TWI785698B - Method for monitoring abnormalities in injection molding process, electronic device, and storage medium - Google Patents

Abstract

The present application provides a method for monitoring abnormalities in an injection molding process, an electronic device, and a storage medium. The method includes: acquiring data of a plurality of components in an injection molding process; using a pre-trained abnormality monitoring model to perform an abnormal analysis on data of each component, and obtaining a process risk value and at least one influencing parameter of each component; determining whether the component is abnormal according to the process risk value; when a target component is determined to be abnormal according to the process risk value, adjusting equipment parameters of the target component according to the at least one influencing parameter of the target component. By utilizing the present application, abnormal situations in the molding process can be discovered in time, thereby reducing a defective rate of molded products.

Description

Abnormal monitoring method, electronic equipment and storage medium of injection molding process

The present application relates to the field of information analysis, in particular to an abnormality monitoring method, electronic equipment and a storage medium of an injection molding process.

At present, the quality monitoring of injection molding is realized through the inspection of the finished product quality. For the continuous production of injection molding production sites, quality supervisors inspect the quality of finished products through regular inspections or random inspections. However, the period from product production to detection of abnormal products in this way is usually longer, and more waste products may be generated during this period. In addition, quality supervisors will notify relevant personnel to deal with abnormalities in the quality of finished products. This process takes a lot of time and cannot provide timely feedback. At the same time, relevant personnel need to handle it according to their own experience, and the processing process relies heavily on personnel capabilities and is highly uncontrollable.

In view of the above, it is necessary to provide a method for monitoring abnormalities in the injection molding process, electronic equipment and storage media, which can detect abnormalities in the molding process in time, thereby reducing the defect rate of molded products.

The present application provides an abnormality monitoring method in the injection molding process, the method comprising: obtaining information on multiple components in the injection molding process; calling a pre-trained abnormality monitoring model to perform abnormality analysis on the information of each component, and obtaining the information of each component Process risk value and at least one influencing parameter; Determine whether the corresponding component is abnormal according to the process risk value; when it is determined that the target component is abnormal according to the process risk value, adjust the equipment parameters of the target component according to at least one influencing parameter of the target component.

In a possible implementation manner, the acquiring information of multiple components in the injection molding process includes: acquiring information of a controller of an injection molding machine, information of a first sensor of a mold, and information of a second sensor of a mold temperature controller.

In a possible implementation manner, after acquiring the controller information of the injection molding machine, the first sensor information of the mold, and the second sensor information of the mold temperature machine, the method further includes: according to the controller The section information in the information divides the controller information to obtain multiple section information, and different section information corresponds to different injection molding processes.

In a possible implementation, the calling the pre-trained abnormality monitoring model to analyze the abnormality of each component information, and obtaining the process risk value and at least one impact parameter of each component includes: calling the pre-trained abnormality monitoring The model performs abnormal analysis on the controller information of the injection molding machine to obtain the first process risk value and at least one first influencing parameter; calls the pre-trained abnormal monitoring model to perform abnormal analysis on the first sensor information of the mold , to obtain the second process risk value and at least one second influencing parameter; call the pre-trained abnormality monitoring model to analyze the abnormality of the second sensor information of the mold temperature machine, and obtain the third process risk value and at least one first Three influence parameters.

In a possible implementation manner, before calling the pre-trained abnormality monitoring model to perform abnormality analysis on the information of each component, the method further includes: setting the molding work cycle of the injection molding machine; Periodically invoke the pre-trained anomaly monitoring model to analyze the information of each component abnormally.

In a possible implementation manner, the method further includes: period, draw the first parameter period change state diagram according to the at least one first influence parameter, draw the second parameter period change state diagram according to the at least one second influence parameter, draw the third parameter according to the at least one third influence parameter Parameter cycle change state diagram.

In a possible implementation manner, the determining whether the corresponding component is abnormal according to the process risk value includes: determining whether the first process risk value is greater than a preset first threshold, when the first process risk value When it is greater than the preset first threshold, a first result is output, and the first result indicates that there is an abnormality in the injection molding machine; it is judged whether the second process risk value is greater than the preset second threshold, and when the first When the risk value of the second process is greater than the preset second threshold, the second result is output, and the mold is abnormal in the second result; it is judged whether the risk value of the third process is greater than the preset third threshold, and when the second result is abnormal; When the third process risk value is greater than the preset third threshold, a third result is output, and the third result is that the mold temperature controller is abnormal.

In a possible implementation manner, when it is determined according to the process risk value that there is an abnormality in the target component, adjusting the equipment parameter of the target component according to at least one influencing parameter of the target component includes: when determining that the injection molding machine When an abnormality occurs, input the controller information and the at least one first influencing parameter into a pre-trained intelligent adjustment model, and the intelligent adjustment model outputs at least one first equipment parameter of the injection molding machine, according to the At least one first equipment parameter to adjust the equipment parameters of the injection molding machine; when it is determined that the mold is abnormal, input the first sensor information and the at least one second influencing parameter to the pre-trained intelligent adjustment model, the intelligent adjustment model outputs at least one second equipment parameter of the mold, and adjusts the equipment parameter of the mold according to the at least one second equipment parameter; when it is determined that the mold temperature machine is abnormal, the The second sensor information and the at least one third influencing parameter are input to the pre-trained intelligent adjustment model, and the intelligent adjustment model outputs at least one third equipment parameter of the mold temperature controller, according to the at least one first Three Equipment Parameters Adjust the equipment parameters of the mould.

The present application also provides an electronic device, the electronic device includes a processor and a memory, and the processor is used to implement the abnormality monitoring method of the injection molding process when executing the computer program stored in the memory.

The present application also provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method for monitoring abnormalities in the injection molding process is realized.

The abnormal monitoring method and related equipment of the injection molding process disclosed in this application can detect system abnormalities in time, reduce the generation of defective products, save raw materials, and improve injection molding by collecting molding process information in real time, monitoring abnormal conditions and giving timely feedback prompts. The effective value time of the device.

S21~S24: Steps

1: Electronic equipment

11: Memory

12: Processor

13: Communication bus

FIG. 1 is a schematic structural diagram of an electronic device in a preferred embodiment of a method for monitoring abnormalities in an injection molding process provided by an embodiment of the present application.

Fig. 2 is a flow chart of a preferred embodiment of a method for monitoring abnormalities in an injection molding process provided by an embodiment of the present application.

In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

Please refer to FIG. 1 , which is a schematic diagram of an electronic device according to an embodiment of the present application. As shown in FIG. 1 , the electronic device 1 includes, but is not limited to, a memory 11 and at least one processor 12 . The above components can be connected through a communication bus 13 or can be directly connected.

The electronic device 1 may be a computer, a mobile phone, a tablet computer, a personal digital assistant (Personal Digital Assistant, PDA), etc., which are installed with application programs. Those skilled in the art can understand that the schematic diagram 1 is only an example of the electronic device 1, and does not constitute a limitation on the electronic device 1. It includes more or less components than shown in the figure, or some components are combined, or different components. For example, the electronic device 1 may also include input and output devices, network access devices, bus bars, and the like.

As shown in FIG. 2 , it is a flowchart of a preferred embodiment of the abnormality monitoring method of the injection molding process of the present application. The abnormality monitoring method of the injection molding process is applied in the electronic device 1 . According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted. In this embodiment, the abnormality monitoring method of the injection molding process includes: S21. Obtain information of multiple components in the injection molding process.

In one embodiment of the present application, the acquisition of information on multiple components in the injection molding process includes:

(1) Obtain the controller information of the injection molding machine. The injection molding machine is the main molding equipment for making thermoplastic or thermosetting plastic into various shapes of plastic products. Wherein, the injection molding machine can heat the plastic, apply high pressure to the molten plastic, and make it inject to fill the cavity of the mold. During specific implementation, a programmable logic controller (Programmable Logic Controller, PLC) communication board interface communicates with the injection molding machine to collect PLC information in real time, wherein the PLC information includes: the injection molding machine system parameters, the The switch status of the injection molding machine and the internal information of the injection molding machine. Send the PLC information to the electronic device through the PLC communication board interface.

(2) Obtain the information of the first sensor of the mould. The mold is made in proportion according to the shape and structure of the real object in the injection molding process, and the material is made into a tool of a certain shape by pressing or pouring. During specific implementation, a temperature sensor is set to measure the temperature information of the mold through the temperature sensor, and a distance sensor is set to measure the position information and size information of the mold through the distance sensor. Sending the temperature information of the mould, the position information of the mould, and the dimension information of the mould, as the first sensor information, to the electronic device.

(3) Obtain the information of the second sensor of the mold temperature controller. The mold temperature controller is a kind of heating and Mechanical equipment for cooling action, used for industrial temperature control, used to control the temperature of the mold during the injection molding process. During specific implementation, the distance sensor is set to measure the size information of the internal circulation pump of the mold temperature controller, the size information of the heating pipe and the size information of the electrical accessories through the distance sensor, and the temperature sensor is set to pass through the distance sensor. The temperature sensor measures the temperature information of the circulation pump inside the mold temperature controller, the temperature information of the heating pipe and the temperature information of the electrical accessories. The size information of the loop pump, the size data of the heating tube, the size information of the electrical accessories, the temperature information of the loop pump, the temperature information of the heating tube, and the temperature information of the electrical accessories sent to the electronic device as the second sensor information.

By obtaining the information of each component in the injection molding process, it is possible to analyze the abnormality of the injection molding process more comprehensively, so that the analysis results are more accurate.

As an optional implementation manner, after the step S21, the method further includes: segmenting the controller information according to the section information in the controller information to obtain a plurality of section information, different section information The information corresponds to different injection molding processes.

In one embodiment of the present application, injection molding is a cycle process, and each cycle cycle includes: quantitative feeding, melting and plasticizing, pressure injection, mold filling and cooling, and mold opening and taking. After the plastic part is taken out, the mold is closed again, and the next cycle is performed. According to the loop cycle, the controller information can be divided into feeding process information, plasticizing process information, injection process information, cooling process information and pick-up process information.

By dividing the information of the controller, when performing abnormality analysis on the information, the process where the abnormal information is located can be accurately located, thereby improving the efficiency of abnormality monitoring.

S22. Invoke the pre-trained abnormality monitoring model to analyze the abnormality of the information of each component, and obtain the process risk value and at least one influencing parameter of each component.

In one embodiment of the present application, the abnormality monitoring model is pre-trained, and during specific implementation, Obtain a first training information set, the first training information set includes a preset first number of first training information, log the first training information into a pre-built neural network framework for training, and obtain the abnormality monitoring model. Obtain a first test information set, the first test information set includes a preset second amount of first test information, use the first test information to test the abnormality monitoring model and obtain a first test penetration rate. When the test transmittance is greater than the preset first transmittance threshold, the abnormality monitoring model is used as the trained abnormality monitor model; when the first test transmittance is less than the preset first transmittance threshold, Adjusting model parameters of the abnormality monitoring model until the first test transmittance is greater than a preset first transmittance threshold.

In one embodiment of the present application, the calling of the pre-trained anomaly monitoring model to perform anomaly analysis on the information of each component, and obtaining the process risk value and at least one influencing parameter of each component include:

(1) Invoking the trained abnormality monitoring model to analyze the abnormality of the controller information of the injection molding machine to obtain the first process risk value and the parameter value of at least one first influencing parameter. During specific implementation, input the feeding process information into the trained abnormality monitoring model, and output the first feeding risk value; input the plasticizing process information into the trained abnormality monitoring model, and output the first plasticizing risk value; input the injection process information to the trained abnormal monitoring model, and output the first injection risk value; input the cooling process information to the trained abnormal monitoring model, and output the first cooling risk value; input the Describe the pickup process information to the abnormal monitoring model that has been trained, and output the first pickup risk value; set the proportion of each section according to the importance of the section, for example, the feeding process accounts for 10%, and the plasticizing process accounted for 30%, the injection process accounted for 20%, the cooling process accounted for 30% and the pick-up process accounted for 10%; according to the first feeding risk value, the first plasticizing risk value, the first injection The risk value, the first cooling risk value, the first pick-up risk value, and the proportion of each process section obtain the first process risk value. Wherein, the greater the risk value of the first process, the table The higher the possibility that the injection molding machine is abnormal, the greater the risk value of the injection molding process. The first influencing parameter is the equipment parameter of the injection molding machine, including: injection volume, injection pressure, injection rate, plasticizing capacity, mold clamping area, mold clamping force, mold opening and closing speed, and empty cycle time.

(2) Invoking the trained abnormality monitoring model to analyze the abnormality of the first sensor information of the mold to obtain a second process risk value and a parameter value of at least one second influencing parameter. Wherein, the greater the risk value of the second process, the higher the possibility of abnormality in the mold, and the greater the risk value of the molding process. The second influencing parameter is the equipment parameter of the mold, including: material type, tensile strength, thickness, burr height, double-sided clearance, sag depth and regrinding life.

(3) Invoking the trained abnormality monitoring model to analyze the abnormality of the second sensor information of the mold temperature controller to obtain a third process risk value and a parameter value of at least one third influencing parameter. Wherein, the greater the risk value of the third process, the higher the possibility of abnormality in the mold temperature controller, and the greater the risk value of the mold making process. The third influencing parameter is the equipment parameter of the mold temperature controller, including: temperature control range, temperature control accuracy, heat transfer medium, expansion tank capacity, pump power and pump pressure.

The process risk value and influence parameters of the injection molding machine, the process risk value and influence parameters of the mold, and the process risk value and influence parameters of the mold temperature machine are obtained by using the pre-trained abnormality monitoring model, replacing the traditional Manual monitoring method can improve the accuracy and efficiency of monitoring.

As an optional implementation manner, before the step S22, the method further includes: setting the molding work cycle of the injection molding machine; calling the pre-trained abnormality monitoring model to monitor the information of each component every time the molding work cycle Exception analysis.

As an optional implementation manner, after the step S22, the method further includes: drawing a first parameter periodic change state diagram according to the at least one first influencing parameter every working cycle of the molding, according to the at least one A second influencing parameter is used to draw a state diagram of the periodical variation of the second parameter, and a diagram of the periodical variation of the third parameter is drawn according to the at least one third influencing parameter.

S23. Determine whether the corresponding component is abnormal according to the process risk value.

In one embodiment of the present application, the determining whether the corresponding component is abnormal according to the process risk value includes: determining whether the first process risk value is greater than a preset first threshold, when the first process risk value When the value is greater than the preset first threshold, output the first result, the first result indicates that there is an abnormality in the injection molding machine; judge whether the second process risk value is greater than the preset second threshold, when the When the second process risk value is greater than the preset second threshold value, output a second result that the mold is abnormal; determine whether the third process risk value is greater than the preset third threshold value, when When the third process risk value is greater than the preset third threshold, a third result is output, and the third result is that the mold temperature controller is abnormal.

Exemplarily, the first threshold is set to 0.3, the second threshold is set to 0.6, and the third threshold is set to 0.5. The risk value of the first process is 0.1, the risk value of the second process is 0.5, and the risk value of the third process is 0.7, wherein the risk value of the third process is greater than the third threshold, and the output of the model There is an abnormality prompt in the temperature machine.

By using the process risk value to judge whether there is an abnormality in each component, through real-time collection of molding process information, monitoring of abnormal conditions and timely feedback prompts, it is possible to detect system abnormalities in a timely manner, reduce the occurrence of defective products, save raw materials, and improve injection molding equipment. effective value time.

S24. When it is determined according to the process risk value that there is an abnormality in the target component, adjust an equipment parameter of the target component according to at least one influencing parameter of the target component.

In one embodiment of the present application, the intelligent adjustment model is trained in advance. During specific implementation, a second training information set is obtained, the second training information set includes a preset third amount of second training information, and the second The training information is logged into the pre-built neural network framework for training to obtain the intelligent adjustment model. Obtain a second test information set, the second test information set includes a preset fourth amount of second test information, use the second test information to test the smart adjustment model and obtain a second test transmittance. When the second test transmittance is greater than the preset second transmittance threshold, use the intelligent adjustment model as the trained intelligent adjustment model; when the second test transmittance is less than the preset second transmittance threshold , adjusting the model parameters of the smart adjustment model until the second test transmittance is greater than the preset second transmittance threshold.

In one embodiment of the present application, when it is determined according to the process risk value that there is an abnormality in the target component, adjusting the equipment parameters of the target component according to at least one influencing parameter of the target component includes: when determining that the injection molding When an abnormality occurs in the injection molding machine, input the controller information and the parameter value of the at least one first influencing parameter into a pre-trained intelligent adjustment model, and the intelligent adjustment model outputs at least one first equipment parameter of the injection molding machine , adjusting the equipment parameters of the injection molding machine according to the at least one first equipment parameter; when it is determined that the mold is abnormal, inputting the first sensor information and the parameter value of the at least one second influencing parameter To the pre-trained intelligent adjustment model, the intelligent adjustment model outputs at least one second equipment parameter of the mold, and adjusts the equipment parameter of the mold according to the at least one second equipment parameter; when the mold temperature controller is determined When an abnormality occurs, the second sensor information and the parameter value of the at least one third influencing parameter are input to the pre-trained intelligent adjustment model, and the intelligent adjustment model outputs at least one first parameter value of the mold temperature controller. Three parameter values of equipment parameters, adjusting the equipment parameters of the mold according to the at least one third equipment parameter.

By using the intelligent adjustment model to adjust the equipment parameters of the injection molding machine, the mold, and the mold temperature controller, on the one hand, intelligent adjustment can be realized, the waste of manpower and material resources can be reduced, and the accuracy of equipment adjustment can be increased. It can provide important reference information for on-site personnel to debug equipment.

Please continue to refer to FIG. 1 , in this embodiment, the memory 11 may be an internal memory of the electronic device 1 , that is, a memory built in the electronic device 1 . In other embodiments, the memory 11 may also be an external memory of the electronic device 1 , that is, a memory externally connected to the electronic device 1 .

In some embodiments, the memory 11 is used to store program codes and various information, and realize high-speed and automatic access to programs or information during the operation of the electronic device 1 .

The memory 11 may include a random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital ( Secure Digital (SD) card, flash memory card (Flash Card), at least one disk memory device, flash memory device, or other volatile solid state memory devices.

In one embodiment, the processor 12 may be a central processing unit (Central Processing Unit, CPU), and may also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application-specific integrated circuits (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may be any other conventional processor and the like.

If the program codes and various information in the memory 11 are realized in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the methods of the above-mentioned embodiments, such as realizing the abnormality monitoring method of the injection molding process, which can also be completed by instructing related hardware through computer programs, and the computer programs can be stored In a computer-readable storage medium, when the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be realized. Wherein, the computer program includes computer program code, and the computer program code may be in the form of original code, object code, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read- Only Memory) etc.

It can be understood that the above-described module division is a logical function division, which implements In actual implementation, there may be another division method. In addition, each functional module in each embodiment of the present application may be integrated into the same processing unit, or each module may exist separately physically, or two or more modules may be integrated into the same unit. The above-mentioned integrated modules can be implemented in the form of hardware, or in the form of hardware plus software function modules.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application without limitation. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be Make modifications or equivalent replacements without departing from the spirit and scope of the technical solutions of the present application.

S21~S24: Steps

Claims (10)

A method for monitoring abnormalities in an injection molding process, wherein the method for monitoring abnormalities in an injection molding process includes: obtaining information on multiple components in the injection molding process, including: obtaining controller information of an injection molding machine; Segmentation to obtain information on multiple sections, and information on different sections corresponds to different injection molding processes; call the pre-trained anomaly monitoring model to analyze the information of each component abnormally, and obtain the process risk value and at least one impact of each component Parameters, including: calling the trained abnormality monitoring model to analyze the abnormality of the controller information of the injection molding machine, and obtaining the first process risk value and the parameter value of at least one first influencing parameter, including: combining the multiple The section information is respectively input into the abnormality monitoring model that has been trained to obtain the corresponding risk value; the proportion of each section is set according to the importance of each section; according to the risk values corresponding to the multiple section information, and the According to the proportion of each process section, the first process risk value is obtained; according to the process risk value, it is judged whether the corresponding component is abnormal; An influencing parameter adjusts a device parameter of the target component. The abnormality monitoring method of the injection molding process as described in claim 1, wherein the acquisition of information of multiple components in the injection molding process further includes: information of the first sensor of the mold and the second sensor of the mold temperature machine information; the acquisition of the controller information of the injection molding machine includes: communicating with the injection molding machine through the programmable logic controller communication board interface, and collecting the controller information of the injection molding machine in real time, including: the injection molding machine system parameters , the switching state of the injection molding machine and the internal information of the injection molding machine; and obtaining the controller information of the injection molding machine from the PLC communication board interface. The abnormality monitoring method of the injection molding process according to claim 2, wherein the first sensor information of the mold includes: the temperature information of the mold measured by the temperature sensor, the distance sensor The measured position information and size information of the mold; the second sensor information of the mold temperature controller includes: the size information of the internal circulation pump of the mold temperature controller sensed by the distance sensor, The size information of the heating tube and the size information of the electrical accessories, the temperature information of the internal circulation pump of the mold temperature controller, the temperature information of the heating tube and the temperature information of the electrical accessories are sensed by the temperature sensor. The abnormality monitoring method of the injection molding process as described in claim 2 or claim 3, wherein, the abnormality analysis is performed on the information of each component by calling the pre-trained abnormality monitoring model, and the process risk value and the process risk value of each component are obtained. The at least one influencing parameter further includes: calling the pre-trained abnormal monitoring model to analyze the abnormality of the first sensor information of the mold to obtain the second process risk value and at least one second influencing parameter; calling the pre-trained abnormal The monitoring model performs anomaly analysis on the information of the second sensor of the mold temperature controller to obtain a third process risk value and at least one third influencing parameter. The abnormality monitoring method of the injection molding process as described in claim 4, wherein, before calling the pre-trained abnormality monitoring model to perform abnormality analysis on the information of each component, the method further includes: setting the molding of the injection molding machine Working cycle: calling the pre-trained abnormality monitoring model to analyze the abnormality of the information of each component every said molding working cycle. The abnormality monitoring method of the injection molding process as claimed in item 5, wherein the method further includes: Every molding working cycle, according to the at least one first influencing parameter, draw a first parameter periodical change state diagram, according to the at least one second influencing parameter, draw a second parameter periodical change state diagram, according to the at least one first influencing parameter Three influence parameters Draw the state diagram of the period change of the third parameter. The abnormality monitoring method of the injection molding process according to claim 6, wherein the determining whether the corresponding component is abnormal according to the process risk value includes: determining whether the first process risk value is greater than a preset first threshold , when the first process risk value is greater than the preset first threshold, output a first result, the first result indicates that there is an abnormality in the injection molding machine; judge whether the second process risk value is greater than a preset The second threshold of the second process, when the second process risk value is greater than the preset second threshold, output the second result, the second result is abnormal in the mold; judge whether the third process risk value is greater than A preset third threshold, when the third process risk value is greater than the preset third threshold, a third result is output, and the third result is that the mold temperature controller is abnormal. The abnormality monitoring method of the injection molding process according to claim 7, wherein, when it is determined according to the process risk value that there is an abnormality in the target component, the equipment for the target component is adjusted according to at least one influencing parameter of the target component The parameters include: when it is determined that the injection molding machine is abnormal, input the controller information and the at least one first influencing parameter into a pre-trained intelligent adjustment model, and the intelligent adjustment model outputs at least A first equipment parameter, adjusting the equipment parameters of the injection molding machine according to the at least one first equipment parameter; when it is determined that the mold is abnormal, the first sensor information and the at least one second influence The parameters are input to the pre-trained intelligent adjustment model, and the intelligent adjustment model outputs the model at least one second equipment parameter of the tool, and adjust the equipment parameter of the mold according to the at least one second equipment parameter; when it is determined that the mold temperature machine is abnormal, the information of the second sensor and the at least A third influencing parameter is input to the pre-trained intelligent adjustment model, the intelligent adjustment model outputs at least one third equipment parameter of the mold temperature controller, and the equipment parameter of the mold is adjusted according to the at least one third equipment parameter . An electronic device, wherein the electronic device includes a processor and a memory, and the processor is used to execute a computer program stored in the memory to realize the injection molding process as described in any one of claim 1 to claim 8 exception monitoring method. A computer-readable storage medium, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the injection molding as described in any one of claim 1 to claim 8 is realized An exception monitoring method for the process.

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