Injection mold temperature control method, system and equipment based on industrial internet of things and medium

Abstract

The invention provides an injection mold temperature control method, system and equipment based on industrial Internet of Things and a medium. The method comprises the following steps: controlling a plurality of miniature temperature sensors in a temperature monitoring network to carry out temperature acquisition on a target injection mold to obtain multi-point temperature data; based on the multi-point temperature data, temperature field analysis is conducted on the target injection mold, and a temperature distribution model in the target injection mold is generated; identifying a temperature deviation area according to the temperature distribution model, and generating a temperature regulation and control instruction of each temperature control unit according to the temperature deviation area; and controlling the corresponding temperature control unit in the mold temperature control network to perform uniform temperature control on the target injection mold based on the temperature regulation and control instruction of each temperature control unit. And uniform distribution of the temperature in the mold is realized through multi-point monitoring and intelligent regulation and control of the Internet of Things, and the quality and the yield of injection molding products are improved.

Description

Technical Field

[0001] The present application relates to the field of Internet of Things, and in particular to an injection mold temperature control method, system, equipment and medium based on the Industrial Internet of Things. Background Art

[0002] Injection molding, a method for processing plastic products, is widely used in the automotive, electronics, and medical fields. During the injection molding process, the lack of effective monitoring and control of temperature uniformity within the mold leads to uneven temperature distribution within the mold. Uneven mold temperature can cause inconsistent flow of the plastic melt within the mold cavity, resulting in large differences in local shrinkage in the product. This can lead to quality defects such as warping, deformation, sink marks, and bubbles, resulting in low quality and yield rates for the injection molded products. Summary of the Invention

[0003] The main purpose of this application is to provide an injection mold temperature control method, system, equipment and medium based on the Industrial Internet of Things, aiming to solve the technical problem in the prior art of uneven temperature distribution of injection molds leading to low quality and yield of injection molded products.

[0004] To achieve the above-mentioned objectives, the present application provides an injection mold temperature control method based on the Industrial Internet of Things, which is applied to the Industrial Internet of Things injection mold temperature control system. The Industrial Internet of Things control system includes a management platform, a sensor network platform and an object platform. The object platform includes multiple micro temperature sensors, and the management platform includes multiple temperature control units; the method includes: controlling multiple micro temperature sensors in the temperature monitoring network to collect temperature of the target injection mold and obtain multi-point temperature data; based on the multi-point temperature data, performing temperature field analysis on the target injection mold and generating a temperature distribution model inside the target injection mold; identifying temperature deviation areas according to the temperature distribution model, and generating temperature control instructions for each of the temperature control units according to the temperature deviation areas; based on the temperature control instructions of each of the temperature control units, controlling the corresponding temperature control units in the mold temperature control network to uniformly control the temperature of the target injection mold.

[0005] Optionally, controlling the multiple micro temperature sensors in the temperature monitoring network to collect temperature of the target injection mold and obtain multi-point temperature data includes: monitoring the current process stage of the target injection mold, and generating a temperature collection instruction if the current process stage is a preset process stage; sending the temperature collection instruction to the temperature monitoring network, controlling the multiple micro temperature sensors to synchronously collect temperature and obtain multiple temperature collection values; and associating the multiple temperature collection values ​​with the multiple micro temperature sensors to form the multi-point temperature data.

[0006] Optionally, the preset process stage is a mold preheating completion stage, an injection molding stabilization stage, or a cooling completion stage.

[0007] Optionally, based on the multi-point temperature data, the temperature field analysis of the target injection mold is performed to generate a temperature distribution model inside the target injection mold, including: obtaining a three-dimensional structural model of the target injection mold, and extracting a target mold internal temperature simulator bound to the three-dimensional structural model; configuring the target mold internal temperature simulator according to the multi-point temperature data, executing mold internal temperature simulation, and generating a temperature distribution model inside the target injection mold.

[0008] Optionally, before obtaining the three-dimensional structural model of the target injection mold, the method further includes: extracting structural features of the three-dimensional structural model to obtain multiple mold structural features; constructing a mold retrieval statement based on the multiple mold structural features, and retrieving multiple candidate three-dimensional models with similar structures to the three-dimensional structural model in the mold database through the mold retrieval statement; obtaining mold internal temperature simulators bound to the multiple candidate three-dimensional models to obtain multiple candidate mold internal temperature simulators; fusing the multiple candidate mold internal temperature simulators according to the structural similarity between each candidate three-dimensional model and the three-dimensional structural model to generate an initial mold internal temperature simulator; performing multiple heat conduction simulations on the target injection mold based on the three-dimensional structural model to obtain a simulation data set, the simulation data set including multiple groups of simulation data, the simulation data including simulated multi-point temperature data and simulated mold internal temperature distribution data; optimizing and adjusting the parameters of the initial mold internal temperature simulator according to the simulation data to obtain the target mold internal temperature simulator.

[0009] Optionally, the identifying of the temperature deviation area according to the temperature distribution model and generating the temperature control instructions for each of the temperature control units according to the temperature deviation area include: obtaining the corresponding expected temperature distribution model based on the current process stage; comparing and analyzing the temperature distribution model according to the expected temperature distribution model to determine the temperature deviation area and the corresponding temperature deviation value; determining the temperature control area to be adjusted based on the temperature deviation area, and generating the temperature deviation parameters of the temperature control area to be adjusted based on the temperature deviation value; obtaining the distribution information of multiple temperature control units in the target injection mold, and determining the temperature control unit corresponding to the temperature control area to be adjusted based on the distribution information, and determining the temperature control unit to be adjusted; generating the temperature control instructions for the temperature control unit to be adjusted according to the temperature deviation parameters.

[0010] Optionally, generating the temperature control instruction of the temperature-controlled unit to be adjusted based on the temperature deviation parameter includes: obtaining the temperature control data of the temperature-controlled unit to be adjusted and the temperature response data of the temperature-controlled area to be adjusted; establishing a temperature mapping relationship between the temperature-controlled unit to be adjusted and the temperature-controlled area to be adjusted based on the temperature control data and the temperature response data; mapping and converting the temperature deviation parameter based on the temperature mapping relationship to obtain the temperature control adjustment parameter of the temperature-controlled unit to be adjusted; and generating the temperature control instruction of the temperature-controlled unit to be adjusted based on the temperature control adjustment parameter.

[0011] In addition, to achieve the above-mentioned purpose, the present application provides an injection mold temperature control system based on the industrial Internet of Things. The industrial Internet of Things control system includes a management platform, a sensor network platform and an object platform, the object platform includes multiple micro temperature sensors, and the management platform includes multiple temperature control units; the management platform includes: a data acquisition module, which is used to control the multiple micro temperature sensors in the temperature monitoring network to collect temperature of the target injection mold and obtain multi-point temperature data; a temperature field analysis module, which is used to perform temperature field analysis on the target injection mold based on the multi-point temperature data and generate a temperature distribution model inside the target injection mold; an instruction generation module, which is used to identify the temperature deviation area according to the temperature distribution model and generate temperature control instructions for each of the temperature control units according to the temperature deviation area; a temperature control execution module, which is used to control the corresponding temperature control unit in the mold temperature control network to uniformly control the temperature of the target injection mold based on the temperature control instructions of each temperature control unit.

[0012] Furthermore, to achieve the above-mentioned objectives, the present application further provides an electronic device comprising a memory and a processor. The memory is used to store a computer software program; the processor is used to read and execute the computer software program, thereby implementing any of the above-mentioned possible implementations of the method for temperature control of an injection mold based on the Industrial Internet of Things.

[0013] In addition, the present application also provides a non-transitory computer-readable storage medium, which stores a computer software program. When the computer software program is executed by a processor, it implements the injection mold temperature control method based on the industrial Internet of Things in any of the above possible implementation methods.

[0014] The beneficial effects that this application can achieve are as follows: The temperature monitoring network and the mold temperature control network are connected to the IoT control center to form a unified information processing and command issuance center, realizing the coordinated management of data collection and temperature control execution, and ensuring efficient temperature control of the injection mold; the temperature monitoring network includes multiple micro temperature sensors that can monitor the temperature at different positions of the injection mold and provide comprehensive temperature collection capabilities; the mold temperature control network includes multiple temperature control units that can implement targeted temperature adjustments for different areas of the mold to ensure the accuracy and flexibility of temperature control.

[0015] By controlling multiple micro temperature sensors in the temperature monitoring network to collect the temperature of the target injection mold and obtain multi-point temperature data, multi-point monitoring of the mold temperature is realized, providing a comprehensive temperature information basis; by performing temperature field analysis on the target injection mold based on multi-point temperature data, a temperature distribution model inside the target injection mold is generated, and a complete temperature field distribution model can be constructed from discrete temperature point data, providing data support for subsequent precise control, and realizing an overall grasp of the temperature state inside the mold; temperature deviation areas are identified according to the temperature distribution model, and temperature control instructions for each temperature control unit are generated according to the temperature deviation areas, which determines which areas inside the mold have abnormal temperatures, and generates targeted control instructions for each temperature control unit, realizing precise and personalized temperature control; based on the temperature control instructions of each temperature control unit, the corresponding temperature control unit in the mold temperature control network is controlled to uniformly control the temperature of the target injection mold.

[0016] Through the above technical methods, the Internet of Things technology is used to organically combine temperature monitoring and temperature control, realizing comprehensive monitoring, intelligent analysis and precise control of the injection mold temperature, effectively solving the problem of uneven mold temperature distribution in the existing technology, and improving the temperature uniformity during the injection mold processing process, thereby improving the quality and yield of injection molded products. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of the process of the injection mold temperature control method based on the Industrial Internet of Things provided in this application; Figure 2 A schematic diagram of the structure of the injection mold temperature control system based on the Industrial Internet of Things provided in this application; Figure 3 A schematic diagram of the structure of the electronic device provided for this application; Figure 4 A schematic diagram of the structure of a computer-readable storage medium provided in this application.

[0018] In the accompanying drawings, the components represented by the reference numerals are as follows: Data acquisition module 11 , temperature field analysis module 12 , instruction generation module 13 , temperature control execution module 14 , electronic device 200 , memory 210 , processor 220 , computer program 211 , computer readable storage medium 300 .

[0019] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0021] It should be noted that all directional indications in the embodiments of the present application (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain preset posture (as shown in the accompanying drawings). If the preset posture changes, the directional indication will also change accordingly.

[0022] In this application, unless otherwise specified or limited, the terms "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can mean fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. For those skilled in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0023] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the meaning of "and / or" appearing throughout the text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme in which A and B are satisfied at the same time. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0024] The first embodiment of this application provides an injection mold temperature control method based on the Industrial Internet of Things. The method is applied to an Industrial Internet of Things injection mold temperature control system. The Industrial Internet of Things control system includes a management platform, a sensor network platform, and an object platform. The object platform includes multiple micro temperature sensors, and the management platform includes multiple temperature control units.

[0025] Specifically, the method is applied to an IoT control center, which serves as the core control unit and connects to both a temperature monitoring network and a mold temperature control network via IoT communication protocols, forming a complete temperature monitoring and control loop. The temperature monitoring network consists of multiple miniature temperature sensors that collect real-time temperature data at key points in the injection mold. The mold temperature control network, on the other hand, consists of multiple independently controllable temperature control units, each responsible for regulating the temperature of a specific area of ​​the mold.

[0026] like Figure 1 As shown, the injection mold temperature control method includes: S1: Control multiple micro temperature sensors in the temperature monitoring network to collect temperature data from the target injection mold and obtain multi-point temperature data; S2: Based on the multi-point temperature data, the temperature field of the target injection mold is analyzed to generate a temperature distribution model inside the target injection mold; S3: Identify the temperature deviation area according to the temperature distribution model, and generate temperature control instructions for each temperature control unit according to the temperature deviation area; S4: Based on the temperature control instructions of each temperature control unit, control the corresponding temperature control unit in the mold temperature control network to uniformly control the temperature of the target injection mold.

[0027] Specifically, first, the IoT control center sends a temperature collection instruction to the temperature monitoring network, controlling multiple micro temperature sensors deployed at key locations of the injection mold to synchronously start temperature collection. These micro temperature sensors use high-precision thermocouples or infrared temperature measurement technology to accurately monitor temperature changes at preset points on the mold in real time. When the temperature monitoring network receives the temperature collection instruction, each temperature sensor collects temperature data from the target injection mold according to the preset sampling frequency and accuracy requirements, and transmits the collected temperature values ​​to the IoT control center in real time via the IoT communication protocol. After receiving these temperature data from different locations, the IoT control center integrates them into multi-point temperature data. Multi-point temperature data not only contains temperature value information, but also contains corresponding spatial position coordinate information and timestamp information, providing a comprehensive and accurate data basis for subsequent temperature field analysis.

[0028] Secondly, based on the acquired multi-point temperature data, the IoT control center conducts a comprehensive temperature field analysis and modeling of the target injection mold through temperature field analysis, deriving the complete temperature distribution state within the entire mold. In specific implementation, the IoT control center first spatially maps the multi-point temperature data with the three-dimensional structural model of the target injection mold to determine the precise location of each temperature sampling point within the mold structure. Subsequently, it calls a pre-configured heat conduction mathematical model, which combines Fourier's law of heat conduction with finite element analysis methods to accurately simulate the heat transfer pattern within the mold. By inputting the measured multi-point temperature data into the model and performing iterative calculations, a continuous temperature field distribution within the injection mold is generated, creating a temperature distribution model within the target injection mold. By generating a temperature distribution model within the target injection mold, the technical limitation of traditional temperature control methods, which can only obtain surface temperatures but cannot understand the internal temperature state, is overcome, providing a basis for the subsequent precise identification of temperature deviation areas and the implementation of precise temperature control.

[0029] The IoT control center then determines the target temperature requirements for each area of ​​the target injection mold based on the injection molding process's technical requirements and the current production stage, including the standard temperature value and the allowable temperature fluctuation range. These standard temperature values ​​are pre-set based on the injection molding material's characteristics, mold structure, and product quality requirements. The resulting temperature distribution model is then compared with the standard temperature requirements to identify temperature deviation areas and their deviation values. Temperature deviation areas are mold regions where the difference between the actual temperature and the standard temperature exceeds a preset threshold. These areas require temperature control to ensure injection molding quality. Based on the identified temperature deviation areas, the IoT control center identifies the specific mold areas requiring control and calculates the corresponding temperature deviation parameters. The IoT control center then generates specific temperature control instructions for the corresponding temperature control unit based on the temperature deviation parameters for each temperature deviation area, incorporating factors such as the mold's thermal response characteristics. These temperature control instructions include parameters such as the temperature increase or decrease action to be executed by the temperature control unit, the target temperature value, and the control rate. This precise deviation identification and control instruction generation mechanism based on the temperature distribution model enables precise temperature control of the injection mold, effectively improving the quality consistency and production efficiency of injection molded products.

[0030] The IoT control center then transmits the generated temperature control instructions to each corresponding temperature control unit in the mold temperature control network via the IoT communication protocol. Upon receiving its dedicated control instructions, each temperature control unit immediately configures its operating state according to the corresponding temperature control instructions, including technical parameters such as heating power, cooling flow rate, and temperature change rate. Next, each temperature control unit synchronously initiates temperature control operations based on the received temperature control instructions. For areas requiring temperature increase, the corresponding temperature control unit activates the heating element, precisely controlling the heating power and duration. For areas requiring temperature reduction, the temperature control unit adjusts the flow rate and temperature of the cooling medium to ensure a smooth cooling process. Throughout the entire control process, each temperature control unit adjusts its output power in real time to ensure a smooth temperature curve and avoid thermal stress caused by drastic temperature fluctuations. Simultaneously, the temperature monitoring network continuously collects real-time temperature data from all points on the mold and feeds it back to the IoT control center. Based on this feedback, the IoT control center evaluates the temperature control effectiveness in real time and dynamically optimizes the temperature control instructions as necessary to ensure the stability and accuracy of the temperature control process. The coordinated working mechanism of distributed temperature control units can effectively eliminate temperature unevenness inside the mold, ensure temperature consistency in all areas of the mold, and reduce product defects caused by uneven temperature, such as warping and uneven shrinkage, thereby significantly improving the quality and yield of injection molded products.

[0031] As an optional implementation, multiple micro temperature sensors in the temperature monitoring network are controlled to collect temperature data from the target injection mold to obtain multi-point temperature data, including: S11: monitoring the current process stage of the target injection mold, and generating a temperature acquisition instruction if the current process stage is a preset process stage; S12: Sending a temperature acquisition instruction to the temperature monitoring network to control multiple micro temperature sensors to synchronously acquire temperature and obtain multiple temperature acquisition values; S13: Associating the multiple temperature acquisition values ​​with the multiple micro temperature sensors to form multi-point temperature data.

[0032] Specifically, the IoT control center continuously monitors the current process stage of the target injection mold by interacting with the injection molding equipment control system in real time. When it detects that the current process stage matches the preset process stage, a temperature collection event is triggered, automatically generating a temperature collection instruction. This process-stage-based triggering mechanism ensures that temperature data collection occurs at the necessary moments during the process, improving the effectiveness of the collected data. The IoT control center then transmits the generated temperature collection instruction to the temperature monitoring network via the IoT communication protocol. This instruction includes technical parameters such as sampling frequency, accuracy requirements, and data format. Upon receiving the instruction, the temperature monitoring network coordinates multiple micro-temperature sensors in the network to synchronize temperature collection according to a unified time base. Each micro-temperature sensor accurately measures the mold temperature at its location according to the instruction and converts the collected temperature value into a standardized digital signal, generating multiple collected temperature values. After receiving the collected temperature values ​​uploaded by each micro-temperature sensor, the IoT control center performs information correlation processing. Each collected temperature value is associated with metadata such as the corresponding micro-temperature sensor's location coordinates, sensor ID, and collection timestamp to construct structured multi-point temperature data. This association processing gives the temperature data spatial location attributes and time attributes, which facilitates subsequent temperature field analysis.

[0033] Through the above steps, intelligent triggering, multi-point synchronous acquisition and data association processing based on the process stage are realized, ensuring the accuracy, synchronization and integrity of multi-point temperature data, and laying a data foundation for temperature field analysis and precise temperature control.

[0034] As an optional implementation, the preset process stage is the mold preheating completion stage, the injection molding stabilization stage or the cooling completion stage.

[0035] Specifically, the preset process stages refer to the mold preheating completion stage, the injection stabilization stage, and the cooling completion stage in the injection molding process.

[0036] The mold preheat completion stage refers to the state of the injection mold after it has been preheated and reached the preset operating temperature. Temperature acquisition and analysis during this stage can verify whether the preheat temperature across all areas of the mold is uniform and within the temperature range required by the process. Because injection molding requires high initial mold temperature uniformity, temperature monitoring and control during the preheat completion stage ensures consistent mold temperature before injection begins, preventing product defects caused by uneven initial temperatures.

[0037] The injection molding stabilization phase occurs when the plastic melt is injected into the mold cavity and reaches a stable flow state. During this phase, the melt temperature influences various areas within the mold, creating a new temperature distribution pattern. Temperature acquisition and control during this phase effectively regulates localized temperature anomalies caused by melt injection, ensuring the flow and uniformity of the plastic melt within the mold cavity, thereby improving the internal structure and surface quality of the product.

[0038] The cooling completion stage refers to the state of a plastic product after solidification and cooling within the mold and before the mold is opened for removal. Temperature acquisition and analysis during this stage can verify whether cooling is uniform across all mold areas and whether the required temperature conditions for demolding have been met. Because the uniformity of the cooling process directly impacts the residual stress distribution and dimensional stability of the product, temperature control during the cooling completion stage can reduce defects such as warping and deformation caused by uneven cooling.

[0039] By accurately collecting and controlling the temperature during the preset process stages, the temperature field management of the injection molding process is achieved, effectively improving the quality stability and production efficiency of the injection molded products.

[0040] As an optional embodiment, based on the multi-point temperature data, a temperature field analysis is performed on the target injection mold to generate a temperature distribution model inside the target injection mold, including: S21: obtaining a three-dimensional structural model of a target injection mold, and extracting a target mold internal temperature simulator bound to the three-dimensional structural model; S22: configuring a target mold internal temperature simulator according to the multi-point temperature data, executing a mold internal temperature simulation, and generating a temperature distribution model inside the target injection mold.

[0041] Specifically, first, the IoT control center retrieves the three-dimensional structural model of the target injection mold from the mold database. The three-dimensional structural model is an accurate geometric representation of the target injection mold, containing key information such as the mold's external dimensions, internal structure, and material distribution. After obtaining the three-dimensional structural model, the target mold internal temperature simulator, which is pre-bound to the three-dimensional structural model, is further extracted from the mold database. The target mold internal temperature simulator is a dedicated thermodynamic calculation engine that can infer the complete temperature distribution state inside the target injection mold based on a limited number of temperature measurement points. The parameters of the target mold internal temperature simulator have been optimized for the target injection mold structure and can accurately simulate the heat conduction characteristics under the mold structure.

[0042] The IoT control center then inputs the acquired multi-point temperature data into the target mold's internal temperature simulator as the boundary conditions and initial values ​​for the simulation calculation. The temperature simulator performs numerical simulations of heat conduction based on these measured temperature data, combined with physical parameters such as the mold material's thermal conductivity and specific heat capacity. During the calculation process, the target mold's internal temperature simulator uses an improved finite element analysis method to discretize the mold space into a large number of computational units. By solving the thermal balance equation for each unit, it ultimately generates a continuous temperature field distribution covering the entire internal space of the mold, obtaining a temperature distribution model for the target injection mold. This model accurately describes the temperature values ​​and temperature gradient distribution at each point within the target injection mold.

[0043] Through these steps, we achieve a precise mapping from a limited number of temperature sampling points to a complete temperature field, overcoming the incomplete temperature field information caused by the limited number of measurement points in traditional temperature measurement methods. The resulting temperature distribution model provides comprehensive and accurate data support for subsequent temperature deviation identification and precise temperature control.

[0044] As an optional embodiment, before obtaining the three-dimensional structural model of the target injection mold, the method further includes: S23: extracting structural features from the three-dimensional structural model to obtain multiple mold structural features; S24: constructing a mold search statement based on the multiple mold structure features, and searching the mold database for multiple candidate three-dimensional models with structures similar to the three-dimensional structure model using the mold search statement; S25: Acquire multiple mold internal temperature simulators bound to the candidate three-dimensional models to obtain multiple candidate mold internal temperature simulators; S26: fusing multiple candidate mold internal temperature simulators based on the structural similarity between each candidate 3D model and the 3D structural model to generate an initial mold internal temperature simulator; S27: Based on the three-dimensional structural model, multiple heat conduction simulations are performed on the target injection mold to obtain a simulation data set, where the simulation data set includes multiple sets of simulation data, each of which includes simulated multi-point temperature data and simulated mold internal temperature distribution data; S28: Optimizing and adjusting parameters of the initial mold internal temperature simulator according to the simulation data to obtain a target mold internal temperature simulator.

[0045] Specifically, when constructing the target mold internal temperature simulator, first, the three-dimensional structural model of the target injection mold is subjected to feature extraction processing to obtain multiple mold structural features. For example, a geometric feature analysis algorithm is used to extract key structural features from the three-dimensional structural model, including but not limited to characteristic parameters such as the mold outline, internal cavity structure, cooling channel layout, wall thickness distribution, and material partitioning. These structural features are represented in the form of digital feature vectors and can accurately describe the structural characteristics of the target injection mold. Then, based on the multiple extracted mold structural features, a standardized mold retrieval statement is constructed. The mold retrieval statement uses a specific grammatical structure and contains a description of each mold structural feature and its weight coefficient. The mold retrieval statement is then submitted to the mold database to retrieve candidate three-dimensional models with similar structures to the target injection mold. The retrieval process uses a similarity matching algorithm to identify mold models with similar structural characteristics and return multiple candidate three-dimensional models based on the degree of similarity.

[0046] Afterwards, the mold internal temperature simulators bound to each of the retrieved multiple candidate 3D models are retrieved from the mold database as multiple candidate mold internal temperature simulators. These candidate mold internal temperature simulators have been parameter-optimized and verified for the corresponding mold structure and have high simulation accuracy. These candidate mold internal temperature simulators and their parameter configuration information are read as the basic resources for constructing the target mold internal temperature simulator. Next, based on the structural similarity between each candidate 3D model and the 3D structural model of the target injection mold, the weight coefficient of each candidate mold internal temperature simulator during the fusion process is calculated. Subsequently, through simulator fusion, the core parameters, calculation models, and thermal conductivity characteristics of the multiple candidate mold internal temperature simulators are weightedly fused to generate an initial mold internal temperature simulator suitable for the target injection mold. This initial mold internal temperature simulator inherits the advantages and characteristics of each candidate mold internal temperature simulator and initially possesses the ability to simulate the thermal conductivity behavior of the target injection mold.

[0047] Then, based on the three-dimensional structural model of the target injection mold, multiple sets of different boundary conditions and initial conditions were set, and multiple heat conduction simulation experiments were conducted on the target injection mold. Each simulation experiment generated a set of simulation data, including multi-point temperature data during the simulation process (i.e., simulated multi-point temperature data), as well as complete mold internal temperature distribution data (i.e., simulated mold internal temperature distribution data). The data from all simulation experiments were integrated into a simulation dataset, which comprehensively covered the heat conduction behavior characteristics of the target injection mold under different temperature conditions. Subsequently, the parameters of the generated initial mold internal temperature simulator were optimized and adjusted based on the simulation dataset. During the optimization process, the error between the temperature distribution predicted by the initial mold internal temperature simulator and the temperature distribution generated by the simulation was compared. Through iterative optimization, the internal parameters of the initial mold internal temperature simulator were continuously adjusted to reduce the prediction error and improve the simulation accuracy. After sufficient training and optimization, a target mold internal temperature simulator that is highly customized for the target injection mold is ultimately obtained.

[0048] Through the above steps, the intelligent construction and adaptive optimization of the target mold internal temperature simulator are achieved, so that the target mold internal temperature simulator can accurately adapt to the structural characteristics and heat conduction behavior of the target injection mold, improve the generation accuracy of the temperature distribution model, and provide a more reliable decision-making basis for subsequent temperature control.

[0049] As an optional implementation, identifying the temperature deviation area according to the temperature distribution model and generating the temperature control instructions for each temperature control unit according to the temperature deviation area include: S31: Based on the current process stage, obtain the corresponding temperature distribution expectation model; S32: Compare and analyze the temperature distribution model according to the expected temperature distribution model to determine the temperature deviation area and the corresponding temperature deviation value; S33: determining a temperature control area to be adjusted based on the temperature deviation area, and generating a temperature deviation parameter of the temperature control area to be adjusted based on the temperature deviation value; S34: Obtain distribution information of multiple temperature control units in the target injection mold, and determine the temperature control unit corresponding to the temperature control area to be adjusted based on the distribution information, thereby determining the temperature control unit to be adjusted; S35: Generate a temperature control instruction for the temperature control unit to be adjusted according to the temperature deviation parameter.

[0050] Specifically, the IoT control center first identifies the current stage of the injection molding process, including the mold preheating completion stage, the injection stabilization stage, or the cooling completion stage. Subsequently, the expected temperature distribution model corresponding to the current stage is retrieved from the process database. This expected temperature distribution model is a predefined ideal temperature distribution state based on process requirements and product quality standards. It contains the target temperature values ​​that each area of ​​the mold should reach during the current process stage and the allowable temperature fluctuation range. The actual generated temperature distribution model is then compared and analyzed with the obtained expected temperature distribution model. This comparison process uses a grid comparison method to discretize the mold space into multiple calculation units, calculating the difference between the actual temperature and the expected temperature of each unit one by one. When the temperature difference exceeds a preset threshold, the corresponding area is marked as a temperature deviation area, and the temperature deviation value of this area is recorded. The temperature deviation value includes both the numerical value and the direction of the deviation (high or low).

[0051] Then, based on the identified temperature deviation regions, adjacent temperature deviation units are merged into continuous target temperature control regions through regional clustering. For each target temperature control region, parameters such as the average temperature deviation, maximum temperature deviation, and area are calculated. Based on these parameters, standardized temperature deviation parameters are generated. The temperature deviation parameters describe the direction and magnitude of temperature adjustment required for the target temperature control region. Subsequently, the spatial distribution information of each temperature control unit in the mold temperature control network is obtained, including its location coordinates, control range, and affected area. Next, a spatial mapping analysis is performed between the target temperature control regions and the distribution information of the temperature control units to determine which temperature control units are affected by each target temperature control region. Based on this information, the target temperature control units that need to participate in temperature control are identified. Finally, specific temperature control instructions are generated for each target temperature control unit based on the temperature deviation parameters of the target temperature control region. These temperature control instructions contain parameters such as the action type (heating or cooling), target temperature, temperature change rate, and control duration required by the temperature control unit.

[0052] Through the above steps, the entire process from temperature distribution difference detection to precise temperature control instruction generation is realized, and a precise mapping relationship between temperature state and control action is established, providing technical support for achieving high-precision and uniform control of mold temperature.

[0053] As an optional implementation, generating a temperature control instruction for the temperature control unit to be adjusted according to the temperature deviation parameter includes: S351: Acquire temperature control data of the temperature control unit to be adjusted and temperature response data of the temperature control area to be adjusted; S352: Establishing a temperature mapping relationship between the temperature control unit to be adjusted and the temperature control area to be adjusted according to the temperature control data and the temperature response data; S353: Mapping and converting the temperature deviation parameter based on the temperature mapping relationship to obtain the temperature control adjustment parameter of the temperature control unit to be adjusted; S354: Generate a temperature control instruction for the temperature control unit to be adjusted according to the temperature control adjustment parameter.

[0054] Specifically, the IoT control center first acquires historical temperature control data for the temperature control unit to be adjusted and temperature response data for the temperature control area to be adjusted. The temperature control data records the output characteristics of the temperature control unit under different control parameters, including key parameters such as heating / cooling power, temperature change rate, and control delay. The temperature response data records the temperature change characteristics of the mold area under the influence of the temperature control unit, including information such as the temperature response curve, heat transfer rate, and temperature stabilization time. This historical data, automatically recorded during actual operation, accurately reflects the thermodynamic relationship between the temperature control unit and the mold area for the specific target injection mold structure. Then, based on the acquired temperature control and temperature response data, a machine learning algorithm is used to construct a temperature mapping relationship between the temperature control unit to be adjusted and the temperature control area to be adjusted. For example, the temperature control data of the temperature control unit is used as input features, and the temperature response data of the corresponding temperature control area is used as output features. A multi-layer neural network structure is used to capture the nonlinear relationships and cross-influences in temperature transfer, thereby generating a temperature mapping relationship.

[0055] Then, based on the acquired temperature deviation parameters and the temperature mapping relationship, a reverse mapping calculation is performed. This reverse mapping determines the temperature control input required to accurately offset the temperature deviation in the target temperature-controlled area. The calculation results are output as temperature control adjustment parameters, including control direction, control intensity, and action time. This parameter conversion based on the mapping relationship ensures a quantitative correspondence between the temperature control action and the temperature deviation, effectively avoiding the over- and under-control issues common in traditional control methods. Next, based on the acquired temperature control adjustment parameters and the control protocol of the temperature control unit, a standardized temperature control instruction is generated. This temperature control instruction can include complete information such as the temperature control unit's device ID, control mode, target temperature / power value, control timing, and feedback requirements, allowing it to be directly parsed and executed by the temperature control unit. Furthermore, the instruction generation process considers the temperature control unit's safety margins and operating limitations, ensuring that the generated temperature control instruction effectively regulates the temperature without causing equipment overload or damage.

[0056] Through the above steps, a precise conversion mechanism from temperature deviation parameters to specific control instructions was established, which solved the technical problem of inaccurate control parameter determination in traditional temperature control methods, achieved high-precision closed-loop control of mold temperature, and provided a guarantee for improving the quality of injection molded products.

[0057] The second embodiment of the present application provides an injection mold temperature control system based on the industrial Internet of Things, such as Figure 2 As shown, the system includes a management platform 101, a sensor network platform 102, and an object platform 103 that are communicatively connected in sequence. The object platform 103 includes multiple micro temperature sensors, and the management platform 101 includes multiple temperature control units. The management platform 101 also includes a data acquisition module 11, a temperature field analysis module 12, an instruction generation module 13, and a temperature control execution module 14. The data acquisition module 11 is used to control the multiple micro temperature sensors in the temperature monitoring network to collect temperature data from the target injection mold and obtain multi-point temperature data; the temperature field analysis module 12 is used to perform temperature field analysis on the target injection mold based on the multi-point temperature data and generate a temperature distribution model inside the target injection mold; the instruction generation module 13 is used to identify temperature deviation areas based on the temperature distribution model and generate temperature control instructions for each of the temperature control units based on the temperature deviation areas; and the temperature control execution module 14 is used to control the corresponding temperature control units in the mold temperature control network to uniformly control the temperature of the target injection mold based on the temperature control instructions of each of the temperature control units.

[0058] As an optional embodiment, the data acquisition module 11 includes an acquisition instruction generation unit, a temperature acquisition unit, and an information association unit. The acquisition instruction generation unit is configured to monitor the current process stage of the target injection mold and, if the current process stage is a preset process stage, generate a temperature acquisition instruction. The temperature acquisition unit is configured to send the temperature acquisition instruction to the temperature monitoring network, control the multiple micro-temperature sensors to synchronously acquire temperature, and obtain multiple temperature acquisition values. The information association unit is configured to associate the multiple temperature acquisition values ​​with the multiple micro-temperature sensors to form the multi-point temperature data.

[0059] As an optional implementation, the preset process stage is a mold preheating completion stage, an injection molding stabilization stage, or a cooling completion stage.

[0060] As an optional embodiment, the temperature field analysis module 12 includes a simulator extraction unit and a temperature simulation unit. The simulator extraction unit is used to obtain a three-dimensional structural model of the target injection mold and extract a target mold internal temperature simulator bound to the three-dimensional structural model. The temperature simulation unit is used to configure the target mold internal temperature simulator based on the multi-point temperature data, perform mold internal temperature simulation, and generate a temperature distribution model inside the target injection mold.

[0061] As an optional implementation, the temperature field analysis module 12 further includes a feature extraction unit, a model retrieval unit, a candidate simulation question acquisition unit, an initial simulator generation unit, a heat conduction simulation unit, and a parameter optimization and adjustment unit. Among them, the feature extraction unit is used to extract structural features of the three-dimensional structure model and obtain multiple mold structure features; the model retrieval unit is used to construct a mold retrieval statement based on the multiple mold structure features, and retrieve multiple candidate three-dimensional models with similar structures to the three-dimensional structure model in the mold database through the mold retrieval statement; the candidate simulation question acquisition unit is used to obtain the mold internal temperature simulators bound to the multiple candidate three-dimensional models and obtain multiple candidate mold internal temperature simulators; the initial simulator generation unit is used to fuse the multiple candidate mold internal temperature simulators according to the structural similarity between each candidate three-dimensional model and the three-dimensional structure model to generate an initial mold internal temperature simulator; the heat conduction simulation unit is used to perform multiple heat conduction simulations on the target injection mold based on the three-dimensional structure model to obtain a simulation data set, wherein the simulation data set includes multiple groups of simulation data, and the simulation data includes simulated multi-point temperature data and simulated mold internal temperature distribution data; the parameter optimization adjustment unit is used to optimize and adjust the parameters of the initial mold internal temperature simulator according to the simulation data to obtain the target mold internal temperature simulator.

[0062] As an optional embodiment, the instruction generation module 13 includes an expected model acquisition unit, a comparison and analysis unit, a unit for determining an area to be adjusted, a unit for determining a temperature control unit to be adjusted, and a temperature control instruction generation unit. The expected model acquisition unit is used to acquire the corresponding expected temperature distribution model based on the current process stage; the comparison and analysis unit is used to compare and analyze the temperature distribution model according to the expected temperature distribution model to determine the temperature deviation area and the corresponding temperature deviation value; the unit for determining an area to be adjusted is used to determine the temperature control area to be adjusted based on the temperature deviation area, and generate the temperature deviation parameter of the temperature control area to be adjusted based on the temperature deviation value; the unit for determining a temperature control unit to be adjusted is used to acquire the distribution information of multiple temperature control units in the target injection mold, and determine the temperature control unit corresponding to the temperature control area to be adjusted based on the distribution information, and determine the temperature control unit to be adjusted; the temperature control instruction generation unit is used to generate a temperature control instruction for the temperature control unit to be adjusted based on the temperature deviation parameter.

[0063] As an optional embodiment, the temperature control instruction generation unit includes a data acquisition subunit for adjustment, a mapping subunit, a mapping conversion subunit, and a temperature control instruction subunit. The data acquisition subunit for adjustment is used to acquire the temperature control data of the temperature-controlled unit to be adjusted and the temperature response data of the temperature-controlled area to be adjusted; the mapping subunit is used to establish a temperature mapping relationship between the temperature-controlled unit to be adjusted and the temperature-controlled area to be adjusted based on the temperature control data and the temperature response data; the mapping conversion subunit is used to perform mapping conversion on the temperature deviation parameter based on the temperature mapping relationship to obtain the temperature control adjustment parameter of the temperature-controlled unit to be adjusted; and the temperature control instruction subunit is used to generate a temperature control instruction for the temperature-controlled unit to be adjusted based on the temperature control adjustment parameter.

[0064] A third embodiment of the present application provides an electronic device, such as Figure 3 As shown, the electronic device includes a memory 210, a processor 220, and a computer program 211 stored in the memory 210 and executable on the processor 220. When the processor 220 executes the computer program 211, the injection mold temperature control method based on the industrial Internet of Things in any possible implementation method of the first embodiment is implemented.

[0065] The fourth embodiment of the present application provides a computer-readable storage medium, such as Figure 4 As shown, a computer program 211 is stored on the computer-readable storage medium 300. When the computer program 211 is executed by the processor, the temperature control method for an injection mold based on the industrial Internet of Things in any possible implementation manner of the first embodiment is implemented.

[0066] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A temperature control method for injection molds based on industrial Internet of Things, characterized in that: The method is applied to an industrial Internet of Things injection mold temperature control system, wherein the industrial Internet of Things control system includes a management platform, a sensor network platform, and an object platform, wherein the object platform includes multiple micro temperature sensors, and the management platform includes multiple temperature control units; the method includes: Controlling multiple micro temperature sensors in the temperature monitoring network to collect temperature of a target injection mold and obtain multi-point temperature data; Based on the multi-point temperature data, performing a temperature field analysis on the target injection mold to generate a temperature distribution model inside the target injection mold; Identifying a temperature deviation area according to the temperature distribution model, and generating a temperature control instruction for each of the temperature control units according to the temperature deviation area; Based on the temperature control instructions of each temperature control unit, the corresponding temperature control unit in the mold temperature control network is controlled to uniformly control the temperature of the target injection mold.

2. The method according to claim 1, characterized in that The controlling of the plurality of micro temperature sensors in the temperature monitoring network to collect temperature of the target injection mold and obtain multi-point temperature data includes: monitoring a current process stage of the target injection mold, and generating a temperature acquisition instruction if the current process stage is a preset process stage; Sending the temperature acquisition instruction to the temperature monitoring network to control the multiple micro temperature sensors to synchronously acquire temperature and obtain multiple temperature acquisition values; The multiple temperature collection values ​​are associated with the multiple micro temperature sensors to form the multi-point temperature data.

3. The method according to claim 2, characterized in that The preset process stage is a mold preheating completion stage, an injection molding stabilization stage, or a cooling completion stage.

4. The method according to claim 1, wherein The step of performing temperature field analysis on the target injection mold based on the multi-point temperature data to generate a temperature distribution model inside the target injection mold includes: Acquire a three-dimensional structural model of the target injection mold, and extract a target mold internal temperature simulator bound to the three-dimensional structural model; The target mold internal temperature simulator is configured according to the multi-point temperature data, and a mold internal temperature simulation is performed to generate a temperature distribution model inside the target injection mold.

5. The method according to claim 4, characterized in that Before obtaining the three-dimensional structural model of the target injection mold, the method further includes: Extracting structural features from the three-dimensional structural model to obtain multiple mold structural features; Constructing a mold search statement based on the multiple mold structural features, and searching a mold database for multiple candidate three-dimensional models with structures similar to the three-dimensional structural model using the mold search statement; Acquire mold internal temperature simulators bound to the multiple candidate three-dimensional models to obtain multiple candidate mold internal temperature simulators; fusing the plurality of candidate mold internal temperature simulators according to the structural similarity between each candidate three-dimensional model and the three-dimensional structural model to generate an initial mold internal temperature simulator; Based on the three-dimensional structural model, multiple heat conduction simulations are performed on the target injection mold to obtain a simulation data set, wherein the simulation data set includes multiple groups of simulation data, and the simulation data includes simulated multi-point temperature data and simulated mold internal temperature distribution data; Parameters of the initial mold internal temperature simulator are optimized and adjusted according to the simulation data to obtain the target mold internal temperature simulator.

6. The method according to claim 2, characterized in that The identifying of the temperature deviation area according to the temperature distribution model and generating the temperature control instruction of each temperature control unit according to the temperature deviation area includes: Based on the current process stage, obtaining a corresponding expected temperature distribution model; Comparing and analyzing the temperature distribution model according to the expected temperature distribution model to determine the temperature deviation area and the corresponding temperature deviation value; Determining a temperature control area to be adjusted based on the temperature deviation area, and generating a temperature deviation parameter of the temperature control area to be adjusted based on the temperature deviation value; Obtaining distribution information of a plurality of temperature control units in the target injection mold, and determining a temperature control unit corresponding to the temperature control area to be adjusted based on the distribution information, thereby determining the temperature control unit to be adjusted; A temperature control instruction for the temperature control unit to be adjusted is generated according to the temperature deviation parameter.

7. The method according to claim 6, characterized in that Generating a temperature control instruction for the temperature control unit to be adjusted according to the temperature deviation parameter includes: Acquiring temperature control data of the temperature control unit to be adjusted and temperature response data of the temperature control area to be adjusted; Establishing a temperature mapping relationship between the temperature control unit to be adjusted and the temperature control area to be adjusted according to the temperature control data and the temperature response data; Performing mapping conversion on the temperature deviation parameter based on the temperature mapping relationship to obtain the temperature control adjustment parameter of the temperature control unit to be adjusted; A temperature control instruction for the temperature control unit to be adjusted is generated according to the temperature control adjustment parameter.

8. An injection mold temperature control system based on industrial Internet of Things, characterized in that: The industrial Internet of Things control system includes a management platform, a sensor network platform and an object platform. The object platform includes multiple micro temperature sensors, and the management platform includes multiple temperature control units. The management platform includes: A data acquisition module is used to control multiple micro temperature sensors in the temperature monitoring network to collect temperature of the target injection mold and obtain multi-point temperature data; a temperature field analysis module, configured to perform temperature field analysis on the target injection mold based on the multi-point temperature data, and generate a temperature distribution model inside the target injection mold; an instruction generation module, configured to identify a temperature deviation area according to the temperature distribution model, and generate a temperature control instruction for each of the temperature control units according to the temperature deviation area; The temperature control execution module is used to control the corresponding temperature control unit in the mold temperature control network to uniformly control the temperature of the target injection mold based on the temperature control instructions of each temperature control unit.

9. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor, configured to read and execute the computer software program, thereby implementing the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that The storage medium stores a computer software program, which implements the method according to any one of claims 1 to 7 when executed by a processor.

Images