A process parameter intelligent monitoring method and system for injection molding

Abstract

The application relates to the technical field of electric digital data processing, in particular to a process parameter intelligent monitoring method and system for injection molding, wherein the process parameter intelligent monitoring method for injection molding comprises the following steps: determining an injection real-time solidification speed based on a cooling temperature change curve in an injection molding process; determining an environmental temperature-noise ratio based on historical cooling temperature values and environmental uncontrollability indexes; determining an injection cooling effect index of the injection molding process based on a solidification speed change characteristic of a normal injection process; and determining an abnormal identification result of a process parameter in the injection molding process based on the environmental temperature-noise ratio and the injection cooling effect index. Through the introduction of the environmental temperature-noise ratio, statistical interference of environmental temperature fluctuation is quantified as an independent index, and correlation operation is carried out with real-time and historical solidification speed difference, so that root cause positioning is completed at the moment of abnormality, and misjudgment and waste caused by manual experience troubleshooting are avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital data processing, in particular to a process parameter intelligent monitoring method and system for injection molding. BACKGROUND

[0002] Injection molding is a high-efficiency manufacturing process that obtains products by injecting molten thermoplastic or thermosetting plastic into a mold cavity and then solidifying it by cooling. In this process, process parameters such as temperature, pressure, speed, and displacement directly determine the dimensional accuracy, appearance quality, and mechanical properties of the product. The cooling stage is particularly critical: the molten plastic gradually cools from high temperature to demolding temperature in the mold, and the uniformity and stability of its cooling rate not only affect the molding cycle length, but also relate to the residual stress, warping deformation, and final performance of the product. To achieve real-time control of the cooling process, existing technologies usually use temperature sensors placed in the mold, cooling circuit, and environment to continuously collect cooling water temperature, mold temperature, and workshop environment temperature, and record and display the data through a central control system, providing a basis for adjustment for the operator.

[0003] However, the above-mentioned existing technology has the following common defects in actual application: when an abnormality occurs in the cooling stage (such as uneven cooling of the product, prolonged cycle, or excessive deformation), the system can only indicate that the temperature is out of limit, but cannot distinguish whether the abnormality is caused by environmental temperature fluctuation or equipment cooling circuit failure; this defect requires the operator to rely on experience to repeatedly troubleshoot, which not only prolongs the downtime and debugging time, but also reduces the accuracy of abnormality disposal and production efficiency. SUMMARY

[0004] The present application provides a process parameter intelligent monitoring method and system for injection molding to solve the existing problems.

[0005] The process parameter intelligent monitoring method and system for injection molding provided by the present application adopts the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a process parameter intelligent monitoring method for injection molding, which comprises: determining an injection real-time solidification speed based on a cooling temperature change curve in the injection molding process; determining an environmental temperature noise ratio based on historical cooling temperature values and an environmental uncontrollability index; wherein the environmental uncontrollability index is used to represent the interference degree of environmental temperature fluctuation on the injection molding process; the environmental temperature noise ratio is used to represent the probability of injection cooling abnormality caused by environmental temperature fluctuation in the injection molding process; determining an injection cooling effect index of the injection molding process based on the solidification speed change characteristics of the normal injection process; and determining an abnormality recognition result of the process parameters in the injection molding process based on the environmental temperature noise ratio and the injection cooling effect index.

[0007] Further, the cooling temperature change curve in the injection molding process is based on determining the injection real-time solidification speed, comprising: performing frequency domain transformation on the cooling temperature change curve in the injection molding process to obtain frequency domain feature information of the cooling temperature change curve; determining a cooling change characteristic period based on a period length corresponding to the lowest frequency component in the frequency domain feature information; dividing the target time period into a plurality of target periods with the cooling change characteristic period as a step; wherein the target time period is a time period corresponding to the cooling temperature change curve; determining the injection real-time solidification speed of each target period.

[0008] Further, determining the injection real-time solidification speed of the target period comprises: determining the cooling speed of the target period based on the initial temperature value and the final temperature value in the target period; calculating the local slope value of each sampling point in the cooling temperature change curve in the target period to obtain a plurality of local slope values; determining a credibility weight based on the dispersion degree between a plurality of local slope values; and determining the injection real-time solidification speed of the target period by weighting calculation of the cooling speed using the credibility weight.

[0009] Further, determining the environmental uncontrollability index comprises: obtaining an environmental temperature sequence in the injection molding process; calculating the deviation of each environmental temperature value in the environmental temperature sequence with respect to the environmental temperature sequence; and performing normalized accumulation processing on all the deviations to determine the environmental uncontrollability index.

[0010] Further, the determination of the environmental temperature noise ratio comprises: obtaining the historical solidification speed corresponding to each historical period in the historical cooling period corresponding to the target time period; calculating the difference between the injection real-time solidification speed of the target period and the historical solidification speed of the historical period; and performing correlation operation on the difference and the environmental uncontrollability index to obtain the environmental temperature noise ratio.

[0011] Further, the determination of the injection cooling effect index of the injection molding process comprises: sequentially comparing the injection real-time solidification speeds of adjacent target periods in the time direction to determine the decreasing trend persistence degree of the injection real-time solidification speed; and determining the injection cooling effect index of the injection molding process based on the decreasing trend persistence degree; wherein the injection cooling effect index is directly proportional to the decreasing trend persistence degree.

[0012] Further, the method further comprises: determining the abnormality recognition result of the process parameter in the injection molding process based on the ambient temperature noise ratio and the injection cooling effect index; if the injection cooling effect index is lower than a first preset threshold, determining that the process parameter of the injection molding process is abnormal; if the ambient temperature noise ratio is not lower than a second preset threshold when it is determined that the process parameter of the injection molding process is abnormal, determining that the abnormality is caused by an abnormal ambient temperature; if the ambient temperature noise ratio is lower than the second preset threshold, determining that the abnormality is caused by an abnormal internal cooling cycle of the equipment; and outputting the abnormality recognition result; wherein the abnormality recognition result comprises an abnormality determination result and an abnormality cause.

[0013] Further, the method further comprises: triggering an alarm when it is determined that the process parameter of the injection molding process is abnormal.

[0014] Further, the method further comprises: determining the cooling temperature change curve, comprising: acquiring discrete temperature data arranged in time sequence within a target time period; and performing smooth fitting on the discrete temperature data to generate the cooling temperature change curve.

[0015] In a second aspect, an embodiment of the present application provides an intelligent process parameter monitoring system for injection molding, comprising: an ambient temperature sensor, an equipment cooling temperature sensor, a data acquisition subsystem and a data analysis terminal, wherein the ambient temperature sensor and the equipment cooling temperature sensor are in communication connection with the data acquisition subsystem, and the data acquisition subsystem is in communication connection with the data analysis terminal.

[0016] The ambient temperature sensor is arranged in a workshop where an injection molding machine is located, and is used to collect ambient temperature data.

[0017] The equipment cooling temperature sensor is arranged on a cooling system of the injection molding machine, and is used to collect cooling temperature during the injection molding process.

[0018] The data acquisition subsystem is used to convert received analog signals into digital signals, and send the digital signals to the data analysis terminal.

[0019] The data analysis terminal is used for determining an injection molding real-time solidification speed based on a cooling temperature change curve in the injection molding process; determining an environmental temperature noise ratio based on a historical cooling temperature value and an environmental uncontrollability index; wherein the environmental uncontrollability index is used for representing an interference degree of environmental temperature fluctuation on the injection molding process; the environmental temperature noise ratio is used for representing a probability of injection molding cooling abnormality caused by environmental temperature fluctuation in the injection molding process; determining an injection molding cooling effect index of the injection molding process based on a solidification speed change characteristic of a normal injection molding process; and determining an abnormality recognition result of a process parameter in the injection molding process based on the environmental temperature noise ratio and the injection molding cooling effect index.

[0020] The technical scheme of the present application has the following beneficial effects:

[0021] In the embodiment of the present application, the injection molding real-time solidification speed is determined based on a cooling temperature change curve in the injection molding process; the environmental temperature noise ratio is determined based on a historical cooling temperature value and an environmental uncontrollability index; the injection molding cooling effect index of the injection molding process is determined based on a solidification speed change characteristic of a normal injection molding process; and the abnormality recognition result of a process parameter in the injection molding process is determined based on the environmental temperature noise ratio and the injection molding cooling effect index. Thus, by introducing the environmental temperature noise ratio, the statistical interference of environmental temperature fluctuation is quantified as an independent index, and the correlation operation is performed with the real-time-historical solidification speed difference, so that the root cause positioning is completed at the moment of abnormality occurrence, and the misjudgment and waste caused by manual experience troubleshooting are avoided. On the other hand, in order to improve the robustness of solidification speed estimation, the above-mentioned scheme adopts a double constraint mechanism of macro cooling rate and local slope dispersion credibility weighted when calculating the injection molding real-time solidification speed of each target period. The macro value reflects the overall cooling capacity, and the local slope dispersion suppresses high-frequency noise. The finally output solidification speed not only retains the physical meaning, but also reduces the deviation caused by external disturbance, so that the threshold setting of subsequent abnormality judgment is more engineering repeatable. On the other hand, in order to accurately evaluate the cooling process health degree, the above-mentioned scheme proposes to use the decreasing trend persistence degree as the injection molding cooling effect index. The coherence of the monotonically decreasing solidification speed along the time axis is quantified as a single proportion value. The index directly maps the heat exchange efficiency of the cooling system. When the index is lower than the preset threshold, an abnormal alarm is triggered, the change from post-event statistics to online early warning is realized, and the product warping, internal stress and cycle extension caused by uneven cooling are reduced. On the other hand, after the abnormality is confirmed, the system immediately performs a linkage alarm, synchronously pushes the abnormality judgment result, root cause category and corresponding intervention suggestion to an operation terminal, forms a detection-diagnosis-prompting closed loop, shortens the response time from abnormality occurrence to manual intervention, thereby reducing the scrap rate and energy consumption, and further improving the overall production efficiency and product quality consistency of the injection molding production line. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0023] Figure 1 A flowchart of a process parameter intelligent monitoring method for injection molding provided by an embodiment of the present application;

[0024] Figure 2 A structural diagram of a process parameter intelligent monitoring system for injection molding provided by an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined purposes, the following describes the process parameter intelligent monitoring method and system for injection molding according to the present application, its specific implementation, structure, features and effects in detail, as follows. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0027] The following specifically describes the specific scheme of the process parameter intelligent monitoring method and system for injection molding provided by the present application in combination with the drawings.

[0028] Please refer to Figure 1 which shows a process parameter intelligent monitoring method for injection molding provided by an embodiment of the present application, comprising:

[0029] Step S110: determining the real-time solidification speed of injection based on the cooling temperature change curve in the injection molding process.

[0030] It is required to explain that the above injection molding is a plastic forming process which takes high molecular materials as the main processing object and realizes batch production of products by the synergistic effect of injection and molding. Its process can be summarized as follows: thermoplastic or thermosetting plastic particles (or their modified composite materials) are melted and plasticized in the cylinder by external heating and screw shearing action to form a homogeneous melt with good fluidity; then, under the driving of precisely controlled injection pressure and speed, the melt is injected into a closed mold cavity at high speed through the nozzle and runner system; in the cavity, the melt is compensated by pressure holding and shrinkage, and then the heat is quickly taken away by the mold cooling system, so that the product is solidified and shaped according to the cavity geometry; finally, the mold is opened, and the molded plastic product is demolded and taken out by the ejection mechanism, completing a molding cycle. This process has the advantages of short molding cycle, high dimensional accuracy of products, good surface quality and high degree of automation, and is widely used in the mass production of complex and precise plastic parts in the fields of automobile, electronics, household appliances, medical treatment, packaging, etc.

[0031] It is further required to explain that the process parameters of injection molding refer to all quantifiable physical quantities that can directly regulate the state transition of materials, the flow filling behavior, the heat exchange efficiency and the final molding quality in the whole process from the plastic melt entering the mold from the cylinder to the final cooling into a product. These parameters can be generally divided into four categories: temperature, pressure, speed and time, and displacement and position. Among them:

[0032] Temperature parameters include the set temperature of each heating section of the cylinder, the actual temperature of the melt, the mold temperature and the environmental temperature, which determine the melting degree, fluidity, cooling rate and crystallization behavior of the plastic;

[0033] Pressure parameters include injection pressure, holding pressure and real-time cavity pressure, which are used to drive the melt to fill, shrink and suppress shrinkage and bubbles;

[0034] Speed and time parameters include injection speed, screw speed, holding time, cooling time and the whole molding cycle, which jointly affect the flow length of the melt front, shear heat generation, molding efficiency, and product orientation and residual stress;

[0035] Displacement and position parameters mainly refer to screw position, mold opening and closing stroke and ejection stroke, which are used to accurately control metering, injection amount and demolding action.

[0036] By synergistically adjusting the above parameters, the best dimensional accuracy, appearance quality and mechanical properties can be obtained under different materials, molds and product requirements.

[0037] In addition, in injection molding processing, the melt temperature directly determines the flowability and filling property of the plastic, and is the primary control factor affecting the quality of the product; the cooling stage is also critical, the plastic gradually cools from the molten state in the mold to the solid state, and the temperature and its change rate in this stage have a decisive effect on the appearance, dimensional stability and mechanical properties of the product. However, due to the influence of workshop environment temperature fluctuation and injection molding machine self-heat dissipation, the temperature in the cooling stage changes dynamically, making it difficult to keep the cooling speed constant: if the cooling is too fast, it is easy to make the material shrink sharply, resulting in internal stress or warping deformation; if the cooling is too slow, it prolongs the molding cycle, increases the production cost and reduces the production efficiency. In the injection molding cooling stage, the temperature of the cooling water pipeline and the environment temperature need to be precisely controlled: the former directly determines the heat dissipation efficiency of the cavity, and the latter can be actively adjusted through workshop refrigeration or ventilation means. However, temperature abnormalities on the equipment side are often hidden and easily confused with environmental fluctuations, making it difficult for the monitoring system to accurately trace the source, and thus leading to misjudgment.

[0038] To effectively distinguish between the two types of abnormalities, the embodiment of the present application first takes the solidification speed as the core monitoring object, and when the solidification speed deviates from the expectation, the current environment temperature and its historical data are compared in real time to calculate the environment temperature noise ratio; this ratio quantifies the degree of interference of the environment temperature fluctuation on the cooling process, thereby clearly pointing the abnormal reason to "environment temperature abnormality" or "equipment cooling cycle abnormality", avoiding the ambiguous judgment of traditional monitoring methods.

[0039] Based on this, the above-mentioned intelligent monitoring method for process parameters of injection molding can first acquire temperature continuous change data in the cooling process obtained through a sensor, which reflects the real-time process of injection cooling, from the high temperature at the beginning to the low temperature result kept unchanged after solidification. Specifically:

[0040] Preferably, in an embodiment of the present application, the cooling temperature change curve is determined, including: acquiring discrete temperature data arranged in time sequence within a target time period; smoothing and fitting the discrete temperature data to generate a cooling temperature change curve. For example, in the embodiment:

[0041] The response time of the cooling area temperature sensor is 2 seconds, that is, a temperature value data is generated every 2 seconds, and the acquired temperature value sequence is: wherein, represents the temperature data at time

[0042] The historical temperature data under normal cooling conditions is acquired, which is a finite-length discrete sequence: wherein, represents the temperature data at historical time

[0043] ​​Finally, the above temperature value sequence is fitted into a continuous curve by least square method.

[0044] It is required to be explained that in the above scheme, the determination of the cooling temperature change curve is realized in a "discrete-continuous" two-step strategy. Specifically: first, the instantaneous temperature value of the cooling area is continuously collected in a fixed sampling period within the set target time period, obtaining a set of discrete temperature data arranged in time sequence. These discrete data may contain high-frequency noise caused by sensor resolution, environmental disturbance or short-term thermal fluctuation, which can easily amplify errors if directly used for subsequent calculation. Therefore, the second step is to perform smoothing fitting processing on the discrete temperature data: a smooth curve is constructed in the continuous time domain through a mathematical fitting algorithm, which completely retains the macro trend and key details of temperature change with time while effectively suppressing measurement noise, thereby generating a high-precision, derivable and physically meaningful cooling temperature change curve, providing a reliable data basis for subsequent extraction of injection real-time solidification speed and anomaly diagnosis.

[0045] It is further required to be explained that the above mathematical fitting algorithm can use least square method, which is a mathematical optimization technique. The basic idea is that between a given set of observation data and a function model to be fitted, the error sum of squares between the observation value and the model prediction value is minimized by adjusting the model parameters, so as to obtain the best fitting curve or surface that best reflects the overall trend of the data. It can be understood that the mathematical fitting algorithm such as least square method is a mature known technology, and its specific implementation mode can be referred to related technology, and the embodiment of the present application will not be described here.

[0046] Preferably, in one embodiment of the present application, the above step S110 can include: performing frequency domain transformation on the cooling temperature change curve in the injection molding process to obtain frequency domain feature information of the cooling temperature change curve; determining a cooling change characteristic time period based on the period length corresponding to the lowest frequency component in the frequency domain feature information; dividing the target time period into a plurality of target time periods with the cooling change characteristic time period as the step; wherein the target time period is the time period corresponding to the cooling temperature change curve; determining the injection real-time solidification speed of each target time period. This implementation mode, for example, obtains the frequency domain information of the continuous curve by the method of FFT (Fast Fourier Transform), and the minimum frequency value in the frequency domain corresponds to the period length of the cooling change characteristic time period. The corresponding time period As a period with cooling change characteristics, the period is obtained based on the Nyquist sampling theorem, and the purpose is to ensure that every detail feature of temperature change is obtained. Then, the real-time cooling area temperature data at the current time is taken as The base unit is divided into several time periods, each time period has an independent temperature cooling speed change characteristic, that is, has a single solidification speed. The above-mentioned Nyquist sampling theorem points out that: in order to restore the original continuous signal from the discrete sampling data without distortion, the sampling frequency must be at least twice the highest frequency contained in the signal; only in this way, frequency aliasing can be avoided, and all details can be correctly preserved. It can be understood that the Nyquist sampling theorem is a relatively mature known technology, and the specific sampling mode can be referred to the related technology, and the embodiments of the present application will not be described again.

[0047] It is necessary to explain that: the above-mentioned scheme is to accurately intercept the minimum complete period which can represent the real solidification process of the material from the macro-continuous cooling temperature change curve, first, the curve is processed by frequency domain transformation. Through frequency domain transformation, the temperature fluctuation information in the time domain is mapped to the amplitude-frequency distribution in the frequency domain, so as to extract the lowest frequency component with the most concentrated energy in the curve; the period length corresponding to the lowest frequency component is defined as the cooling change characteristic period. The physical meaning of the period is that: its length just covers the whole process of typical material cooling-solidification, neither loses the key thermodynamic characteristics because of being too short, nor mixes in redundant noise because of being too long. Subsequently, the cooling change characteristic period is taken as a fixed step, and the whole target time period (i.e. the time interval corresponding to the complete cooling stage) is divided without overlap and with equal length, to obtain a series of sequentially arranged target time periods. Each target time period is an independent analysis unit, and the internal temperature change is considered to have statistical consistency. Finally, the injection real-time solidification speed of each target time period is calculated: first, the temperature difference between the start and end of the period is calculated, then the average cooling rate is obtained by combining the period length, and further modified by the credibility weighting, so as to output the unique and comparable injection real-time solidification speed value of the period. Through the above steps, the complex continuous cooling process can be adaptively divided into several sub-processes with clear physical meaning without relying on artificial experience, providing a high-resolution, traceable solidification speed sequence for subsequent anomaly diagnosis.

[0048] Preferably, in an embodiment of the present application, the step S110 of determining the injection real-time solidification speed of the target time period comprises: determining the cooling speed of the target time period based on the initial temperature value and the final temperature value in the target time period; calculating the local slope value of each sampling point in the target time period in the cooling temperature change curve to obtain a plurality of local slope values; determining the credibility weight based on the dispersion degree between the plurality of local slope values; and calculating the cooling speed by using the credibility weight to determine the injection real-time solidification speed of the target time period. For example, the different solidification speeds represent different cooling states at the current time, specifically: a plurality of time periods of the cooling area in the current injection solidification stage are obtained, the cooling speed corresponding to the first time period is calculated: ​

[0049]

[0050] in, Indicates the first Injection cooling rate during a specific time period; This indicates the initial temperature value during that period; This indicates the final temperature value during that period;

[0051] The average temperature change is obtained by subtracting the initial and final temperature values, and is taken as the cooling rate. The larger this value, the higher the cooling rate during the post-injection molding cooling stage.

[0052] Furthermore, the cooling rate is not equal to the curing rate because changes in other temperature values ​​are ignored during the time interval, even though these changes may contain more important cooling characteristics. Therefore, the real-time temperature sequence can be fitted using the least squares method to obtain its continuous curve, and the slope value of the curve corresponding to each temperature point can be obtained. The slope value indicates the real-time cooling (curing) rate over a fixed period of time.

[0053] When multiple slope values ​​are relatively close within a certain period (i.e., the mean slope difference), The smaller, the first The mean of the slope difference between adjacent time periods indicates that the cooling rates are relatively close, which means that the injection molding cooling rate is less affected by noise and other factors. This rate value is highly reliable and can be used as the curing rate to participate in subsequent anomaly monitoring and anomaly cause analysis.

[0054] Therefore, the curing speed can be quantified as:

[0055]

[0056] in, Indicates the first The curing speed corresponding to each time period; This is the credibility weight.

[0057] By setting a confidence limit on the cooling rate after injection molding, i.e., setting a confidence weight. When the cooling rate changes normally within a given period, i.e., within a minimum positive cycle period. If the internal velocity changes normally, then the corresponding different times... The slope value calculated below The lower the difference between them, the higher the credibility.

[0058] Because it is proved that the solidification process is stable under the injection cooling time, and there is no additional noise (such as the instantaneous pressure difference of the cooling liquid or bubbles, which causes the cooling speed to change) to affect it.

[0059] Therefore, the lower the average slope difference, the closer the solidification speed value to the real cooling speed; otherwise, it will be less than or much less than the cooling speed, the purpose is to reduce the impact of noise period on injection process monitoring.

[0060] Further, in the injection cooling process, different periods correspond to their specific solidification speed. From the end of injection to the beginning of cooling solidification, to the end of cooling solidification, the corresponding solidification speed sequence is: .

[0061] It should be noted that the core idea of the above scheme is to use the macro cooling speed to depict the overall cooling intensity of the target period, use the local slope dispersion to measure the stability of temperature change in the period, and then combine the two into an injection real-time solidification speed that reflects both the average cooling capacity and the process stability constraint. Specifically:

[0062] First, the macro cooling speed is obtained by the ratio of the temperature difference between the beginning and the end of the period and the length of the period - it directly reflects the overall efficiency of the plastic from how many degrees to how many degrees in the period. However, the average speed cannot distinguish between two cases: one is that the temperature almost uniformly decreases, and the curve is smooth; the other is that the temperature is fast and slow, and the curve is shaking. The latter is often caused by noise such as instantaneous pressure change of the cooling medium, local bubbles, etc., and its average speed is high, but it does not represent stable solidification of the material. Therefore, a quantitative evaluation of the smoothness of the curve can be introduced.

[0063] To this end, the above scheme selects several sampling points in the period, calculates the local slope of the cooling temperature change curve at each point, and obtains a set of local slope values. The lower the dispersion of this set of values, the more uniform the temperature change with time, the less the noise, and the more reliable the data; the higher the dispersion, the greater the disturbance, and the lower the data reliability. The dispersion is normalized to a reliability weight, which ranges from 0 to 1: when the dispersion tends to zero, the weight tends to 1, and when the dispersion is larger, the weight tends to 0.

[0064] Finally, multiply the macro cooling speed by the reliability weight: when the curve is smooth and the local slope is almost the same, the weight is close to 1, and the obtained injection real-time solidification speed is almost equal to the macro cooling speed; when the curve is shaking and the local slope difference is large, the weight is reduced, and the injection real-time solidification speed is also discounted accordingly. In this way, the physical meaning of the average cooling capacity is retained, and the false high-efficiency cooling caused by abnormal disturbance is automatically suppressed, so as to output an injection real-time solidification speed that can more truly reflect the stable solidification process of the material.

[0065] Step S120: Determine the ambient temperature-to-noise ratio based on historical cooling temperature values ​​and environmental uncontrollability indicators; wherein, the environmental uncontrollability indicators are used to characterize the degree of interference of ambient temperature fluctuations on the injection molding process; the ambient temperature-to-noise ratio is used to characterize the probability of abnormal injection cooling caused by ambient temperature fluctuations during the injection molding process.

[0066] It should be noted that the method for determining the curing rate sequence in step S110 can be used to obtain the historical temperature values ​​and corresponding curing rate sequences of the same material during normal cooling. .

[0067] Preferably, in one embodiment of the present invention, determining the environmental uncontrollability index includes: acquiring an environmental temperature sequence during the injection molding process; calculating the deviation of each environmental temperature value from the environmental temperature sequence for each environmental temperature value in the sequence; and performing normalized summation on all deviations to determine the environmental uncontrollability index. An example of this implementation is:

[0068] By comparing the curing speed of historical data with the current ambient temperature, the ambient temperature-to-noise ratio (ANR) can be preliminarily determined, i.e., the potential impact of ambient temperature changes on the cooling rate. The greater the impact, the higher the ANR, indicating that the injection molding cooling anomaly is more likely caused by unstable ambient temperature rather than issues with the equipment's coolant circulation. When there is a significant difference between the historical curing speed under normal cooling conditions and the current time period, and simultaneously, the ambient temperature fluctuates considerably (i.e., the ambient temperature is more uncontrollable), the ANR will obviously be higher. Specifically:

[0069] Obtain the ambient temperature sequence:

[0070]

[0071] in, Indicates time The ambient temperature value; the ambient temperature sequence is a set of temperature changes within a complete cooling phase.

[0072] Determine the uncontrollability indicators of ambient temperature:

[0073]

[0074] in, This indicates the uncontrollability of ambient temperature. Indicates time The Z-score corresponding to the ambient temperature value; This represents the total number of elements in the sequence.

[0075] It should be noted that the above deviation can be expressed using temperature values. The corresponding Z-score, i.e., Z-score, is used to characterize. The greater the value of the Z-score, the greater the outlier. When the Z-score average score within the temperature sequence is greater, it is closer to 1 after normalization, and the inverse result The greater the above Z-score is, the higher the corresponding environmental temperature variability index is, and the higher the uncontrollability is. The above Z-score is a standardized index in statistics for measuring the deviation between a data point and the mean of the data set, which is defined as the difference between the data point and the mean divided by the standard deviation of the data set; the numerical value directly reflects the relative position and dispersion of the data point relative to the overall distribution. It can be understood that the Z-score is a relatively mature known technology, and its specific calculation method can be referred to related technologies, and the embodiments of the present application will not be repeated.

[0076] It needs to be further explained that the main idea of the above scheme is that during the injection cooling stage, the environmental temperature is not constant, but presents fluctuations in time sequence. Different fluctuation modes have different interference degrees on the cooling process. If the environmental temperature at a certain time deviates from the overall average level, the abnormal energy at that time is higher, and the disturbance to the cooling rate is greater; on the contrary, if all the temperatures at all times are closely distributed around the mean value, the environmental disturbance is weak and the controllability is strong. Therefore, the above scheme quantifies the environmental uncontrollability as the cumulative effect of the outlier degree of each temperature value in the entire sequence. Specifically, the environmental temperature sequence during the entire cooling stage can be obtained as the overall sample. For any temperature value in the sequence, the deviation degree (i.e., the difference between the value and the mean of the sequence) is calculated. The greater the deviation degree, the more abnormal the temperature at that time, and the greater the potential interference. Then, all the deviation degrees are normalized so that temperature sequences of different magnitudes or dimensions can be compared uniformly, and the normalized deviation degrees are accumulated. The accumulation result is the environmental uncontrollability index: if the accumulation value is high, there are a large number of temperature points deviating from the mean value in the sequence, the environmental fluctuation is violent, and the uncontrollability is strong; if the accumulation value is low, the temperature distribution is concentrated, and the environmental disturbance is controllable. In this way, the index directly describes the unpredictable disturbance strength of the environmental temperature on the injection cooling process with a single value, providing a quantitative basis for subsequent differentiation of environmental abnormalities and equipment abnormalities.

[0077] Preferably, in an embodiment of the present application, the step S120 of determining the environmental temperature-to-noise ratio comprises: obtaining historical solidification speeds corresponding to each historical period in the historical cooling period corresponding to the target period; calculating the difference between the real-time solidification speed of the injection in the target period and the historical solidification speed of the historical period; and performing correlation operation on the difference and the environmental uncontrollability index to obtain the environmental temperature-to-noise ratio. For example:

[0078] The difference between the solidification speed sequence of the normal cooling historical temperature value at different time periods and the solidification speed sequence of the current several time periods is calculated:

[0079]

[0080] wherein, represents the difference between the two; represents the total amount of time involved in calculating the difference;

[0081] While the environmental temperature is relatively high in uncontrolled, When the environmental temperature noise ratio is also relatively high, because it is more likely that the injection molding cooling stage is abnormal due to unstable environmental temperature; therefore, the environmental temperature noise ratio can be quantified as:

[0082]

[0083] wherein, represents the environmental temperature noise ratio; represents the Pearson correlation coefficient between the two; the higher the correlation coefficient, the higher the environmental temperature noise ratio.

[0084] It should be noted that the above scheme defines the environmental temperature noise ratio as the possibility weight of the environmental temperature fluctuation contribution to the abnormality when the current cooling abnormality occurs. The core idea is that if the abnormality is mainly caused by environmental disturbance, the current solidification speed should deviate from the historical normal level, and this deviation is strongly correlated with the environmental uncontrollability index; otherwise, if the deviation is not related to the environmental fluctuation, the correlation is weak, and the abnormality is more likely to be caused by the device itself. Specifically: first, select the historical cooling records of the same material, mold and process setting as the target time period, and extract the historical solidification speed of each historical period as the reference. Then, calculate the difference between the real-time solidification speed of the current injection molding period and the corresponding historical value; the greater the difference, the more abnormal the current cooling behavior. Next, the difference sequence is correlated with the environmental uncontrollability index sequence calculated synchronously (such as Pearson correlation). When the correlation is positive and the value is high, it indicates that the abnormality amplitude is amplified synchronously with the environmental fluctuation, and the environmental noise is the main cause, so a high environmental temperature noise ratio is given; if the correlation is low or even reversed, it means that the abnormality amplitude is decoupled from the environmental fluctuation, and should be attributed to the cooling loop failure of the device. Thus, the environmental temperature noise ratio quantifies the explainable degree of the environmental temperature to the current cooling abnormality with a single value, providing a decision threshold for subsequent abnormality cause positioning.

[0085] Further need to be explained is that the above correlation operation can use the Pearson correlation coefficient, which is a statistical index for measuring the linear correlation strength and direction between two groups of continuous variables, and its value range is -1 to 1: the closer the absolute value is to 1, the stronger the linear correlation, the symbol represents positive or negative correlation, and close to 0 indicates weak or no linear correlation. It can be understood that the Pearson correlation coefficient is a relatively mature known technology, and its specific calculation method can be referred to related technologies, and the embodiments of the present application will not be repeated.

[0086] Step S130: determining the injection cooling effect index of the injection molding process based on the solidification speed variation characteristic of the normal injection molding process.

[0087] It is to be noted that the solidification speed variation characteristic of the normal injection molding process refers to:

[0088] After injection, the cooling process generally goes through three stages:

[0089] (1) Initial stage: after the plastic starts to flow out of the mold, the melt temperature is high and the cooling rate is fast. At this time, the outer layer of the plastic starts to solidify, but the inside is still in a molten state. The temperature change in this stage is relatively rapid.

[0090] (2) Transition stage: as the cooling progresses, the outer layer gradually solidifies and the internal temperature begins to gradually decrease, and the cooling rate gradually slows down. At this time, the heat conduction of the plastic gradually approaches stability, and the cooling rate is affected by the mold temperature and the ambient temperature.

[0091] (3) Stable stage: at this time, the temperature of the plastic approaches the ambient temperature or the mold temperature, and the cooling speed tends to be flat, eventually reaching a stable state of molding.

[0092] That is, after injection, the solidification molding speed under normal conditions presents the characteristics of high, low, and constant (zero).

[0093] If the solidification speed presents the above characteristics and the temperature noise ratio is low, the injection cooling abnormality is low;

[0094] On the contrary, when the above characteristics are not met and the temperature noise ratio is low, the influence of unstable ambient temperature can be ruled out, and the abnormality caused by problems such as device itself cooling liquid circulation can be ruled out.

[0095] Preferably, in an embodiment of the present application, the step S130 can include: comparing the injection real-time solidification speed of adjacent target periods in the time direction in sequence to determine the duration of the decreasing trend of the injection real-time solidification speed; determining the injection cooling effect index of the injection molding process based on the duration of the decreasing trend; wherein the injection cooling effect index is proportional to the duration of the decreasing trend. For example, the implementation mode is:

[0096] The injection cooling effect refers to the solidification speed variation characteristic in different periods, which is the case that the solidification speed value in the current period is high (after normalization), and with the passage of time, the value becomes low, and there is no case that the subsequent solidification speed value is greater than the previous one, that is, the injection cooling effect is considered to be high;

[0097] The calculation method of the injection cooling effect index can be:

[0098]

[0099] wherein, represents the injection cooling effect index; represents the sign function;

[0100] When the solidification speed continuously decreases, the function result value is always 1, and the sum value tends to 1, because the output of the sign function is 1 when the internal value is greater than 0. Therefore, The closer the value is to 1, the higher the injection cooling effect is.

[0101] It should be noted that the above scheme abstracts the injection cooling effect as the continuity of the decrease of the solidification speed over time, that is, in the normal cooling process, the plastic melt successively experiences three stages of rapid heat dissipation, slow heat dissipation, and constant temperature, and the solidification speed should present a smooth decrease from high to low and finally tend to zero. If this decreasing trend is continuously maintained, it indicates that the cooling water circuit and mold temperature control are good, and the cooling effect is excellent. If the trend is interrupted or rises, it indicates that the cooling is uneven, local overheating or circulation failure, and the cooling effect is deteriorated. Therefore, the above scheme can compare the injection real-time solidification speed of adjacent target periods in the time direction, and count the proportion of the number of continuous occurrences of the speed of the latter period being lower than that of the former period in the total comparison times. The proportion is the degree of continuity of the decreasing trend. The higher the proportion, the more continuous the speed decreases, and the higher the injection cooling effect index. The lower the proportion, the more the decreasing trend is interrupted, and the injection cooling effect index decreases accordingly, thereby directly quantifying the continuity of the decreasing trend over time in the current cooling process with a single value.

[0102] Step S140: determining the abnormality recognition result of the process parameters in the injection molding process based on the ambient temperature noise ratio and the injection cooling effect index.

[0103] Preferably, in an embodiment of the present application, the step S140 can include: if the injection cooling effect index is lower than a first preset threshold, determining that the process parameters of the injection molding process are abnormal; if it is determined that the process parameters of the injection molding process are abnormal, if the ambient temperature noise ratio is not lower than a second preset threshold, determining that the abnormality is caused by the ambient temperature; and if the ambient temperature noise ratio is lower than the second preset threshold, determining that the abnormality is caused by the internal cooling circulation of the equipment; outputting the abnormality recognition result; wherein the abnormality recognition result includes the abnormality determination result and the abnormality cause. For example, when the cooling is determined to be abnormal. When the ambient temperature noise ratio the ambient temperature is determined to be abnormal; otherwise, when the ambient temperature noise ratio At this time, it is identified that the device internal cold liquid and the like circulation is abnormal. The abnormal monitoring result and the corresponding identification result are output, so as to achieve the purpose of monitoring the injection molding cooling process and identifying the abnormal reason through the process parameters in the injection molding process.

[0104] It should be noted that: the above scheme completes the process parameter abnormality identification by two-level logic of first determining abnormality and then determining root cause. Specifically: first, the injection molding cooling effect index is used as an abnormal trigger: the index reflects the overall efficiency of the cooling system through the coherence degree of the solidification speed decreasing trend, when it is lower than the first preset threshold, it indicates that the current cooling behavior has deviated from the normal interval, and the system immediately gives a determination that there is an abnormality. Subsequently, on the basis of confirming the abnormality, the ambient temperature noise ratio is introduced as the basis for distinguishing the root cause. The ambient temperature noise ratio quantifies the linear correlation strength between the ambient temperature fluctuation and the abnormal amplitude, when it is not lower than the second preset threshold, it means that the abnormality is highly coupled with the environmental disturbance, so the abnormal reason can be locked as the environmental temperature abnormality; otherwise, if the temperature noise ratio is lower than the threshold, it means that the abnormal amplitude is decoupled from the environmental fluctuation, and the abnormal reason is determined as the device internal cooling circulation abnormality. Finally, the complete abnormality identification result containing the abnormality determination result + abnormal reason is output, which provides accurate guidance for on-site intervention and avoids repeated debugging and downtime caused by unknown root cause in traditional methods.

[0105] Preferably, in an embodiment of the present application, the intelligent monitoring method for process parameters for injection molding can further include: when it is determined that the process parameters of the injection molding process are abnormal, triggering an alarm.

[0106] It should be noted that: the above scheme adds an instant alarm mechanism on the basis of two-level logic abnormality identification, when the step S140 determines that the process parameters of the injection molding process are abnormal, an alarm signal can be generated immediately. The alarm signal is synchronously pushed to the operation terminal in the form of sound, light, text and picture or network message, so that the on-site personnel can know the abnormality at the first time; at the same time, the alarm content is attached with the already clear abnormal reason (environmental temperature abnormality or device internal cooling circulation abnormality) and the recommended intervention measures, realizing the closed-loop prompt of abnormality-root cause-countermeasure, thereby shortening the time window from fault discovery to processing, reducing the scrap rate and downtime loss.

[0107] Please refer to Figure 2 Based on the same inventive concept, the present application also provides an intelligent monitoring system 200 for process parameters for injection molding, comprising: an ambient temperature sensor 210, a device cooling temperature sensor 220, a data acquisition subsystem 230 and a data analysis terminal 240, the ambient temperature sensor 210 and the device cooling temperature sensor 220 are respectively in communication connection with the data acquisition subsystem 230, and the data acquisition subsystem 230 is in communication connection with the data analysis terminal 240, wherein:

[0108] The environmental temperature sensor 210 is arranged in the workshop where the injection molding machine is located, and is used to collect environmental temperature data.

[0109] The equipment cooling temperature sensor 220 is arranged on the cooling system of the injection molding machine, and is used to collect the cooling temperature during the injection molding process.

[0110] The data acquisition subsystem 230 is used to convert the received analog signals into digital signals and send the digital signals to the data analysis terminal.

[0111] The data analysis terminal 240 is used to determine the real-time solidification speed of injection molding based on the cooling temperature change curve during the injection molding process, determine the environmental temperature noise ratio based on the historical cooling temperature value and the environmental uncontrollability index, wherein the environmental uncontrollability index is used to represent the interference degree of the environmental temperature fluctuation on the injection molding process; the environmental temperature noise ratio is used to represent the probability of injection cooling abnormality caused by environmental temperature fluctuation during the injection molding process; the solidification speed change characteristic of the normal injection process is used to determine the injection cooling effect index of the injection molding process; and the environmental temperature noise ratio and the injection cooling effect index are used to determine the abnormal identification result of the process parameters during the injection molding process.

[0112] The environmental temperature sensor 210 can be installed at a key position in the workshop, such as near the injection molding machine, near the window, etc., to monitor the environmental temperature change. The environmental temperature sensor 210 can adopt a digital temperature sensor DHT22, the measurement range is -40℃ to 80℃, the resolution is 0.1℃, the accuracy is ±0.5℃, the response time is 1 second, and the output signal is a digital signal (I2C).

[0113] The equipment cooling temperature sensor 220 can be installed on the cooling system (cooling water pipeline) of the injection molding machine, and is used to monitor the cooling liquid temperature and the running state of the cooling system. The equipment cooling temperature sensor 220 can select an RTD sensor PT100, the measurement range is -200℃ to 850℃, the resolution is 0.1℃, the accuracy is ±0.1℃, the response time is 1 second, and the output signal is a temperature signal converted from a digital current signal (4-20mA).

[0114] The environmental temperature sensor 210 and the equipment cooling temperature sensor 220 can be connected to the data acquisition subsystem 230, which is responsible for converting the analog signals of the sensors into digital signals, processing and storing. The data acquisition subsystem 230 can transmit the collected temperature data to the numerical control analysis terminal 240 through a wireless network. When the numerical control analysis terminal 240 detects temperature abnormalities (such as temperature exceeding the set range), it can issue an alarm and feedback to the operator through the operation interface, and suggest taking measures to intervene. In addition, the alarm threshold can be configured on the numerical control analysis terminal 240, and if the data exceeds the preset range, the system will automatically alarm.

[0115] It should be noted that the above data analysis terminal 240 can be used to execute the intelligent monitoring method of process parameters for injection molding provided by the embodiments of the present application, the implementation principle and the technical effects generated in the foregoing method embodiments have been introduced, for brief description, the system embodiments not mentioned can refer to the corresponding content in any one of the foregoing method embodiments.

[0116] Thus far, the present application is completed.

[0117] To sum up, in the embodiments of the present application, the cooling temperature change curve in the injection molding process is determined based on the real-time solidification speed of injection molding; the environmental temperature noise ratio is determined based on the historical cooling temperature value and the environmental uncontrollable index; the injection cooling effect index of the injection molding process is determined based on the solidification speed change characteristics of the normal injection molding process; and the abnormal identification result of the process parameters in the injection molding process is determined based on the environmental temperature noise ratio and the injection cooling effect index. The present application quantifies the statistical disturbance of the environmental temperature fluctuation as an independent index by introducing the environmental temperature noise ratio, and performs correlation operation with the real-time-historical solidification speed difference, so that the root cause positioning is completed at the moment of abnormality occurrence, avoiding the misjudgment and waste caused by manual experience troubleshooting; on the other hand, to improve the robustness of the solidification speed estimation, the above scheme adopts a double constraint mechanism of macro cooling rate and local slope dispersion credibility weighted when calculating the real-time solidification speed of injection molding in each target period, the macro value reflects the overall cooling capacity, and the local slope dispersion suppresses high-frequency noise, so that the final output solidification speed not only retains the physical meaning, but also reduces the deviation caused by external disturbance, making the threshold setting of subsequent abnormal judgment more engineering repeatable; on the other hand, to accurately evaluate the cooling process health degree, the above scheme proposes to use the decreasing trend persistence degree as the injection cooling effect index, the coherence of the monotonically decreasing solidification speed along the time axis is quantified as a single proportion value; the index directly maps the heat exchange efficiency of the cooling system, when the index is lower than the preset threshold, an abnormal alarm is triggered, realizing the change from post-event statistics to online early warning, reducing the product warping, internal stress and cycle extension caused by uneven cooling; on the other hand, after the abnormality is confirmed, the system immediately performs linkage alarm, synchronously pushes the abnormal judgment result, root cause category and corresponding intervention suggestion to the operation terminal, forming a detection-diagnosis-prompting closed loop, which shortens the response time from abnormality occurrence to manual intervention, thereby reducing the scrap rate and energy consumption, and further improving the overall production efficiency and product quality consistency of the injection molding production line.

[0118] The above only describes the preferred embodiments of the present application and should not be used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for intelligent monitoring of process parameters in injection molding, characterized in that, The method includes: Based on the cooling temperature change curve during the injection molding process, the real-time curing speed of the injection molding is determined. Obtain the ambient temperature sequence during the injection molding process; For each ambient temperature value in the ambient temperature sequence, calculate the deviation of the ambient temperature value from the ambient temperature sequence; All the aforementioned deviations are normalized and summed to determine the environmental uncontrollability index; Based on historical cooling temperature values ​​and environmental uncontrollability indicators, the historical curing speed corresponding to each historical period in the historical cooling time period corresponding to the target time period is obtained. Calculate the difference between the real-time curing speed of the injection molding during the target time period and the historical curing speed during the historical time period; The correlation between the difference degree and the environmental uncontrollability index is calculated to obtain the environmental temperature-to-noise ratio; wherein, the environmental uncontrollability index is used to characterize the degree of interference of environmental temperature fluctuations on the injection molding process; the environmental temperature-to-noise ratio is used to characterize the probability of abnormal injection cooling caused by environmental temperature fluctuations during the injection molding process. Based on the curing speed variation characteristics of the normal injection molding process, the real-time curing speed of the injection molding is compared sequentially along the time direction in adjacent target time periods to determine the degree of persistence of the decreasing trend of the real-time curing speed of the injection molding. Based on the degree of persistence of the decreasing trend, an injection cooling effect index for the injection molding process is determined; wherein, the injection cooling effect index is directly proportional to the degree of persistence of the decreasing trend; If the injection cooling effect index is lower than the first preset threshold, it is determined that the process parameters of the injection molding process are abnormal. When it is determined that there is an abnormality in the process parameters of the injection molding process, if the ambient temperature-to-noise ratio is not lower than the second preset threshold, the cause of the abnormality is determined to be an abnormal ambient temperature; and if the ambient temperature-to-noise ratio is lower than the second preset threshold, the cause of the abnormality is determined to be an abnormal cooling circulation inside the equipment. Output anomaly identification results; wherein, the anomaly identification results include anomaly determination results and anomaly causes.

2. The intelligent monitoring method for process parameters in injection molding according to claim 1, characterized in that, The determination of the real-time curing speed based on the cooling temperature change curve during the injection molding process includes: The frequency domain transformation of the cooling temperature change curve during the injection molding process is performed to obtain the frequency domain feature information of the cooling temperature change curve; Based on the period length corresponding to the lowest frequency component in the frequency domain feature information, the characteristic time period of cooling change is determined; Using the cooling change characteristic time period as a step size, the target time period is divided into multiple target time periods; wherein, the target time period is the time period corresponding to the cooling temperature change curve; Determine the real-time curing speed of injection molding for each of the target time periods.

3. The intelligent monitoring method for process parameters in injection molding according to claim 2, characterized in that, Determining the real-time curing speed of the injection molding during the target time period includes: The cooling rate for the target time period is determined based on the initial and final temperature values ​​within the target time period. Calculate the local slope value of each sampling point in the cooling temperature change curve within the target time period, and obtain multiple local slope values; The confidence weight is determined based on the degree of dispersion among the multiple local slope values; Using the confidence weight, the cooling rate is weighted and calculated to determine the real-time curing rate of the injection molding during the target time period.

4. The intelligent monitoring method for process parameters in injection molding according to claim 1, characterized in that, The method further includes: An alarm is triggered when it is determined that there is an abnormality in the process parameters of the injection molding process.

5. The intelligent monitoring method for process parameters in injection molding according to any one of claims 1-4, characterized in that, Determining the cooling temperature change curve includes: Obtain discrete temperature data arranged in chronological order within a target time period; The discrete temperature data is smoothed and fitted to generate the cooling temperature change curve.

6. An intelligent monitoring system for process parameters in injection molding, characterized in that, include: The system comprises an ambient temperature sensor, an equipment cooling temperature sensor, a data acquisition subsystem, and a data analysis terminal. The ambient temperature sensor and the equipment cooling temperature sensor are respectively communicatively connected to the data acquisition subsystem, and the data acquisition subsystem is communicatively connected to the data analysis terminal. The ambient temperature sensor is installed in the workshop where the injection molding machine is located to collect ambient temperature data; The cooling temperature sensor is installed on the cooling system of the injection molding machine to collect the cooling temperature during the injection molding process. The data acquisition subsystem is used to convert the received analog signals into digital signals and send the digital signals to the data analysis terminal; The data analysis terminal is used to determine the real-time curing speed of injection molding based on the cooling temperature change curve during the injection molding process; acquire the ambient temperature sequence during the injection molding process; calculate the deviation of each ambient temperature value relative to the ambient temperature sequence for each ambient temperature value in the ambient temperature sequence; perform normalized accumulation processing on all the deviations to determine the environmental uncontrollability index; acquire the historical curing speed corresponding to each historical time period in the historical cooling time period corresponding to the target time period based on historical cooling temperature values ​​and the environmental uncontrollability index; calculate the difference between the real-time curing speed of injection molding in the target time period and the historical curing speed in the historical time period; perform correlation calculation on the difference and the environmental uncontrollability index to obtain the ambient temperature-to-noise ratio; wherein, the environmental uncontrollability index is used to characterize the degree of interference of ambient temperature fluctuations on the injection molding process; the ambient temperature-to-noise ratio is used to characterize the interference caused by ambient temperature fluctuations during the injection molding process. The probability of abnormal injection molding cooling caused by ambient temperature fluctuations; based on the curing speed variation characteristics of a normal injection molding process, the real-time curing speed of the injection molding process is compared sequentially along the time direction with adjacent target time periods to determine the degree of persistence of the decreasing trend of the real-time curing speed; based on the degree of persistence of the decreasing trend, the injection molding cooling effect index of the injection molding process is determined; wherein, the injection molding cooling effect index is proportional to the degree of persistence of the decreasing trend; if the injection molding cooling effect index is lower than a first preset threshold, the process parameters of the injection molding process are determined to be abnormal; when the process parameters of the injection molding process are determined to be abnormal, if the ambient temperature-to-noise ratio is not lower than a second preset threshold, the cause of the abnormality is determined to be an abnormal ambient temperature; and if the ambient temperature-to-noise ratio is lower than the second preset threshold, the cause of the abnormality is determined to be an abnormal cooling circulation inside the equipment; an abnormality identification result is output; wherein, the abnormality identification result includes an abnormality determination result and an abnormality cause.

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