Intelligent adjustment method and system for mold temperature controller under diversified temperature control mode

Abstract

The application discloses a mold temperature controller intelligent adjustment method and system under diversified temperature control modes, relates to the mold temperature control technical field, and the method comprises the following steps: determining mold process execution information connected with the mold temperature controller, analyzing a temperature control process, positioning a cold-heat alternating node and corresponding temperature parameters, obtaining a mold temperature controller medium conveying loop and relative position distribution information of the mold temperature controller medium conveying loop and the mold, combining a heat conductivity coefficient of a molding material, constructing an intelligent temperature diffusion model, taking a first temperature parameter as a control origin, a second temperature parameter as a uniform control target, calling an intelligent model simulation to generate temperature control parameters, adjusting the mold temperature controller according to the temperature control parameters at the cold-heat alternating node, and combining process information to perform uniform cooling control. Therefore, the technical effects of improving control precision, improving mold temperature uniformity, improving the quality and stability of plastic molding are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mold temperature control, in particular to a mold temperature controller intelligent adjustment method and system under diversified temperature control modes. BACKGROUND

[0002] As a key equipment for controlling mold temperature, the parameter control of the mold temperature controller has a crucial influence on product quality and production efficiency. The existing mold temperature controller mainly adopts a single control mode in terms of temperature control, that is, the mold is heated to a set temperature range through a heating system to ensure that the mold surface temperature 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. The control accuracy of this traditional control mode at the cold-hot alternating node of the mold is insufficient, and accurate temperature conversion cannot be achieved, 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 cold-hot alternating process, the delivery circuit itself will be affected by the high-temperature medium. When the cooling medium continues to flow in the delivery circuit, due to the temperature change of the pipeline itself, the temperature of the medium will be affected, and then the cooling effect and temperature uniformity of the mold will be affected. SUMMARY

[0003] The present application provides a mold temperature controller intelligent adjustment method and system under diversified temperature control modes to solve the technical problems of poor control accuracy at the cold-hot alternating node, temperature change of the delivery circuit affecting the temperature of the medium, and affecting the molding quality and efficiency, and to achieve the technical effects of improving control accuracy, improving mold temperature uniformity, and improving the quality and stability of plastic molding.

[0004] In a first aspect, the present application provides a mold temperature controller intelligent adjustment method under diversified temperature control modes, wherein the mold temperature controller intelligent adjustment method under diversified temperature control modes comprises:

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

[0006] Performing mold temperature control process analysis based on the mold process execution information, locating the cold-hot alternating node, and the corresponding cold-hot alternating temperature parameters.

[0007] Obtaining a delivery circuit in the mold temperature controller for temperature control of the mold, and relative position distribution information of the delivery circuit and the mold.

[0008] Determining the thermal conductivity coefficient of the molding material information analysis in the mold, combining the relative position distribution information, and constructing an intelligent temperature diffusion model.

[0009] The first temperature parameter in the cold-heat alternating temperature parameter is taken as a control origin, and a second temperature parameter is taken as a mold global uniform control target.

[0010] After the mold temperature controller is controlled by the cold-heat alternating temperature control parameter at the cold-heat alternating node, a temperature uniformity descending mode is executed in combination with the mold process execution information.

[0011] In a feasible implementation manner, mold temperature control flow analysis is performed based on the mold process execution information, and the cold-heat alternating node is located, including:

[0012] The mold process execution information is analyzed, and a continuous temperature requirement data sequence in a process execution process is determined.

[0013] The cold-heat alternating node is located based on the continuous temperature requirement data sequence, which is between a continuous rising trend and a continuous descending trend, and the cold-heat alternating node is generated.

[0014] In a feasible implementation manner, the first temperature parameter in the cold-heat alternating temperature parameter is taken as a control origin, and a second temperature parameter is taken as a mold global uniform control target, the intelligent temperature diffusion model is called for control simulation, and the cold-heat alternating temperature control parameter is generated, including:

[0015] The first temperature parameter is analyzed by the intelligent temperature diffusion model in a reverse diffusion manner, and a residual influence temperature of a conveying medium loop is determined.

[0016] After the control origin is adjusted by the residual influence temperature of the conveying medium loop, the intelligent temperature diffusion model is called for multiple control simulations based on the adjusted control origin, and multiple sets of control simulation results are recorded.

[0017] A first control simulation result set in which temperatures of all positions of the mold meet the second temperature parameter is screened from the multiple sets of control simulation results.

[0018] The mold global temperature uniformity of each simulation result in the first control simulation result set is analyzed, a set of simulation results corresponding to a temperature control parameter in which a uniformity index meets a preset uniformity index is screened, and the cold-heat alternating temperature control parameter is generated.

[0019] In a feasible implementation manner, the first temperature parameter is analyzed by the intelligent temperature diffusion model in a reverse diffusion manner, and a residual influence temperature of a conveying medium loop is determined, including:

[0020] The first temperature parameter is loaded to the intelligent temperature diffusion model as a temperature diffused to each position of the mold.

[0021] The intelligent temperature diffusion model is called to perform reverse temperature conduction analysis to generate the residual influence temperature of the delivery loop.

[0022] In an implementable manner, the intelligent temperature diffusion model is called to perform multiple control simulations, and multiple sets of control simulation results are recorded, including:

[0023] A throttle valve in the delivery loop is identified, and a set of loop adjustable modes is determined according to the throttle valve position.

[0024] Each loop adjustable mode in the set of loop adjustable modes is determined, and the intelligent temperature diffusion model is adjusted and matched with the mode to perform control simulation based on the each loop adjustable mode to generate the multiple sets of control simulation results, wherein each set of control simulation results has identification information including temperature control parameters and throttle valve control parameters.

[0025] In an implementable manner, the information of the molding material in the mold is analyzed to determine the thermal conductivity coefficient, and the intelligent temperature diffusion model is constructed in combination with the relative position distribution information, including:

[0026] The temperature mapping samples from the mold surface to the inner cavity are collected with the molding material information as an index element, and the temperature mapping samples include a temperature distribution data set corresponding to each inner cavity position and the outer surface.

[0027] The thermal conductivity coefficient of the material from the mold surface to the inner cavity of the mold is analyzed based on the temperature mapping samples.

[0028] The thermal conductivity coefficient between the delivery loop and the mold is determined, and the temperature diffusion relationship from the delivery loop to the mold surface and the material in the inner cavity of the mold is analyzed in combination with the thermal conductivity coefficient of the material from the mold surface to the inner cavity of the mold and the relative position distribution information to generate the intelligent temperature diffusion model.

[0029] In an implementable manner, the multiple sets of control simulation results are generated, and the method further includes:

[0030] Each loop distribution feature of the flow circulation loop relative to the mold corresponding to the each loop adjustable mode is determined.

[0031] Uniform probability analysis of temperature control is performed based on the each loop distribution feature to generate each uniform probability.

[0032] Based on the each uniform probability, a loop adjustable mode with a uniform probability greater than or equal to a preset uniform probability threshold is screened.

[0033] Control simulation is performed based on the screened loop adjustable mode to generate the multiple sets of control simulation results.

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

[0035] A temperature control parameter influence sample database corresponding to a preset temperature uniform drop gradient is constructed.

[0036] A drop gradient corresponding to the temperature uniform drop mode is determined based on the mold process execution information, and temperature control parameter influence analysis is performed in the temperature control parameter influence sample database to generate uniform drop temperature control parameters.

[0037] The uniform control in the temperature uniform drop mode is performed with the uniform drop temperature control parameters.

[0038] In a feasible implementation, a media loop for temperature control of a mold in the mold temperature machine is acquired, and relative position distribution information of the media loop and the mold is acquired, including:

[0039] A reference coordinate is constructed, and position information of the media loop and mold position information are mapped to the reference coordinate system to generate the relative position distribution information.

[0040] In a second aspect, the present application further provides a mold temperature machine intelligent adjustment system in a diversified temperature control mode, wherein the mold temperature machine intelligent adjustment system in the diversified temperature control mode includes:

[0041] A process acquisition module is configured to determine mold process execution information connected to a mold temperature machine.

[0042] An alternating node positioning module is configured to analyze a mold temperature control process based on the mold process execution information, position cold and hot alternating nodes, and corresponding cold and hot alternating temperature parameters.

[0043] A loop information acquisition module is configured to acquire a media loop for temperature control of a mold in the mold temperature machine, and relative position distribution information of the media loop and the mold.

[0044] A diffusion model construction module is configured to determine a heat conduction coefficient based on information analysis of a molding material in a mold, and construct an intelligent temperature diffusion model in combination with the relative position distribution information.

[0045] A simulation decision module is configured to use a first temperature parameter in the cold and hot alternating temperature parameters as a control origin, use a second temperature parameter as a mold global uniform control target, call the intelligent temperature diffusion model for control simulation, and generate cold and hot alternating temperature control parameters.

[0046] A uniform control module is configured to control the mold temperature machine with the cold and hot alternating temperature control parameters at the cold and hot alternating nodes, and then perform uniform control in a temperature uniform drop mode in combination with the mold process execution information.

[0047] The application discloses a mold temperature controller intelligent adjustment method and system under a diversified temperature control mode, and relates to the technical field of mold temperature control. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 FIG. 1 is a flowchart of the mold temperature controller intelligent adjustment method under the diversified temperature control mode of the application.

[0049] Figure 2 FIG. 2 is a structural diagram of the mold temperature controller intelligent adjustment system under the diversified temperature control mode of the application.

[0050] The technical field of the application is mold temperature control. DETAILED DESCRIPTION

[0051] The above technical solutions will be described in detail below in combination with the accompanying drawings and specific embodiments, so as to better understand the above technical solutions. Obviously, the described embodiments are only part of the embodiments of the application, not all embodiments of the application, and it should be understood that the application is not limited to the example embodiments for explaining the application. Based on the embodiments of the application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the application. In addition, it should be noted that, for convenience of description, only the parts related to the application are shown in the drawings, not all.

[0052] Embodiment one, as Figure 1Fig. 1 is a flowchart of the intelligent adjustment method of the mold temperature controller under the diversified temperature control mode of the present application, wherein the intelligent adjustment method of the mold temperature controller under the diversified temperature control mode comprises the following steps:

[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. Different parameters are required for different molds and products to meet the quality requirements. These information will serve as the basis for mold temperature control to ensure that the mold undergoes temperature changes according to the predetermined process flow.

[0055] For example, the process execution information can include the preheating temperature of the mold as 180℃, the holding time as 5 minutes, and then the cooling rate as 20℃ per minute to room temperature in an injection mold.

[0056] S200: Analyze the mold temperature control process based on the mold process execution information, locate the cold-hot alternating node, and the corresponding cold-hot alternating temperature parameters.

[0057] Specifically, by studying and evaluating the temperature changes of the mold during the production process, the temperature requirements and change trends of the mold at different stages are determined, and the cold-hot alternating node is located. The cold-hot alternating node refers to the turning point of the temperature change of the mold during heating and cooling, i.e., the key moment when the mold changes from heating to cooling or from cooling to heating. The cold-hot alternating temperature parameters refer to the specific values of the mold temperature at the cold-hot alternating node. For example, they include the mold transition temperature when the cold-hot alternating is triggered and the transition target temperature after the expected transition.

[0058] In some embodiments, analyzing the mold temperature control process based on the mold process execution information, locating the cold-hot alternating node, comprises:

[0059] Parsing the mold process execution information to determine a continuous temperature requirement data sequence in the process execution, and locating a temperature control node between the continuous rising trend and the continuous falling trend based on the continuous temperature requirement data sequence to generate the cold-hot alternating node.

[0060] Specifically, the continuous temperature requirement data sequence refers to a set of temperature requirement data of the mold arranged in time sequence during the production process. The temperature control node refers to the turning point of the mold temperature change, i.e., the key time point when the mold changes from heating to cooling or from cooling to heating.

[0061] Specifically, first, according to the temperature setting and time arrangement in the mold process execution information, the temperature change curve of the mold in the production process is analyzed to determine the temperature change trend of the mold at different stages, and the cold and hot alternating nodes, i.e. the time when the mold is converted from a heated state to a cooled state, are located; for example, in an injection mold, the process execution information may show that the mold needs to be heated from room temperature to 180℃ and maintained until the injection is completed, and then cooled to 50℃ at a rate of 20℃ per minute. At this time, the corresponding cold and hot alternating temperature parameters are 180℃ and 50℃, respectively.

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

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

[0064] Specifically, by obtaining the media delivery circuit in the mold temperature controller for temperature control of the mold, and establishing the relative position distribution information of the media delivery circuit and the mold, it is helpful to optimize the temperature control of the mold and improve the stability and efficiency of the molding process, wherein the media delivery circuit (media delivery circuit) refers to a piping system for delivering cooling or heating medium (such as water, oil or heat-conducting fluid) to the mold, which functions to control the temperature of the mold to keep it within a set range.

[0065] In some embodiments, obtaining the media delivery circuit in the mold temperature controller for temperature control of the mold, and the relative position distribution information of the media delivery circuit and the mold, comprises:

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

[0067] Specifically, in order to unify the spatial position information of the media delivery circuit and the mold, first, a unified reference coordinate system is established in the equipment, which can be established based on the equipment coordinate system (with the mold temperature controller rack 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 CAD design drawings or by three-dimensional scanning method, the geometric information of the pipeline structure is extracted, and the key positions such as the inlet, outlet, bending point and branch point of each pipeline in the reference coordinate system are recorded, thereby generating a spatial path model of the media delivery circuit; at the same time, based on CNC machining data, 3D modeling files or sensor measurement, the position information of the mold is determined.

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

[0069] Further, the position information of the mold and the delivery circuit is unified into the reference coordinate system by using a coordinate transformation algorithm (such as translation and rotation transformation) to calculate the relative position relationship therebetween, preferably including the coverage rate of the delivery circuit to each region of the mold and the distance distribution of the delivery circuit to each surface of the mold.

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

[0071] S400: determining the molding material information in the mold to analyze the thermal conductivity coefficient, combining the relative position distribution information to construct an intelligent temperature diffusion model.

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

[0073] By constructing the intelligent temperature diffusion model, the diffusion of temperature in the mold can be accurately predicted, thereby providing a scientific basis for the control of the mold temperature machine, which is helpful to improve the precision of mold temperature control and reduce the product quality problems caused by uneven temperature.

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

[0075] The temperature mapping sample is collected by taking the material information of the forming material as an index element, and includes a temperature distribution data set corresponding to each inner cavity position and the outer surface; the thermal conductivity of the material from the mold surface to the mold cavity is analyzed based on the temperature mapping sample; the thermal conductivity between the delivery circuit and the mold is determined, and the temperature diffusion relationship from the delivery circuit to the mold surface and the material in the mold cavity is analyzed in combination with the thermal conductivity of the material from the mold surface to the mold cavity and the relative position distribution information, and the intelligent temperature diffusion model is generated.

[0076] Specifically, the temperature mapping sample refers to the temperature distribution data set collected at different positions from the mold surface to the inner cavity, including the temperature value corresponding to each inner cavity position and the outer surface, for analyzing the distribution of temperature in the mold.

[0077] Specifically, first, the types and characteristics of the materials used in the mold are taken as the basis for searching and organizing data, and the corresponding temperature mapping sample is obtained, including the temperature distribution data set corresponding to each inner cavity position and the outer surface; then, the temperature variation law from the mold surface to the inner cavity is analyzed based on the collected temperature mapping sample, and the thermal conductivity of the material from the mold surface to the mold cavity is obtained, for example, if the temperature of the mold surface is 180℃ and the temperature of the inner cavity is 170℃, the temperature gradient can be calculated as 10℃, then the thermal conductivity is calculated according to the Fourier heat conduction law combined with the temperature gradient and the heat flux density. For example, if the heat flux density is 100W / m 2 , the thermal conductivity is 10W / 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, which helps to reduce the temperature control error caused by inaccurate thermal conductivity and improve the prediction accuracy of the model.

[0078] Further, the thermal conductivity between the delivery circuit and the mold is determined, which can be obtained by experimental measurement or by consulting relevant material data books; then, the thermal conductivity of the material from the mold surface to the inner cavity and the relative position distribution information are combined to analyze how the temperature is transmitted from the delivery circuit to the mold surface and the inner cavity material; for example, if the 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 200W / m·K, then the mathematical formula reflecting the diffusion speed and distribution of temperature in the delivery circuit to the mold surface and the inner cavity material can be derived according to the heat transfer dynamics, and the intelligent temperature diffusion model is defined to simulate and predict the diffusion process of temperature in the mold.

[0079] Optionally, finite element analysis (FEA) or numerical calculation method (such as finite difference method FDM) is used to establish a temperature field distribution equation in combination with the thermal conductivity, relative position distribution and temperature diffusion relationship, to simulate the heat diffusion of the medium delivery circuit to each part of the mold. An intelligent temperature diffusion model is constructed.

[0080] S500: Taking the first temperature parameter in the cold-heat alternating temperature parameter as a control origin and the second temperature parameter as a mold global uniform control target, calling the intelligent temperature diffusion model for control simulation to generate a cold-heat alternating temperature control parameter.

[0081] Specifically, the first temperature parameter generally refers to the initial temperature of the mold when it is switched from a heating state to a cooling state, and the second temperature parameter refers to the target temperature to which the mold is cooled; the mold global uniform control target refers to the target value that the temperature needs to reach uniformly in the entire mold area, and the value of the mold global uniform control target corresponds to the second temperature parameter described above.

[0082] For example, assuming that the first temperature parameter is 180℃ and the second temperature parameter is 50℃; then, these parameters are input into the intelligent temperature diffusion model, taking the first temperature parameter as the control origin and the second temperature parameter as the mold global uniform control target for control simulation. During the simulation process, the intelligent temperature diffusion model will simulate the diffusion process of the temperature from the medium delivery circuit to the mold surface and the internal cavity material according to the thermal conductivity and relative position distribution information of the mold, and through multiple simulations, the control parameters (such as the flow rate and temperature of the medium delivery circuit) of the mold temperature machine are continuously adjusted, and then the optimal temperature control parameter is found, so that the mold temperature can quickly and uniformly drop from 180℃ (the first temperature parameter) to 50℃ (the first temperature parameter) for actual mold temperature machine control.

[0083] In some embodiments, taking the first temperature parameter in the cold-heat alternating temperature parameter as a control origin and the second temperature parameter as a mold global uniform control target, calling the intelligent temperature diffusion model for control simulation to generate a cold-heat alternating temperature control parameter, includes:

[0084] determining a residual influence temperature of the media loop based on the first temperature parameter through the reverse diffusion analysis of the intelligent temperature diffusion model; adjusting the control origin based on the residual influence temperature of the media loop, and calling the intelligent temperature diffusion model to perform multiple control simulations based on the adjusted control origin, and recording multiple sets of control simulation results; selecting a first control simulation result set in which the temperature of all positions of the mold meets the second temperature parameter from the multiple sets of control simulation results; performing mold global temperature uniformity analysis on each simulation result in the first control simulation result set, selecting a set of simulation results corresponding to the temperature control parameter whose uniformity index meets the preset uniformity index, and generating the cold and hot alternating temperature control parameter.

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

[0086] Further, the control origin is adjusted based on the residual influence temperature of the media loop to compensate for the influence of the media loop on the temperature of the mold. For example, the adjustment method includes shifting the temperature of the control origin or setting a temperature shift curve that changes over time (the shift rate of the control origin at different times). Then, the intelligent temperature diffusion model is used to simulate the control process of the mold temperature controller multiple times to obtain the mold temperature control effect (i.e., the control simulation result) under different control strategies (temperature control parameters), wherein the control simulation result includes the time series temperature distribution data of the mold.

[0087] Specifically, after recording multiple sets of control simulation results, the results that meet the conditions are selected from these results. For example, the first control simulation result set refers to a set of simulation results that meet the condition that the temperature of all positions of the mold reaches the second temperature parameter (e.g., within the interval of the second temperature parameter ± a preset value). For example, if the second temperature parameter is 50°C and the neighborhood radius is 1°C, the first simulation result shows that there are three areas with an internal cavity temperature of 48°C, which does not meet the condition. The second simulation result shows that there are two areas with an internal cavity temperature of 49.2°C and one area with an internal cavity temperature of 50.3°C, which meets the condition. By selecting the simulation results that meet the condition, it can be ensured that the temperature of all positions of the mold during the cold and hot alternating process can reach the target value, thereby helping to improve the precision of the mold temperature control and reducing the product quality problems caused by temperature non-uniformity.

[0088] Further, the temperature of all positions of the mold is analyzed to determine the uniformity of the temperature distribution, wherein 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 for screening simulation results that meet the conditions; for example, if the preset uniformity index is that the temperature deviation is less than ±1℃, the temperature data in each simulation result needs to be analyzed. If the temperature deviation of a certain simulation result is within ±1℃, it is considered that the result meets the condition, so as to screen out the temperature control parameters corresponding to the simulation results that meet the preset uniformity index, and generate the cold-heat alternating temperature control parameters. Through the mold global temperature uniformity analysis, the uniformity of the temperature distribution of the mold during the cold-heat alternating process can be ensured, and the product quality problems caused by uneven temperature can be reduced.

[0089] In some implementations, based on the first temperature parameter, the reverse diffusion analysis is performed through the intelligent temperature diffusion model to determine the residual influence temperature of the media loop, including:

[0090] The first temperature parameter is loaded as the temperature diffused to each position of the mold into the intelligent temperature diffusion model; the reverse temperature conduction analysis is performed through the intelligent temperature diffusion model to generate the residual influence temperature of the media loop.

[0091] Specifically, first, the initial temperature of the mold during the cold-heat alternating process, i.e., the first temperature parameter, is input into the intelligent temperature diffusion model as the initial condition of the model, for example, including setting the initial temperature of the media loop as the first temperature parameter; then the diffusion process of the temperature in the mold is simulated, i.e., the temperature starts from 180℃ and gradually diffuses from the media loop to each position of the mold, and finally reaches a stable temperature distribution, so as to determine the residual influence temperature of the media loop.

[0092] The residual influence temperature of the media loop is generated through the reverse temperature conduction analysis, which can more accurately understand the influence of the media loop on the temperature of the mold, help to improve the precision of the temperature control of the mold, and reduce the temperature fluctuation of the mold caused by the temperature change of the media loop.

[0093] In some implementations, the intelligent temperature diffusion model is called for multiple control simulations, and multiple sets of control simulation results are recorded, including:

[0094] The throttle valve in the media loop is identified, the set of adjustable modes of the loop is determined according to the position of the throttle valve; each adjustable mode of the set of adjustable modes of the loop is determined, the intelligent temperature diffusion model is adjusted and matched in mode, and then control simulation is performed based on each adjustable mode to generate the multiple sets of control simulation results, wherein each set of control simulation results has identification information containing temperature control parameters and throttle valve control parameters.

[0095] Specifically, first, the throttle valves in the media delivery circuit are identified, and the circuit adjustable mode set is determined according to the positions of the throttle valves; for example, assuming that there are two throttle valves in the media delivery circuit, one in the main circuit and one in the return circuit, by adjusting the opening degrees of the two throttle valves, different circuit adjustable modes can be formed, for example, the throttle valve in the main circuit is used to control the flow in the circuit, and the throttle valve in the return circuit is used to more accurately control the temperature of the circuit by controlling the return mixing ratio. By identifying the throttle valves and determining the circuit adjustable mode set, the adjustable range and control strategy of the media 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 parameters for controlling the mold temperature, such as heating power, cooling rate, etc.; the throttle valve control parameters refer to the opening degree, position, etc. of the throttle valve, which will affect the flow and temperature of the media delivery circuit.

[0097] Further, the intelligent temperature diffusion model is adjusted by mode matching, control simulation is performed based on each circuit adjustable mode, and during the simulation process, each set of simulation results, including temperature control parameters and throttle valve control parameters, are recorded. For example, the simulation results of mode 1 show that by fully opening the throttle valve, the mold temperature can be reduced from 180°C to 50°C in 10 minutes; the simulation results of mode 2 show that by half opening the throttle valve, the mold temperature can be reduced from 180°C to 50°C in 12 minutes; the simulation results of mode 3 show that by fully closing the throttle valve, the mold temperature can be reduced from 180°C to 50°C in 15 minutes, and each set of simulation results has identification information containing temperature control parameters and throttle valve control parameters for subsequent analysis and screening.

[0098] By determining the circuit adjustable mode and adjusting by mode matching, the influence of different control strategies on mold temperature control can be comprehensively understood, which helps to find the best control parameters and throttle valve control strategy, and improves the accuracy and stability of mold temperature control.

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

[0100] determining the respective circuit distribution characteristics of the flow-through circuit corresponding to the respective circuit adjustable modes relative to the mold; performing uniform probability analysis of temperature control based on the respective circuit distribution characteristics to generate respective uniform probabilities; selecting, based on the respective uniform probabilities, a circuit adjustable mode with a uniform probability greater than or equal to a preset uniform probability threshold; and performing control simulation based on the selected circuit adjustable mode to generate the multiple sets of control simulation results.

[0101] Specifically, the specific circuit adjustable mode refers to each specific throttle control strategy in the set of circuit adjustable modes; and the circuit distribution feature of the flow circuit relative to the mold refers to the specific layout and positional relationship of the flow circuit in the mold, including information such as the length, diameter, and branches of the circuit.

[0102] Specifically, the uniform probability analysis refers to analyzing the uniformity of the temperature distribution in the mold according to the distribution feature of the flow circuit, and the uniform probability is the probability of uniform temperature distribution in the mold under a specific circuit distribution feature; and optionally, statistical analysis based on historical mold temperature control records is performed for each circuit distribution feature, such as collecting temperature data under different circuit distribution features in past production processes, analyzing the probability distribution of the data, and determining the temperature distribution uniformity under different circuit distribution features, thereby generating each uniform probability. Through uniform probability analysis, the influence of different circuit distribution features on the uniformity of the mold temperature distribution can be more accurately understood, which helps to optimize the control process of the mold temperature machine and improve the precision and stability of the mold temperature control.

[0103] For example, mode 1 is full throttle, and the flow circuit distribution feature is: length 10 meters, diameter 1 centimeter, and fewer branches. By collecting temperature data under similar conditions in past production processes, it is found that the probability of uniform temperature distribution is 0.8. Mode 2 is half throttle, and the flow circuit distribution feature is: length 12 meters, diameter 1.5 centimeters, and more branches. By collecting temperature data under similar conditions in past production processes, it is found that the probability of uniform temperature distribution is 0.7. Mode 3 is full throttle, and the flow circuit distribution feature is: length 15 meters, diameter 2 centimeters, and more branches. By collecting temperature data under similar conditions in past production processes, it is found that the probability of uniform temperature distribution is 0.6.

[0104] Further, according to the pre-set standard of temperature distribution uniformity (pre-set uniform probability threshold), the circuit adjustable mode that meets the requirement of temperature distribution uniformity is selected from the plurality of circuit adjustable modes, so as to more accurately select the suitable control strategy and improve the precision and stability of the mold temperature control; and then, the selected circuit adjustable mode is simulated by the intelligent temperature diffusion model, and each simulation result is recorded during the simulation, including the temperature control parameters and the throttle control parameters.

[0105] Through the pre-selection based on the uniform probability, the circuit adjustable mode with poor uniformity performance can be excluded as early as possible, thereby reducing the number of schemes that need to be simulated subsequently and improving the overall control decision efficiency.

[0106] S600: After controlling the mold temperature machine with the cold-hot alternating temperature control parameters at the cold-hot alternating node, the uniform control in the temperature uniformity descending mode is performed in combination with the mold process execution information.

[0107] Further, the determined cold-heat alternating temperature control parameters are fed back to the mold temperature controller, and the mold temperature controller adjusts the process parameters and execution steps (such as controlling the opening degree of the throttle valve, adjusting the heating / cooling power, etc.) required to be followed at the cold-heat alternating node based on the cold-heat alternating temperature control parameters, so that the temperature of the target mold decreases at a uniform rate.

[0108] By combining the cold-heat alternating temperature control parameters and the mold process execution information at the cold-heat alternating node, the temperature change of the mold can be more accurately controlled, and the product quality problems caused by temperature fluctuations can be reduced; at the same time, through the uniform control in the temperature uniform decrease mode, the cooling process of the mold can be optimized, the production efficiency can be improved, and the energy consumption can be reduced, thereby improving the economic benefits of the entire production process.

[0109] In some embodiments, the uniform control in the temperature uniform decrease mode is performed in combination with the mold process execution information, including:

[0110] A temperature control parameter influence sample database corresponding to a preset temperature uniform decrease gradient is constructed; a decrease gradient corresponding to the temperature uniform decrease mode is determined based on the mold process execution information, temperature control parameter influence analysis is performed in the temperature control parameter influence sample database, and uniform decrease temperature control parameters are generated; and the uniform control in the temperature uniform decrease mode is performed with the uniform decrease temperature control parameters.

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

[0112] For example, it is assumed that the mold needs to decrease from 180℃ to 50℃, which can be completed within 10 minutes, 12 minutes, or 15 minutes, and then historical data including the influence of different temperature control parameters (such as heating power, cooling rate, throttle valve opening degree, etc.) on the decrease of the mold temperature is collected. For example, experimental data shows that when the cooling is completed within 10 minutes, the cooling power is 100%, and the cooling rate is 10℃ / minute; when the cooling is completed within 12 minutes, the cooling power is 80%, and the cooling rate is 8℃ / minute; and when the cooling is completed within 15 minutes, the cooling power is 60%, and the cooling rate is 6℃ / minute. These data will be sorted and stored in the temperature control parameter influence sample database.

[0113] Then, a descending gradient corresponding to the temperature uniform descending mode is determined. For example, assuming that the mold needs to be cooled from 180℃ to 50℃ in 10 minutes, the descending gradient is 10℃ / min. Then, in the temperature control parameter influence sample database, data corresponding to the descending gradient is searched to generate the uniform descending temperature control parameter. For example, the database shows that when the cooling is completed in 10 minutes, the refrigeration power is 100%, the cooling rate is 10℃ / min, and the throttle opening is 100%. These parameters will be used as the uniform descending temperature control parameter for the actual mold temperature controller.

[0114] By executing the uniform control in the temperature uniform descending mode through the uniform descending temperature control parameter, the temperature change of the mold can be more accurately controlled, and the product quality problems caused by too fast temperature drop can be reduced.

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

[0116] By determining the mold process execution parameter connected with the mold temperature controller, analyzing the mold temperature control process based on the process execution parameter, identifying the cold-heat alternating node and the corresponding temperature parameter, obtaining the delivery circuit information of the mold temperature controller for regulating the mold temperature, and determining the relative position distribution thereof and the mold, determining the molding material information in the mold, analyzing the thermal conductivity coefficient, and constructing the intelligent temperature diffusion model in combination with the relative position distribution information, taking the first temperature parameter in the cold-heat alternating temperature parameter as the control reference and the second temperature parameter as the mold overall balanced control target, calling the intelligent temperature diffusion model for simulation analysis to generate the cold-heat alternating temperature control parameter, and regulating the mold temperature controller according to the temperature control parameter at the cold-heat alternating node and in combination with the mold process execution parameter to execute the balanced control in the temperature uniform descending mode, the technical effects of improving the control precision, improving the mold temperature uniformity, improving the quality and stability of the plastic molding can be achieved.

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

[0118] Based on the same idea as the mold temperature controller intelligent adjustment method in the diversified temperature control mode in the embodiments, the present application also provides a mold temperature controller intelligent adjustment system in the diversified temperature control mode, which comprises:

[0119] The process acquisition module 11 is used to determine the mold process execution information connected with the mold temperature controller.

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

[0121] The loop information acquisition module 13 is configured to acquire a carrier loop in the mold temperature machine for temperature control of the mold, and relative position distribution information of the carrier loop and the mold.

[0122] The diffusion model construction module 14 is configured to determine a heat conduction coefficient of the molding material in the mold based on information analysis, and construct an intelligent temperature diffusion model in combination with the relative position distribution information.

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

[0124] The uniform control module 16 is configured to perform uniform control in a temperature uniformity reduction mode in combination with the mold process execution information after the mold temperature machine is controlled by the cold-hot alternating temperature control parameters at the cold-hot alternating nodes.

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

[0126] The mold process execution information analysis unit is configured to analyze the mold process execution information and determine a continuous temperature requirement data sequence in a process execution process.

[0127] The cold-hot alternating node generation unit is configured to position a temperature control node between a continuous rising trend and a continuous falling trend based on the continuous temperature requirement data sequence, and generate the cold-hot alternating node.

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

[0129] The carrier loop residual influence temperature determination unit is configured to determine a carrier loop residual influence temperature by performing reverse diffusion analysis on the intelligent temperature diffusion model based on the first temperature parameter.

[0130] The control origin adjustment and simulation recording unit is configured to adjust the control origin based on the carrier loop residual influence temperature, call the intelligent temperature diffusion model for multiple control simulations based on the adjusted control origin, and record multiple sets of control simulation results.

[0131] The first control simulation result set screening unit is configured to screen a first control simulation result set in which all position temperatures of the mold meet the second temperature parameter from the multiple sets of control simulation results.

[0132] The cold and hot alternating temperature control parameter generation unit is configured to perform mold global temperature uniformity analysis on each simulation result in the first control simulation result set, screen temperature control parameters corresponding to a group of simulation results with uniform indexes meeting preset uniform indexes, and generate the cold and hot alternating temperature control parameters.

[0133] In some implementations, the simulation decision module 15 includes a messenger loop residual influence temperature determination unit configured to:

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

[0135] The messenger loop residual influence temperature generation subunit is configured to perform reverse temperature conduction analysis through the intelligent temperature diffusion model to generate the messenger loop residual influence temperature.

[0136] In some implementations, the simulation decision module 15 includes a control origin adjustment and simulation recording unit configured to:

[0137] The adjustable mode set determination subunit is configured to identify a throttle valve in the messenger loop, determine a set of adjustable modes of the loop according to the position of the throttle valve.

[0138] The loop adjustable mode determination and control simulation subunit is configured to determine each adjustable mode of the set of adjustable modes of the loop, perform control simulation based on each adjustable mode of the loop after mode matching adjustment of the intelligent temperature diffusion model, and generate a plurality of sets of control simulation results, each set of control simulation results having 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 each loop distribution feature of the flow circulation loop relative to the mold corresponding to each adjustable mode of the loop. Performing uniform probability analysis of temperature control with each loop distribution feature to generate each uniform probability. Screening the loop adjustable mode with a uniform probability greater than or equal to a preset uniform probability threshold based on each uniform probability. Performing control simulation with the screened loop adjustable mode to generate the plurality of sets of control simulation results.

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

[0141] The relative position calibration unit is configured to construct a reference coordinate system, map the position information of the messenger loop and the position information of the mold 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 collection unit is configured to collect temperature mapping samples from the mold surface to the inner cavity, taking the molding material information as an index element, and the temperature mapping samples include a temperature distribution data set corresponding to each inner cavity position and the outer surface.

[0144] A thermal conductivity analysis unit is configured to analyze the thermal conductivity of the material from the mold surface to the inner cavity of the mold based on the temperature mapping samples.

[0145] An intelligent temperature diffusion model generation unit is configured to determine the thermal conductivity between the delivery circuit and the mold, and analyze the temperature diffusion relationship from the delivery circuit to the mold surface and the material in the inner cavity of the mold based on the thermal conductivity of the material from the mold surface to the inner cavity of the mold and the relative position distribution information, to 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 is configured to construct a temperature control parameter influence sample database corresponding to a preset temperature uniformity descending gradient.

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

[0149] A temperature uniformity descending mode control unit is configured to perform uniformity control in a temperature uniformity descending mode based on the uniformity descending temperature control parameters.

[0150] It should be understood that the embodiments mentioned in the specification focus on their differences from other embodiments, and the specific embodiments in the first embodiment are also applicable to the mold temperature controller intelligent adjustment system in the diversified temperature control mode described in the second embodiment. For the sake of brevity of the specification, no further expansion is made here.

[0151] It should be understood that the embodiments disclosed in the present application and the above description can enable those skilled in the art to implement the present application. Meanwhile, the present application is not limited to the aforementioned part of the embodiments, and it should be understood that those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent replacement of some technical features; and such 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 application, and should be included in the protection scope of the present application.

Claims

1. A method for intelligent adjustment of a mold temperature controller in a diversified temperature control mode, characterized in that, The method comprises the following steps: determining mold process execution information connected with a mold temperature machine; based on the mold process execution information, performing mold temperature control process analysis, locating cold and hot alternating nodes, and corresponding cold and hot alternating temperature parameters; obtaining a carrier loop in the mold temperature machine for temperature control of the mold, and relative position distribution information of the carrier loop and the mold; determining the thermal conductivity of the molding material information analysis in the mold, combining the relative position distribution information, and constructing an intelligent temperature diffusion model; taking the first temperature parameter in the cold and hot alternating temperature parameter as a control origin, taking the second temperature parameter as a mold global uniform control target, calling the intelligent temperature diffusion model for control simulation, and generating cold and hot alternating temperature control parameters; after controlling the mold temperature machine with the cold and hot alternating temperature control parameters at the cold and hot alternating nodes, and then combining the mold process execution information to execute uniform control in the temperature uniformity descending mode; wherein, taking the first temperature parameter in the cold and hot alternating temperature parameter as a control origin, taking the second temperature parameter as a mold global uniform control target, calling the intelligent temperature diffusion model for control simulation, and generating cold and hot alternating temperature control parameters, comprising: based on the first temperature parameter, performing reverse diffusion analysis through the intelligent temperature diffusion model to determine the residual influence temperature of the carrier loop; after adjusting the control origin based on the residual influence temperature of the carrier loop, calling the intelligent temperature diffusion model for multiple control simulations based on the adjusted control origin, and recording multiple sets of control simulation results; selecting a first control simulation result set in which the temperature of all positions of the mold meets the second temperature parameter from the multiple sets of control simulation results; performing mold global temperature uniformity analysis on each simulation result in the first control simulation result set, selecting the temperature control parameter corresponding to the simulation result set that meets the preset uniformity index, and generating the cold and hot alternating temperature control parameter.

2. The mold temperature controller intelligent adjustment method in diversified temperature control mode according to claim 1, characterized in that, based on the mold process execution information, performing mold temperature control process analysis, locating cold and hot alternating nodes, comprising: analyzing the mold process execution information to determine the continuous temperature demand data sequence in the process execution; based on the continuous temperature demand data sequence, locating the temperature control node between the continuous rising trend and the continuous descending trend, and generating the cold and hot alternating node.

3. The mold temperature controller intelligent adjustment method in diversified temperature control mode according to claim 1, characterized in that, based on the first temperature parameter, performing reverse diffusion analysis through the intelligent temperature diffusion model to determine the residual influence temperature of the carrier loop, comprising: loading the first temperature parameter as the temperature diffused to each position of the mold to the intelligent temperature diffusion model; performing reverse temperature conduction analysis through the intelligent temperature diffusion model to generate the residual influence temperature of the carrier loop.

4. The mold temperature controller intelligent adjustment method in diversified temperature control mode according to claim 1, characterized in that, calling the intelligent temperature diffusion model for multiple control simulations, and recording multiple sets of control simulation results, comprising: identifying the throttle valve in the carrier loop, and determining a loop adjustable mode set according to the throttle valve position; Determine each loop adjustable mode in the loop adjustable mode set, and control simulation is performed based on each loop adjustable mode after mode matching adjustment of the intelligent temperature diffusion model, and multiple sets of control simulation results are generated, wherein each set of control simulation result has identification information including temperature control parameters and throttle valve control parameters.

5. The mold temperature controller intelligent adjustment method in diversified temperature control mode according to claim 1, characterized in that, Determine the molding material information analysis heat conduction coefficient in the mold, and construct an intelligent temperature diffusion model combined with the relative position distribution information, including: Collect temperature mapping samples from the mold surface to the inner cavity by taking 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; Analyze the heat conduction coefficient of the material from the mold surface to the mold inner cavity by the temperature mapping samples; Determine the heat conduction coefficient between the delivery loop and the mold, and analyze the temperature diffusion relationship from the delivery loop to the mold surface and the material in the mold inner cavity combined with the heat conduction coefficient of the material from the mold surface to the mold inner cavity and the relative position distribution information, and generate the intelligent temperature diffusion model.

6. The mold temperature controller intelligent adjustment method in diversified temperature control mode according to claim 4, wherein, Generating the multiple sets of control simulation results also includes: Determine the loop distribution characteristics of each loop adjustable mode corresponding to the flow loop relative to the mold; Generate each uniform probability by performing uniform probability analysis of temperature control based on the loop distribution characteristics; Screen the loop adjustable mode with uniform probability greater than or equal to the preset uniform probability threshold based on the uniform probability; Generate the multiple sets of control simulation results by performing control simulation based on the screened loop adjustable mode.

7. The mold temperature controller intelligent adjustment method in diversified temperature control mode according to claim 1, wherein, Perform uniform control in the temperature uniformity descending mode combined with the mold process execution information, including: Construct a preset temperature uniformity descending gradient corresponding temperature control parameter influence sample database; Determine the descending gradient corresponding to the temperature uniformity descending 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 uniformity descending temperature control parameters; Perform uniform control in the temperature uniformity descending mode based on the uniformity descending temperature control parameters.

8. The mold temperature controller intelligent adjustment method in diversified temperature control mode according to claim 1, wherein, Obtain the delivery loop in the mold temperature machine for temperature control of the mold, and the relative position distribution information of the delivery loop and the mold, including: Construct a reference coordinate system, map the position information of the delivery loop and the position information of the mold to the reference coordinate system, and generate the relative position distribution information.

9. The mold temperature controller intelligent adjustment system under the diversified temperature control mode, characterized in that, The mold temperature machine intelligent adjustment system in the diversified temperature control mode is used to execute the mold temperature machine intelligent adjustment method in the diversified temperature control mode in any one of claims 1-8, including: A process acquisition module for determining mold process execution information connected to the mold temperature machine; An alternating node positioning module for analyzing the mold temperature control process based on the mold process execution information, positioning the cold and hot alternating nodes, and corresponding cold and hot alternating temperature parameters; A loop information acquisition module for obtaining the delivery loop in the mold temperature machine for temperature control of the mold, and the relative position distribution information of the delivery loop and the mold; The diffusion model construction module is configured to determine the information analysis of the forming material in the mold, analyze the heat conduction coefficient, combine the relative position distribution information, and construct an intelligent temperature diffusion model. The simulation decision module is configured to take a first temperature parameter in the cold-heat alternating temperature parameter as a control origin, take a second temperature parameter as a mold global uniform control target, call the intelligent temperature diffusion model for control simulation, and generate a cold-heat alternating temperature control parameter. The uniform control module is configured to control the mold temperature machine by the cold-heat alternating temperature control parameter at the cold-heat alternating node, and then combine the mold process execution information to execute uniform control in a temperature uniformity descending mode.

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