Automatic injection molding machine operation monitoring system and method

Abstract

The present invention relates to the technical field of abnormal monitoring of injection molding processing, and specifically to an automatic injection molding machine operation monitoring system and method. During the injection molding process, due to the inclusion of multiple stages such as mold filling, pressure holding, and cooling, the mold temperature and injection pressure will fluctuate with the stage changes. In order to accurately monitor abnormal conditions, real-time and historical mold temperature and injection pressure time series data are obtained. By analyzing the similarity between the real-time time series data and the historical time series data of the injection pressure, the stage time periods of the current injection molding process are divided. Considering the impact of changes in production conditions on the duration of the stages, it is necessary to analyze the difference in duration between real-time and historical data in the stage time period to accurately locate the comparison moment. At the same time, combined with the temperature difference, the similarity of the change trend and the difference in stage duration, the consistency of the current moment and the comparison moment state is verified to ensure the accuracy of the calculation of the abnormal degree value, thereby improving the accuracy of the automatic injection molding machine operation monitoring.

Description

Technical Field

[0001] The invention relates to the technical field of injection molding abnormality monitoring, and in particular to an automatic injection molding machine operation monitoring system and method. Background Art

[0002] With the rapid development of industrial automation, automatic injection molding machines are increasingly used in the manufacturing industry. Automatic injection molding machines achieve efficient and accurate plastic molding by precisely controlling key parameters such as mold temperature and injection pressure. Among them, changes in mold temperature will directly affect the quality and production efficiency of molded products. Therefore, real-time monitoring and abnormality detection of the mold temperature of automatic injection molding machines are of great significance for ensuring product quality and improving production efficiency.

[0003] The prior art usually relies on setting a fixed temperature threshold when monitoring the abnormal temperature of the mold during the operation of the automatic injection molding machine. However, since the injection molding process usually includes multiple stages, the mold temperature varies in different stages, and the mold temperature will change due to changes in production conditions, such as changes in material type, injection speed, ambient temperature, etc., so relying solely on a fixed temperature threshold for abnormal monitoring will lead to inaccurate results, causing false alarms or missed alarms. Summary of the invention

[0004] In order to solve the technical problem that the abnormal monitoring results will be inaccurate when relying on a fixed temperature threshold for abnormal monitoring, resulting in false alarms or missed alarms, the purpose of the present invention is to provide an automatic injection molding machine operation monitoring system and method, and the technical solutions adopted are as follows:

[0005] Obtain the real-time mold temperature timing data and real-time injection pressure timing data of the current injection molding process of the automatic injection molding machine; obtain the historical mold temperature timing data and historical injection pressure timing data of the historical injection molding process of the automatic injection molding machine;

[0006] Analyze the similarity between the data value change trends in the real-time injection pressure time series data and the historical injection pressure time series data, divide the real-time injection pressure time series data into injection molding process stages, and obtain the stage time period;

[0007] Based on the difference in duration between the current injection molding process and the corresponding stage time period in the historical injection molding process, a relative time ratio is obtained, and based on the relative time ratio, a comparison moment corresponding to each moment in the current injection molding process in the historical injection molding process is determined;

[0008] Analyze the temperature difference, temperature change similarity, and relative time proportion of each moment between each moment in the current injection molding process and the corresponding comparison moment, and determine the abnormal degree value of the mold temperature at each moment in the current injection molding process; based on the abnormal degree value, perform abnormal monitoring on the operation of the automatic injection molding machine.

[0009] Furthermore, the historical injection molding process at least includes a mold filling stage, a pressure holding stage and a cooling stage.

[0010] Furthermore, the method for obtaining the phase time period includes:

[0011] In each historical injection molding process, the moment corresponding to the transition of the phases is taken as the target moment;

[0012] In the real-time injection pressure time series data and the historical injection pressure time series data, respectively, in a preset neighborhood corresponding to each moment, based on the difference between the data values, determine the change trend characteristic value corresponding to each moment;

[0013] Compare the change trend characteristic value at each moment in the real-time injection pressure time series data with the change trend characteristic value at the target moment in the historical injection pressure time series data, and determine the moment when the stage transition occurs in the current injection molding process as the segmentation moment;

[0014] The current injection molding process is segmented according to the segmentation moment to obtain all stage time periods in the current injection molding process. If there is no segmentation moment in the real-time injection pressure timing data, the current injection molding process includes one stage time period.

[0015] Furthermore, the method for obtaining the segmentation time includes:

[0016] Take two adjacent stages as a stage combination, and take the minimum value of the change trend characteristic value at the target moment corresponding to the same stage combination in all historical injection molding processes as the change standard characteristic value of the stage combination;

[0017] In the current injection molding process, the change trend characteristic value of each moment is compared with the change standard characteristic value of the first stage combination in turn. When the change trend characteristic value is greater than or equal to the change standard characteristic value, the moment corresponding to the current change trend characteristic value is used as the segmentation moment; the change trend characteristic value of each moment after the segmentation moment is compared with the change standard characteristic value of the second stage combination in turn. When the change trend characteristic value is greater than or equal to the change standard characteristic value, the moment corresponding to the current change trend characteristic value is used as the segmentation moment;

[0018] And so on, all the segmentation moments in the current injection molding process are obtained.

[0019] Further, the method of obtaining a relative time ratio based on the difference in duration between the current injection molding process and the corresponding stage time period in the historical injection molding process, and determining the comparison moment corresponding to each moment in the current injection molding process in the historical injection molding process based on the relative time ratio includes:

[0020] Combine the current injection molding process with each historical injection molding process to obtain an injection molding process combination;

[0021] In each injection molding process combination, if the current injection molding process only includes one stage time period, then in the current injection molding process, based on the change trend between the temperature values ​​at the corresponding position in the historical injection molding process, the relative proportion of time corresponding to each moment is determined;

[0022] If the current injection molding process includes at least two stage time periods, for each moment in the first stage time period of the current injection molding process, the relative time ratio is determined based on the change trend between the temperature values ​​at the corresponding position time in the historical injection molding process; for each moment in the other stage time periods, the relative time ratio corresponding to each moment is determined based on the ratio between the stage time period before each moment and the duration of the same stage time period in the historical injection molding process;

[0023] Based on the relative proportion of the position sequence number and time of each moment in the current injection molding process in the corresponding stage time period, the comparison moment of each moment in the current injection molding process in the same stage time period of each historical injection molding process is determined.

[0024] Furthermore, the method for obtaining the abnormality degree value includes:

[0025] In each injection molding process combination, based on the difference between the mold temperature at each moment in the current injection molding process and the corresponding comparison moment, determining the initial abnormal value at each moment in the current injection molding process;

[0026] Determine a first credible factor of an initial abnormal value at each moment in the current injection molding process based on a similarity between a temperature change trend of the mold temperature at each moment in the current injection molding process and a temperature change trend of the mold temperature at the corresponding comparison moment;

[0027] Determine a second credible factor of the initial abnormal value at each moment in the current injection molding process based on the relative time ratio between the phase time period to which each moment belongs and the phase time period to which the corresponding comparison moment belongs in the current injection molding process;

[0028] According to the first credible factor, the second credible factor and the initial abnormal value corresponding to each moment in all injection molding process combinations at each moment in the current injection molding process, the abnormality degree value at each moment in the current injection molding process is obtained, and the first credible factor, the second credible factor and the initial abnormal value are all positively correlated with the abnormality degree value.

[0029] Furthermore, the method for obtaining the first trust factor includes:

[0030] Obtain the real-time data curve corresponding to the real-time mold temperature time series data, and obtain the historical data curve corresponding to the historical mold temperature time series data;

[0031] In each injection molding process combination, the difference between the mold temperature slope value at each moment in the current injection molding process and the mold temperature slope value at the corresponding comparison moment is used as a difference factor;

[0032] A first credible factor corresponding to each moment in the current injection molding process is obtained according to the difference factor, and the difference factor is negatively correlated with the first credible factor.

[0033] Furthermore, the method for obtaining the second trust factor includes:

[0034] The difference between the relative proportion of time at each moment in the current injection molding process and the preset constant is used as a deviation factor;

[0035] Based on the deviation factor, a second credible factor corresponding to each moment in the current injection molding process is obtained, and the second credible factor is negatively correlated with the deviation factor.

[0036] Further, the abnormality monitoring of the operation of the automatic injection molding machine based on the abnormality degree value includes:

[0037] In the current injection molding process, the moment when the abnormality value is greater than or equal to the preset abnormality threshold is regarded as an abnormal moment and an early warning is issued.

[0038] The present invention also proposes an automatic injection molding machine operation monitoring system, the system comprising:

[0039] A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the steps of any one of the methods can be implemented when the processor executes the computer program.

[0040] The present invention has the following beneficial effects:

[0041] Since the injection molding process includes multiple stages, such as the mold filling stage, the pressure holding stage, and the cooling stage, the injection pressure data in different stages will have different changes, and the mold temperature will also change accordingly. In order to more accurately judge the abnormal situation at each moment in the current injection molding process, it is necessary to judge the stage of the current injection molding process. Therefore, the real-time mold temperature timing data and real-time injection pressure timing data in the current process of the automatic injection molding machine, as well as the historical mold temperature timing data and historical injection pressure timing data in the historical injection molding process of the automatic injection molding machine are obtained. Then, the similarity between the data value change trends in the real-time injection pressure timing data and the historical injection pressure timing data is analyzed, and the current injection molding process is divided into the injection molding stage to obtain the stage time period. In the subsequent process, the temperature changes in the corresponding stage time periods in the current injection molding process and the historical injection molding process can be analyzed, so as to more accurately reflect the abnormal situation of the mold temperature in the current injection molding process. Furthermore, since different production conditions such as material type, injection speed, ambient temperature, etc. will affect the duration of different stages when they change, which will be manifested as a lag or advance in time when they are in the same state. Therefore, in order to more accurately locate the moment consistent with the state of each moment in the current injection molding process in the historical injection molding process, the difference in duration between the current injection molding process and the corresponding stage time period in the historical injection molding process can be analyzed, so as to find the comparison moment corresponding to each moment in the current injection molding process. Further, in order to ensure the accuracy of the calculation result of the abnormality degree value, while analyzing the temperature difference between each moment in the current injection molding process and the corresponding comparison moment, the similarity of temperature changes and the difference in duration of the corresponding stage time period are combined to verify the consistency of the state and production conditions of each moment in the current injection molding process and the corresponding comparison moment, and finally obtain a more accurate abnormality degree value, and perform abnormal monitoring of the operation of the automatic injection molding machine based on the abnormality degree value, so that the monitoring result is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 A method flow chart of an automatic injection molding machine operation monitoring method provided by one embodiment of the present invention;

[0044] Figure 2 A method flow chart of a method for obtaining a phase time period provided by an embodiment of the present invention;

[0045] Figure 3 A method flow chart of a method for obtaining an abnormality degree value provided by an embodiment of the present invention;

[0046] Figure 4 A system block diagram of an automatic injection molding machine operation monitoring system provided by one embodiment of the present invention;

[0047] Figure 5 A schematic diagram of the system structure of an automatic injection molding machine operation monitoring system provided by one embodiment of the present invention;

[0048] Figure 6 A schematic diagram of a computer-readable storage medium provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the automatic injection molding machine operation monitoring system and method proposed by the present invention, its specific implementation, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0050] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0051] The specific scheme of an automatic injection molding machine operation monitoring system and method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0052] See also Figure 1 , which shows a method flow chart of an automatic injection molding machine operation monitoring method provided by an embodiment of the present invention, the method comprising the following steps:

[0053] Step S1: obtaining real-time mold temperature timing data and real-time injection pressure timing data during the current injection molding process of the automatic injection molding machine; obtaining historical mold temperature timing data and historical injection pressure timing data during the historical injection molding process of the automatic injection molding machine.

[0054] Automatic injection molding machine is a kind of equipment used to produce plastic products, which can achieve a high degree of automation and intelligence in the production process. During the operation of the automatic injection molding machine, the temperature change of the mold will directly affect the quality of the molded product and production efficiency. Therefore, monitoring whether the mold temperature is abnormal during the operation of the automatic injection molding machine is a key link to ensure the quality of the molded product and production efficiency.

[0055] The injection molding process can be roughly divided into the filling stage: the automatic injection molding machine applies high pressure to inject the molten plastic into the mold. At this time, the injection pressure will rise rapidly until the molten plastic fills the entire mold cavity; the holding stage: when the mold is full of plastic, the automatic injection molding machine switches to the holding stage to ensure that the plastic fully fills the mold details and ensures the quality of the finished product. At this time, the injection pressure will remain basically stable; the cooling stage: the injection pressure will gradually decrease until the mold is finally released and the injection molded part is taken out. Therefore, during the injection molding process, the change of injection pressure is directly related to each stage. Therefore, when analyzing the abnormal situation of mold temperature, while obtaining the mold temperature timing data, the injection pressure timing data can be obtained, which is convenient for the subsequent division of the injection molding stages contained in the current injection molding process. The injection molding process can also be divided more finely, for example, including: preheating stage, filling stage, front holding stage, rear holding stage, cooling stage, etc.

[0056] The temperature sensor can be used to obtain the real-time mold temperature timing data of the automatic injection molding machine during the current injection molding process, and the pressure sensor can be used to obtain the real-time injection pressure timing data of the automatic injection molding machine during the current injection molding process. Since a complete injection molding process takes about 3 minutes, in the embodiment of the present invention, the sampling period of the real-time timing data is set to 1 minute, and the sampling time interval is set to one second. In addition, if it is a specific material, such as PVC, the complete injection molding process may take about one and a half hours, so the setting of the sampling period of the real-time timing data and the setting of the sampling time interval can be adjusted according to the implementation scenario, and are not limited here.

[0057] Since historical data is usually stored in a database or external storage medium, the historical mold temperature timing data and historical injection pressure timing data of the historical injection molding process of the automatic injection molding machine can be obtained in the database or external storage medium. It should be noted that each historical injection molding process is a complete injection molding process, and the transition time points between different stages in the injection molding process are all known, and the corresponding moment when the stage transition occurs is used as the target moment. For example, from moment 1 to moment 4 is the mold filling stage, and from moment 4 to moment 80 is the pressure holding stage, then moment 4 is used as a target moment.

[0058] At this point, the required time series data can be obtained to provide data support for subsequent calculation processes.

[0059] Step S2: Analyze the similarity between the data value change trends in the real-time injection pressure time series data and the historical injection pressure time series data, divide the real-time injection pressure time series data into stages of the injection molding process, and obtain stage time periods.

[0060] During the operation of the automatic injection molding machine, the duration of different stages will change due to various factors such as material properties, ambient temperature, etc. Therefore, in order to more accurately monitor the injection molding process, it is necessary to divide the current injection molding process into stages. Specifically, since the injection pressure has obvious change characteristics in different stages, the similarity between the data value change trends in the real-time injection pressure time series data and the historical injection pressure time series data can be analyzed, and the real-time injection pressure time series data can be divided into the injection molding process stages to obtain the stage time periods.

[0061] Preferably, in one embodiment of the present invention, the method for obtaining the phase time period includes:

[0062] See also Figure 2 , which shows a method flow chart of a method for obtaining a phase time period in one embodiment of the present invention, the method comprising the following steps:

[0063] Step S201: In the real-time injection pressure time series data and the historical injection pressure time series data, respectively, in a preset neighborhood corresponding to each moment, based on the difference between data values, determine the change trend characteristic value corresponding to each moment.

[0064] In order to more accurately capture the changing trend of the injection pressure data and facilitate a more accurate stage division of the current injection molding process, the data values, that is, the differences between the pressure values, can be analyzed within the local range corresponding to each moment, thereby capturing the subtle changes in the injection pressure time series data and more accurately determining the changing trend characteristic values ​​corresponding to each moment.

[0065] Therefore, in the real-time injection pressure time series data and the historical injection pressure time series data, each moment is taken as the center, and a preset first number of moments are selected before and after the time series of each moment, so as to determine the preset neighborhood corresponding to each moment, and in the preset neighborhood, other moments except the central moment are taken as neighborhood moments, and then the difference between the data values ​​of the central moment and the neighborhood moments on each side is analyzed, so as to obtain the change trend factor corresponding to the central moment on each side. The change trend factor corresponding to the central moment reflects the change trend of the data points on one side of the central moment, which is helpful to determine whether the data change trend on both sides of the central moment has changed in the future. The formula model of the change trend factor includes:

[0066] ;

[0067] ;

[0068] in, Indicates The change trend factor between the central moment and the neighboring moments before the time series; Indicates The number of neighboring moments before the central moment; Indicates The injection pressure data value at the central moment; Indicates The time sequence before the central moment The injection pressure data value at each neighboring moment; Indicates The central moment and the previous The time interval between neighboring moments; Indicates The change trend factor between the central moment and the neighboring moments after the time series; Indicates The number of neighboring moments after the central moment sequence; Indicates The first time after the central moment The injection pressure data value at each neighboring moment; Indicates The central moment and the sequence after The time interval between neighboring moments.

[0069] In the formula model of the change trend factor, the difference between the injection pressure data values ​​between the central moment and the neighboring moment before the time series is When is positive, and the larger it is, the pressure data value at the center moment has a greater increase than the injection pressure data value at the neighboring moment before the time series, showing an upward data trend. The smaller the time interval is, the closer the time distance between the center moment and the neighboring moment is, and the calculated change trend will be more credible. Therefore, when The value is positive, and the larger it is, it means that before the center moment, the injection pressure data value has a more obvious upward trend. On the contrary, if If the value is negative and smaller, it means that before the central moment, the injection pressure data value has a more obvious downward trend. Similarly, when the difference between the injection pressure data value between the neighboring moment after the central moment and the central moment is positive, and the larger it is, the greater the pressure data value of the injection pressure at the neighboring moment after the central moment is compared with the pressure data value at the central moment, showing an upward data trend. The smaller the time interval is, the closer the time distance between the central moment and the neighboring moment is, and the more credible the calculated change trend will be. Therefore, when The value is positive, and the larger it is, the more obvious the injection pressure data value is after the center moment. On the contrary, if The smaller the value is, the more obvious the downward trend of the injection pressure data value is before the center moment.

[0070] Finally, the change trend factors corresponding to both sides are integrated to obtain the change trend characteristic value corresponding to the central moment. The formula model of the change trend characteristic value includes:

[0071] ;

[0072] in, Indicates The characteristic value of the changing trend at the central moment; Indicates The change trend factor between the central moment and the neighboring moments before the time series; Indicates The change trend factor between a central moment and the neighboring moments after the time series.

[0073] In the formula model of the characteristic value of the changing trend, based on the above analysis, it can be seen that when The value is positive, and the larger it is, it means that before the center moment, the injection pressure data value has a more obvious upward trend. On the contrary, if If the value is negative and smaller, it means that before the center moment, the injection pressure data value has a more obvious downward trend. The value is positive, and the larger it is, the more obvious the injection pressure data value is after the center moment. On the contrary, if If the value is negative and smaller, it means that after the center moment, the injection pressure data value has a more obvious downward trend. Because, if the change trend of the injection pressure data value before and after the center moment is consistent, then and Both are positive or negative. If the change trend of the injection pressure data value before and after the center time sequence changes, then and Based on the above logic, we can find the difference between the trend factors corresponding to the front and back sides of the central time series, that is, we can get the absolute value of the difference. At this time, the smaller the absolute value of the difference is, the more likely it is that the injection pressure data value has a consistent change trend within the preset neighborhood, and the center moment is less likely to be the transition moment of different stages in the injection molding process. On the contrary, the larger the absolute value of the difference is, the more likely it is that the injection pressure data value has a different change trend within the preset neighborhood, and the center moment is more likely to be the transition moment of different stages in the injection molding process.

[0074] It should be noted that, when the preset first number is set to 5, the size of the preset neighborhood is 11. The specific value can be adjusted according to the implementation scenario and is not limited here. For the first moment and the last moment, since there is only one side with a neighborhood moment, the trend change characteristic value is set to a fixed value, and the specific value is 0.

[0075] At this point, through the above calculations, the real-time injection pressure time series data and the change trend characteristic value corresponding to each moment in each historical injection pressure time series data can be obtained. This value reflects the change trend of the injection pressure data value within a local range, which can be used to subsequently determine whether the stage has changed during the injection molding process.

[0076] Step S202: Compare the change trend characteristic value at each moment in the real-time injection pressure time series data with the change trend characteristic value at the target moment in the injection pressure time series data, and determine the moment when the stage transition occurs in the current injection molding process as the segmentation moment.

[0077] Based on step S201, the characteristic value of the change trend at all moments can be obtained. Since in the historical injection molding process, the moments when the transitions occur in different stages are known, that is, the divisions of different stages are known, two adjacent stages can be used as a stage combination in all historical injection molding processes. In this embodiment of the present invention, taking the injection molding process including three stages as an example, the first stage and the second stage can form a stage combination, and similarly, the second stage and the third stage form a stage combination.

[0078] Then, in all historical injection molding processes, the minimum value of the change trend characteristic value at the target moment corresponding to the same stage combination is used as the change standard characteristic value of the stage combination. In other embodiments of the present invention, the mean, mode, median, etc. of the change trend characteristic value at the target moment corresponding to the same stage combination can also be used as the change standard characteristic value of the stage combination, which is not limited here.

[0079] Finally, in the current injection molding process, the change trend characteristic value of each moment is compared with the change standard characteristic value of the first stage combination in turn. When the change trend characteristic value is greater than or equal to the change standard characteristic value, it means that a more obvious data trend change has occurred before and after the time sequence at the moment corresponding to the current change trend characteristic value, and a stage transition is more likely to occur. Therefore, the moment corresponding to the current change trend characteristic value is taken as the segmentation moment; then, the change trend characteristic value of each moment after the segmentation moment is compared with the change standard characteristic value of the second stage combination in turn. When the change trend characteristic value is greater than or equal to the change standard characteristic value, the moment corresponding to the current change trend characteristic value is taken as the segmentation moment;

[0080] And so on, all the segmentation moments in the current injection molding process are obtained.

[0081] The process of obtaining the above segmentation moments is illustrated by an example: the standard characteristic value of the change of the first stage combination in the historical injection molding process is recorded as characteristic value a, and the standard characteristic value of the change of the second stage combination is recorded as characteristic value b. The current injection molding process contains a total of 5 moments, which are recorded as moment 1, moment 2, moment 3, moment 4, and moment 5 respectively.

[0082] The first case: compare the change trend characteristic value of moment 1 with characteristic value a. If the change trend characteristic value of moment 1 is less than characteristic value a, then compare the change trend characteristic value of moment 2 with characteristic value a. If the change trend characteristic value of moment 2 is greater than or equal to characteristic value a at this time, then take moment 2 as a segmentation moment; then, starting from moment 3, compare moment 3 with characteristic value b. If the change trend characteristic value of moment 1 is less than characteristic value b, then continue to compare the change trend characteristic value of moment 4 with characteristic value b. If the change trend characteristic value of moment 4 is greater than or equal to characteristic value b at this time, then take moment 4 as a segmentation moment. Because there are only two stage combinations, it can be stopped when two segmentation moments are found. At this time, moment 1 to moment 3 are the first stage of the current injection molding process, moment 3 to moment 4 are the second stage of the current injection molding process, and moment 4 to moment 5 are the third stage of the current injection molding process.

[0083] The second case: compare the change trend characteristic value at moment 1 with the characteristic value a. If the change trend characteristic value at moment 1 is less than the characteristic value a, then compare the change trend characteristic value at moment 2 with the characteristic value a. If the change trend characteristic value at moment 2 is less than the characteristic value a, then compare moment 3 with the characteristic value a. If the change trend characteristic value at moment 3 is less than the characteristic value a, then continue to compare the change trend characteristic value at moment 4 with the characteristic value a. If the change trend characteristic value at moment 4 is still less than the characteristic value a, then compare the change trend characteristic value at moment 5 with the characteristic value a. If the change trend characteristic value at moment 5 is still less than the characteristic value a, then there is no split moment in the current injection molding process, that is, the current injection molding process is in the first stage.

[0084] Step S203: segment the current injection molding process according to the segmentation time to obtain all stage time periods in the current injection molding process.

[0085] In step S202, all the segmentation moments in the current injection molding process can be obtained, and then the current injection molding process can be segmented based on the segmentation moments: the time period between the first moment to the first segmentation moment is the first stage time period, the time period between the first segmentation moment to the second segmentation moment is the second stage time period, and so on, all the stage time periods in the current injection molding process can be obtained.

[0086] If there is no split time, the current injection molding process is regarded as a phase time period.

[0087] It should be noted that, in the second case in the example of step S202, it is explained that the current injection molding process is not a complete first stage time period.

[0088] Step S3: Based on the difference in duration between the current injection molding process and the corresponding stage time period in the historical injection molding process, a relative time ratio is obtained, and based on the relative time ratio, a comparison moment corresponding to each moment in the current injection molding process in the historical injection molding process is determined.

[0089] Since there will be differences in process parameters such as speed between different injection molding processes, as well as the influence of vibration of automatic injection molding machine equipment, ambient temperature and other conditions, there will be differences in duration, which will result in different durations even in the same stage. Therefore, it is necessary to compare the difference between the duration of the current injection molding process and the same stage time period in the historical injection molding process, so as to find the corresponding relationship on the time axis, and then determine the comparison moment of each moment in the current injection molding process in the historical injection molding process. At this time, the comparison moment can be considered to be a time point that is in the same stage as the moment in the injection molding process, with similar process conditions and environmental conditions. Then, when performing abnormal analysis later, by comparing with the comparison moment, more accurate abnormal monitoring results can be obtained.

[0090] Preferably, in one embodiment of the present invention, the method for obtaining the comparison time includes:

[0091] In order to establish a connection between the current injection molding process and the historical injection molding processes, the current injection molding process may be combined with each historical injection molding process to obtain an injection molding process combination.

[0092] Then, in each injection molding process combination, if the current injection molding process only contains one stage time period, because the temperature change rate can reflect the duration to a certain extent, based on the change trend between the temperature values ​​of the current injection molding process and the second preset number of moments in the historical injection molding process, the duration ratio between the stage time periods is determined, and then the time relative ratio corresponding to each moment is obtained. The formula model of the time relative ratio includes:

[0093] ;

[0094] in, Indicated in The relative proportion of time corresponding to the first phase time period between the current injection molding process and the historical injection molding process in the injection molding process combination; Indicates a preset second quantity; Indicates that in the current injection molding process, The temperature value at a certain moment; Indicates that in the current injection molding process, The temperature value at a certain moment; Indicated in In the historical injection molding process of the injection molding process combination, the The temperature value at a certain moment; Indicated in In the historical injection molding process of the injection molding process combination, the The temperature value at a certain moment; Indicates the preset first parameter.

[0095] In the time relative proportion formula model, the second number of moments preset before are selected, and then the temperature change rate of each of the second number of moments preset before is analyzed in the historical injection molding process and the current injection molding process, that is, and , since the faster the temperature changes, the shorter the duration should be, so the duration is inversely proportional to the temperature change rate, so the temperature change rate in the historical injection molding process is used as the denominator, and the temperature change rate in the current injection molding process is used as the numerator to obtain the proportional factor Finally, the proportional factors obtained according to the preset second number of moments are integrated to obtain the relative time ratio between the current injection molding process and the historical injection molding in each injection molding process combination, which is also used as the relative time ratio corresponding to each moment in the current injection molding process.

[0096] It should be noted that the preset second number is set to 5, and the specific value can be adjusted according to the implementation scenario and is not limited here; the preset first parameter The function of is to prevent the denominator from being 0. Here, the value can be 0.001. The specific value can be adjusted according to the implementation scenario and is not limited here.

[0097] If the current injection molding process includes at least two stage time periods, for each moment in the first stage time period of the current injection molding process, the relative time ratio is obtained based on the change trend between the temperature values ​​at the corresponding position moments in the historical injection molding process, and the specific process is the same as above.

[0098] Since it is uncertain whether the last stage time period is a complete stage in the injection molding process, for each moment in the other stage time periods except the first stage time period, the duration ratio between the stage time periods is estimated based on the ratio between the stage time period before each moment and the duration of the same stage time period in the historical injection molding process, and then the relative time ratio corresponding to each moment is obtained. The formula model of the relative time ratio includes:

[0099] ;

[0100] in, Indicated in In the combination of injection molding processes, the current injection molding process and the first The relative proportion of time between the phases; Indicates In the combination of injection molding processes, the current injection molding process and the first The total number of phase time periods between phase time periods; Indicates that during the current injection molding process, The time period between the stages The duration of each phase; Indicated in In the historical injection molding process of the injection molding process combination, the The time period between the stages The duration of each phase time period.

[0101] In the formula model of time relative proportion, since the last stage time period in the current injection molding process is not necessarily a complete stage in the injection molding process, when the current injection molding process contains at least two stage time periods, for other stage time periods except the first stage time period, the ratio of the duration of the previous stage time period to the duration of the corresponding stage time period in the historical injection molding process is analyzed. , and then combine the previous stage time period of each stage time period , which is used to estimate the relative proportion of time corresponding to each stage time period, and finally the relative proportion of time corresponding to each moment in each stage time period except the first stage time period in the current injection molding process can be obtained.

[0102] So far, through the above process, we have analyzed various situations and obtained the relative proportion of time corresponding to each moment in the current injection molding process, which is uniformly recorded as Then, based on the relative proportion of the position and time of each moment in the current injection molding process in the corresponding stage time period, the comparison moment of each moment in the current injection molding process in the same stage time period of each historical injection molding process is determined.

[0103] In each injection molding process combination, the ratio of the position of each moment in the current injection molding process in its corresponding stage time period to the corresponding relative time ratio is used as the position of each moment in the current injection molding process in the historical injection molding process, so as to determine the comparison moment in the historical injection molding process. The formula model of the position of the comparison moment includes:

[0104] ;

[0105] in, Indicated in In the combination of injection molding processes, the current injection molding process In the period of the phase The position of the comparison moment in the historical injection process; Indicates the current injection process. In the period of the phase The position at a moment; Indicates the current injection process. In the period of the phase The relative time ratio corresponding to each moment.

[0106] In the above formula model, since the relative time ratio represents the duration ratio between the stage time period in the current injection molding process and the stage time period in the historical injection molding process, when determining the position of the comparison moment in the historical injection molding process based on the position of each moment in the current injection molding process, the relative time ratio should be placed in the denominator. Therefore, the above formula is constructed to determine the position of the comparison moment.

[0107] The method of determining the comparison moment is illustrated by an example: for example, if the relative time ratio of each moment in the cooling stage of the current injection molding process is 3 / 2, then in the cooling stage of the current injection molding process, the moment that ranks second, that is, in the second position, should correspond to the moment 4 / 3 in the cooling stage of the historical injection molding process.

[0108] So far, through the above process, the comparison moment corresponding to each moment in the current injection molding process in each historical injection molding process can be obtained.

[0109] Step S4: Analyze the temperature difference, temperature change similarity, and relative time proportion of each moment between each moment in the current injection molding process and the corresponding comparison moment, and determine the abnormality degree value of the mold temperature at each moment in the current injection molding process; perform abnormal monitoring on the operation of the automatic injection molding machine based on the abnormality degree value.

[0110] By comparing the temperature difference between each moment in the current injection molding process and all the corresponding comparison moments, the fluctuation of the mold temperature in the current injection molding process can be directly reflected, which helps to identify potential abnormalities. However, since the process parameters in the current injection molding process may be different from those in the historical injection molding process, and environmental factors may also change, in order to obtain more accurate abnormal judgment results, the temperature difference can be adjusted according to the similarity of the change trend between each moment and the corresponding comparison moment and the relative proportion of time at each moment, so as to determine the abnormal degree value of the mold temperature at each moment in the current injection molding process. Finally, the operation of the automatic injection molding machine is monitored abnormally based on the abnormal degree value.

[0111] Preferably, in one embodiment of the present invention, the method for obtaining the abnormality degree value includes:

[0112] See also Figure 3 , which shows a method flow chart of a method for obtaining an abnormality degree value in one embodiment of the present invention, the method comprises the following steps:

[0113] Step S301: In each injection molding process combination, based on the difference between the mold temperature at each moment in the current injection molding process and the corresponding comparison moment, determine the initial abnormal value at each moment in the current injection molding process.

[0114] The formula model for initial outliers includes:

[0115] ;

[0116] in, Indicated in In the combination of injection molding processes, the current injection molding process The initial outlier value at the moment; Indicates that during the current injection molding process, The temperature value at a certain moment; Indicates the current injection process. At this moment, The temperature values ​​at the corresponding comparison moments in the historical injection molding process of a combination of injection molding processes.

[0117] In the formula model of the initial outlier, in each injection process combination, the difference in temperature value between each moment in the current injection process and the corresponding comparison moment is calculated. The larger the difference is, the more likely it is that the mold temperature at each moment in the current injection molding process is abnormal.

[0118] Step S302: In each injection molding process combination, based on the similarity between the temperature change trend of the mold temperature corresponding to each moment in the current injection molding process and the temperature change trend of the mold temperature at the corresponding comparison moment, determine the first credible factor of the initial abnormal value at each moment in the current injection molding process.

[0119] Since some abnormalities may be caused by process requirements or external factors, it is not accurate to judge the abnormality only based on the difference in temperature values. During the injection molding process, when there are the same process requirements and external factors, the change trend of the temperature value should be relatively consistent. Therefore, by analyzing the change trend of the mold temperature at each moment in the current injection molding process and the mold temperature at the comparison moment, the first credible factor is obtained, thereby improving the accuracy of the judgment of the abnormality degree value.

[0120] Get the real-time data curve corresponding to the real-time mold temperature timing data, and get the historical data curve corresponding to the historical mold temperature timing data.

[0121] Then, in each injection molding process combination, the difference between the mold temperature slope value at each moment in the current injection molding process and the mold temperature slope value at the corresponding comparison moment is used as the difference factor.

[0122] The first credible factor corresponding to each moment in the current injection molding process is obtained according to the difference factor, and the difference factor is negatively correlated with the first credible factor. The formula model of the first credible factor includes:

[0123] ;

[0124] in, Indicated in In the combination of injection molding processes, the current injection molding process The first credible factor corresponding to the moment; Indicates that during the current injection molding process, The mold temperature slope value at a certain moment; Indicates the current injection process. At this moment, The mold temperature slope value at the corresponding comparison time in the historical injection process of the injection process combination; Indicates the preset second parameter.

[0125] In the formula model of the first credible factor, in each injection process combination, the difference between the mold temperature slope value at each moment in the current injection process and the mold temperature slope value at the corresponding comparison moment is calculated as the difference factor The larger the difference factor is, to a certain extent, it means that there may be a large difference between the current injection molding process and the historical injection molding process in terms of process requirements or environmental factors. On the contrary, the smaller the difference factor is, the more consistent the process requirements or environmental factors are between the current injection molding process and the historical injection molding process. On this basis, abnormal analysis can better characterize the correct abnormal situation. Therefore, the difference factor is negatively correlated to obtain the first credible factor corresponding to each moment in the current injection molding process.

[0126] It should be noted that the second parameter is preset The function of is to prevent the denominator from being 0. Here, the value can be 0.001. The specific value can be adjusted according to the implementation scenario and is not limited here.

[0127] Step S303: In each injection molding process combination, based on the relative time ratio between the phase time period of each moment in the current injection molding process and the phase time period of the corresponding comparison moment, determine the second credible factor of the initial abnormal value at each moment in the current injection molding process.

[0128] Since the difference between the duration of the stage time periods can also represent the consistency of the process requirements, and the duration of the stage time periods can be represented by the relative proportion of time in the aforementioned steps, the difference between the relative proportion of time at each moment in the current injection molding process and the preset constant is used as the deviation factor.

[0129] Finally, based on the deviation factor, the second credible factor corresponding to each moment in the current injection molding process is obtained, and the second credible factor is negatively correlated with the deviation factor. The formula model of the second credible factor includes:

[0130] ;

[0131] in, Indicated in In the combination of injection molding processes, the current injection molding process The second credible factor corresponding to the moment; Indicated in In the combination of injection molding processes, the current injection molding process The relative proportion of time corresponding to each moment; Indicates a preset constant; Indicates the preset third parameter.

[0132] In the formula model of the second credible factor, when the relative proportion of time is closer to 1, it means that in the injection molding process combination, the duration of the stage time period in the current injection molding process is more similar to that in the historical injection molding process, that is, the deviation factor The smaller it is, the more consistent the process requirements are in the two injection molding processes. Similarly, on this basis, abnormal analysis can better characterize the correct abnormal situation. Therefore, the deviation factor is negatively correlated to obtain the second credible factor corresponding to each moment in the current injection molding process.

[0133] It should be noted that the preset constant Set to 1; preset the third parameter The function of is to prevent the denominator from being 0. Here, the value can be 0.001. The specific value can be adjusted according to the implementation scenario and is not limited here.

[0134] Step S304: in all injection molding process combinations, according to the first credible factor, the second credible factor and the initial abnormal value corresponding to each moment in the current injection molding process, obtain the abnormality degree value at each moment in the current injection molding process.

[0135] The first credibility factor, the second credibility factor and the initial abnormal value are all positively correlated with the abnormal degree value. The formula model of the abnormal degree value can be specifically, for example:

[0136] ;

[0137] in, Indicates the current injection process. The abnormality value at each moment; Indicates the total number of injection molding process combinations; Indicated in In the combination of injection molding processes, the current injection molding process The initial outlier value at the moment; Indicated in In the combination of injection molding processes, the current injection molding process The first credible factor corresponding to the moment; Indicated in In the combination of injection molding processes, the current injection molding process The second credible factor corresponding to the moment; Represents the normalization function.

[0138] In the formula model of the abnormality degree value, when the first credible factor is larger, it means that the process requirements and external factors between the current injection molding process and the historical injection molding process are more consistent. On this basis, the analysis of abnormal conditions will be more accurate. Similarly, when the second credible factor is larger, it also means that the process requirements between the current injection molding process and the historical injection molding process are more consistent. Therefore, the calculated abnormality degree value can better represent the real abnormal situation. Therefore, the sum of the first credible factor and the second credible factor is used as the adjustment factor. Then multiply the adjustment factor by the abnormality value at each moment in the current injection molding process to obtain the abnormality factor at each moment in the current injection molding process under each injection molding process combination. Finally, all abnormal factors at each moment in the current injection molding process are integrated, averaged and normalized to obtain the abnormal degree value at each moment in the current injection molding process.

[0139] At this point, by comprehensively considering multiple factors, the abnormality level value at each moment in the current injection molding process can be obtained. The abnormality level value at this time can more comprehensively and accurately characterize the abnormal situation at each moment.

[0140] Finally, the operation of the automatic injection molding machine can be monitored based on the abnormality level value corresponding to each moment in the current injection molding process.

[0141] Preferably, in one embodiment of the present invention, abnormal monitoring of the operation of the automatic injection molding machine is performed based on the abnormality degree value, including:

[0142] Since the larger the abnormality value is, the more likely the mold temperature is abnormal at that moment, so in the current injection molding process, the moment when the abnormality value is greater than or equal to the preset abnormality threshold is regarded as an abnormal moment and an early warning is issued.

[0143] It should be noted that the abnormal threshold is set to 0.6, and the specific value can be adjusted according to the implementation scenario and is not limited here.

[0144] In other embodiments of the present invention, the method for obtaining the abnormal moment may further include:

[0145] The abnormality degree values ​​of all moments in the current injection molding process are arranged in descending order to obtain a sorting sequence. In the sorting sequence, the third number of moments preset before are taken as abnormal moments. The third number is set to one fifth of the duration of the current injection molding process rounded up. The specific value can be adjusted according to the implementation scenario and is not limited here.

[0146] It should be noted that, in order to facilitate calculation, all indicator data involved in the calculation in the embodiment of the present invention are preprocessed to eliminate the dimension effect. The specific means of eliminating the dimension effect are technical means well known to those skilled in the art and are not limited here.

[0147] In summary, since the injection molding process includes multiple stages, such as the mold filling stage, the pressure holding stage, and the cooling stage, the injection pressure data in different stages will have different changes, and the mold temperature will also change accordingly. In order to more accurately judge the abnormal situation at each moment in the current injection molding process, it is necessary to judge the stage of the current injection molding process. Therefore, the real-time mold temperature timing data and real-time injection pressure timing data of the current process of the automatic injection molding machine, as well as the historical mold temperature timing data and historical injection pressure timing data of the historical injection molding process of the automatic injection molding machine are obtained. Then, the similarity between the data value change trends in the real-time injection pressure timing data and the historical injection pressure timing data is analyzed, and the current injection molding process is divided into injection molding stages to obtain the stage time period. In the subsequent process, the temperature changes in the corresponding stage time periods of the current injection molding process and the historical injection molding process can be analyzed, so as to more accurately reflect the abnormal situation of the mold temperature in the current injection molding process. Furthermore, since different production conditions such as material type, injection speed, ambient temperature, etc. will affect the duration of different stages when they change, which will be manifested as a lag or advance in time when they are in the same state. Therefore, in order to more accurately locate the moment consistent with the state of each moment in the current injection molding process in the historical injection molding process, the difference in duration between the current injection molding process and the corresponding stage time period in the historical injection molding process can be analyzed, so as to find the comparison moment corresponding to each moment in the current injection molding process. Further, in order to ensure the accuracy of the calculation result of the abnormality degree value, while analyzing the temperature difference between each moment in the current injection molding process and the corresponding comparison moment, the similarity of temperature changes and the difference in duration of the corresponding stage time period are combined to verify the consistency of the state and production conditions of each moment in the current injection molding process and the corresponding comparison moment, and finally obtain a more accurate abnormality degree value, and perform abnormal monitoring of the operation of the automatic injection molding machine based on the abnormality degree value, so that the monitoring result is more accurate.

[0148] This embodiment also provides an automatic injection molding machine operation monitoring system. Figure 4 , which shows a system block diagram, including a data acquisition module 401, used to implement step S1 in the above method; a stage division module 402, used to implement step S2 in the above method; a comparison moment analysis module 403, used to implement step S3 in the above method; and an abnormality monitoring module 404, used to implement step S4 in the above method.

[0149] It should be noted that: the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the automatic injection molding machine operation monitoring system and the automatic injection molding machine operation monitoring method embodiment provided in the above embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0150] See also Figure 5 , which shows a system structure diagram of an automatic injection molding machine operation monitoring system provided by an embodiment of the present invention, including a processor 500, a memory 501, a bus 502 and a communication interface 503, wherein the processor 500, the communication interface 503 and the memory 501 are connected via the bus 502; wherein the memory 501 may include a high-speed random access memory, the bus 502 may be an ISA bus, a PCI bus or an EISA bus, etc., and the processor 500 may be an integrated circuit chip with signal processing capabilities; the memory 501 stores at least one instruction, at least one program, a code set or an instruction set, and when the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor, the steps in a data security transmission method of a digital energy air compressor station are implemented.

[0151] The present invention also provides a computer-readable storage medium corresponding to the method provided in the above-mentioned embodiment. Figure 6 , the storage medium shown is a CD, on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, it will execute the method provided by any of the aforementioned embodiments.

[0152] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), read-only memory (ROM), and other optical and magnetic storage media, which are not listed here one by one.

[0153] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0154] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for monitoring the operation of an automatic injection molding machine, characterized in that: The method comprises: Obtain the real-time mold temperature timing data and real-time injection pressure timing data of the current injection molding process of the automatic injection molding machine; obtain the historical mold temperature timing data and historical injection pressure timing data of the historical injection molding process of the automatic injection molding machine; Analyze the similarity between the data value change trends in the real-time injection pressure time series data and the historical injection pressure time series data, divide the real-time injection pressure time series data into injection molding process stages, and obtain the stage time period; Based on the difference in duration between the current injection molding process and the corresponding stage time period in the historical injection molding process, a relative time ratio is obtained, and based on the relative time ratio, a comparison moment corresponding to each moment in the current injection molding process in the historical injection molding process is determined; Analyze the temperature difference, temperature change similarity, and relative time proportion of each moment in the current injection molding process between each moment and the corresponding comparison moment, and determine the abnormality degree value of the mold temperature at each moment in the current injection molding process; perform abnormal monitoring on the operation of the automatic injection molding machine based on the abnormality degree value; The method of obtaining a relative time ratio based on the difference in duration between the current injection molding process and the corresponding stage time period in the historical injection molding process, and determining the comparison moment corresponding to each moment in the current injection molding process in the historical injection molding process based on the relative time ratio, includes: Combine the current injection molding process with each historical injection molding process to obtain an injection molding process combination; In each injection molding process combination, if the current injection molding process only includes one stage time period, then in the current injection molding process, based on the change trend between the temperature values ​​at the corresponding position in the historical injection molding process, the relative proportion of time corresponding to each moment is determined; If the current injection molding process includes at least two stage time periods, for each moment in the first stage time period of the current injection molding process, the relative time ratio is determined based on the change trend between the temperature values ​​at the corresponding position time in the historical injection molding process; for each moment in the other stage time periods, the relative time ratio corresponding to each moment is determined based on the ratio between the stage time period before each moment and the duration of the same stage time period in the historical injection molding process; Based on the relative proportion of the position sequence number and time of each moment in the current injection molding process in the corresponding stage time period, the comparison moment of each moment in the current injection molding process in the same stage time period of each historical injection molding process is determined.

2. The method for monitoring the operation of an automatic injection molding machine according to claim 1, characterized in that: The historical injection molding process at least includes a mold filling stage, a pressure holding stage and a cooling stage.

3. The method for monitoring the operation of an automatic injection molding machine according to claim 2, characterized in that: The method for obtaining the stage time period includes: In each historical injection molding process, the moment corresponding to the stage transition is taken as the target moment; In the real-time injection pressure time series data and the historical injection pressure time series data, respectively, in a preset neighborhood corresponding to each moment, based on the difference between the data values, determine the change trend characteristic value corresponding to each moment; Compare the change trend characteristic value at each moment in the real-time injection pressure time series data with the change trend characteristic value at the target moment in the historical injection pressure time series data, and determine the moment when the stage transition occurs in the current injection molding process as the segmentation moment; The current injection molding process is segmented according to the segmentation moment to obtain all stage time periods in the current injection molding process. If there is no segmentation moment in the real-time injection pressure timing data, the current injection molding process includes one stage time period.

4. The method for monitoring the operation of an automatic injection molding machine according to claim 3, characterized in that: The method for obtaining the segmentation time includes: Take two adjacent stages as a stage combination, and take the minimum value of the change trend characteristic value at the target moment corresponding to the same stage combination in all historical injection molding processes as the change standard characteristic value of the stage combination; In the current injection molding process, the change trend characteristic value of each moment is compared with the change standard characteristic value of the first stage combination in turn. When the change trend characteristic value is greater than or equal to the change standard characteristic value, the moment corresponding to the current change trend characteristic value is used as the segmentation moment; the change trend characteristic value of each moment after the segmentation moment is compared with the change standard characteristic value of the second stage combination in turn. When the change trend characteristic value is greater than or equal to the change standard characteristic value, the moment corresponding to the current change trend characteristic value is used as the segmentation moment; And so on, all the segmentation moments in the current injection molding process are obtained.

5. The method for monitoring the operation of an automatic injection molding machine according to claim 1, characterized in that: The method for obtaining the abnormality degree value includes: In each injection molding process combination, based on the difference between the mold temperature at each moment in the current injection molding process and the corresponding comparison moment, determining the initial abnormal value at each moment in the current injection molding process; Determine a first credible factor of an initial abnormal value at each moment in the current injection molding process based on a similarity between a temperature change trend of the mold temperature at each moment in the current injection molding process and a temperature change trend of the mold temperature at the corresponding comparison moment; Determine a second credible factor of the initial abnormal value at each moment in the current injection molding process based on the relative time ratio between the phase time period to which each moment belongs and the phase time period to which the corresponding comparison moment belongs in the current injection molding process; According to the first credible factor, the second credible factor and the initial abnormal value corresponding to each moment in all injection molding process combinations at each moment in the current injection molding process, the abnormality degree value at each moment in the current injection molding process is obtained, and the first credible factor, the second credible factor and the initial abnormal value are all positively correlated with the abnormality degree value.

6. The method for monitoring the operation of an automatic injection molding machine according to claim 5, characterized in that: The method for obtaining the first trust factor includes: Obtain the real-time data curve corresponding to the real-time mold temperature time series data, and obtain the historical data curve corresponding to the historical mold temperature time series data; In each injection molding process combination, the difference between the mold temperature slope value at each moment in the current injection molding process and the mold temperature slope value at the corresponding comparison moment is used as a difference factor; A first credible factor corresponding to each moment in the current injection molding process is obtained according to the difference factor, and the difference factor is negatively correlated with the first credible factor.

7. The method for monitoring the operation of an automatic injection molding machine according to claim 5, characterized in that: The method for obtaining the second trust factor includes: The difference between the relative proportion of time at each moment in the current injection molding process and the preset constant is used as a deviation factor; Based on the deviation factor, a second credible factor corresponding to each moment in the current injection molding process is obtained, and the second credible factor is negatively correlated with the deviation factor.

8. The method for monitoring the operation of an automatic injection molding machine according to claim 1, characterized in that: The abnormality monitoring of the operation of the automatic injection molding machine based on the abnormality degree value includes: In the current injection molding process, the moment when the abnormality value is greater than or equal to the preset abnormality threshold is regarded as an abnormal moment and an early warning is issued.

9. An automatic injection molding machine operation monitoring system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 can be implemented.

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