each production cycle, used to describe the time rhythm of the injection molding equipment in each production cycle. At this time, through... The parameter vector representation method transforms mold temperature and cycle time series into vector form, quantifies the cycle time difference between different devices into numerical form, and calculates the degree of difference. Taking injection molding machines and mold temperature controllers as examples, mold temperature and cycle time series correspond to vector components and time series, respectively. The difference is quantified by calculating Euclidean distance. For example, when the cycle time of the injection molding machine is [2.5, 2.6] seconds, while that of the mold temperature controller is [2.4, 2.5] seconds, the calculated difference is 0.1 seconds. This difference value can effectively reflect the cycle time deviation between devices, thereby timely identifying the impact of small fluctuations in the precision production process.

[0033] Furthermore, based on the calculated cycle time difference, the weights are adjusted and a weighted average is performed to more accurately quantify the cycle time deviation and ensure parameter fusion in real-time adjustments. Finally, the quantified linkage parameter set will be used for support vector classification to determine the degree of deviation and generate a real-time synchronization instruction sequence. This synchronization instruction sequence will be transmitted to relevant equipment to adjust the operating cycle of the equipment, ensuring that multiple devices operate synchronously during the production process, forming a unified production cycle. This effectively reduces instability in the molding of precision parts and improves production consistency and product quality.

[0034] The above technical solution can automatically adjust the equipment operating status based on data collected by sensors, combined with time series analysis and classification methods, optimize the temperature, pressure and cycle control in the injection molding process, thereby improving overall production efficiency and the dimensional accuracy of parts.

[0035] Step S3: The set of linkage parameters is processed using a classification method to obtain a deviation index. If the deviation index exceeds the allowable range, a synchronization instruction sequence is generated and transmitted to the relevant equipment to obtain a unified production cycle. In step S3, the set of linkage parameters is processed using a classification method to obtain a deviation index, including: The linkage parameter set is classified using the support vector classification method to generate classification results that characterize the degree of rhythm difference. The rhythm deviation is calculated based on the classification results, and the range of the rhythm deviation is defined by a preset classification boundary. A deviation index for evaluating the synchronization of linkage equipment is determined, and a real-time judgment mechanism is generated.

[0036] Specifically, the linkage parameter set is classified using the support vector classification method to generate classification results that characterize the degree of beat difference. This classification method maps data points to a high-dimensional feature space and uses a kernel function, i.e., a radial basis function, to handle nonlinear relationships and optimizes the hyperplane to maximize the interval between class data points, thereby ensuring effective separation between different classes. The classification results are presented in the form of numerical scores, such as 1 for slight difference, 2 for moderate difference, and 3 for severe difference. The deviation index is calculated by weighted averaging to reflect the overall synchronization status.

[0037] Based on the preset classification boundary range, the deviation index is defined within the range of 0 to 5, where 0 to 2 represents an acceptable range, and values ​​above 3 trigger an alarm. If the deviation index exceeds the allowable range, a synchronization command sequence is automatically generated and transmitted to the equipment to adjust the equipment cycle time to achieve a unified production cycle. The real-time performance of this process is fully guaranteed. In particular, when the barrel temperature fluctuates slightly by 0.5 degrees Celsius, the cycle time sequence in the linkage parameter set shows that the injection molding machine cycle is 10 seconds, the robot cycle is 10.2 seconds, the classification result is moderate difference, and the deviation index is 2.5. Through the preset range judgment mechanism, a synchronization command is generated in real time to adjust to a unified 10-second cycle. The robot arm undertakes automatic part picking and transfer operations during the injection molding process and is also used as the core node for production cycle division. It is used to identify the production cycle boundary, participate in the construction of the linkage parameter set, and ensure the coordination and consistency of the entire injection molding production line through synchronization command adjustment, thereby supporting the efficient and collaborative operation of automotive interior injection molding equipment. Furthermore, by integrating historical data with real-time fluctuation data, the system further corrects deviations through predictive models and enhances the handling of minute cycle deviations, ensuring coordinated operation between equipment. Especially on production lines with high precision requirements, when the barrel temperature exceeds the threshold of 0.3 degrees Celsius, the real-time adjustment mechanism will automatically correct the deviations, ensuring efficient and stable production.

[0038] The above technical solution, through the combination of time series analysis, classification algorithms, and prediction models, solves the problems of cycle time differences and parameter fluctuations between equipment in injection molding production, improves the synchronization and accuracy of production, and ensures the efficiency of the automotive interior injection molding production process and the stability of product quality.

[0039] Further, in step S3, if the deviation index exceeds the allowable range, a synchronization instruction sequence is generated and transmitted to the relevant equipment to obtain a unified production cycle, including: If the deviation index exceeds the preset allowable range, a synchronization instruction sequence is generated based on the deviation index. The synchronization instruction sequence is transmitted to the injection molding machine and the robot, and the equipment operation cycle is adjusted to generate a unified production cycle. The cycle difference is quantified by an optimization algorithm to correct minor deviations.

[0040] Specifically, in one implementation, when the cycle deviation generated after processing the linkage parameter set using a classification method exceeds a preset allowable range, a synchronization command sequence is triggered to achieve a unified production cycle between injection molding machines. Specifically, firstly, it is determined whether the deviation exceeds a cycle threshold range of, for example, 0.1 to 0.5 seconds. If the determination result is that it exceeds the limit, the specific value of the deviation (e.g., injection molding machine delay of 0.3 seconds) is extracted, and mapped according to the difference ratio between this value and the robot's cycle time. The corresponding control command template is then invoked to generate a synchronization command sequence containing injection molding machine waiting time compensation and robot advance action commands. This sequence is transmitted to the injection molding machine and robot control systems via an interactive network to achieve cycle time adjustment between the machines.

[0041] After receiving the synchronization command sequence, the injection molding machine control unit adjusts the operating rhythm by modifying the injection cycle parameters. Simultaneously, the robotic arm controller adjusts the timing of its gripping action based on the command sequence, thus unifying the production cycle to a standardized 15-second interval. Subsequently, a cycle time monitoring mechanism is implemented to track the equipment's operating rhythm in real time, ensuring all participating equipment operates under the same clock reference and eliminating cycle time inconsistencies caused by parameter fluctuations.

[0042] To further optimize the synchronization adjustment process, the system introduces a particle swarm optimization algorithm to quantify and correct beat differences. This algorithm initializes a particle swarm containing different beat adjustment schemes, assigning each particle's "position" and "velocity" to represent the delay adjustment value and its rate of change, respectively. It iteratively updates the position of each particle, bringing it closer to the solution with the minimum beat difference. The fitness function uses the sum of squared beat differences as the evaluation metric, updating the local and global optima in each iteration, ultimately controlling the beat difference to within 0.05 seconds, achieving precise beat synchronization.

[0043] For example, in the high-precision injection molding production of automotive interior parts, if a 0.3-second delay in the injection molding machine cycle is detected, the system will generate a synchronization instruction sequence containing a 0.2-second delay for the injection molding machine and a 0.2-second advance for the robotic arm. Through particle swarm optimization, after 50 iterations, the cycle time difference between the equipment is compressed from 0.3 seconds to 0.02 seconds, ensuring coordinated equipment operation and consistent product dimensions. Simultaneously, to address different causes of deviation, the synchronization instructions can be adapted to different compensation mechanisms based on the type of deviation. For example, for cycle time delays caused by minor fluctuations in barrel temperature, a temperature adjustment instruction is automatically introduced; while for deviations caused by pressure fluctuations, corresponding injection parameter compensation logic is introduced. This entire process, through data-driven control decisions, achieves high-precision collaborative control of minute deviations in the production process, effectively improving the stability and yield of the automotive interior injection molding production line.

[0044] Step S4: Extract data from the unified production cycle using a prediction model to predict parameter fluctuations, adjust equipment parameters, and determine the final process configuration; specifically including: The cycle matching data is extracted from the unified production cycle, and the cycle matching data is processed by a neural network model to generate a predicted parameter fluctuation sequence. The barrel parameters are adjusted by integrating historical data and real-time adjustment cycle data. The final process configuration is generated by integrating the predicted parameter fluctuation sequence and the adjusted parameters.

[0045] Specifically, based on a unified production cycle formed by equipment such as injection molding machines and robotic arms, cycle matching data is first extracted from this unified production cycle. This cycle matching data is the start and end points of each production cycle identified based on the time series of the synchronous control of the injection molding machine and the robotic arm. The time series data formed by combining the temperature and pressure data corresponding to these key nodes is further vectorized to provide a standardized input format for subsequent intelligent prediction. Subsequently, the above cycle matching data is input into a neural network model with time series learning capabilities, namely a Long Short-Term Memory (LSTM) network. This network structure dynamically adjusts the retention of historical information through a gating mechanism. The degree and current state update logic can effectively capture the potential impact of small fluctuations in barrel temperature on injection pressure. In the model processing, the input data is first normalized to ensure the consistency of the data scale of each parameter, thereby improving the stability and convergence speed of network training. Then, the historical temperature and pressure information to be retained is determined by the forget gate, new time step information is introduced by the input gate, and the current prediction output is generated by the output gate, thereby generating a prediction parameter fluctuation sequence including timestamp and fluctuation amplitude. This prediction sequence can be used in actual injection molding production to estimate the possible deviations in barrel temperature or injection pressure in the next few production cycles.

[0046] After obtaining the predicted data, the operating parameters of the feed cylinder recorded in the historical database, such as humidity control values ​​and temperature setpoints, are further integrated with the real-time adjustment data obtained during the current cycle optimization process. A weighted average method is used for fusion calculation to form feed cylinder adjustment parameters with predictive and real-time response capabilities. The weight allocation of historical data and real-time data is preset according to the equipment response delay characteristics and production stability requirements. For example, it can be set to a ratio of 0.6 to 0.4 to ensure that the feed cylinder can still achieve stable material supply under different material moisture fluctuations.

[0047] Finally, the predicted parameter fluctuation sequence is further integrated with the adjusted barrel parameters to generate the final process configuration suitable for the current injection molding production cycle. This configuration not only includes the control parameter values ​​of all key equipment, including the upper limit of injection molding machine temperature, injection pressure threshold, and barrel temperature and humidity setpoints, but also incorporates time series information to form a stable equipment coordination operation configuration table. System verification ensures that the process configuration has sustainable executability and multi-equipment collaborative stability over multiple unified production cycles in the future. Through the above data acquisition, processing, prediction, and optimization configuration process, dynamic predictive control of injection molding equipment parameters and closed-loop optimization of process configuration are effectively achieved, improving the intelligence level and product consistency quality level of the automotive interior parts injection molding process.

[0048] The present invention also provides a production data acquisition and monitoring system for automotive interior injection molding equipment, used to implement the above-mentioned method, the system comprising: The model building unit is used to obtain real-time operating status data from the injection molding equipment through the communication node to obtain the equipment status dataset, and to process the equipment status dataset using time series analysis to construct a parameter-coupled model. The parameter determination unit is used to input the real-time collected barrel temperature sequence and injection pressure sequence into the parameter coupling model, determine whether the temperature parameter fluctuation exceeds the preset threshold, and if it does, it integrates the injection pressure data to generate mold temperature controller control instructions and temperature optimization configuration, and obtains the barrel temperature optimization configuration through the barrel fluctuation amplitude, and transmits the temperature optimization configuration to relevant devices through the interactive network to determine the linkage parameter set. The calculation unit is used to process the set of linkage parameters using a classification method to obtain a deviation index. If the deviation index exceeds the allowable range, a synchronization instruction sequence is generated and transmitted to the relevant equipment to obtain a unified production cycle. The equipment adjustment unit is used to extract data from the unified production cycle through a prediction model to predict parameter fluctuations, adjust equipment parameters, and determine the final process configuration.

[0049] The present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the above-described method.

[0050] In summary, this invention collects real-time operating status data of barrel temperature, injection pressure, and mold temperature through communication nodes, and extracts dynamic change characteristics using time series decomposition methods. Furthermore, it establishes a parameter coupling model, which reveals the coupling relationship between minute fluctuations in barrel temperature and unstable injection pressure, providing a data foundation for subsequent judgment and control. Based on the parameter coupling model, real-time temperature fluctuations are monitored. If the amplitude exceeds a threshold, injection pressure data is automatically integrated, and a coupling coefficient is calculated to generate an adjustment factor, thus forming an optimized mold temperature controller control command. This control command not only optimizes the barrel temperature configuration but also ensures parameter consistency across multiple devices under the same production conditions through real-time distribution via an interactive network. By classifying the linkage parameter set and calculating deviation indicators, if asynchronous cycle times are detected between devices, the system automatically generates a synchronization command. The sequence enables a unified production cycle for equipment such as injection molding machines, mold temperature controllers, and robotic arms. This step ensures time consistency between equipment, effectively reducing the negative impact of cycle time deviations on product dimensions and process stability. Finally, predictive models are used to analyze parameters under the unified production cycle, predicting potential temperature and pressure fluctuations in advance. Based on historical data and real-time cycle results, equipment configurations are adjusted to generate the final process parameter table. This not only improves the predictability of future operating conditions but also achieves adaptive control of the entire process. Through the synergy of the above technical solutions, precise monitoring and dynamic adjustment of multi-source data for automotive interior injection molding equipment are achieved. The steps form a mutually supportive closed loop, effectively solving the problems of amplified parameter fluctuations, inconsistent cycle times, and unstable product quality in existing technologies, thus realizing intelligent, refined, and efficient production processes.

[0051] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for collecting and monitoring production data in automotive interior injection molding equipment, characterized in that, include: Step S1: Obtain real-time operating status data from the injection molding equipment via a communication node to obtain an equipment status dataset. Process the equipment status dataset using time series analysis to construct a parameter coupling model. This includes: performing time series decomposition on the equipment status dataset to obtain dynamic change characteristics; analyzing the time series correlation between barrel temperature fluctuations and injection pressure; constructing a coupling effect model that includes barrel temperature fluctuations and injection pressure instability; obtaining the correspondence between barrel temperature fluctuations and injection pressure based on the coupling effect model; constructing a parameter coupling model based on the correspondence; using the barrel temperature fluctuation data and injection pressure data as training data to train the parameter coupling model; the communication node collects data once per second via a wireless sensor. Step S2: Input the real-time collected barrel temperature sequence and injection pressure sequence into the parameter coupling model, determine whether the temperature parameter fluctuation exceeds a preset threshold. If it does, fuse the injection pressure data to generate a mold temperature controller control command and a temperature optimization configuration, and obtain the barrel temperature optimization configuration through the barrel fluctuation amplitude. Transmit the temperature optimization configuration to the relevant equipment through the interactive network to determine the linkage parameter set. In step S2, determining whether the temperature parameter fluctuation exceeds a preset threshold according to the parameter coupling model, and if it does, fuse the pressure data to adjust the control command and generate an optimization configuration, includes: inputting the real-time collected barrel temperature sequence and injection pressure sequence into the parameter coupling model, and determining whether the barrel temperature micro-fluctuation amplitude exceeds a preset threshold. If it does, fuse the injection pressure data to generate a mold temperature controller control factor, obtain the mold temperature controller control command according to the control factor, and also generate a barrel temperature optimization configuration containing an optimized temperature value through the barrel fluctuation amplitude and the temperature optimization configuration of the mold temperature controller. Repeat this step to obtain the mold temperature controller control command and temperature optimization configuration in real time and transmit them to the mold temperature controller to achieve real-time adjustment cycle. Step S3: The set of linkage parameters is processed using a classification method to obtain a deviation index. If the deviation index exceeds the allowable range, a synchronization instruction sequence is generated and transmitted to the relevant equipment to obtain a unified production cycle. Step S4: Extract data from the unified production cycle using the prediction model to predict parameter fluctuations, adjust equipment parameters, and determine the final process configuration.

2. The method as described in claim 1, characterized in that, In step S1, real-time operating status data is obtained from the injection molding equipment through the communication node to obtain the equipment status dataset, including: By collecting barrel temperature, mold temperature, and injection pressure data through communication nodes deployed in the injection molding machine, mold temperature controller, and dryer, an equipment status dataset containing the barrel temperature, mold temperature, and injection pressure is generated. Feature extraction is performed on the small fluctuation data of the barrel temperature to determine the influence characteristics of the small fluctuations of the barrel temperature on the injection pressure, and an equipment status dataset for subsequent analysis is generated.

3. The method as described in claim 2, characterized in that, In step S2, the optimized configuration is transmitted to the relevant devices via the interactive network to determine the linkage parameter set, including: The adjusted mold temperature value is extracted from the temperature optimization configuration and transmitted to the injection molding machine and mold temperature controller through an interactive network to generate a linkage parameter set containing mold temperature and cycle time series. The cycle time difference between devices in the linkage parameter set is quantified using the parameter vector representation method to determine the linkage parameter set used for subsequent classification.

4. The method as described in claim 1, characterized in that, In step S3, the set of linkage parameters is processed using a classification method to obtain a deviation index, including: The set of linkage parameters is classified using the support vector classification method to generate classification results that characterize the degree of rhythm difference. Based on the classification results, a rhythm deviation index is calculated. The range of the rhythm deviation index is defined by a preset classification boundary to determine the deviation index used to evaluate the synchronization of linkage equipment and generate a real-time judgment mechanism.

5. The method as described in claim 4, characterized in that, In step S3, if the deviation index exceeds the allowable range, a synchronization instruction sequence is generated and transmitted to the relevant equipment to obtain a unified production cycle, including: If the deviation index exceeds the preset allowable range, a synchronization instruction sequence is generated based on the deviation index. The synchronization instruction sequence is transmitted to the injection molding machine and the robot, and the equipment operation cycle is adjusted to generate a unified production cycle. The cycle difference is quantified by an optimization algorithm to correct minor deviations.

6. The method as described in claim 1, characterized in that, Step S4 includes: Cycle matching data is extracted from the unified production cycle, and the cycle matching data is processed using a neural network model to generate a predicted parameter fluctuation sequence. The dryer parameters are adjusted by integrating historical data and real-time adjustment cycle data, and the final process configuration is generated by integrating the predicted parameter fluctuation sequence and the adjusted parameters.

7. A production data acquisition and monitoring system for automotive interior injection molding equipment, used to implement the method as described in any one of claims 1-6, characterized in that, The system includes: The model building unit is used to obtain real-time operating status data from the injection molding equipment through the communication node to obtain the equipment status dataset, and to process the equipment status dataset using time series analysis to construct a parameter-coupled model. The parameter determination unit is used to input the real-time collected barrel temperature sequence and injection pressure sequence into the parameter coupling model, determine whether the temperature parameter fluctuation exceeds the preset threshold, and if it does, it integrates the injection pressure data to generate mold temperature controller control instructions and temperature optimization configuration, and obtains the barrel temperature optimization configuration through the barrel fluctuation amplitude, and transmits the temperature optimization configuration to relevant devices through the interactive network to determine the linkage parameter set. The calculation unit is used to process the set of linkage parameters using a classification method to obtain a deviation index. If the deviation index exceeds the allowable range, a synchronization instruction sequence is generated and transmitted to the relevant equipment to obtain a unified production cycle. The equipment adjustment unit is used to extract data from the unified production cycle through a prediction model to predict parameter fluctuations, adjust equipment parameters, and determine the final process configuration.

8. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the method as described in any one of claims 1-6.

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