Operation state monitoring method and system for injection molding equipment

Abstract

The invention relates to the technical field of electronic digital data processing, in particular to an operation state monitoring method and system for injection molding equipment, and the method comprises the steps: collecting a multi-dimensional operation parameter time sequence of the injection molding equipment at different moments, collecting parameters of the same dimension into data sets, and constructing an isolated tree based on each data set; after sorting tree nodes of the isolated tree according to a data average value, distributing a tree node sequence number for each item of data; the combination of the dimension parameters at the same moment is regarded as a data point, the distribution relation of any two data points on the isolated tree is compared, the same-group degree of the data points is calculated, and the data points with the same-group degree higher than a set threshold value are classified into the same group; calculating the abnormal degree of the data points; and if the abnormal degree exceeds a set threshold value, determining that the data point is an abnormal data point and giving an alarm at the same time. The problem that the accuracy and reliability of state detection are not high is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and more specifically, to a method and system for monitoring the operating status of injection molding equipment. Background Art

[0002] Injection molding equipment is a key equipment in the production of plastic products. The stability of its operating state directly affects product quality, production efficiency and the service life of the equipment. However, during the injection molding process, the operating state of the equipment is usually affected by a variety of complex factors, including the temperature, vibration, pressure and speed of the equipment. There is a high degree of nonlinear correlation and coupling between these parameters, and any abnormality may lead to product quality problems or equipment failures.

[0003] At present, most of the operating status monitoring methods of injection molding equipment mainly rely on manual inspections or simple monitoring of a single operating parameter, such as independent judgment of temperature, pressure or vibration by setting thresholds. However, these traditional methods have obvious shortcomings. Manual inspections are not only inefficient and have poor real-time performance, but are also easily affected by insufficient human experience or subjective misjudgment; and single parameter monitoring fails to fully consider the interaction between multiple parameters and their impact on the overall operating status, especially in complex production environments, which can easily lead to missed or false alarms of abnormal detection. In addition, with the rapid development of industrial automation and intelligent technology, the complexity of injection molding processes and equipment is increasing. Existing monitoring technologies are unable to cope with diverse working conditions and high-frequency abnormal conditions, and cannot meet the higher requirements of modern production for equipment stability, abnormal response speed, and fault location accuracy.

[0004] The patent application document with application publication number CN118348925A discloses a device operation status monitoring system and monitoring method. The patent application document obtains the external operation status and internal operation parameters of the device in real time through a video acquisition unit and a data acquisition unit. The processor combines the two to judge the overall operation status of the device and issues an early warning in the event of an abnormality, thereby achieving accurate positioning and rapid maintenance of equipment abnormalities, improving production efficiency and reducing risks and losses.

[0005] However, the above technical solution only relies on the processor to simply combine and judge the operating status of the equipment, and does not fully consider the correlation between multi-dimensional operating parameters in complex scenarios and the interference of abnormalities on the detection results. It is difficult to effectively cope with diverse and complex environments, resulting in low accuracy and reliability of injection molding equipment status detection. Summary of the invention

[0006] In order to solve the problem of low accuracy and reliability of injection molding equipment status detection mentioned in the above background technology, the present invention provides solutions in the following aspects.

[0007] In a first aspect, the present invention provides a method for monitoring the operating status of an injection molding device, comprising: collecting the operating parameter time series of the injection molding device at different times, wherein the operating parameter time series includes parameter time series of multiple dimensions, and treating the parameters of the same dimension at different times as a data set; constructing an isolation tree based on each data set, and sorting the tree nodes based on the average value of the data in each tree node of the isolation tree to obtain the sequence number of the tree node corresponding to each data in the data set; treating the parameters of each dimension at the same time as a data point, and calculating the degree of same group of any two data points, wherein the first Data points and The degree of homogeneity of the data points , where is the total number of parameter items of each dimension in the data point, For the Of the data points The item data corresponds to the sequence number of the tree node, For the Of the data points The item data corresponds to the sequence number of the tree node, For the The total number of tree nodes corresponding to the item data; the data points with a degree of same group greater than the set same group threshold are recorded as the same group data points; calculate the The degree of abnormality of the data point , where For the The initial abnormality of the data point, For the data points is the number of data points in the same group, For the The average value of the total number of tree nodes corresponding to each data point, is the total number of data points; and a data point whose abnormality degree is greater than a set abnormality threshold is an abnormal data point.

[0008] The above technical solution can efficiently detect abnormal conditions of injection molding equipment based on isolation tree construction and multi-dimensional parameter analysis. By calculating the degree of grouping and abnormality of data points, the system can accurately identify small deviations or abnormal patterns in equipment operation, thereby improving the accuracy of equipment status monitoring. Since this method takes into account the coordinated changes of multi-dimensional parameters, it can comprehensively capture potential problems in equipment operation and avoid misjudgments that may be caused by single-dimensional parameters. In addition, combined with dynamically set abnormal thresholds and intelligent data analysis, the system has stronger adaptability and flexibility under different working conditions, further improving the reliability of detection. Overall, this technical solution effectively reduces the omission of equipment failures, improves the fault prediction capability of injection molding equipment, and provides more accurate and timely decision support for equipment maintenance and production scheduling.

[0009] Furthermore, the construction of the isolation tree is specifically as follows: randomly selecting a feature in each tree node of the isolation tree, and randomly selecting a split value between the maximum value and the minimum value of the feature, and splitting the data set according to the split value; until the tree node reaches a predetermined maximum depth or the amount of data in the tree node reaches a preset quantity threshold.

[0010] The above technical solution constructs an efficient anomaly detection model by randomly selecting features and performing data segmentation at each node of the isolation tree, and by using recursive segmentation. Since the segmentation of each node is based on the random selection of features, this randomness helps to avoid overfitting and improves the adaptability of the model to different data distributions. By controlling the maximum depth of the tree and the threshold of the amount of node data, the system can fully explore the potential patterns in the data while ensuring computational efficiency. This construction method ensures the stability and efficiency of the isolation tree when processing large-scale, high-dimensional data, thereby improving the accuracy and sensitivity of anomaly detection.

[0011] Furthermore, the operating parameter timing includes a temperature timing, a vibration timing, a pressure timing and a speed timing of the injection molding equipment.

[0012] Furthermore, a temperature sensor is used to collect the temperature time series of the injection molding equipment, a vibration sensor is used to collect the vibration time series of the injection molding equipment, a pressure sensor is used to collect the pressure time series of the injection molding equipment, and a speed sensor is used to collect the speed time series of the injection molding equipment.

[0013] Furthermore, it also includes data cleaning of the operating parameter timing.

[0014] The above technical solution can effectively remove noise, fill missing values, and correct outliers through data cleaning, thereby preventing inaccurate or erroneous data from interfering with subsequent analysis and prediction.

[0015] Furthermore, an isolation forest algorithm is used to obtain the initial abnormality degree of the data point.

[0016] The above technical solution uses the isolation forest algorithm to obtain the initial abnormality of the data point, and the system can efficiently and accurately identify potential abnormalities in the operation of the equipment. The isolation forest algorithm performs anomaly assessment based on the isolation of data points in the feature space, and can effectively distinguish between normal and abnormal data, especially when processing high-dimensional and large-scale data.

[0017] Furthermore, the injection molding equipment automatically issues an alarm when an abnormal data point is detected.

[0018] The above technical solution significantly improves the response speed and fault prevention capability of injection molding equipment by automatically issuing an alarm when abnormal data points are detected. The system can capture abnormal conditions in equipment operation in real time and issue an alarm in time when problems first occur, avoiding further deterioration of equipment failures or affecting production progress. This automated alarm mechanism reduces reliance on manual monitoring, improves the system's monitoring efficiency and accuracy, and also reduces losses caused by human negligence or delayed response. In addition, through timely alarms, operators can quickly take emergency measures, such as adjusting equipment parameters or performing maintenance, to ensure that the equipment operates in a safe operating state, thereby reducing downtime, improving production efficiency, and enhancing equipment reliability and sustainability.

[0019] In a second aspect, the present invention provides an operation status monitoring system for injection molding equipment, comprising a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, any one of the above-described methods for monitoring the operation status of injection molding equipment is implemented.

[0020] The beneficial effects of the present invention are: The present invention effectively improves the accuracy and reliability of monitoring the operating status of injection molding equipment by adopting multi-dimensional parameter time series analysis, isolated tree construction, abnormality degree calculation and automatic alarm mechanism. By real-time monitoring and data cleaning of parameters such as equipment temperature, vibration, pressure and speed, the system can comprehensively capture the operating status of the equipment and promptly identify potential abnormal behaviors. The application of the isolated tree algorithm enhances the sensitivity and adaptability of anomaly detection, especially when faced with high-dimensional and large-scale data, it can efficiently distinguish between normal and abnormal data points. The automatic alarm mechanism of abnormal data points further improves the response speed of the system, ensuring that timely alarms and emergency measures can be taken when problems occur in the equipment, thereby reducing the occurrence of equipment failures, extending the service life of the equipment, and optimizing production efficiency. Overall, the present invention provides an intelligent and efficient operation monitoring and fault warning solution for injection molding equipment, which greatly improves the operating safety and production stability of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 is a flow chart schematically showing a method for monitoring the operating status of an injection molding device according to an embodiment of the present invention; Figure 2 Schematically shows a structural block diagram of an operation status monitoring system for injection molding equipment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0024] An embodiment of a method for monitoring the operating status of an injection molding device.

[0025] like Figure 1 As shown, a flow chart of a method for monitoring the operating status of an injection molding device according to an embodiment of the present invention includes the following steps: S1: Collect the operating parameter timing of the injection molding equipment at different times, where the operating parameter timing includes parameter timing in multiple dimensions.

[0026] In one embodiment, the operating parameter sequence includes the temperature sequence, vibration sequence, pressure sequence and speed sequence of the injection molding equipment. These parameters are important indicators for evaluating the operating status of the injection molding equipment, and the change of each parameter may directly affect the working efficiency and product quality of the equipment. Therefore, collecting and analyzing these operating parameters is crucial for accurately monitoring the equipment status.

[0027] Specifically: Use temperature sensors to collect the temperature sequence of injection molding equipment. Temperature is one of the important factors affecting the injection molding process. Changes in equipment temperature may be directly related to equipment overheating, poor cooling and other faults; Use vibration sensors to collect the vibration sequence of injection molding equipment. Vibration is a common state feedback during equipment operation. Abnormal vibration fluctuations may be a precursor to equipment failure; Use pressure sensors to collect the pressure sequence of injection molding equipment. Pressure fluctuations may directly affect the molding accuracy and consistency of the product; Use speed sensors to collect the speed sequence of injection molding equipment. Changes in equipment operating speed are directly related to production efficiency and product consistency. By real-time monitoring of speed sequence, it is possible to ensure that the equipment runs smoothly at a predetermined speed. Once speed fluctuations or abnormalities occur, the system can issue an early warning in a timely manner to help operators adjust the equipment status.

[0028] After collecting the time series data of operating parameters such as temperature, vibration, pressure and speed, these data need to be cleaned. Data cleaning is a key link in the entire monitoring process. The purpose is to eliminate noise data, missing values ​​and abnormal data in the collection process to ensure the accuracy and reliability of the analysis results. Through data cleaning, the influence of external environmental interference, sensor failure or error and other factors on data collection can be effectively eliminated.

[0029] During the data cleaning process, the collected raw data will first be filled with missing values ​​and processed for outliers to ensure that the data is complete and has no obvious errors. In addition, the system will correct or remove abnormal fluctuations caused by sensor drift or external factors to prevent these interference factors from affecting subsequent fault diagnosis and prediction results. After data cleaning, the obtained data will have higher quality, providing reliable data support for subsequent equipment status monitoring, fault warning and maintenance decisions.

[0030] Through the above data collection and cleaning process, the system can obtain accurate and reliable time series data of equipment operating parameters. After cleaning, these data can provide high-quality input for anomaly detection, fault prediction and equipment health management, greatly improving the accuracy and real-time performance of equipment status monitoring.

[0031] S2: Take the parameters of each dimension at the same time as a data point and calculate the degree of same group between any two data points.

[0032] In one embodiment, the same dimension parameter at different times is taken as an independent data set; an isolation tree is constructed based on each data set, and the tree nodes are sorted based on the average value of the data in each tree node of the isolation tree to obtain the sequence number of the tree node corresponding to each data in the data set; Among them, constructing an isolation tree is specifically as follows: randomly selecting a feature in each tree node of the isolation tree, and randomly selecting a split value between the maximum value and the minimum value of the feature, and splitting the data set according to the split value; until the tree node reaches a predetermined maximum depth or the number of data in the tree node reaches a preset number threshold.

[0033] Take the parameters of each dimension at the same time as a data point and calculate the degree of same group of any two data points. Data points and The degree of homogeneity of the data points , where is the total number of parameter items of each dimension in the data point, For the Of the data points The item data corresponds to the sequence number of the tree node, For the Of the data points The item data corresponds to the sequence number of the tree node, For the The total number of tree nodes corresponding to the item data; And the data points whose degree of same group is greater than the set same group threshold are recorded as same group data points.

[0034] By calculating the degree of same-grouping of each dimensional parameter as a data point at the same time, this technical solution can quantify the similarity between different data points, thereby effectively identifying similar or consistent operating modes. Specifically, by calculating the degree of same-grouping based on the difference in the sequence numbers of isolated tree nodes, the proximity of different data points in the multidimensional parameter space can be accurately evaluated. This method ensures a comprehensive measurement of the similarity of data points by comprehensively considering the tree node distribution and structure of each dimensional data, and improves the model's ability to identify abnormal data points. In addition, this same-group measurement method can effectively filter noise, improve the accuracy of data analysis, and provide a more reliable basis for subsequent anomaly detection, fault prediction, and status monitoring.

[0035] S3: Calculate the abnormality of each data point based on the same group situation of each data point.

[0036] In one embodiment, an isolation forest algorithm is used to obtain the initial abnormality degree of the data point; The degree of abnormality of the data point , where For the The initial abnormality of the data point, For the data points is the number of data points in the same group, For the The average value of the total number of tree nodes corresponding to each data point, is the total number of data points.

[0037] By using the isolation forest algorithm to obtain the initial abnormality of the data point, and combining the number of data points in the same group, the average value of the tree node distribution, and the total number of data points for comprehensive calculation, this technical solution can effectively quantify the abnormality of each data point. This method can accurately evaluate the abnormality of data points in multidimensional parameter space, and further optimize the abnormality detection results through the relationship with the data points in the same group. Through this comprehensive calculation, the solution enhances the sensitivity to minor anomalies, improves the accuracy and reliability of detection, and thus provides more accurate support for equipment failure warning, abnormal state identification, etc.

[0038] S4: In response to a data point whose abnormality is greater than a set abnormality threshold, an alarm is automatically issued as an abnormal data point.

[0039] The above-mentioned abnormal threshold value may be set to 0.65, and of course, it may also be determined according to actual conditions.

[0040] In one embodiment, when the degree of abnormality of a data point exceeds the set abnormal threshold, the system automatically determines that the data point is an abnormal data point, triggers an alarm in time, and notifies the operator or maintenance personnel to take corresponding measures. The alarm can not only be issued locally on the device, but also transmit information to the management platform or mobile terminal through the remote monitoring system so that relevant personnel can take timely action. The triggering of the alarm can be linked with the control system of the device to automatically start the predetermined emergency response measures, such as suspending production, adjusting parameters or switching to backup equipment, etc., to ensure the continuity of production and the safety of the equipment.

[0041] The solution of the present invention significantly improves the accuracy and real-time performance of the operation status monitoring of the injection molding equipment by combining the monitoring of the multi-dimensional operation parameter time series, the construction of the isolated tree model, and the automatic identification and alarm mechanism of abnormal data points. First, the use of isolated trees and isolated forest algorithms to intelligently analyze the multi-dimensional data of the equipment can efficiently identify minor anomalies in the operation of the equipment and avoid potential faults that may be missed in traditional methods. Secondly, the mechanism of automatic alarm ensures timely response to abnormal situations, reduces human delays and misjudgments, and improves fault prevention capabilities. In addition, through data cleaning and the fusion of multi-sensor data, the system further enhances the quality and reliability of monitoring data and reduces the risk of false alarms caused by noise and outliers. Overall, this solution improves the accuracy of equipment fault prediction, reduces downtime, optimizes production efficiency, and improves the level of intelligence in equipment maintenance, and has good application prospects.

[0042] An embodiment of an operation status monitoring system for injection molding equipment: like Figure 2As shown, a structural block diagram of an operation status monitoring system for injection molding equipment according to an embodiment of the present invention includes a processor and a memory.

[0043] The present invention also provides an operation status monitoring system for injection molding equipment. Figure 2 As shown, the system includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned method for monitoring the operating status of an injection molding device according to the present invention is implemented.

[0044] The operation status monitoring system for injection molding equipment also includes other components familiar to those skilled in the art, such as a communication interface, and its settings and functions are known in the art, so they will not be described in detail here.

[0045] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that may be stored or otherwise maintained by such a computer-readable medium.

[0046] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0047] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for monitoring the operating status of an injection molding device, characterized in that: include: Collecting the time series of operating parameters of the injection molding equipment at different times, wherein the time series of operating parameters includes the time series of parameters in multiple dimensions, and taking the parameters of the same dimension at different times as a data set; constructing an isolation tree based on each data set, and sorting the tree nodes based on the average value of the data in each tree node of the isolation tree, so as to obtain the sequence number of the tree node corresponding to each data in the data set; Take the parameters of each dimension at the same time as a data point and calculate the degree of same group of any two data points. Data points and The degree of homogeneity of the data points , where is the total number of parameter items of each dimension in the data point, For the Of the data points The item data corresponds to the sequence number of the tree node, For the Of the data points The item data corresponds to the sequence number of the tree node, For the The total number of tree nodes corresponding to the item data; Data points whose degree of same group is greater than the set same group threshold are recorded as same group data points; Calculate the The degree of abnormality of the data point , where For the The initial abnormality of the data point, For the data points is the number of data points in the same group, For the The average value of the total number of tree nodes corresponding to each data point, is the total number of data points; In response to the data point having an abnormal degree greater than a set abnormal threshold, the data point is regarded as an abnormal data point.

2. A method for monitoring the operating status of an injection molding device according to claim 1, characterized in that: The construction of the isolation tree is specifically as follows: A feature is randomly selected at each tree node of the isolated tree, and a split value is randomly selected between the maximum value and the minimum value of the feature, and the data set is split according to the split value; Until the tree node reaches a predetermined maximum depth or the amount of data in the tree node reaches a preset threshold.

3. The method for monitoring the operating status of an injection molding device according to claim 1, characterized in that: The operating parameter timing includes a temperature timing, a vibration timing, a pressure timing and a speed timing of the injection molding equipment.

4. A method for monitoring the operating status of an injection molding device according to claim 3, characterized in that: The temperature time series of the injection molding equipment is collected by a temperature sensor, the vibration time series of the injection molding equipment is collected by a vibration sensor, the pressure time series of the injection molding equipment is collected by a pressure sensor, and the speed time series of the injection molding equipment is collected by a speed sensor.

5. The method for monitoring the operating status of an injection molding device according to claim 1, characterized in that: It also includes data cleaning of the operating parameter timing.

6. A method for monitoring the operating status of an injection molding device according to claim 1, characterized in that: The isolation forest algorithm is used to obtain the initial abnormality degree of the data point.

7. A method for monitoring the operating status of an injection molding device according to claim 1, characterized in that: It also includes the injection molding equipment automatically sending out alarms when abnormal data points are detected.

8. An operating status monitoring system for injection molding equipment, characterized in that: The invention comprises a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for monitoring the operating status of an injection molding device as claimed in any one of claims 1 to 7 is implemented.

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