Control method, device and equipment for automobile part injection mold and medium

Abstract

The invention relates to a control method, system and device for an automobile accessory injection mold and a storage medium, and the method comprises the steps that three-dimensional data and injection molding process parameters of the automobile instrument panel injection mold are obtained, and structure construction is conducted on the three-dimensional data based on the injection molding process parameters to obtain an instrument panel mold structure; acquiring real-time operation data of the automobile instrument panel injection mold, and associating the real-time operation data with the instrument panel mold structure to obtain corresponding real-time mold state information; injection molding data of an automobile instrument panel injection mold are obtained, performance prediction is conducted on the instrument panel mold structure according to the injection molding data, and corresponding mold performance information is obtained; performing scheme analysis on the real-time mold state information and the mold performance information to obtain an initial mold control scheme; and performing scheme optimization on the initial mold control scheme according to the adjustment process parameters to obtain a corresponding global adjustment strategy. According to the invention, different production requirements can be flexibly met.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile parts injection molds, and in particular to a control method, device, equipment and medium for automobile parts injection molds. Background Art

[0002] As an important information display and control center inside a car, the manufacturing quality of the car dashboard directly affects the driving experience and vehicle safety. With the rapid development of the automobile industry and the continuous improvement of consumers' requirements for vehicle quality, the design and manufacturing technology of automobile dashboard injection molds are facing increasingly higher challenges. How to improve the accuracy, stability and production efficiency of dashboard injection molds has become one of the focuses of industry research. However, there are still some problems with the current control methods of automobile dashboard injection molds. Traditional control methods often rely on experience and fixed process parameters, and are difficult to adapt to the production needs of dashboards of different materials and shapes. At the same time, the existing control methods lack comprehensive monitoring and analysis of the real-time status of the mold, and cannot promptly discover and solve problems that arise in the production process. Summary of the invention

[0003] The main purpose of the present invention is to provide a control method, device, equipment and medium for an automobile parts injection mold, which can flexibly respond to different production needs and improve the adaptability and flexibility of mold control.

[0004] To achieve the above object, the present invention provides a control method for an automobile parts injection mold, comprising: Acquire three-dimensional data and injection molding process parameters of an automobile instrument panel injection mold, and construct a structure of the three-dimensional data based on the injection molding process parameters to obtain an instrument panel mold structure; Acquire real-time operation data of the automobile instrument panel injection mold, associate the real-time operation data with the instrument panel mold structure, and obtain corresponding real-time mold status information; Acquire injection molding data of the automobile instrument panel injection mold, perform performance prediction on the instrument panel mold structure according to the injection molding data, and obtain corresponding mold performance information; Performing solution analysis on the real-time mold status information and the mold performance information to obtain an initial mold control solution; Performing parameter identification on the injection molding data to obtain corresponding injection molding process parameters, and adaptively adjusting the injection molding process parameters based on the mold performance information to obtain corresponding adjusted process parameters; The initial mold control solution is optimized according to the adjustment process parameters to obtain a corresponding global adjustment strategy.

[0005] Furthermore, the three-dimensional data is structured based on the injection molding process parameters to obtain a dashboard mold structure, including: Meshing the three-dimensional data to obtain an initial mesh model; Extracting characteristics of the injection molding process parameters to obtain melt flow characteristic data, molding temperature data and pressure parameters; Constructing the mold cavity and gate structure of the initial mesh model according to the melt flow characteristic data to obtain a mold base structure; Performing a thermodynamic analysis on the mold base structure according to the molding temperature data and the pressure parameters to obtain thermal stress distribution data; The mold base structure is structurally optimized based on the thermal stress distribution data to obtain the instrument panel mold structure.

[0006] Furthermore, the real-time operation data of the automobile instrument panel injection mold is obtained, and the real-time operation data is associated with the instrument panel mold structure to obtain corresponding real-time mold status information, including: Collecting data on the automobile instrument panel injection mold to obtain corresponding mold temperature data, mold pressure data, mold displacement data, and mold vibration data; Mapping the mold temperature data to the instrument panel mold structure and performing heat flow analysis to obtain the temperature gradient distribution of each part of the mold; Correlation analysis is performed on the mold pressure data and the stress-bearing parts in the instrument panel mold structure to obtain pressure spectrum characteristics; Performing geometric deviation analysis on the instrument panel mold structure according to the mold displacement data to obtain the mold deformation; Associating and calculating the vibration data with the connection structure points in the instrument panel mold structure to obtain a vibration characteristic vector; Based on the instrument panel mold structure, the temperature gradient distribution, the pressure spectrum characteristics, the mold deformation and the vibration characteristic vector are subjected to multi-source data fusion to obtain a comprehensive mold state matrix; Performing state recognition on the comprehensive mold state matrix to obtain corresponding mold state information; Performing real-time evaluation on the mold status information according to a preset status evaluation index system to obtain the real-time mold status information; The step of fusing multi-source data to obtain the comprehensive mold state matrix includes: Dividing the instrument panel mold structure into a plurality of structural sub-regions, each of the structural sub-regions corresponds to a matrix row; Extracting the temperature gradient value of the temperature gradient distribution, the main frequency component of the pressure spectrum, the three-dimensional direction component of the mold deformation and the main vibration component of the vibration eigenvector as the matrix column of each structural sub-region; Arranging the temperature gradient value, the main frequency component, the three-dimensional direction component and the main vibration component in a predefined order to form a regional feature vector for each structural sub-region; The regional feature vectors of all structural sub-regions are combined to form the comprehensive mold state matrix, wherein each row represents one of the structural sub-regions, and each column represents each type of regional feature vector.

[0007] Furthermore, the step of acquiring the injection molding data of the automobile instrument panel injection mold and predicting the performance of the instrument panel mold structure according to the injection molding data to obtain corresponding mold performance information includes: Performing shear rate calculation on the injection molding data to obtain shear rates at different locations during the injection molding process; Calculating the melt viscosity distribution of the instrument panel mold structure based on the shear rate to obtain viscosity distribution data; Performing pressure field simulation based on the viscosity distribution data and the shear rate to obtain rheological property data of the melt in the mold; Performing temperature field analysis on the instrument panel mold structure based on the rheological property data of the melt in the mold to obtain mold temperature distribution data; Performing a warping deformation analysis on the instrument panel mold structure according to the mold temperature distribution data to obtain mold warping deformation data; Performing stress-strain analysis on the instrument panel mold structure based on the mold warping deformation data to obtain mold stress-strain distribution data; Performing a durability evaluation on the instrument panel mold structure according to the mold stress-strain distribution data to obtain mold durability data; A comprehensive performance evaluation is performed on the mold durability data and the mold temperature distribution data to obtain comprehensive mold performance information.

[0008] Furthermore, the real-time mold state information and the mold performance information are analyzed to obtain an initial mold control solution, including: Associating the real-time mold status information with the mold performance information to obtain associated information; Comprehensively evaluating the associated information to obtain a comprehensive mold status evaluation result; Based on the comprehensive mold status evaluation result, the associated information is analyzed for abnormality to obtain mold performance abnormality points; Optimize and adjust the design of the abnormal points of the mold performance to obtain corresponding abnormal improvement strategies; Perform parameter identification on the associated information according to the abnormal improvement strategy to obtain a specific mold parameter adjustment plan; The mold parameter adjustment plan and the abnormality improvement strategy are integrated to obtain the initial mold control plan.

[0009] Further, the performing parameter identification on the injection molding data to obtain corresponding injection molding process parameters, and adaptively adjusting the injection molding process parameters based on the mold performance information to obtain corresponding adjusted process parameters, includes: Decomposing the injection molding data in multiple dimensions to obtain a parameter sequence including four dimensions: temperature, pressure, speed and time; Performing nonlinear time series analysis on the parameter sequence and extracting dynamic features to obtain a dynamic feature set; Constructing an injection molding process parameter space according to the dynamic feature set, and partitioning the injection molding process parameter space according to a spatial clustering algorithm to obtain initial injection molding process parameters; Optimizing and adjusting the initial injection molding process parameters based on the mold performance information to obtain the adjusted process parameters; The optimization and adjustment process includes: Set constraints on process parameters based on mold performance information and define optimization objective functions that include product quality and production efficiency; Iteratively optimizing the initial injection molding process parameters under the constraints according to the optimization objective function; In each iteration, the objective function value of the current parameter combination is evaluated to obtain the evaluation result; Selecting the corresponding parameter combination according to the evaluation result to perform crossover mutation to generate a new parameter combination; When the preset iteration target is reached, the parameter combination obtained by the final iteration is used as the adjustment process parameter.

[0010] Furthermore, the initial mold control scheme is optimized according to the adjustment process parameters to obtain a corresponding global adjustment strategy, including: Performing a parameter hierarchy analysis on the initial mold control scheme according to the adjusted process parameters to obtain a parameter importance ranking; Performing an orthogonal test design on the initial mold control scheme based on the parameter importance ranking to obtain a test scheme; Performing simulation operation on the test scheme to obtain simulation results; Performing a decision tree analysis on the simulation results according to a preset decision rule set to obtain decision modification information; Adaptively adjusting the initial mold control scheme according to the decision modification information to obtain an adaptively adjusted control scheme; Performing pattern matching on the adaptive adjustment control scheme and a preset control pattern library to obtain a control pattern; The adaptive adjustment control scheme is integrated with the control mode to obtain the global adjustment strategy.

[0011] The present invention also provides a control device for an automobile parts injection mold, which is applied to any one of the above-mentioned control methods for an automobile parts injection mold, comprising: An acquisition module, the acquisition module is used to obtain three-dimensional data and injection molding process parameters of the automobile instrument panel injection mold, and to perform structural construction on the three-dimensional data based on the injection molding process parameters to obtain the instrument panel mold structure; An analysis module, the analysis module is used to obtain real-time operation data of the automobile instrument panel injection mold, associate the real-time operation data with the instrument panel mold structure, and obtain corresponding real-time mold status information; An association module, the association module is used to obtain injection molding data of the automobile instrument panel injection mold, perform performance prediction on the instrument panel mold structure according to the injection molding data, and obtain corresponding mold performance information; A processing module, the processing module is used to perform a solution analysis on the real-time mold state information and the mold performance information to obtain an initial mold control solution; A control module, the control module is used to perform parameter identification on the injection molding data to obtain corresponding injection molding process parameters, and adaptively adjust the injection molding process parameters based on the mold performance information to obtain corresponding adjustment process parameters; An execution module is used to optimize the initial mold control solution according to the adjustment process parameters to obtain a corresponding global adjustment strategy.

[0012] The present invention also provides a control device for an automobile parts injection mold, comprising: Memory, used to store programs; The processor is used to execute the program to implement each step of the control method of an automobile parts injection mold described in any one of the above.

[0013] The present invention also provides a medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0014] The control method, device, equipment and medium of an automobile parts injection mold provided by the present invention have the following beneficial effects: By acquiring the three-dimensional data and injection molding process parameters of the automobile instrument panel injection mold and constructing the instrument panel mold structure based on these parameters, the structural characteristics and process requirements of the mold can be more accurately evaluated, providing reliable basic data for subsequent control and optimization. By acquiring the mold operation data in real time and associating it with the mold structure, real-time monitoring and analysis of the mold status are achieved, which helps to timely discover and solve problems in the production process and improve the stability and reliability of production. Performance prediction of the mold structure based on the injection molding data can identify potential performance problems in advance and provide guidance for mold maintenance and optimization, thereby improving the service life and production efficiency of the mold. By comprehensively analyzing the real-time mold status information and mold performance information, the initial mold control plan is formulated, and the refined management of mold operation is achieved, which helps to improve product quality and production efficiency. Parameter identification of the injection molding data and adaptive adjustment based on the mold performance information enable the control method to flexibly respond to the production needs of instrument panels of different materials and shapes, improving the adaptability and flexibility of mold control. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a control method for an automobile parts injection mold provided by the present invention; Figure 2 This is a structural diagram of a control device for an automobile parts injection mold provided by the present invention; Figure 3 The present invention provides a control device structure diagram of an automobile parts injection mold.

[0016] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0019] Reference Figure 1 As shown, the present invention provides a control method for an automobile parts injection mold, comprising: Step S1: acquiring three-dimensional data and injection molding process parameters of an automobile instrument panel injection mold, and constructing a structure of the three-dimensional data based on the injection molding process parameters to obtain an instrument panel mold structure; Step S2: acquiring real-time operation data of the automobile instrument panel injection mold, associating the real-time operation data with the instrument panel mold structure, and obtaining corresponding real-time mold status information; Step S3: acquiring the injection molding data of the automobile instrument panel injection mold, and performing performance prediction on the instrument panel mold structure according to the injection molding data to obtain corresponding mold performance information; Step S4: performing solution analysis on the real-time mold state information and the mold performance information to obtain an initial mold control solution; Step S5: performing parameter identification on the injection molding data to obtain corresponding injection molding process parameters, and adaptively adjusting the injection molding process parameters based on the mold performance information to obtain corresponding adjustment process parameters; Step S6: Optimizing the initial mold control solution according to the adjustment process parameters to obtain a corresponding global adjustment strategy.

[0020] Based on the above steps, the detailed process is as follows: Step S1: Obtain the three-dimensional data and injection molding process parameters of the automobile instrument panel injection mold. The three-dimensional data describes the geometric shape and size of the mold in detail. The injection molding process parameters include various specific data required in the injection molding process, such as temperature, pressure, speed, etc.

[0021] The acquired 3D data is structured based on the injection molding process parameters. The 3D model and process parameters are simulated and simulated. By simulating the injection molding process, the feasibility and potential problems of the mold, such as stress concentration and deformation, are effectively evaluated. A complete and detailed dashboard mold structure is obtained, which includes geometric information and related process parameters.

[0022] Step S2: Obtain real-time operating data of the automobile dashboard injection mold during the actual production process. These data are usually collected through various sensors and monitoring equipment, and can reflect the operating status of the mold in real time, including temperature, pressure, speed, position and other information.

[0023] The real-time operation data is associated with the constructed mold structure. This process is usually achieved through data fusion technology, which maps the real-time data to different parts of the mold structure to generate a dynamic, real-time updated mold status information.

[0024] Step S3: Obtaining the injection molding data of the automobile instrument panel injection molding mold, the injection molding data including the injection molding cycle time, injection molding pressure curve, temperature curve, etc. The injection molding data reflects the operation status of the mold and the product quality in each injection molding cycle.

[0025] The performance of the dashboard mold structure is predicted based on the injection molding data, and the performance change trend of the mold is predicted by analyzing historical data and real-time data. For example, the mold's service life, possible failure points, and the time required for maintenance can be predicted.

[0026] Step S4: Integrate the real-time mold status information and mold performance information, and analyze the association and difference between the two types of information by using data mining technology.

[0027] Identify the problems and optimization space in the current mold control, generate an initial mold control plan, and evaluate the feasibility and expected effect of the initial control plan.

[0028] Step S5: Perform data preprocessing and feature extraction on the injection molding data, and identify key injection molding process parameters by using parameter identification algorithms such as principal component analysis (PCA) or independent component analysis (ICA).

[0029] Establish the relationship information between mold performance and injection molding process parameters, adaptively adjust the injection molding process parameters, and verify the adjusted process parameters.

[0030] Step S6: Import the adjusted process parameters into the initial control scheme, and use the Pareto optimization algorithm to consider production efficiency, product quality, mold life and energy consumption, and set the weights: production efficiency 30%, product quality 40%, mold life 20%, and energy consumption 10%.

[0031] Optimize the initial control scheme and get the corresponding optimization scheme: Plan A: Focus on product quality and slightly reduce production efficiency Option B: Balancing various indicators Solution C: Focus on production efficiency and appropriately reduce mold life By comprehensively evaluating each option, the balanced option B is selected as the final global adjustment strategy.

[0032] For example, the selected solution B specifically includes: mold temperature: 176.5°C, injection pressure: 73MPa, holding time: 5.5 seconds, production cycle: 28 seconds per piece, and mold maintenance cycle: every 8,000 injection cycles.

[0033] The present invention provides a control method for an automobile parts injection mold. By acquiring the three-dimensional data and injection molding process parameters of the automobile instrument panel injection mold and constructing the instrument panel mold structure based on these parameters, the structural characteristics and process requirements of the mold can be more accurately evaluated, and reliable basic data can be provided for subsequent control and optimization. By acquiring the mold operation data in real time and associating it with the mold structure, real-time monitoring and analysis of the mold state are realized, which helps to timely discover and solve problems in the production process and improve the stability and reliability of production. The performance of the mold structure is predicted based on the injection molding data, and potential performance problems can be identified in advance, providing guidance for the maintenance and optimization of the mold, thereby improving the service life and production efficiency of the mold. By comprehensively analyzing the real-time mold state information and mold performance information, an initial mold control plan is formulated, and refined management of mold operation is realized, which helps to improve product quality and production efficiency. The injection molding data is parameter-identified and adaptively adjusted based on the mold performance information, so that the control method can flexibly respond to the production requirements of instrument panels of different materials and shapes, and improves the adaptability and flexibility of mold control.

[0034] In one embodiment, the three-dimensional data is structured based on the injection molding process parameters to obtain the instrument panel mold structure, including: The three-dimensional data of the instrument panel is meshed to generate a mesh model composed of triangular or quadrilateral units. The density and quality of the mesh directly affect the accuracy of subsequent analysis, so it is necessary to reasonably set the mesh parameters according to the structural characteristics of the instrument panel.

[0035] The characteristics of the injection molding process parameters are extracted to obtain melt flow characteristic data, molding temperature data and pressure parameters. The injection molding process parameters include material characteristics, injection speed, holding time, etc. These injection molding process parameters are simulated and calculated to obtain the flow characteristic data such as the flow path and velocity distribution of the melt in the mold cavity, as well as the temperature change data and pressure distribution data of each part during the molding process.

[0036] The mold cavity and gate structure of the initial mesh model are constructed based on the melt flow characteristic data to obtain the mold basic structure. According to the melt flow characteristic data, the location, size and number of the gate are determined, and the layout of the runner system is designed. The cavity structure is optimized according to the flow path, such as adding guide grooves, etc., to ensure that the melt can evenly fill the cavity. In this way, the mold basic structure including the cavity, gate and runner system is obtained.

[0037] According to the molding temperature data and pressure parameters, the mold base structure is thermodynamically analyzed to obtain the thermal stress distribution data. The molding temperature data and pressure parameters are imported into the finite element analysis software to perform transient thermal analysis and structural stress analysis on the mold base structure. The temperature field distribution and stress field distribution of each part of the mold during the injection molding process are calculated to determine the hot spot area and stress concentration area of ​​the mold.

[0038] Based on the thermal stress distribution data, the mold base structure is optimized to obtain the dashboard mold structure. For hot spots, the design of the cooling system is optimized, such as adjusting the position and size of the cooling water channel; for stress concentration areas, local reinforcement is performed by adding ribs and adjusting structural transitions. At the same time, considering the opening and closing, ejection and other functional requirements of the mold, the parting surface, ejection system and other structures are designed. After multiple rounds of iterative optimization, the dashboard mold structure that meets various performance requirements is finally obtained.

[0039] This embodiment provides an accurate data basis for mold structure design by meshing three-dimensional data and extracting the characteristics of injection molding process parameters, effectively improving the accuracy and reliability of mold design. The mold cavity and gate structure are constructed based on the melt flow characteristic data to ensure a high degree of match between the mold structure and the injection molding process, which is beneficial to improving the molding quality of the product. Thermodynamic analysis based on molding temperature data and pressure parameters can accurately identify the hot spots and stress concentration areas of the mold, providing a clear direction for subsequent structural optimization. Through multiple rounds of iterative optimization of the mold basic structure, the final dashboard mold structure is more perfect in performance and function, greatly reducing the problems that may occur during the actual mold trial.

[0040] In one embodiment, real-time operation data of an automobile dashboard injection mold is obtained, and the real-time operation data is associated with the dashboard mold structure to obtain corresponding real-time mold status information, including: Data collection is performed on the injection mold of the automobile instrument panel, and the collected data includes mold temperature data, mold pressure data, mold displacement data and mold vibration data. Temperature data is collected through thermocouples or infrared sensors, pressure data is collected through pressure sensors, displacement data is collected through displacement sensors, and vibration data is collected through acceleration sensors. These data reflect the various physical state changes of the mold during the injection molding process.

[0041] The collected mold temperature data is mapped to the dashboard mold structure, and a heat flow analysis is performed to obtain the temperature gradient distribution of each part of the mold. The heat flow analysis uses the finite element method to discretize the mold structure into grid units, calculate the temperature change rate of each unit, and thus obtain the temperature gradient distribution of the entire mold. This step helps to identify hot spots and uneven temperature distribution in the mold.

[0042] The mold pressure data is correlated with the stress-bearing parts in the instrument panel mold structure to obtain the pressure spectrum characteristics. The correlation analysis combines the pressure data with the geometric information of the mold structure to identify the key parts that bear greater pressure. The pressure data of these parts are Fourier transformed to obtain the pressure spectrum, and the main frequency components are extracted as the pressure spectrum characteristics. This step helps to find the stress concentration areas and periodic pressure changes in the mold.

[0043] Based on the mold displacement data, the instrument panel mold structure is subjected to geometric deviation analysis to obtain the mold deformation. Geometric deviation analysis calculates the deformation in three-dimensional space by comparing the difference between the actual position of the mold and the ideal position. The deformation includes displacement components in the x, y, and z directions, which are the three-dimensional directions of length, width, and height, respectively, and reflect the degree of deformation of the mold during the injection molding process.

[0044] The vibration data is correlated with the connection points in the dashboard mold structure to obtain the vibration feature vector. The correlation calculation combines the vibration data with the location information of the connection points in the mold structure to identify the key connection points with large vibrations. The vibration data of these connection points is analyzed by principal component analysis to extract the main vibration components as vibration feature vectors. This step helps to find loose or unstable connections in the mold.

[0045] Based on the dashboard mold structure, the temperature gradient distribution, pressure spectrum characteristics, mold deformation and vibration feature vector are fused to obtain a comprehensive mold state matrix. The multi-source data fusion process is as follows: First, the dashboard mold structure is divided into several structural sub-areas, each of which corresponds to a row of the comprehensive mold state matrix. The division of each structural sub-area is based on the geometric characteristics and functional partitions of the mold to ensure that the status of each part of the mold can be fully reflected.

[0046] For each structural sub-region, the temperature gradient value of the temperature gradient distribution, the main frequency component of the pressure spectrum, the three-dimensional directional component of the mold deformation and the main vibration component of the vibration eigenvector are extracted. These extracted eigenvalues ​​constitute the regional eigenvector of the structural sub-region. In the regional eigenvector, the temperature gradient value reflects the severity of the temperature change in the region, the main frequency component of the pressure spectrum indicates the main periodic pressure borne by the region, the three-dimensional directional component of the deformation describes the deformation of the region, and the main vibration component reflects the vibration characteristics of the region.

[0047] The temperature gradient value, main frequency component, three-dimensional direction component and main vibration component are arranged in a predefined order to form the regional feature vector of each structural sub-region. The predefined order is usually: temperature gradient value, pressure frequency component, x-direction deformation, y-direction deformation, z-direction deformation, and main vibration component. This arrangement order ensures the orderly organization of different types of data.

[0048] The regional feature vectors of all structural sub-regions are combined to form a comprehensive mold state matrix. In this matrix, each row represents a structural sub-region, and each column represents each regional feature vector. Such a matrix structure enables the overall state information of the mold to be systematically and structured.

[0049] The state recognition of the comprehensive mold state matrix is ​​performed to obtain the corresponding mold state information. The state recognition adopts machine learning algorithms, such as support vector machines or neural networks, which take the comprehensive mold state matrix as input and output various mold state categories, such as normal, slightly abnormal, and seriously abnormal.

[0050] The mold status information is evaluated in real time according to the preset status evaluation index system to obtain real-time mold status information. The status evaluation index system includes evaluation standards in multiple dimensions such as temperature uniformity, pressure stability, deformation degree, vibration intensity, etc. By comparing the mold status information with these standards, a quantitative evaluation result of the current state of the mold is given to form real-time mold status information.

[0051] This embodiment realizes comprehensive and real-time monitoring of the mold status by collecting and fusion analyzing multi-source data of the automobile dashboard injection mold. This method can not only capture the changes in the temperature, pressure, displacement and vibration of the mold, but also associate these data with the mold structure to obtain more accurate and meaningful status information. By dividing the mold structure into sub-areas and constructing a comprehensive mold status matrix, the complex mold status information is systematically and structured, which is convenient for subsequent status identification and evaluation. This multi-dimensional and multi-scale status monitoring method greatly improves the accuracy and comprehensiveness of mold status evaluation and provides a reliable basis for precise control and preventive maintenance of the mold.

[0052] In one embodiment, the injection molding data of the automobile instrument panel injection mold is obtained, and the performance of the instrument panel mold structure is predicted based on the injection molding data to obtain the corresponding mold performance information, including: The shear rate of the injection molding data is calculated to obtain the shear rate of different parts during the injection molding process. The shear rate is a physical quantity that describes the melt flow velocity gradient and has an important influence on the melt flow behavior. The mold cavity shape, injection pressure, injection speed and other factors are considered in the calculation. The finite element analysis method is used to discretize the mold cavity into grid units, and the local shear rate is calculated in each unit.

[0053] Based on the calculated shear rate, the melt viscosity distribution of the instrument panel mold structure is calculated to obtain the viscosity distribution data. Melt viscosity is an important parameter that characterizes the flow resistance of the melt.

[0054] The pressure field simulation is performed using the viscosity distribution data and shear rate to obtain the rheological characteristics of the melt in the mold. The pressure field simulation is numerically solved using the finite volume method. The simulation results include rheological parameters such as pressure and velocity at each point in the mold, reflecting the flow characteristics of the melt in the mold.

[0055] Based on the rheological characteristics of the melt in the mold, the temperature field analysis of the instrument panel mold structure is carried out to obtain the mold temperature distribution data. The temperature field analysis uses the heat conduction equation, taking into account the convection heat transfer caused by the melt flow and the heat dissipation of the mold cooling system, and calculates the temperature distribution of each part of the mold. The temperature distribution has an important influence on the product molding quality and mold life.

[0056] The instrument panel mold structure is subjected to warpage deformation analysis based on the mold temperature distribution data to obtain the mold warpage deformation data. The warpage deformation analysis is based on the thermal-structural coupling model, taking into account the thermal stress and shrinkage stress caused by uneven temperature distribution, and calculating the deformation of each part of the mold. Warpage deformation will affect the product dimensional accuracy and appearance quality.

[0057] Based on the mold warpage deformation data, the stress and strain analysis of the instrument panel mold structure is carried out to obtain the mold stress and strain distribution data. The stress and strain analysis uses a linear elastic mechanics model, considers the elastic modulus and Poisson's ratio of the mold material, and calculates the stress and strain state of each part of the mold. The stress and strain distribution reflects the force condition of the mold and is an important basis for evaluating the strength and stiffness of the mold.

[0058] The durability of the instrument panel mold structure is evaluated based on the mold stress-strain distribution data to obtain the mold durability data. The durability evaluation is based on the fatigue life theory and the SN curve of the mold material to calculate the expected service life of the mold under cyclic loads. The durability data reflects the long-term performance of the mold.

[0059] The mold durability data and mold temperature distribution data are comprehensively evaluated to obtain the comprehensive performance information of the mold. The comprehensive performance evaluation adopts a multi-objective optimization method, takes durability and temperature uniformity as evaluation indicators, and comprehensively considers the long-term use performance of the mold and the product molding quality to obtain the comprehensive performance score of the mold.

[0060] This embodiment achieves accurate prediction of the performance of the instrument panel mold by performing a comprehensive analysis and multi-step simulation of the injection molding data. From shear rate calculation to comprehensive performance evaluation, key physical parameters and process factors are considered in each step to ensure the accuracy and reliability of the prediction results. The use of advanced numerical simulation methods, such as finite element analysis and thermal-structural coupling models, can deeply reveal the complex physical processes inside the mold, providing a scientific basis for mold design and optimization. The long-term performance of the mold is predicted through durability evaluation, which helps to improve the service life and production efficiency of the mold. The comprehensive performance evaluation adopts a multi-objective optimization method to balance multiple indicators such as mold life and product quality, providing a comprehensive reference for mold design and production decisions.

[0061] In one embodiment, a solution analysis is performed on the real-time mold state information and mold performance information to obtain an initial mold control solution, including: The collected real-time mold status information is associated with the pre-stored mold performance information. Real-time mold status information includes real-time monitoring data such as mold temperature, pressure, injection speed, etc. Mold performance information includes mold design parameters, material properties, historical production data, etc. The association process establishes a corresponding relationship between the real-time status and the performance index through a data matching algorithm to obtain associated information reflecting the current working status of the mold.

[0062] Comprehensively evaluate the relevant information to obtain the comprehensive mold status evaluation result. The evaluation process adopts a multi-dimensional scoring model to quantitatively score the mold status from aspects such as mold life, product quality, and production efficiency, and calculates a comprehensive score based on the weight of each dimension as the mold status evaluation result.

[0063] Based on the comprehensive mold status evaluation results, the associated information is analyzed for abnormalities to identify abnormal mold performance points. Abnormal analysis uses statistical process control methods to compare the evaluation results with the preset normal range, and data points outside the range are marked as abnormal points. These abnormal points reflect that the mold has performance problems in some aspects.

[0064] Optimize and adjust the design of the detected abnormal points in mold performance and formulate corresponding abnormal improvement strategies. According to the type and degree of the abnormal points, combined with mold structure and process knowledge, design targeted improvement measures, such as adjusting the cooling system, optimizing the pouring system, modifying the mold structure, etc., to form a specific abnormal improvement strategy.

[0065] According to the abnormal improvement strategy, the parameters of the associated information are identified to obtain a specific mold parameter adjustment plan. Through simulation and data analysis, the specific parameters and their value ranges that need to be adjusted to implement the improvement strategy are determined, such as the adjustment plan of key process parameters such as mold temperature, injection pressure, and holding time.

[0066] Integrate the mold parameter adjustment plan and abnormal improvement strategy to form a complete initial mold control plan.

[0067] This embodiment achieves a comprehensive grasp of the current working status of the mold by associating real-time mold status information with mold performance information, providing a rich data basis for subsequent analysis. The use of a multi-dimensional scoring model for comprehensive evaluation makes the quantification of mold status more accurate and objective, which helps to discover potential problems in a timely manner. Abnormal analysis based on the evaluation results can quickly locate abnormal points in mold performance, improving the efficiency and accuracy of problem diagnosis. The optimization and adjustment strategy formulated for abnormal points ensures the pertinence and effectiveness of improvement measures. The specific adjustment plan obtained through parameter identification provides operational guidance for mold control and improves the accuracy and reliability of control. The initial mold control plan finally integrated takes into account structural optimization and parameter adjustment, providing a systematic solution for improving mold performance and product quality.

[0068] In one embodiment, parameter identification is performed on the injection molding data to obtain corresponding injection molding process parameters, and the injection molding process parameters are adaptively adjusted based on the mold performance information to obtain corresponding adjusted process parameters, including: The injection molding data is multi-dimensionally decomposed into parameter sequences of four dimensions: temperature, pressure, speed, and time. These parameter sequences are then processed by nonlinear time series analysis methods to extract dynamic features and form a dynamic feature set. Dynamic features include but are not limited to parameter change trends, periodicity, mutation points, etc.

[0069] Based on the obtained dynamic feature set, the injection molding process parameter space is constructed. This space is a multi-dimensional parameter representation, and each dimension corresponds to a process parameter. Use a spatial clustering algorithm (such as K-means or DBSCAN) to partition this parameter space to obtain the initial injection molding process parameters. The spatial clustering algorithm calculates the distance between parameters and clusters similar parameters together to form different parameter combinations.

[0070] The initial injection molding process parameters are optimized and adjusted based on the mold performance information. The mold performance information includes mold material characteristics, structural design, cooling system efficiency, etc. The optimization and adjustment process first sets the constraints of the process parameters according to the mold performance information, such as upper and lower temperature limits, pressure range, etc. At the same time, the optimization objective function including product quality and production efficiency is defined, which may involve the weighted sum of multiple factors such as product qualification rate, production cycle, energy consumption, etc.

[0071] Under the constraints, an iterative optimization algorithm (such as a genetic algorithm or a particle swarm optimization algorithm) is used to optimize the initial injection molding process parameters. In each iteration, the objective function value of the current parameter combination is evaluated to obtain an evaluation result. The evaluation result is used to judge the quality of the current parameter combination. According to the evaluation result, a parameter combination with better performance is selected for crossover mutation to generate a new parameter combination. The crossover mutation operation simulates the biological evolution process and explores a better solution by combining and fine-tuning existing parameters.

[0072] The iteration process continues until the preset iteration goal is reached. The iteration goal can be to reach a certain target function value, or the improvement of the target function value after multiple consecutive iterations is less than a predetermined threshold. The parameter combination obtained by the final iteration is the adjusted process parameter, which can achieve a good balance between product quality and production efficiency while meeting the mold performance constraints.

[0073] This embodiment realizes accurate parameter identification of complex injection molding process and improves the accuracy of process parameter setting by multi-dimensional decomposition and nonlinear time series analysis of injection molding data. By constructing injection molding process parameter space and performing spatial clustering, a large number of parameter combinations are effectively simplified into representative initial parameters, greatly improving the efficiency of parameter optimization. Adaptive adjustment is performed based on mold performance information, so that the optimized process parameters can better adapt to the characteristics of different molds, improving the flexibility and versatility of mold use. The iterative optimization algorithm is used to find the best balance between product quality and production efficiency, which not only ensures product quality but also improves production efficiency. By setting constraints and optimizing objective functions, the practicality and reliability of the optimization results are ensured. The whole process realizes the intelligent adjustment of injection molding process parameters, reduces human intervention, and improves the stability and consistency of the production process.

[0074] In one embodiment, the initial mold control solution is optimized according to the adjusted process parameters to obtain a corresponding global adjustment strategy, including: The initial mold control scheme is subjected to parameter hierarchical analysis to obtain the parameter importance ranking. Parameter hierarchical analysis is a multi-criteria decision-making method that decomposes complex problems into various components by establishing a hierarchical model, and compares these factors in pairs to finally obtain the relative importance of each parameter. In this embodiment, a hierarchical analysis is performed on various process parameters of the injection mold, such as temperature, pressure, time, etc., and the weight of each parameter is calculated to obtain the ranking result of the parameter importance.

[0075] Based on the parameter importance ranking, an orthogonal test design is performed on the initial mold control scheme to obtain the test scheme. Orthogonal test design is an efficient experimental design method that reduces the number of tests by selecting representative test point combinations. According to the parameter importance ranking, key parameters are selected, an orthogonal test table is designed, and the level of each parameter is determined to obtain a set of optimized test schemes.

[0076] The test scheme is simulated and run to obtain simulation results. Simulation is to conduct virtual experiments on the test scheme through computer simulation software to predict the situations that may occur in actual production. By using professional injection molding simulation software, inputting various parameters in the test scheme, and performing virtual injection molding process, simulation results can be obtained, including indicators such as product quality and production efficiency.

[0077] According to the preset decision rule set, the simulation results are analyzed by decision tree to obtain decision modification information. The decision rule set is a series of judgment criteria developed based on expert experience and historical data, which is used to evaluate the quality of simulation results. Decision tree analysis is an intuitive data mining method that represents the decision process by constructing a tree structure. The simulation results are compared with the decision rule set, a decision tree is constructed, and the impact of each parameter on the results is analyzed, so as to obtain the parameters that need to be modified and the modification direction.

[0078] The initial mold control scheme is adaptively adjusted according to the decision modification information to obtain an adaptive adjustment control scheme. Adaptive adjustment refers to the process of automatically adjusting control parameters according to feedback information. According to the decision modification information, the relevant parameters in the initial mold control scheme are adjusted, such as adjusting the injection temperature, pressure or time, so as to obtain an optimized adaptive adjustment control scheme.

[0079] The adaptive control scheme is matched with the preset control mode library to obtain the control mode. The control mode library is a database containing a variety of typical injection molding process modes, each of which corresponds to a specific product type and production requirements. The parameters of the adaptive control scheme are compared with each mode in the control mode library, and the best matching control mode is selected as the optimization direction.

[0080] The adaptive adjustment control scheme is integrated with the control mode to obtain the global adjustment strategy.

[0081] This embodiment realizes the scientific optimization of mold control parameters through parameter hierarchy analysis and orthogonal experimental design, greatly improving the efficiency and accuracy of parameter adjustment. The combination of simulation operation and decision tree analysis makes it possible to predict and evaluate the effects of various parameter combinations before actual production, effectively reducing trial and error costs and waste of resources. The adaptive adjustment mechanism can flexibly adjust the control scheme according to real-time feedback, improving the flexibility and adaptability of mold control. The integration of the adaptive adjustment scheme with the preset control mode not only ensures the reliability of the control scheme, but also takes into account personalized needs, achieving a balance between standardization and customization of mold control. The entire optimization process forms a closed-loop control strategy that can continuously improve the mold control effect, improve product quality and production efficiency.

[0082] Reference Figure 2 As shown, the present invention also provides a control device for an automobile parts injection mold, which is applied to any one of the above-mentioned control methods for an automobile parts injection mold, comprising: An acquisition module is used to obtain the three-dimensional data and injection molding process parameters of the automobile instrument panel injection mold, and to construct the structure of the three-dimensional data based on the injection molding process parameters to obtain the instrument panel mold structure; An analysis module, which is used to obtain real-time operation data of the automobile instrument panel injection mold, associate the real-time operation data with the instrument panel mold structure, and obtain corresponding real-time mold status information; The association module is used to obtain the injection molding data of the automobile instrument panel injection mold, and predict the performance of the instrument panel mold structure according to the injection molding data to obtain the corresponding mold performance information; A processing module is used to analyze the real-time mold status information and mold performance information to obtain an initial mold control solution; A control module, the control module is used to perform parameter recognition on the injection molding data to obtain corresponding injection molding process parameters, and adaptively adjust the injection molding process parameters based on the mold performance information to obtain corresponding adjustment process parameters; The execution module is used to optimize the initial mold control plan according to the adjusted process parameters to obtain the corresponding global adjustment strategy.

[0083] The present invention provides a control device for an automobile parts injection mold. By acquiring the three-dimensional data and injection molding process parameters of an automobile instrument panel injection mold and constructing the instrument panel mold structure based on these parameters, the structural characteristics and process requirements of the mold can be more accurately evaluated, and reliable basic data can be provided for subsequent control and optimization. By acquiring the mold operation data in real time and associating it with the mold structure, real-time monitoring and analysis of the mold state are achieved, which helps to timely discover and solve problems in the production process and improve the stability and reliability of production. The performance prediction of the mold structure based on the injection molding data can identify potential performance problems in advance, provide guidance for the maintenance and optimization of the mold, and thus improve the service life and production efficiency of the mold. By comprehensively analyzing the real-time mold state information and mold performance information, an initial mold control plan is formulated, and refined management of mold operation is achieved, which helps to improve product quality and production efficiency. The injection molding data is parameter-identified and adaptively adjusted based on the mold performance information, so that the control method can flexibly respond to the production requirements of instrument panels of different materials and shapes, and improve the adaptability and flexibility of mold control.

[0084] Reference Figure 3 As shown, the present invention also provides a control device for an automobile parts injection mold, comprising: Memory, used to store programs; The processor is used to execute the program to implement each step of any one of the above-mentioned control methods for an automobile parts injection mold.

[0085] In this embodiment, the processor and the memory may be connected via a bus or other means. The memory may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk, or a solid-state drive. The processor may be a general-purpose processor, such as a central processing unit, a digital signal processor, an application-specific integrated circuit, or one or more integrated circuits configured to implement the embodiments of the present invention.

[0086] The present invention also provides a medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0087] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0088] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A control method for an automobile parts injection mold, characterized in that: include: Acquire three-dimensional data and injection molding process parameters of an automobile instrument panel injection mold, and construct a structure of the three-dimensional data based on the injection molding process parameters to obtain an instrument panel mold structure; Acquire real-time operation data of the automobile instrument panel injection mold, associate the real-time operation data with the instrument panel mold structure, and obtain corresponding real-time mold status information; Acquire injection molding data of the automobile instrument panel injection mold, perform performance prediction on the instrument panel mold structure according to the injection molding data, and obtain corresponding mold performance information; Performing solution analysis on the real-time mold status information and the mold performance information to obtain an initial mold control solution; Performing parameter identification on the injection molding data to obtain corresponding injection molding process parameters, and adaptively adjusting the injection molding process parameters based on the mold performance information to obtain corresponding adjusted process parameters; The initial mold control solution is optimized according to the adjustment process parameters to obtain a corresponding global adjustment strategy.

2. The control method of the automobile parts injection mold according to claim 1, characterized in that: The step of constructing the structure of the three-dimensional data based on the injection molding process parameters to obtain a dashboard mold structure includes: Meshing the three-dimensional data to obtain an initial mesh model; Extracting characteristics of the injection molding process parameters to obtain melt flow characteristic data, molding temperature data and pressure parameters; Constructing the mold cavity and gate structure of the initial mesh model according to the melt flow characteristic data to obtain a mold base structure; Performing a thermodynamic analysis on the mold base structure according to the molding temperature data and the pressure parameters to obtain thermal stress distribution data; The mold base structure is structurally optimized based on the thermal stress distribution data to obtain the instrument panel mold structure.

3. The control method of the automobile parts injection mold according to claim 1, characterized in that: The step of acquiring the real-time operation data of the automobile instrument panel injection mold and associating the real-time operation data with the instrument panel mold structure to obtain corresponding real-time mold status information includes: Collecting data on the automobile instrument panel injection mold to obtain corresponding mold temperature data, mold pressure data, mold displacement data, and mold vibration data; Mapping the mold temperature data to the instrument panel mold structure and performing heat flow analysis to obtain the temperature gradient distribution of each part of the mold; Correlation analysis is performed on the mold pressure data and the stress-bearing parts in the instrument panel mold structure to obtain pressure spectrum characteristics; Performing geometric deviation analysis on the instrument panel mold structure according to the mold displacement data to obtain the mold deformation; Associating and calculating the vibration data with the connection structure points in the instrument panel mold structure to obtain a vibration characteristic vector; Based on the instrument panel mold structure, the temperature gradient distribution, the pressure spectrum characteristics, the mold deformation and the vibration characteristic vector are multi-source data fused to obtain a comprehensive mold state matrix; Performing state recognition on the comprehensive mold state matrix to obtain corresponding mold state information; Performing real-time evaluation on the mold status information according to a preset status evaluation index system to obtain the real-time mold status information; The step of fusing multi-source data to obtain the comprehensive mold state matrix includes: Dividing the instrument panel mold structure into a plurality of structural sub-regions, each of the structural sub-regions corresponds to a matrix row; Extracting the temperature gradient value of the temperature gradient distribution, the main frequency component of the pressure spectrum, the three-dimensional direction component of the mold deformation and the main vibration component of the vibration eigenvector as the matrix column of each structural sub-region; Arranging the temperature gradient value, the main frequency component, the three-dimensional direction component and the main vibration component in a predefined order to form a regional feature vector for each structural sub-region; The regional feature vectors of all structural sub-regions are combined to form the comprehensive mold state matrix, wherein each row represents one of the structural sub-regions, and each column represents each type of regional feature vector.

4. The control method of the automobile parts injection mold according to claim 1, characterized in that: The step of acquiring the injection molding data of the automobile instrument panel injection mold, and predicting the performance of the instrument panel mold structure according to the injection molding data to obtain corresponding mold performance information includes: Performing shear rate calculation on the injection molding data to obtain shear rates at different locations during the injection molding process; Calculating the melt viscosity distribution of the instrument panel mold structure based on the shear rate to obtain viscosity distribution data; Performing pressure field simulation based on the viscosity distribution data and the shear rate to obtain rheological property data of the melt in the mold; Performing temperature field analysis on the instrument panel mold structure based on the rheological property data of the melt in the mold to obtain mold temperature distribution data; Performing a warping deformation analysis on the instrument panel mold structure according to the mold temperature distribution data to obtain mold warping deformation data; Performing stress-strain analysis on the instrument panel mold structure based on the mold warping deformation data to obtain mold stress-strain distribution data; Performing a durability evaluation on the instrument panel mold structure according to the mold stress-strain distribution data to obtain mold durability data; A comprehensive performance evaluation is performed on the mold durability data and the mold temperature distribution data to obtain comprehensive mold performance information.

5. The control method of the automobile parts injection mold according to claim 1, characterized in that: The performing of scheme analysis on the real-time mold state information and the mold performance information to obtain an initial mold control scheme includes: Associating the real-time mold status information with the mold performance information to obtain associated information; Comprehensively evaluating the associated information to obtain a comprehensive mold status evaluation result; Based on the comprehensive mold status evaluation result, the associated information is analyzed for abnormality to obtain mold performance abnormality points; Optimize and adjust the design of the abnormal points of the mold performance to obtain corresponding abnormal improvement strategies; Perform parameter identification on the associated information according to the abnormal improvement strategy to obtain a specific mold parameter adjustment plan; The mold parameter adjustment plan and the abnormality improvement strategy are integrated to obtain the initial mold control plan.

6. The control method of the automobile parts injection mold according to claim 1, characterized in that: The performing parameter identification on the injection molding data to obtain corresponding injection molding process parameters, and adaptively adjusting the injection molding process parameters based on the mold performance information to obtain corresponding adjusted process parameters, includes: Decomposing the injection molding data in multiple dimensions to obtain a parameter sequence including four dimensions: temperature, pressure, speed and time; Performing nonlinear time series analysis on the parameter sequence and extracting dynamic features to obtain a dynamic feature set; Constructing an injection molding process parameter space according to the dynamic feature set, and partitioning the injection molding process parameter space according to a spatial clustering algorithm to obtain initial injection molding process parameters; Optimizing and adjusting the initial injection molding process parameters based on the mold performance information to obtain the adjusted process parameters; The optimization and adjustment process includes: Set constraints on process parameters based on mold performance information and define optimization objective functions that include product quality and production efficiency; Iteratively optimizing the initial injection molding process parameters under the constraints according to the optimization objective function; In each iteration, the objective function value of the current parameter combination is evaluated to obtain the evaluation result; Selecting the corresponding parameter combination according to the evaluation result to perform crossover mutation to generate a new parameter combination; When the preset iteration target is reached, the parameter combination obtained by the final iteration is used as the adjustment process parameter.

7. The control method of the automobile parts injection mold according to claim 1, characterized in that: The optimizing the initial mold control scheme according to the adjusted process parameters to obtain a corresponding global adjustment strategy includes: Performing a parameter hierarchy analysis on the initial mold control scheme according to the adjusted process parameters to obtain a parameter importance ranking; Performing an orthogonal test design on the initial mold control scheme based on the parameter importance ranking to obtain a test scheme; Performing simulation operation on the test scheme to obtain simulation results; Performing a decision tree analysis on the simulation results according to a preset decision rule set to obtain decision modification information; Adaptively adjusting the initial mold control scheme according to the decision modification information to obtain an adaptively adjusted control scheme; Performing pattern matching on the adaptive adjustment control scheme and a preset control pattern library to obtain a control pattern; The adaptive adjustment control scheme is integrated with the control mode to obtain the global adjustment strategy.

8. A control device for an automobile parts injection mold, characterized in that: The control method for the automobile parts injection mold applied to any one of claims 1 to 7 above comprises: An acquisition module, the acquisition module is used to obtain three-dimensional data and injection molding process parameters of the automobile instrument panel injection mold, and to perform structural construction on the three-dimensional data based on the injection molding process parameters to obtain the instrument panel mold structure; An analysis module, the analysis module is used to obtain real-time operation data of the automobile instrument panel injection mold, associate the real-time operation data with the instrument panel mold structure, and obtain corresponding real-time mold status information; An association module, the association module is used to obtain injection molding data of the automobile instrument panel injection mold, perform performance prediction on the instrument panel mold structure according to the injection molding data, and obtain corresponding mold performance information; A processing module, the processing module is used to perform a solution analysis on the real-time mold state information and the mold performance information to obtain an initial mold control solution; A control module, the control module is used to perform parameter identification on the injection molding data to obtain corresponding injection molding process parameters, and adaptively adjust the injection molding process parameters based on the mold performance information to obtain corresponding adjustment process parameters; An execution module is used to optimize the initial mold control solution according to the adjustment process parameters to obtain a corresponding global adjustment strategy.

9. A control device for an automobile parts injection mold, characterized in that: include: Memory, used to store programs; The processor is used to execute the program to implement the various steps of the control method of an automobile parts injection mold as described in any one of claims 1 to 7.

10. A medium, characterized in that Computer instructions are stored, and the computer instructions are used to make a computer execute the method according to any one of claims 1 to 7.

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