Intelligent adjusting method and system for mold temperature controller in diversified temperature control mode

Abstract

The invention discloses an intelligent adjusting method and system for a mold temperature controller in a diversified temperature control mode, and relates to the technical field of mold temperature control, and the method comprises the steps: determining the process execution information of a mold connected with the mold temperature controller, analyzing a temperature control process, and positioning cold and heat alternation nodes and corresponding temperature parameters. The relative position distribution information of the mold temperature controller medium feeding loop and the mold is obtained; and combining the heat conductivity coefficient of the forming material to construct an intelligent temperature diffusion model. And by taking the first temperature parameter as a control original point and the second temperature parameter as a uniform control target, calling the intelligent model to simulate and generate a temperature control parameter. And adjusting the mold temperature machine according to the temperature control parameters at the cooling and heating alternation node, and executing uniform cooling control in combination with the process information. And therefore, the technical effects of improving the control precision, improving the temperature uniformity of the mold and improving the quality and stability of plastic molding are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of mold temperature control, and particularly to an intelligent adjustment method and system for a mold temperature controller under a diversified temperature control mode. Background Art

[0002] As a key device for controlling the temperature of a mold, the parameter control of a mold temperature controller has a crucial impact on product quality and production efficiency. The existing mold temperature controllers mainly adopt a single control mode in temperature control, that is, the mold is heated to a set temperature range through a heating system to ensure that the surface temperature of the mold is suitable for the flow of molten plastic. After the plastic melt is injected into the mold, the mold starts to cool, so that the plastic gradually solidifies and forms, and changes from a heated mold to a cooled mold. This traditional control mode has insufficient control accuracy at the hot and cold alternating nodes of the mold, and it is difficult to achieve precise temperature conversion, which may cause large fluctuations in the mold temperature during the alternating process, affecting the molding quality of the plastic. At the same time, during the hot and cold alternating process, the medium delivery loop itself will be affected by the high-temperature medium. When the cooling medium continues to flow in the medium delivery loop, due to the temperature change of the pipeline itself, it will affect the temperature of the medium, and further affect the cooling effect and temperature uniformity of the mold. Summary of the Invention

[0003] The present invention provides an intelligent adjustment method and system for a mold temperature controller under a diversified temperature control mode to solve the technical problems in the prior art such as poor control accuracy at the hot and cold alternating nodes, the influence of the temperature change of the medium delivery loop on the medium temperature, and the influence on the molding quality and molding efficiency, and to achieve the technical effects of improving the control accuracy, improving the temperature uniformity of the mold, and improving the quality and stability of plastic molding.

[0004] In a first aspect, the present invention provides an intelligent adjustment method for a mold temperature controller under a diversified temperature control mode, wherein the intelligent adjustment method for a mold temperature controller under a diversified temperature control mode includes:

[0005] Determine the mold process execution information connected to the mold temperature controller.

[0006] Based on the mold process execution information, analyze the mold temperature control process, locate the hot and cold alternating nodes, and the corresponding hot and cold alternating temperature parameters.

[0007] Obtain the medium delivery loop for controlling the temperature of the mold in the mold temperature controller, and the relative position distribution information of the medium delivery loop and the mold.

[0008] Determine the thermal conductivity by analyzing the molding material information in the mold, and construct an intelligent temperature diffusion model in combination with the relative position distribution information.

[0009] Taking the first temperature parameter in the hot and cold alternating temperature parameters as the control origin and the second temperature parameter as the target for uniform control of the entire mold area, the intelligent temperature diffusion model is called for control simulation to generate hot and cold alternating temperature control parameters.

[0010] After controlling the mold temperature machine with the hot and cold alternating temperature control parameters at the hot and cold alternating node, uniform control in the temperature uniform drop mode is then performed in combination with the mold process execution information.

[0011] In a feasible implementation manner, based on the mold process execution information, a mold temperature control process analysis is performed to locate the hot and cold alternating node, including:

[0012] Analyze the mold process execution information to determine the continuous temperature demand data sequence during the process execution.

[0013] Based on the continuous temperature demand data sequence, locate the temperature control node between the continuous rising trend and the continuous falling trend to generate the hot and cold alternating node.

[0014] In a feasible implementation manner, taking the first temperature parameter in the hot and cold alternating temperature parameters as the control origin and the second temperature parameter as the target for uniform control of the entire mold area, the intelligent temperature diffusion model is called for control simulation to generate hot and cold alternating temperature control parameters, including:

[0015] Based on the first temperature parameter, perform reverse diffusion analysis through the intelligent temperature diffusion model to determine the influence temperature of the residual in the medium delivery loop.

[0016] After adjusting the control origin with the influence temperature of the residual in the medium delivery loop, based on the adjusted control origin, call the intelligent temperature diffusion model for multiple control simulations and record multiple groups of control simulation results.

[0017] In the multiple groups of control simulation results, screen the first set of control simulation results where the temperatures at all positions of the mold satisfy the second temperature parameter.

[0018] Perform a mold-wide temperature uniformity analysis on each simulation result in the first set of control simulation results, screen the temperature control parameters corresponding to a set of simulation results whose uniformity index meets the preset uniformity index, and generate the hot and cold alternating temperature control parameters.

[0019] In a feasible implementation manner, based on the first temperature parameter, perform reverse diffusion analysis through the intelligent temperature diffusion model to determine the influence temperature of the residual in the medium delivery loop, including:

[0020] Take the first temperature parameter as the temperature diffused to each position of the mold and load it into the intelligent temperature diffusion model.

[0021] Perform reverse temperature conduction analysis through the intelligent temperature diffusion model to generate the residual influence temperature of the medium delivery loop.

[0022] In a feasible implementation, call the intelligent temperature diffusion model to perform multiple control simulations and record multiple groups of control simulation results, including:

[0023] Identify the throttle valve in the medium delivery loop and determine the set of adjustable loop modes based on the throttle valve position.

[0024] Determine each adjustable loop mode in the set of adjustable loop modes, perform mode matching adjustment on the intelligent temperature diffusion model, and then perform control simulations respectively based on each adjustable loop mode to generate the multiple groups of control simulation results. Among them, each group of control simulation results has identification information including temperature control parameters and throttle valve control parameters.

[0025] In a feasible implementation, determine the thermal conductivity of the molding material information in the mold, and combine the relative position distribution information to construct an intelligent temperature diffusion model, including:

[0026] Using the molding material information as an index element, collect temperature mapping samples from the mold surface to the inner cavity. The temperature mapping samples include temperature distribution data sets corresponding to each inner cavity position and the outer surface.

[0027] Analyze the thermal conductivity of the material from the mold surface to the mold inner cavity using the temperature mapping samples.

[0028] Determine the thermal conductivity between the medium delivery loop and the mold, and combine the thermal conductivity of the material from the mold surface to the mold inner cavity and the relative position distribution information to analyze the temperature diffusion relationship from the medium delivery loop to the mold surface and the mold inner cavity material, and generate the intelligent temperature diffusion model.

[0029] In a feasible implementation, generating the multiple groups of control simulation results further includes:

[0030] Determine the distribution characteristics of each circulation loop corresponding to each adjustable loop mode with respect to each loop of the mold.

[0031] Perform a uniform probability analysis of temperature control based on the distribution characteristics of each loop to generate each uniform probability.

[0032] Based on each uniform probability, screen out the adjustable loop modes whose uniform probability is greater than or equal to a preset uniform probability threshold.

[0033] Perform control simulations respectively with the screened adjustable loop modes to generate the multiple groups of control simulation results.

[0034] In a feasible implementation, uniform control in the temperature uniform decrease mode is performed in combination with the mold process execution information, including:

[0035] Construct a temperature control parameter influence sample database corresponding to a preset temperature uniform decrease gradient.

[0036] Based on the mold process execution information, determine the decrease gradient corresponding to the temperature uniform decrease mode, perform temperature control parameter influence analysis in the temperature control parameter influence sample database, and generate uniform decrease temperature control parameters.

[0037] Execute uniform control in the temperature uniform decrease mode with the uniform decrease temperature control parameters.

[0038] In a feasible implementation, obtain the medium delivery loop for controlling the temperature of the mold in the mold temperature control machine, and the relative position distribution information between the medium delivery loop and the mold, including:

[0039] Construct a reference coordinate system, map the position information of the medium delivery loop and the mold position information to the reference coordinate system, and generate the relative position distribution information.

[0040] In a second aspect, the present invention also provides a mold temperature control machine intelligent adjustment system under a diversified temperature control mode. Among them, the mold temperature control machine intelligent adjustment system under the diversified temperature control mode includes:

[0041] A process acquisition module for determining the mold process execution information connected to the mold temperature control machine.

[0042] An alternating node positioning module for performing mold temperature control process analysis based on the mold process execution information, positioning the hot and cold alternating nodes, and the corresponding hot and cold alternating temperature parameters.

[0043] A loop information acquisition module for obtaining the medium delivery loop for controlling the temperature of the mold in the mold temperature control machine, and the relative position distribution information between the medium delivery loop and the mold.

[0044] A diffusion model construction module for determining the thermal conductivity by analyzing the molding material information in the mold, and constructing an intelligent temperature diffusion model in combination with the relative position distribution information.

[0045] A simulation decision module for taking the first temperature parameter in the hot and cold alternating temperature parameters as the control origin, taking the second temperature parameter as the mold global uniform control target, calling the intelligent temperature diffusion model for control simulation, and generating hot and cold alternating temperature control parameters.

[0046] A uniform control module for, at the hot and cold alternating nodes, after controlling the mold temperature control machine with the hot and cold alternating temperature control parameters, and then performing uniform control in the temperature uniform decrease mode in combination with the mold process execution information.

[0047] The present invention discloses an intelligent adjustment method and system for a mold temperature controller under a diversified temperature control mode, including: determining the mold process execution parameters connected to the mold temperature controller; analyzing the mold temperature control process based on the process execution parameters, identifying the cold and hot alternating nodes and their corresponding temperature parameters; obtaining the information of the heat transfer medium circuit used by the mold temperature controller to control the mold temperature, and determining its relative position distribution with respect to the mold; determining the information of the molding material in the mold, analyzing the thermal conductivity, and constructing an intelligent temperature diffusion model in combination with the relative position distribution information; taking the first temperature parameter in the cold and hot alternating temperature parameters as the control reference and the second temperature parameter as the overall equilibrium control target of the mold, calling the intelligent temperature diffusion model for simulation analysis, generating cold and hot alternating temperature control parameters; at the cold and hot alternating nodes, regulating the mold temperature controller according to the temperature control parameters, and combining with the mold process execution parameters, performing equilibrium control under the temperature uniform decrease mode. The intelligent adjustment method and system for a mold temperature controller under a diversified temperature control mode disclosed by the present invention solve the technical problems of poor control accuracy at the cold and hot alternating nodes, the influence of the temperature change of the heat transfer medium circuit on the medium temperature, and the influence on the molding quality and molding efficiency, and achieve the technical effects of improving the control accuracy, improving the mold temperature uniformity, and improving the quality and stability of plastic molding. Description of the Drawings

[0048] Figure 1 It is a schematic flow chart of the intelligent adjustment method for a mold temperature controller under a diversified temperature control mode of the present invention;

[0049] Figure 2 It is a schematic structural diagram of the intelligent adjustment system for a mold temperature controller under a diversified temperature control mode of the present invention.

[0050] Description of the reference numerals: Process acquisition module 11, Alternating node positioning module 12, Loop information acquisition module 13, Diffusion model construction module 14, Simulation decision module 15, Uniform control module 16. Detailed Embodiments

[0051] The following will describe the above technical solutions in detail in combination with the drawings in the specification and specific embodiments to better understand the above technical solutions. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments for explaining the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention. In addition, it should be noted that for the sake of description, only the parts related to the present invention are shown in the drawings rather than all of them.

[0052] Embodiment 1, as Figure 1, is a schematic flow diagram of the intelligent adjustment method of the mold temperature controller under the diversified temperature control mode of the present invention. Among them, the intelligent adjustment method of the mold temperature controller under the diversified temperature control mode includes:

[0053] S100: Determine the mold process execution information connected to the mold temperature controller.

[0054] Specifically, the mold process execution information refers to the process parameters and execution steps that the mold temperature controller needs to follow during the mold production process, including the target temperature of the mold, the holding time, the cooling rate, etc. For different molds and products, different parameters are required to meet the quality requirements. These information will be used as the basis for the control of the mold temperature controller to ensure that the mold changes temperature according to the predetermined process flow.

[0055] Exemplarily, the process information corresponding to the mold connected to the mold temperature controller can be obtained through the control system of the mold temperature controller. For example, in an injection mold, the process execution information may include that the preheating temperature of the mold is 180 °C, the holding time is 5 minutes, and then it cools to room temperature at a rate of 20 °C per minute.

[0056] S200: Based on the mold process execution information, conduct a mold temperature control process analysis to locate the hot and cold alternating nodes and the corresponding hot and cold alternating temperature parameters.

[0057] Specifically, by studying and evaluating the temperature change of the mold during the production process, the temperature requirements and change trends of the mold at different stages are determined, and the hot and cold alternating nodes are located. The hot and cold alternating nodes refer to the turning points of the temperature change of the mold during the heating and cooling processes, that is, the critical moments when the mold changes from the heating state to the cooling state or from the cooling state to the heating state; the hot and cold alternating temperature parameters refer to the specific values of the mold temperature corresponding to the hot and cold alternating nodes. Exemplarily, it includes the mold conversion temperature when triggering the hot and cold alternation and the conversion target temperature expected to be reached after the conversion.

[0058] In some embodiments, based on the mold process execution information, conducting a mold temperature control process analysis to locate the hot and cold alternating nodes includes:

[0059] Analyze the mold process execution information to determine the continuous temperature requirement data sequence during the process execution; based on the continuous temperature requirement data sequence, locate the temperature control nodes between the continuously rising trend and the continuously falling trend, and generate the hot and cold alternating nodes.

[0060] Specifically, the continuous temperature requirement data sequence refers to the set of temperature requirement data arranged in chronological order during the mold production process; the temperature control node refers to the turning point of the mold temperature change, that is, the critical time point when the mold changes from the heating state to the cooling state or from the cooling state to the heating state.

[0061] Specifically, first, according to the temperature setting and time arrangement in the mold process execution information, analyze the temperature change curve of the mold during the production process to determine the temperature change trend of the mold at different stages and locate the hot and cold alternating nodes, that is, the moment when the mold changes from the heating state to the cooling state; for example, in an injection mold, the process execution information may show that the mold needs to be heated from room temperature to 180 °C and maintained until injection is completed, and then cooled to 50 °C at a rate of 20 °C per minute. The corresponding hot and cold alternating temperature parameters are 180 °C and 50 °C respectively.

[0062] Optionally, by using mathematical methods, obtain the extreme points and / or maximum and minimum points in the temperature change curve as temperature control nodes, and extract the coordinate values of multiple temperature control nodes and adjacent temperature control nodes as hot and cold alternating temperature parameters.

[0063] S300: Obtain the medium delivery circuit in the mold temperature controller for controlling the temperature of the mold, and the relative position distribution information between the medium delivery circuit and the mold.

[0064] Specifically, by obtaining the medium delivery circuit in the mold temperature controller for controlling the temperature of the mold and establishing the relative position distribution information between the medium delivery circuit and the mold, it helps to optimize the temperature control of the mold and improve the stability and efficiency of the molding process. Among them, the medium delivery circuit (medium transportation circuit) refers to the pipeline system for delivering cooling or heating media (such as water, oil or heat-conducting fluid) to the mold, and its function is to control the mold temperature to keep it within the set range.

[0065] In some embodiments, obtaining the medium delivery circuit in the mold temperature controller for controlling the temperature of the mold, and the relative position distribution information between the medium delivery circuit and the mold includes:

[0066] Construct a reference coordinate system, map the position information of the medium delivery circuit and the mold position information to the reference coordinate system, and generate the relative position distribution information.

[0067] Specifically, in order to unify the spatial position information of the medium delivery circuit and the mold, first, establish a unified reference coordinate system within the equipment. This coordinate system can be established based on the equipment coordinate system (with the mold temperature controller frame or fixed installation base as the reference) or the mold coordinate system (with the mold center or installation surface as the reference); preferably, the reference coordinate system adopts a three-dimensional coordinate system (X, Y, Z); then, based on the CAD design drawing or through the three-dimensional scanning method, extract the geometric information of the pipeline structure, record the key positions such as the inlet, outlet, bending points, and branch points of each pipeline in the reference coordinate system, so as to generate the spatial path model of the medium delivery circuit; at the same time, determine the position information of the mold based on the CNC machining data, 3D modeling file or sensor measurement.

[0068] Exemplarily, if the reference coordinate system is based on the device coordinate system, the position information of the mold includes the shape and size of the mold (describing its geometric characteristics) and the positioning dimensions of the mold (determining its installation method and relative position); assume that the device coordinate system of a certain mold temperature controller is defined as follows: X-axis: along the left-right direction of the device; Y-axis: along the front-back direction of the device; Z-axis: along the up-down direction (vertical direction) of the device. The installation information of a certain mold is as follows: Mold outer dimensions: 300mm (L) × 200mm (W) × 150mm (H); Mold cavity dimensions: 250mm (L) × 180mm (W) × 100mm (H); Reference point coordinates - (X = 500mm, Y = 300mm, Z = 0mm) in the device coordinate system; The mold installation direction is upright (not rotated); Fixed screw hole positions - (X = 480mm, Y = 280mm, Z = 0mm) and (X = 520mm, Y = 280mm, Z = 0mm) in the device coordinate system.

[0069] Further, a coordinate transformation algorithm (such as translation, rotation transformation) is used to unify the position information of the medium delivery circuit and the mold into the reference coordinate system to calculate the relative position relationship between the two. Preferably, it includes the coverage rate of each area of the mold by the medium delivery circuit, the distance distribution from the medium delivery circuit to each surface of the mold, etc.

[0070] Through the above process, the distribution of the medium delivery circuit in the mold is accurately obtained, which provides reliable basic data support for subsequent simulation analysis and helps to improve the accuracy of simulation analysis.

[0071] S400: Determine the thermal conductivity of the molding material information in the mold, and combine the relative position distribution information to construct an intelligent temperature diffusion model.

[0072] Specifically, the molding material information includes information such as the type, thickness, shape, and characteristics of the material used in the mold; the intelligent temperature diffusion model is a mathematical model based on the material thermal conductivity and position distribution information, used to simulate and predict the temperature diffusion in the mold.

[0073] By constructing an intelligent temperature diffusion model, the temperature diffusion in the mold can be accurately predicted, providing a scientific basis for the control of the mold temperature controller, helping to improve the accuracy of mold temperature control, and reducing product quality problems caused by uneven temperature.

[0074] In some embodiments, determining the thermal conductivity of the molding material information in the mold, and combining the relative position distribution information to construct an intelligent temperature diffusion model includes:

[0075] Taking the information of the molding material as an index element, collect temperature mapping samples from the mold surface to the inner cavity. The temperature mapping samples include datasets of temperature distributions corresponding to each inner cavity position and the outer surface. Analyze the thermal conductivity of the material from the mold surface to the mold inner cavity based on the temperature mapping samples. Determine the thermal conductivity between the medium delivery circuit and the mold, and combine the thermal conductivity of the material from the mold surface to the mold inner cavity and the relative position distribution information to analyze the temperature diffusion relationship from the medium delivery circuit to the mold surface and the mold inner cavity material, and generate the intelligent temperature diffusion model.

[0076] Specifically, the temperature mapping sample refers to a set of temperature distribution data collected at different positions from the mold surface to the inner cavity, including the temperature values corresponding to each inner cavity position and the outer surface, and is used to analyze the temperature distribution inside the mold.

[0077] Specifically, first, use information such as the type and characteristics of the materials used in the mold as the basis for searching and organizing data to obtain corresponding temperature mapping samples, including datasets of temperature distributions corresponding to each inner cavity position and the outer surface. Then, based on the collected temperature mapping samples, analyze the temperature change law from the mold surface to the inner cavity to obtain the thermal conductivity of the material from the mold surface to the mold inner cavity. For example, if the temperature on the mold surface is 180 °C and the temperature in the inner cavity is 170 °C, the temperature gradient can be calculated as 10 °C. Then, according to Fourier's law of heat conduction, combined with the temperature gradient and heat flux density, calculate the thermal conductivity. Exemplarily, if the heat flux density is 100 W / m 2 , then the thermal conductivity is 10 W / m·K. In this way, the total thermal conductivity of the material from the mold surface to the inner cavity, which combines multiple interfaces, can be analyzed, providing basic data for the construction of the intelligent temperature diffusion model, helping to reduce temperature control errors caused by inaccurate thermal conductivity, and improving the prediction accuracy of the model.

[0078] Furthermore, determine the thermal conductivity between the medium delivery circuit and the mold, which can be obtained through experimental measurement or by referring to relevant material data manuals. Then, it is necessary to combine the thermal conductivity of the material from the mold surface to the inner cavity and the relative position distribution information to analyze how the temperature is transferred from the medium delivery circuit to the mold surface and the inner cavity material. For example, if the medium delivery circuit is located on one side of the mold, 10 cm away from the mold surface, and the thermal conductivity of the mold material is 200 W / m·K, a mathematical formula reflecting the diffusion speed and distribution of temperature in the medium delivery circuit to the mold surface and the inner cavity material can be derived according to heat transfer kinetics to define the intelligent temperature diffusion model, which is used to simulate and predict the temperature diffusion process in the mold.

[0079] Optionally, using finite element analysis (FEA) or numerical calculation methods (such as the finite difference method FDM), combined with the thermal conductivity, relative position distribution, and temperature diffusion relationship, establish a temperature field distribution equation to simulate the heat diffusion situation from the media delivery loop to various parts of the mold. Construct an intelligent temperature diffusion model.

[0080] S500: Taking the first temperature parameter in the hot and cold alternating temperature parameters as the control origin and the second temperature parameter as the uniform control target for the entire mold area, call the intelligent temperature diffusion model to perform control simulation to generate hot and cold alternating temperature control parameters.

[0081] Specifically, the first temperature parameter usually refers to the initial temperature when the mold changes from the heating state to the cooling state, and the second temperature parameter refers to the target temperature to which the mold cools; the uniform control target for the entire mold area means that within the entire mold area, the temperature needs to reach a uniform target value, and the value of the uniform control target for the entire mold area corresponds to the above-mentioned second temperature parameter.

[0082] Exemplarily, first, determine the first temperature parameter and the second temperature parameter in the hot and cold alternating temperature parameters; for example, assume that the first temperature parameter is 180 °C and the second temperature parameter is 50 °C; then, input these parameters into the intelligent temperature diffusion model, taking the first temperature parameter as the control origin and the second temperature parameter as the uniform control target for the entire mold area, and perform control simulation. During the simulation process, the intelligent temperature diffusion model will, according to the thermal conductivity and relative position distribution information of the mold, simulate the diffusion process of temperature from the media delivery loop to the mold surface and the inner cavity material, and through multiple simulations, continuously adjust the control parameters of the mold temperature controller (such as the flow rate and temperature of the media delivery loop), and then find the optimal temperature control parameters, so that the mold temperature can quickly and uniformly drop from 180 °C (the first temperature parameter) to 50 °C (the first temperature parameter) for actual mold temperature controller control.

[0083] In some embodiments, taking the first temperature parameter in the hot and cold alternating temperature parameters as the control origin and the second temperature parameter as the uniform control target for the entire mold area, calling the intelligent temperature diffusion model to perform control simulation to generate hot and cold alternating temperature control parameters, includes:

[0084] Based on the first temperature parameter, perform reverse diffusion analysis through the intelligent temperature diffusion model to determine the residual influence temperature of the media delivery loop; after adjusting the control origin with the residual influence temperature of the media delivery loop, based on the adjusted control origin, call the intelligent temperature diffusion model to perform multiple control simulations, and record multiple groups of control simulation results; screen the first control simulation result set in which the temperatures at all positions of the mold meet the second temperature parameter from the multiple groups of control simulation results; perform mold global temperature uniformity analysis on each simulation result in the first control simulation result set, and screen the temperature control parameters corresponding to a group of simulation results whose uniformity index meets the preset uniformity index to generate the cold and hot alternating temperature control parameters.

[0085] Specifically, reverse diffusion analysis refers to starting from the first temperature parameter through the intelligent temperature diffusion model, combining the heat capacity and heat transfer coefficient of the media delivery loop, the temperature, heat capacity and mass flow rate of the cooling or heating medium, and reversely calculating the temperature influence of the media delivery loop in the mold. The residual influence temperature of the media delivery loop reflects the influence of the temperature change of the media delivery loop itself on the mold temperature during the cold and hot alternating process. For example, if the model shows that the residual influence of the temperature of the media delivery loop in the mold is 10°C, this means that during the cold and hot alternating process, the temperature change of the media delivery loop will cause a 10°C fluctuation in the mold temperature.

[0086] Furthermore, according to the residual influence temperature of the media delivery loop, adjust the control origin to compensate for the influence of the media delivery loop on the mold temperature. Exemplarily, the adjustment method includes offsetting the temperature of the control origin or setting a temperature offset curve that changes with time (the offset rate of the control origin at different times); then, through the intelligent temperature diffusion model, combined with the adjusted control origin, simulate the control process of the mold temperature controller multiple times to obtain the mold temperature control effects (i.e., control simulation results) under different control strategies (temperature control parameters), where the control simulation results include the sequential temperature distribution data of the mold.

[0087] Specifically, after recording multiple groups of control simulation results, select and screen the results that meet the conditions from these results. Exemplarily, the obtained first control simulation result set refers to the set of simulation results that meet the condition that the temperatures at all positions of the mold reach the second temperature parameter (such as being in the interval of the second temperature parameter ± preset value); Exemplarily, assume the second temperature parameter is 50°C and the neighborhood radius is 1°C. The first simulation result shows that there are 3 areas where the cavity temperature is 48°C, which does not meet the condition; the second simulation result shows that there are 2 areas where the cavity temperature is 49.2°C and 1 area where the cavity temperature is 50.3°C, which meets the condition. By screening the simulation results that meet the conditions, it can be ensured that the temperatures at all positions of the mold can reach the target value during the cold and hot alternating process, which helps to improve the accuracy of mold temperature control and reduce product quality problems caused by uneven temperature.

[0088] Furthermore, the temperatures at all positions of the mold are analyzed to determine the uniformity of the temperature distribution. Here, the uniformity index is an index for measuring the uniformity of the temperature distribution, such as the deviation or standard deviation of the temperature in the mold cavity; the preset uniformity index is a preset temperature uniformity standard used to screen out the simulation results that meet the conditions. Exemplarily, if the preset uniformity index is set as the temperature deviation being less than ±1 °C, then the temperature data in each simulation result needs to be analyzed. If the temperature deviation of a certain simulation result is within ±1 °C, then this result is considered to meet the conditions, so as to screen out the temperature control parameters corresponding to a set of simulation results whose uniformity index meets the preset uniformity index, and generate the hot and cold alternating temperature control parameters. Through the analysis of the overall mold temperature uniformity, it can ensure the uniformity of the temperature distribution during the hot and cold alternation of the mold, and reduce the product quality problems caused by uneven temperature.

[0089] In some implementation manners, based on the first temperature parameter, reverse diffusion analysis is performed through the intelligent temperature diffusion model to determine the temperature affected by the residual of the medium delivery loop, including:

[0090] Taking the first temperature parameter as the temperature diffused to each position of the mold and loading it into the intelligent temperature diffusion model; performing reverse temperature conduction analysis through the intelligent temperature diffusion model to generate the temperature affected by the residual of the medium delivery loop.

[0091] Specifically, first, the initial temperature of the mold during the hot and cold alternation process, that is, the first temperature parameter, is input into the intelligent temperature diffusion model as the initial condition of the model. Exemplarily, it includes setting the initial temperature of the medium delivery loop as the first temperature parameter; then simulating the diffusion process of the temperature in the mold, that is, the temperature starts from 180 °C and gradually diffuses from the medium delivery loop to each position of the mold, and finally reaches a stable temperature distribution, so as to determine the temperature affected by the residual of the medium delivery loop.

[0092] Generating the temperature affected by the residual of the medium delivery loop through reverse temperature conduction analysis can more accurately understand the influence of the medium delivery loop on the mold temperature, which helps to improve the accuracy of mold temperature control and reduce the mold temperature fluctuations caused by the temperature change of the medium delivery loop.

[0093] In some implementation manners, the intelligent temperature diffusion model is called to perform multiple control simulations, and multiple groups of control simulation results are recorded, including:

[0094] Identifying the throttle valves in the medium delivery loop, determining the set of loop adjustable modes according to the throttle valve positions; determining each loop adjustable mode in the set of loop adjustable modes, performing mode matching adjustment on the intelligent temperature diffusion model, and then performing control simulations respectively based on each loop adjustable mode to generate the multiple groups of control simulation results, where each group of control simulation results has identification information including temperature control parameters and throttle valve control parameters.

[0095] Specifically, first, identify the throttle valves in the medium delivery circuit and determine the set of adjustable circuit modes based on the positions of the throttle valves. Exemplarily, assume there are two throttle valves in the medium delivery circuit, located in the main circuit and the return circuit respectively. By adjusting the opening degrees of these two throttle valves, different adjustable circuit modes can be formed. For example, the throttle valve in the main circuit is used to control the flow rate in the circuit, and the throttle valve in the return circuit can more precisely control the temperature of the circuit by controlling the return mixing ratio. By identifying the throttle valves and determining the set of adjustable circuit modes, the adjustable range and control strategy of the medium delivery circuit can be comprehensively understood. This helps to optimize the control process of the mold temperature controller and improve the accuracy and stability of mold temperature control.

[0096] Specifically, the temperature control parameters refer to the parameters for controlling the mold temperature, such as heating power, cooling rate, etc.; the throttle valve control parameters refer to the control parameters such as the opening degree and position of the throttle valve, and these parameters will affect the flow rate and temperature of the medium delivery circuit.

[0097] Furthermore, perform pattern matching adjustment on the intelligent temperature diffusion model, conduct control simulations respectively based on each adjustable circuit mode, and record each set of simulation results during the simulation process, including temperature control parameters and throttle valve control parameters. For example, the simulation results of mode 1 show that by fully opening the throttle valve, the mold temperature can be decreased from 180°C to 50°C within 10 minutes; the simulation results of mode 2 show that by half-opening the throttle valve, the mold temperature can be decreased from 180°C to 50°C within 12 minutes; the simulation results of mode 3 show that by fully closing the throttle valve, the mold temperature can be decreased from 180°C to 50°C within 15 minutes. Each set of simulation results has identification information including temperature control parameters and throttle valve control parameters for subsequent analysis and screening.

[0098] By determining the adjustable circuit modes and performing pattern matching adjustment, the influence of different control strategies on mold temperature control can be comprehensively understood, which further helps to find the optimal control parameters and throttle valve control strategy and improve the accuracy and stability of mold temperature control.

[0099] In some implementation manners, generating the multiple sets of control simulation results further includes:

[0100] Determine the distribution characteristics of the flow-through circuits corresponding to each adjustable circuit mode with respect to each circuit of the mold; perform a uniform probability analysis of temperature control based on each circuit distribution characteristic to generate each uniform probability; screen the adjustable circuit modes with uniform probabilities greater than or equal to a preset uniform probability threshold based on each uniform probability; perform control simulations respectively with the screened adjustable circuit modes to generate the multiple sets of control simulation results.

[0101] Specifically, the loop adjustable mode refers to each specific throttle valve control strategy in the set of loop adjustable modes; the distribution characteristics of the circulation loop relative to the mold refer to the specific layout and positional relationship of the medium delivery loop in the mold, including information such as the length, diameter, and branches of the loop.

[0102] Specifically, the uniform probability analysis refers to analyzing the possibility of the temperature distribution uniformity in the mold according to the distribution characteristics of the circulation loop. The uniform probability is the probability that the mold temperature is evenly distributed under specific loop distribution characteristics. Optionally, taking each loop distribution characteristic as an index, statistical analysis based on historical mold temperature control records is performed separately, such as collecting temperature data under different loop distribution characteristics during past production processes, analyzing the probability distribution of these data, so as to determine the temperature distribution uniformity under different loop distribution characteristics, and thus generate each uniform probability. Through the uniform probability analysis, the influence of different loop distribution characteristics on the mold temperature distribution uniformity can be understood more accurately, which helps to optimize the control process of the mold temperature controller and improve the accuracy and stability of the mold temperature control.

[0103] Exemplarily, in Mode 1, the throttle valve is fully open, and the distribution characteristics of the circulation loop are: length 10 meters, diameter 1 centimeter, and fewer branches. By collecting temperature data under similar conditions during past production processes, it is found that the probability of uniform temperature distribution is 0.8. In Mode 2, the throttle valve is half open, and the distribution characteristics of the circulation loop are: length 12 meters, diameter 1.5 centimeters, and more branches. By collecting temperature data under similar conditions during past production processes, it is found that the probability of uniform temperature distribution is 0.7. In Mode 3, the throttle valve is fully closed, and the distribution characteristics of the circulation loop are: length 15 meters, diameter 2 centimeters, and more branches. By collecting temperature data under similar conditions during past production processes, it is found that the probability of uniform temperature distribution is 0.6.

[0104] Furthermore, according to the preset standard of temperature distribution uniformity (preset uniform probability threshold), the loop adjustable modes that meet the requirements of temperature distribution uniformity are screened out from multiple loop adjustable modes to more accurately select suitable control strategies and improve the accuracy and stability of the mold temperature control; then, through the intelligent temperature diffusion model, control simulations are performed on the screened loop adjustable modes respectively, and during the simulation process, the simulation results of each group are recorded, including temperature control parameters and throttle valve control parameters.

[0105] Through the pre-screening based on the uniform probability, the loop adjustable modes with poor non-uniformity performance can be excluded as early as possible, thereby reducing the number of solutions that need to be simulated subsequently and improving the overall control decision-making efficiency.

[0106] S600: After controlling the mold temperature controller with the hot and cold alternating temperature control parameters at the hot and cold alternating node, then perform uniform control in the uniform temperature drop mode in combination with the mold process execution information.

[0107] Further, feedback the determined hot and cold alternating temperature control parameters to the mold temperature controller, and the mold temperature controller adjusts the process parameters and execution steps to be followed at the hot and cold alternating nodes (such as controlling the opening of the throttle valve, adjusting the heating / cooling power, etc.) based on the hot and cold alternating temperature control parameters, so that the temperature of the target mold drops at a uniform rate.

[0108] By combining the hot and cold alternating temperature control parameters and the mold process execution information at the hot and cold alternating nodes, the temperature change of the mold can be controlled more precisely, reducing product quality problems caused by temperature fluctuations; at the same time, through the uniform control in the uniform temperature drop mode, the cooling process of the mold can be optimized, improving production efficiency, reducing energy consumption, and thus enhancing the economic benefits of the entire production process.

[0109] In some embodiments, performing the uniform control in the uniform temperature drop mode in combination with the mold process execution information includes:

[0110] Construct a temperature control parameter influence sample database corresponding to a preset uniform temperature drop gradient; determine the drop gradient corresponding to the uniform temperature drop mode based on the mold process execution information, perform temperature control parameter influence analysis in the temperature control parameter influence sample database, and generate uniform drop temperature control parameters; perform the uniform control in the uniform temperature drop mode with the uniform drop temperature control parameters.

[0111] Specifically, the preset uniform temperature drop gradient refers to the drop rate of the mold temperature set during the cooling process according to the mold process requirements; the temperature control parameter influence sample database refers to a data set collected and organized on the influence of different temperature control parameters on the mold temperature drop.

[0112] Exemplarily, assuming that the mold needs to drop from 180°C to 50°C, it can be completed within 10 minutes, 12 minutes, or 15 minutes. Then, collect historical data on the influence of different temperature control parameters (such as heating power, cooling rate, throttle valve opening, etc.) on the mold temperature drop. For example, the experimental data shows that when the cooling is completed within 10 minutes, the cooling power is 100%, and the cooling rate is 10°C / minute; when the cooling is completed within 12 minutes, the cooling power is 80%, and the cooling rate is 8°C / minute; when the cooling is completed within 15 minutes, the cooling power is 60%, and the cooling rate is 6°C / minute. These data will be organized and stored in the temperature control parameter influence sample database.

[0113] Then, determine the descent gradient corresponding to the temperature uniform descent mode. For example, assume that the mold needs to cool down from 180 °C to 50 °C within 10 minutes, then the descent gradient is 10 °C / minute. Then, in the temperature control parameter influence sample database, search for the data corresponding to this descent gradient to generate the uniform descent temperature control parameters. For example, the database shows that when the cooling is completed within 10 minutes, the refrigeration power is 100%, the cooling rate is 10 °C / minute, and the throttle valve opening is 100%. These parameters will be used as the uniform descent temperature control parameters for the actual mold temperature controller control.

[0114] By performing uniform control in the temperature uniform descent mode with the uniform descent temperature control parameters, the temperature change of the mold can be controlled more precisely, reducing product quality problems caused by too rapid temperature drop.

[0115] In summary, the intelligent adjustment method of the mold temperature controller under the diversified temperature control mode provided by the present invention has the following technical effects:

[0116] By determining the mold process execution parameters connected to the mold temperature controller; analyzing the mold temperature control process based on the process execution parameters, identifying the hot and cold alternating nodes and their corresponding temperature parameters; obtaining the heat transfer medium circuit information of the mold temperature controller for regulating the mold temperature, and determining its relative position distribution with the mold; determining the molding material information in the mold, analyzing the thermal conductivity coefficient, and constructing an intelligent temperature diffusion model in combination with the relative position distribution information; using the first temperature parameter in the hot and cold alternating temperature parameters as the control reference and the second temperature parameter as the overall equilibrium control target of the mold, calling the intelligent temperature diffusion model for simulation analysis to generate the hot and cold alternating temperature control parameters; at the hot and cold alternating nodes, regulating the mold temperature controller according to the temperature control parameters, and combining with the mold process execution parameters, performing equilibrium control in the temperature uniform descent mode, thereby achieving the technical effects of improving the control accuracy, improving the mold temperature uniformity, and improving the quality and stability of plastic molding.

[0117] Embodiment 2, as Figure 2 is a schematic structural diagram of the intelligent adjustment system of the mold temperature controller under the diversified temperature control mode of the present invention. For example, Figure 1 The flow schematic diagram of the intelligent adjustment method of the mold temperature controller under the diversified temperature control mode of the present invention can be implemented through a structure such as Figure 2 shown.

[0118] Based on the same concept as the intelligent adjustment method of the mold temperature controller under the diversified temperature control mode in the above embodiment, the intelligent adjustment system of the mold temperature controller under the diversified temperature control mode provided by the present invention further includes:

[0119] A process acquisition module 11, configured to determine the mold process execution information connected to the mold temperature controller.

[0120] The alternating node positioning module 12 is used to perform mold temperature control process analysis based on the mold process execution information, locate the cold and hot alternating nodes, and the corresponding cold and hot alternating temperature parameters.

[0121] The loop information acquisition module 13 is used to acquire the medium delivery loop for temperature control of the mold in the mold temperature controller, and the relative position distribution information between the medium delivery loop and the mold.

[0122] The diffusion model construction module 14 is used to determine the thermal conductivity by analyzing the molding material information in the mold, and construct an intelligent temperature diffusion model in combination with the relative position distribution information.

[0123] The simulation decision-making module 15 is used to take the first temperature parameter in the cold and hot alternating temperature parameters as the control origin, take the second temperature parameter as the uniform control target for the entire mold area, call the intelligent temperature diffusion model to perform control simulation, and generate cold and hot alternating temperature control parameters.

[0124] The uniform control module 16 is used to, at the cold and hot alternating nodes, after controlling the mold temperature controller with the cold and hot alternating temperature control parameters, then perform uniform control in the temperature uniform decrease mode in combination with the mold process execution information.

[0125] In some embodiments, the alternating node positioning module 12 includes:

[0126] The mold process execution information parsing unit is used to parse the mold process execution information and determine the continuous temperature demand data sequence during the process execution.

[0127] The cold and hot alternating node generation unit is used to locate the temperature control nodes between the continuous rising trend and the continuous falling trend based on the continuous temperature demand data sequence, and generate the cold and hot alternating nodes.

[0128] In some embodiments, the simulation decision-making module 15 includes:

[0129] The influence of the remaining medium delivery loop on temperature determination unit is used to perform reverse diffusion analysis through the intelligent temperature diffusion model based on the first temperature parameter to determine the influence of the remaining medium delivery loop on temperature.

[0130] The control origin adjustment and simulation recording unit is used to adjust the control origin with the influence of the remaining medium delivery loop on temperature, and then based on the adjusted control origin, call the intelligent temperature diffusion model to perform multiple control simulations and record multiple groups of control simulation results.

[0131] The first control simulation result set screening unit is used to screen the first control simulation result set in which the temperatures at all positions of the mold satisfy the second temperature parameter from the multiple groups of control simulation results.

[0132] A hot and cold alternating temperature control parameter generation unit is configured to perform die global temperature uniformity analysis on each simulation result in the first control simulation result set, screen out the temperature control parameters corresponding to a group of simulation results whose uniformity index meets the preset uniformity index, and generate the hot and cold alternating temperature control parameters.

[0133] In some implementation manners, the residual influence temperature determination unit of the feed medium loop in the simulation decision module 15 includes:

[0134] A first temperature parameter loading subunit is configured to load the first temperature parameter as the temperature diffused to each position of the die into the intelligent temperature diffusion model.

[0135] A residual influence temperature generation subunit of the feed medium loop is configured to perform reverse temperature conduction analysis through the intelligent temperature diffusion model to generate the residual influence temperature of the feed medium loop.

[0136] In some implementation manners, the control origin adjustment and simulation recording unit in the simulation decision module 15 includes:

[0137] An adjustable mode set determination subunit is configured to identify the throttle valves in the feed medium loop and determine the loop adjustable mode set according to the throttle valve positions.

[0138] A loop adjustable mode determination and control simulation subunit is configured to determine each loop adjustable mode in the loop adjustable mode set, perform mode matching adjustment on the intelligent temperature diffusion model, and then perform control simulation respectively based on each loop adjustable mode to generate the multiple groups of control simulation results, where each group of control simulation results has identification information including temperature control parameters and throttle valve control parameters.

[0139] In some embodiments, the execution steps of the loop adjustable mode determination and control simulation subunit further include: determining the distribution characteristics of the flow circuits corresponding to the respective loop adjustable modes with respect to each loop of the die. Performing uniform probability analysis of temperature control based on the respective loop distribution characteristics to generate respective uniform probabilities. Screening out the loop adjustable modes with uniform probabilities greater than or equal to a preset uniform probability threshold based on the respective uniform probabilities. Performing control simulation respectively with the screened loop adjustable modes to generate the multiple groups of control simulation results.

[0140] In some embodiments, the loop information acquisition module 13 includes:

[0141] A relative position calibration unit is configured to construct a reference coordinate system, map the position information of the feed medium loop and the die position information to the reference coordinate system, and generate the relative position distribution information.

[0142] In some embodiments, the diffusion model construction module 14 includes:

[0143] A temperature mapping sample acquisition unit, which is used to acquire temperature mapping samples from the mold surface to the inner cavity with the molding material information as the index element, and the temperature mapping samples include temperature distribution data sets corresponding to each inner cavity position and the outer surface.

[0144] A thermal conductivity analysis unit, which is used to analyze the thermal conductivity of the material from the mold surface to the mold inner cavity with the temperature mapping samples.

[0145] An intelligent temperature diffusion model generation unit, which is used to determine the thermal conductivity between the medium delivery circuit and the mold, and combine the thermal conductivity of the material from the mold surface to the mold inner cavity and the relative position distribution information to analyze the temperature diffusion relationship from the medium delivery circuit to the mold surface and the mold inner cavity material, and generate the intelligent temperature diffusion model.

[0146] In some embodiments, the uniform control module 16 includes:

[0147] A temperature control parameter influence sample database construction unit, which is used to construct a temperature control parameter influence sample database corresponding to a preset uniform temperature drop gradient.

[0148] A uniform temperature drop control parameter generation unit, which is used to determine the drop gradient corresponding to the uniform temperature drop mode based on the mold process execution information, perform temperature control parameter influence analysis in the temperature control parameter influence sample database, and generate uniform temperature drop control parameters.

[0149] A temperature uniform drop mode control unit, which is used to perform uniform control in the temperature uniform drop mode with the uniform temperature drop control parameters.

[0150] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the foregoing Embodiment 1 are equally applicable to the intelligent temperature control system of the mold temperature controller under the diversified temperature control modes described in Embodiment 2. For the sake of simplicity of the specification, no further elaboration will be made here.

[0151] It should be understood that the disclosed embodiments of the present invention and the above descriptions can enable those skilled in the art to implement the present invention using the present invention. At the same time, the present invention is not limited to the above-mentioned part of the embodiments. It should be understood that those of ordinary skill in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. Intelligent adjustment method of mold temperature controller under diversified temperature control modes, characterized in that, Including: Determine the mold process execution information connected to the mold temperature controller; Based on the mold process execution information, conduct an analysis of the mold temperature control process, locate the hot and cold alternating nodes, and the corresponding hot and cold alternating temperature parameters; Obtain the medium delivery circuit used for temperature control of the mold in the mold temperature controller, and the relative position distribution information between the medium delivery circuit and the mold; Determine the thermal conductivity by analyzing the molding material information in the mold, and combine the relative position distribution information to construct an intelligent temperature diffusion model; Taking the first temperature parameter in the hot and cold alternating temperature parameters as the control origin and the second temperature parameter as the mold global uniform control target, call the intelligent temperature diffusion model to conduct a control simulation and generate hot and cold alternating temperature control parameters; At the hot and cold alternating nodes, after controlling the mold temperature controller with the hot and cold alternating temperature control parameters, then combine the mold process execution information to perform uniform control in the uniform temperature drop mode.

2. The intelligent adjustment method of the mold temperature controller under the diversified temperature control mode according to claim 1, wherein Based on the mold process execution information, conduct an analysis of the mold temperature control process, and locate the hot and cold alternating nodes, including: Analyze the mold process execution information to determine the continuous temperature demand data sequence during the process execution; Based on the continuous temperature demand data sequence, locate the temperature control nodes between the continuous rising trend and the continuous falling trend, and generate the hot and cold alternating nodes.

3. The intelligent adjustment method of the mold temperature controller under the diversified temperature control mode according to claim 1, characterized in that, Taking the first temperature parameter in the hot and cold alternating temperature parameters as the control origin and the second temperature parameter as the mold global uniform control target, call the intelligent temperature diffusion model to conduct a control simulation and generate hot and cold alternating temperature control parameters, including: Conduct a reverse diffusion analysis through the intelligent temperature diffusion model based on the first temperature parameter to determine the remaining influence temperature of the medium delivery circuit; After adjusting the control origin with the remaining influence temperature of the medium delivery circuit, based on the adjusted control origin, call the intelligent temperature diffusion model to conduct multiple control simulations and record multiple groups of control simulation results; Select the first set of control simulation results in which the temperatures at all positions of the mold satisfy the second temperature parameter from the multiple groups of control simulation results; Conduct an analysis of the mold global temperature uniformity for each simulation result in the first set of control simulation results, and select the temperature control parameters corresponding to a set of simulation results whose uniformity index meets the preset uniformity index to generate the hot and cold alternating temperature control parameters.

4. The intelligent adjustment method of the mold temperature controller under the diversified temperature control mode according to claim 3, characterized in that, Conduct a reverse diffusion analysis through the intelligent temperature diffusion model based on the first temperature parameter to determine the remaining influence temperature of the medium delivery circuit, including: Take the first temperature parameter as the temperature diffused to each position of the mold and load it into the intelligent temperature diffusion model; Conduct a reverse temperature conduction analysis through the intelligent temperature diffusion model to generate the remaining influence temperature of the medium delivery circuit.

5. The intelligent adjustment method of the mold temperature controller under the diversified temperature control mode according to claim 3, characterized in that, Call the intelligent temperature diffusion model to conduct multiple control simulations and record multiple groups of control simulation results, including: Identify the throttle valves in the medium delivery circuit and determine the set of adjustable circuit modes according to the throttle valve positions; Determine each loop adjustable mode in the set of loop adjustable modes, perform mode matching adjustment on the intelligent temperature diffusion model, and then perform control simulation based on each loop adjustable mode to generate the multiple groups of control simulation results, where each group of control simulation results has identification information including temperature control parameters and throttle valve control parameters.

6. The intelligent adjustment method of the mold temperature controller under the diversified temperature control mode according to claim 1, characterized in that, Determine the molding material information in the mold to analyze the thermal conductivity, and combine the relative position distribution information to construct an intelligent temperature diffusion model, including: Using the molding material information as an index element, collect temperature mapping samples from the mold surface to the inner cavity, where the temperature mapping samples include temperature distribution data sets corresponding to each inner cavity position and the outer surface; Analyze the thermal conductivity of the material from the mold surface to the mold inner cavity based on the temperature mapping samples; Determine the thermal conductivity between the medium delivery loop and the mold, and combine the thermal conductivity of the material from the mold surface to the mold inner cavity and the relative position distribution information to analyze the temperature diffusion relationship from the medium delivery loop to the mold surface and the mold inner cavity material, and generate the intelligent temperature diffusion model.

7. The intelligent adjustment method of the mold temperature controller under the diversified temperature control mode according to claim 5, characterized in that, Generating the multiple groups of control simulation results further includes: Determine the distribution characteristics of each loop of the flow circuit corresponding to each loop adjustable mode with respect to the mold; Perform a uniform probability analysis of temperature control based on each loop distribution characteristic to generate each uniform probability; Screen the loop adjustable modes with uniform probabilities greater than or equal to a preset uniform probability threshold based on each uniform probability; Perform control simulation respectively with the screened loop adjustable modes to generate the multiple groups of control simulation results.

8. The intelligent adjustment method of the mold temperature machine under the diversified temperature control mode according to claim 1, characterized in that, Combine the mold process execution information to perform uniform control in the uniform temperature drop mode, including: Construct a temperature control parameter influence sample database corresponding to a preset uniform temperature drop gradient; Determine the drop gradient corresponding to the uniform temperature drop mode based on the mold process execution information, and perform temperature control parameter influence analysis in the temperature control parameter influence sample database to generate uniform drop temperature control parameters; Perform uniform control in the uniform temperature drop mode with the uniform drop temperature control parameters.

9. The intelligent adjustment method of the mold temperature controller under the diversified temperature control mode according to claim 1, characterized in that, Obtain the medium delivery loop used to control the temperature of the mold in the mold temperature controller, and the relative position distribution information between the medium delivery loop and the mold, including: Construct a reference coordinate system, map the position information of the medium delivery loop and the mold position information to the reference coordinate system to generate the relative position distribution information.

10. The intelligent adjustment system of the mold temperature controller under diversified temperature control modes is characterized in that, The intelligent adjustment system of the mold temperature controller under the diversified temperature control mode is used to execute the intelligent adjustment method of the mold temperature controller under the diversified temperature control mode according to any one of claims 1-9, including: A process acquisition module for determining the mold process execution information connected to the mold temperature controller; An alternating node positioning module for analyzing the mold temperature control process based on the mold process execution information, positioning the hot and cold alternating nodes, and the corresponding hot and cold alternating temperature parameters; A loop information acquisition module for obtaining the medium delivery loop used to control the temperature of the mold in the mold temperature controller, and the relative position distribution information between the medium delivery loop and the mold; The diffusion model construction module is used to determine the molding material information in the mold, analyze the thermal conductivity, and construct an intelligent temperature diffusion model in combination with the relative position distribution information; The simulation decision module is used to take the first temperature parameter in the hot and cold alternating temperature parameters as the control origin, take the second temperature parameter as the uniform control target for the entire mold area, call the intelligent temperature diffusion model for control simulation, and generate hot and cold alternating temperature control parameters; The uniform control module is used to, at the hot and cold alternating node, after controlling the mold temperature controller with the hot and cold alternating temperature control parameters, then perform uniform control in the mode of uniform temperature drop in combination with the mold process execution information.

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