A monitoring system and a stamping monitoring system

Abstract

This application relates to the field of equipment monitoring technology, and discloses a monitoring system and a stamping monitoring system. The monitoring system includes a mold identification device, a data acquisition device, and a working condition identification device. The mold identification device is connected to the processing equipment to determine the mold identity of the processing mold on the processing equipment. The data acquisition device is connected to the processing equipment to collect state parameters during the processing process. The working condition identification device is connected to both the data acquisition device and the mold identification device, and is used to determine the current working condition of the processing equipment based on the state parameters during the processing process and the mold identity of the processing mold on the processing equipment. This method enables intelligent monitoring of the working condition of the processing equipment.

Description

Technical Field

[0001] This application is a divisional application of the applicant's patent application filed on July 10, 2023, entitled "A Monitoring System and a Stamping Monitoring System," with application number 202310838463.4. This application relates to the field of equipment monitoring technology, and in particular to a monitoring system and a stamping monitoring system. Background Technology

[0002] Processing equipment such as stamping presses and injection molding machines are widely used in industrial manufacturing sectors such as home appliances and automobiles, and their most significant characteristic is high production efficiency. Taking stamping presses as an example, high-speed stamping presses can achieve more than 200 strokes per minute; however, under the high-speed production cycle of stamping presses, manual monitoring of the die properties and other aspects is required, making it impossible to detect changes in the die properties in a timely manner, resulting in low efficiency and a significant expenditure of labor costs. Summary of the Invention

[0003] The main technical problem addressed by this application is to provide a monitoring system and a stamping monitoring system that can intelligently monitor the operating conditions of processing equipment.

[0004] To solve the above-mentioned technical problems, one technical solution adopted in this application is: to provide a monitoring system, which includes a mold identification device, a data acquisition device, and a working condition identification device; the mold identification device is used to connect with the processing equipment to determine the mold identification of the processing mold on the processing equipment; the data acquisition device is used to connect with the processing equipment to collect the status parameters of the processing equipment during the processing process; the working condition identification device is connected to the data acquisition device and the mold identification device respectively, and is used to determine the current working condition of the processing equipment based on the status parameters of the processing equipment during the processing process and the mold identification of the processing mold on the processing equipment; wherein, the current working condition of the processing equipment includes abnormal production, which includes machine shutdown, machine adjustment, and mold replacement.

[0005] To solve the above-mentioned technical problems, another technical solution adopted in this application is: to provide a stamping monitoring system, which includes a mold identification device, a data acquisition device, and a working condition identification device; the mold identification device is used to connect to the stamping machine to determine the mold identification of the stamping mold on the stamping machine; the data acquisition device is used to connect to the stamping machine to collect the state parameters of the stamping process of the stamping machine; the working condition identification device is connected to the data acquisition device and the mold identification device respectively, and is used to determine the current working condition of the stamping machine based on the state parameters of the stamping process of the stamping machine and the mold identification of the stamping mold on the stamping machine; wherein, the current working condition of the stamping machine includes abnormal production, which includes machine shutdown, machine adjustment, and mold replacement.

[0006] The aforementioned technical solution includes a monitoring system comprising a condition identification device and a data acquisition device. The data acquisition device collects status parameters of the processing equipment during the processing process, while the condition identification device determines the current operating condition of the processing equipment based on these status parameters and the mold identity of the processing mold on the equipment. Therefore, by setting up the condition identification device and the data acquisition device, the current operating condition of the processing equipment can be intelligently and accurately identified without any manual intervention, achieving intelligent monitoring of the processing equipment's operating condition. This allows for more timely adjustments to the processing equipment based on its operating condition. Attached Figure Description

[0007] Figure 1 This is a schematic diagram of the frame structure of one embodiment of the processing equipment provided in this application;

[0008] Figure 2 This is a schematic diagram of the frame structure of one embodiment of the mold identification device provided in this application;

[0009] Figure 3 This is a logic diagram of one embodiment of the working condition identification device provided in this application;

[0010] Figure 4 This is a schematic diagram of the framework structure of one embodiment of the data analysis device provided in this application. Detailed Implementation

[0011] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0012] It should be noted that if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0013] Please refer to the following: Figure 1 and Figure 2 , Figure 1This is a schematic diagram of the frame structure of one embodiment of the processing equipment provided in this application. Figure 2 This is a schematic diagram of the framework structure of one embodiment of the mold identification device provided in this application. The monitoring system 10 includes a mold identification device 11, which includes at least a tag 111 and a mold identification module 112. The tag 111 is used to be mounted on the processing equipment 20 and to send radio frequency signals to the mold identification module 112. The radio frequency signals include tag information characterizing the mold identification of the processing mold on the processing equipment 20. The mold identification module 112 is used to determine the preset operating frequency of each tag in the tag group, including the tag 111, and to receive the tag information sent by each tag at its corresponding preset operating frequency. Based on the tag information sent by each tag at its corresponding preset operating frequency, the mold identification of the processing mold on the processing equipment 20 is determined. In other words, the tag 111 is attached to the processing equipment 20 on which the processing mold is installed, thereby returning tag information that identifies the mold identity of the processing mold on the processing equipment 20. The mold identity recognition module 112 can confirm the mold identity of the processing mold on the processing equipment 20 based on the tag information, thereby knowing the mold identity of the processing mold on the processing equipment 20, so as to determine whether the processing mold on the processing equipment 20 has been replaced. Therefore, by setting up the mold identity recognition device 11 connected to the processing equipment 20, the mold identity of the processing mold on the processing equipment 20 can be intelligently and accurately identified without manual intervention, realizing intelligent monitoring of the processing mold identity and saving labor costs.

[0014] In one implementation, such as Figure 1 As shown, the processing equipment 20 is a stamping machine, and the monitoring system 10 is used to monitor the mold identity of the stamping die on the stamping machine. Of course, in other embodiments, the processing equipment 20 can also be an injection molding machine, and the monitoring system 10 is used to monitor the mold identity of the injection mold on the injection molding machine; this is not limited here.

[0015] In one embodiment, the tag information includes the strength of the radio frequency signal transmitted by tag 111. The mold identification module 112 is used to determine the preset operating frequency of each tag in the tag group, including tag 111. Specifically, the mold identification module 112 is used to acquire the radio frequency signal strength of each tag in the tag group, including tag 111, at each operating frequency, and determine the preset operating frequency of each tag in the tag group, including tag 111, based on the radio frequency signal strength of each tag in the tag group, including tag 111, at each operating frequency. That is, the preset operating frequency of each tag in the tag group, including tag 111, is determined by the radio frequency signal strength of each tag in the tag group, including tag 111, at each operating frequency.

[0016] In complex industrial metal environments, complex reflections of radio frequency (RF) signals can lead to multi-tag interference. This means that a single mold identification module 112 may read multiple different RF tags simultaneously, resulting in ineffective identification of the mold on the processing equipment 20. Theoretically, the strongest signal from the demodulated tag could be selected to identify the nearest tag. However, in practical applications, the highest sensitivity center frequency of the tag shifts after installation in complex metal environments. Furthermore, the RF antenna emitted by the mold identification module 112 during tag reading is a broadband electromagnetic wave, and the center frequency consistency of the tags at the factory has some error. Therefore, the identified nearest tag may be incorrect, leading to an incorrect identification of the mold on the processing equipment 20. In one embodiment, a preset operating frequency is designated as the optimal operating frequency. In this case, for each tag, the mold identification module 112 uses the operating frequency with the strongest RF signal as the optimal operating frequency for that tag.

[0017] Specifically, the mold identification module 112 includes an antenna (not shown in the figure), a signal demodulator 1122, and a mold identification module 1123. The antenna is used to receive radio frequency signals and transmit them to the signal demodulator 1122. The signal demodulator 1122 is used to demodulate the radio frequency signals to obtain tag information. For each tag in the tag group, the mold identification module 1123 is used to select the operating frequency with the strongest radio frequency signal as the optimal operating frequency of the tag. Each tag in the tag group is a tag within the antenna's operating area. The tag information of each tag in the tag group is read by the signal demodulator 1122 and sent to the mold identification module 1123. In other words, the mold identification module 1123 determines the true optimal operating frequency of each tag in the tag group, including tag 111, after environmental influences. Specifically, it uses the operating frequency with the strongest radio frequency signal as the optimal operating frequency. Then, the antenna receives the radio frequency signals transmitted by each tag in the tag group, including tag 111, at its corresponding true optimal operating frequency and sends them to the signal demodulator 1122. The signal demodulator 1122 demodulates these signals to obtain the tag information transmitted by each tag in the tag group, including tag 111, at its corresponding optimal operating frequency, and sends this information to the mold identification module 1123. Then, the mold identification module 1123 determines the mold identity of the processing mold on the processing equipment 20 based on the tag information corresponding to each tag 111 in the tag group, including tag 111. By determining the mold identity of the processing mold on the processing equipment 20 based on the tag information transmitted by each tag in the tag group, including tag 111, the interference caused by the center frequency shift of the radio frequency signal after reflection in complex environments such as metal, the error of the center point of the tag manufacturing frequency, and signal superposition can be effectively avoided.

[0018] In one specific embodiment, the mold identification module 1123 determines the operating frequency of each tag in the tag group, including tag 111, by scanning, and then determines the optimal operating frequency of each tag in the tag group, including tag 111.

[0019] In one embodiment, the tag information also includes the mold identification number of the corresponding processing mold. The mold identification module 112 determines the mold identity of the processing mold on the processing equipment 20 based on the tag information sent by each tag at its respective preset working frequency. Specifically, the mold identification module 112 obtains the read success rate of each tag in the tag group, including tag 111, and uses the mold identification number corresponding to the strongest radio frequency signal strength and the highest read success rate as the mold identity of the processing mold. That is, by comprehensively considering the radio frequency signal strength and read success rate of each tag in the tag group, including tag 111, the mold identity of the processing mold on the processing equipment 20 is determined, thus improving the accuracy of processing mold identification. Of course, in other specific embodiments, only the mold identification number corresponding to the strongest radio frequency signal strength can be used as the mold identity of the processing mold on the processing equipment 20. In other specific embodiments, only the mold identification number corresponding to the highest tag read success rate can be used as the mold identity of the processing mold on the processing equipment 20.

[0020] It should be noted that the reading success rate of tag 111 refers to the ratio between the number of times the signal demodulator 1122 successfully demodulates the radio frequency signal sent by tag 111 to the number of times the signal demodulator 1122 receives the radio frequency signal sent by tag 111.

[0021] In one specific embodiment, the signal demodulator 1122 is a reader. The reader can demodulate the radio frequency signal sent by the tag 111 to obtain tag information that characterizes the mold identity of the processing mold on the processing equipment 20. In the tag communication system, the reader provides energy to the tag 111 to emit electromagnetic waves into the surrounding space. After reaching the tag 111, part of the electromagnetic wave energy is absorbed by the tag 111 to drive the circuit of the tag 111, and another part is emitted in various directions with different intensities. Part of the reflected energy eventually returns to the reader; the tag 111 uses this reflected energy to realize data transmission with the reader.

[0022] In one embodiment, tag 111 is an RFID tag or an NFC tag. In a specific embodiment, tag 111 is an RFID tag operating in the UHF band.

[0023] During the production process, processing equipment 20 typically involves normal production, mold replacement, machine debugging, and equipment maintenance. When a mold is replaced, the process state varies significantly depending on the mold and machine, requiring adjustments to the corresponding pattern recognition parameters; otherwise, the monitoring and analysis accuracy of processing equipment 20 will be severely affected. During a single processing and debugging phase, the relevant process parameters are not fixed, and the production process state is unstable. Therefore, using data samples from this phase for self-learning will also significantly impact the monitoring accuracy of processing equipment 20. After equipment maintenance, the samples also need to be relearned; otherwise, the monitoring accuracy of the processing equipment will also be affected.

[0024] Therefore, please refer to the following: Figure 1 and Figure 3 , Figure 3 This is a logic diagram of one embodiment of the working condition identification device provided in this application. In one embodiment, the monitoring system 10 further includes a working condition identification device 12 and a data acquisition device 13. The data acquisition device 13 is connected to the processing equipment 20. The working condition identification device 12 is connected to the data acquisition device 13 and the mold identity identification module 112 of the mold identity identification device 11. The data acquisition device 13 is used to collect the status parameters of the processing equipment 20 during the processing process. The working condition identification device 12 is used to determine the current working condition of the processing equipment 20 based on the status parameters of the processing equipment 20 and the mold identity of the processing mold on the processing equipment 20. That is, the mold identity identification result and the mold status parameters collected by the data acquisition device 13 are used as the input of the working condition identification device 12 to identify the current working condition of the processing equipment 20. In other words, by setting up a data acquisition device 13 connected to the processing equipment 20 and a working condition identification device 12 connected to the data acquisition device 13 and the mold identification module 112 of the mold identification device 11, the current working condition of the processing equipment 20 can be intelligently and accurately identified without any manual intervention. This realizes the intelligent monitoring of the working condition of the processing equipment 20, so that the processing equipment 20 can be adjusted more promptly according to its working condition.

[0025] In one specific embodiment, the current operating condition of the processing equipment 20 includes abnormal production, which includes shutdown, machine adjustment, and mold change. The operating condition identification device 12 is used to determine the current operating condition of the processing equipment 20 based on the status parameters of the processing equipment 20 and the mold identity of the processing mold on the processing equipment 20. Specifically, the operating condition identification device 12 is used to determine the current operating condition of the processing equipment 20 as mold change when the mold identity of the processing mold on the processing equipment 20 is different from the historical identity corresponding to the historical mold on the processing equipment 20; to determine the current operating condition of the processing equipment 20 as shutdown when the mold identity of the processing mold on the processing equipment 20 is the same as the historical identity corresponding to the historical mold on the processing equipment 20, and no new status parameters are received within a preset time period after the previous status parameter is received; and to determine the current operating condition of the processing equipment 20 as machine adjustment when the mold identity of the processing mold on the processing equipment 20 is the same as the historical identity corresponding to the historical mold on the processing equipment 20, and the time interval between the current time of receiving the status parameter and the previous time of receiving the status parameter is greater than the interval threshold. In other words, when the mold identity changes, the processing equipment 20 is determined to be in a mold-changing state; when the mold identity does not change, and no status parameter data is received within a preset time period after the previous status parameter data was received, it indicates that the processing equipment 20 is currently in a shutdown state, and the processing equipment 20 is determined to be in a shutdown state; when the mold identity does not change, and the time interval between the current time of receiving the status parameter and the previous time of receiving the status parameter is greater than the interval threshold, it indicates that the time interval of collecting the status parameter is uneven, and the processing equipment 20 is operating intermittently, thus determining that the processing equipment 20 is currently in a machine adjustment state. In other words, by setting up the operating condition identification device 12, not only can the fault state of the processing equipment 20 be identified, but also the type of fault state can be identified, which is beneficial for subsequent fault location and rapid repair.

[0026] In one specific embodiment, the current operating condition of the processing equipment 20 also includes normal production. The operating condition identification device 12 is used to determine the current operating condition of the processing equipment 20 based on the status parameters of the processing equipment 20 and the mold identity of the processing mold on the processing equipment 20. Specifically, in response to the time interval between the current time of receiving the status parameter and the previous time of receiving the status parameter being equal to an interval threshold, or a new status parameter being received within a preset time period after the previous time of receiving the status parameter, and the mold identity of the processing mold on the processing equipment being the same as the historical identity corresponding to the historical mold on the processing equipment 20, the current operating condition of the processing equipment 20 is determined to be normal production. That is, when the mold identity has not changed, and no new status parameter data has been received within a preset time period after the previous time of receiving the status parameter data, or the time interval between the current time of receiving the status parameter and the previous time of receiving the status parameter being equal to the interval threshold, it indicates that the time interval of the collected status parameters is stable and uniform. At this time, it indicates that the processing equipment 20 is operating stably and continuously, thereby determining that the processing equipment 20 is currently in a normal production state.

[0027] Please continue reading. Figure 1 In one embodiment, the current operating condition includes abnormal production. The monitoring system 10 further includes a data analysis device 14 and a control device 15. The control device 15 is connected to the operating condition identification device 12 and the data analysis device 14, respectively. The data acquisition device 13 is connected to the data analysis device 14. The data analysis device 14 is used at least to determine the processing mode of the processing mold. The control device 15 is used to suspend the operation of the data analysis device 14 in response to the current operating condition of the processing equipment 20 being abnormal production. That is to say, the monitoring system 10 can also determine the processing mode of the processing mold based on the status parameters collected by the data acquisition device 13 to monitor the processing process. However, considering that when the processing equipment 20 itself is in abnormal production, the processing mold installed on it cannot be processed normally, the monitoring system 10 is also provided with a control device 15 to control the data analysis device 14 to suspend operation when the current operating condition of the processing equipment 20 is abnormal production, thereby reducing energy consumption. In other words, the monitoring system 10 provided in this application can intelligently and accurately identify the processing mode of the processing mold without any manual intervention. This realizes the intelligent monitoring of the processing mode of the processing mold, so that the processing equipment 20 can be adjusted more promptly according to the processing mode of the processing mold.

[0028] In other embodiments, the data analysis device 14 can also classify and characterize quality defects such as embossing caused by scrap material skipping and tearing and burrs caused by punch wear based on the status parameters of the processing equipment 20 during the processing process collected by the data acquisition device 13. Additionally, it can analyze whether the current processing mold, parameter range, etc., are in a healthy state based on the status parameters of the processing equipment 20 during the processing process collected by the data acquisition device. In one specific embodiment, the production count of the processing process can also be optimized based on the analysis and identification results of the data analysis device 14, effectively eliminating counts of air pressure and quality defects, and achieving real-time and accurate statistics of the production defect rate.

[0029] Since the processing equipment 20 may need to adjust or change some process parameters after it returns to normal production from an abnormal state, using the previous process parameters for mold pattern identification may lead to errors and inaccurate subsequent yield statistics. Therefore, in one embodiment, as... Figure 4 As shown, Figure 4 This is a schematic diagram of the framework structure of one embodiment of the data analysis device provided in this application. The data analysis device 14 includes a parameter learning module 141, which is connected to the data acquisition device 13. The parameter learning module 141 is used to learn and generate the parameter range corresponding to the normal processing mode based on the state parameters of the processing equipment 20. The control device 15 is also used to restart the operation of the data analysis device 14 in response to the current working condition of the processing equipment 20 returning to normal production. The data analysis device 14 is also used to relearn and generate a new parameter range based on the new state parameters received after restarting. That is, after the processing equipment 20 re-enters normal production, the control device 15 will also control the data analysis device 14 to restart. At this time, after the data analysis device 14 starts, the parameter learning module 141 will use the new state parameters obtained from the data acquisition device 13 as new samples to relearn and obtain a new parameter range to update the algorithm configuration parameters. In other words, the monitoring system 10 provided in this application can automatically update the set parameter range, thereby improving production efficiency.

[0030] In one specific implementation, such as Figure 4As shown, the data analysis device 14 also includes a quality verification module 142, a cleaning module 143, a feature extraction module 144, and a processing pattern recognition module 145. The quality verification module 142 is connected to the data acquisition device 13. The quality verification module 142, cleaning module 143, and feature extraction module 144 are connected sequentially. The feature extraction module 144 is also connected to the parameter learning module 141 and the processing pattern recognition module 145, respectively. The parameter learning module 141 and the processing pattern recognition module 145 are also connected. The sensor data, i.e., the state parameters, acquired by the data acquisition device 13 must first undergo data quality verification to ensure the normal working state of the sensor and the measured data. After data quality verification, the data must undergo data cleaning preprocessing, which includes, but is not limited to, filtering, noise reduction, and dimensionality reduction. After data cleaning and preprocessing, data feature parameters will be extracted. The relevant features are used in the sample acquisition stage to generate a parameter range for normal working state through self-learning. The features after self-learning will be compared with the feature range generated in the self-learning stage to perform pattern recognition.

[0031] Please continue reading. Figure 1 In one embodiment, the monitoring system 10 further includes an abnormal execution device 16, which is connected to the data analysis device 14 and the control device 15 respectively. The abnormal execution device 16 is at least used to adjust the processing equipment 20 according to the processing mode of the processing mold. The control device 15 is also used to suspend the operation of the abnormal execution device 16 in response to the current working condition of the processing equipment 20 being abnormal production. In other words, the abnormal execution device 16 will provide corresponding feedback based on the data analysis results, such as controlling the processing equipment 20 to stop or trigger an alarm. In addition, since the processing equipment 20 may need to adjust or change some process parameters after it returns from an abnormal production state to a normal production state, if the previous process parameters of the processing equipment 20 are still used to identify the processing mode of the processing mold, it may lead to identification errors, resulting in inaccurate subsequent statistics on the processing yield. Therefore, the data analysis device 14 will be suspended at this time. The abnormal execution device 16 provides corresponding feedback based on the analysis results of the data analysis device 14. Therefore, the operation of the abnormal execution device 16 is unnecessary when the data analysis device 14 is suspended. Thus, when the current operating condition of the processing equipment 20 is abnormal production, the abnormal execution device 16 will also be suspended.

[0032] In one specific embodiment, the abnormal execution device 16 will control the processing equipment 20 to stop and sound an alarm when it detects foreign objects or wear on the processing head inside the processing mold, so as to notify the relevant operators to handle the situation in a timely manner. In other specific embodiments, the abnormal execution device 16 will also control a robot / robotic arm to pick up and sort out the identified defective products. For large-sized products, it will control a moving non-conforming product frame driven by a motor and a guide rail to sort out the products. It can also simultaneously control the conforming product frame to sort out conforming products.

[0033] In one specific embodiment, the monitoring system 10 further includes a data analysis device 14, and a data acquisition device 13 including a sensor 131, a data acquisition card (not shown in the figure), and an edge industrial control computer (not shown in the figure). The data acquisition card is connected to the sensor 131 and the edge industrial control computer, respectively. The sensor 131 is installed on the processing equipment 20, and the edge industrial control computer is connected to the data analysis device 14 and the working condition identification device 12. Specifically, the sensor 131 converts the state parameters of the processing equipment 20 during the processing process into analog electrical signals and sends the analog electrical signals to the data acquisition card. The data acquisition card converts the analog electrical signals into digital signals and sends them to the edge industrial control computer. The edge industrial control computer performs data reconstruction based on the digital signals to obtain the state parameters of the processing equipment 20 during the processing process. Specifically, the sensor 131 converts the state parameters during the processing process into analog electrical signals; after the electrical signals enter the data acquisition card, they are converted into digital signals through AD sampling; the digital signals are sent to the edge industrial control computer through a communication protocol; the data acquisition program deployed in the edge industrial control computer reconstructs the digital signals through protocol parsing to obtain the state parameters of the processing equipment 20.

[0034] In one specific embodiment, sensor 131 includes at least one of a strain sensor, an acceleration sensor, an acoustic emission sensor, a force sensor, and a displacement sensor. Specifically, the strain sensor is disposed on the unloading plate of the processing mold, the acceleration sensor and the acoustic emission sensor are disposed on the machine base of the processing equipment 20, the force sensor is disposed on the column of the processing equipment 20, and the displacement sensor is disposed between the upper and lower templates of the processing mold.

[0035] This application also provides a stamping monitoring system, which includes a mold identification device, comprising at least a tag and a mold identification module. The tag is attached to the stamping machine and sends a radio frequency signal to the mold identification module. The radio frequency signal includes tag information characterizing the mold identity of the stamping die on the stamping machine. The mold identification module determines the preset operating frequency of each tag in the tag group, including the tag, and receives the tag information transmitted by each tag at its respective preset operating frequency. Based on the tag information transmitted by each tag at its respective preset operating frequency, the mold identity of the stamping die on the stamping machine is determined. In other words, the tag is attached to the stamping machine on which the stamping die is located, thereby returning tag information characterizing the mold identity of the stamping die on the stamping machine. The mold identification module can confirm the mold identity of the stamping die on the stamping machine based on the tag information, thus knowing the mold identity of the stamping die on the stamping machine, so as to subsequently determine whether the stamping die on the stamping machine has been replaced. Therefore, by setting up a mold identification device connected to the stamping machine, the mold identification of the stamping mold on the stamping machine can be identified intelligently and accurately without the need for manual operation and intervention throughout the process, realizing intelligent monitoring of the mold identification and saving labor costs.

[0036] If the technical solution of this application involves personal information, the product using this technical solution has clearly informed the user of the personal information processing rules and obtained the user's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using this technical solution has obtained the user's separate consent before processing the sensitive personal information, and also meets the requirement of "express consent". For example, at personal information collection devices such as cameras, clear and prominent signs are set up to inform users that they have entered the scope of personal information collection and that personal information will be collected. If an individual voluntarily enters the collection scope, it is deemed that they have agreed to the collection of their personal information; or on the personal information processing device, with clear signs / information informing users of the personal information processing rules, authorization is obtained from the individual through pop-up information or by asking the individual to upload their personal information; wherein, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the types of personal information processed.

[0037] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A monitoring system, characterized in that, The monitoring system includes: A mold identification device is used to connect to processing equipment to determine the mold identity of the processing mold on the processing equipment; A data acquisition device is used to connect to the processing equipment to collect status parameters of the processing equipment during the processing process; The working condition identification device is connected to the data acquisition device and the mold identification device respectively, and is used to determine the current working condition of the processing equipment based on the status parameters of the processing equipment during the processing process and the mold identification of the processing mold on the processing equipment; wherein, the current working condition of the processing equipment includes abnormal production, and the abnormal production includes machine stoppage, machine adjustment and mold change; The operating condition identification device is used to determine the current operating condition of the processing equipment based on the status parameters during the processing process and the mold identity of the processing mold on the processing equipment. This includes: the operating condition identification device determining the current operating condition of the processing equipment as mold replacement in response to a difference between the mold identity of the processing mold on the processing equipment and the historical identity corresponding to a historical mold on the processing equipment; determining the current operating condition of the processing equipment as shutdown in response to a situation where the mold identity of the processing mold on the processing equipment is the same as the historical identity corresponding to a historical mold on the processing equipment, and no new status parameters have been received within a preset time period after the previous receipt of the status parameters; and determining the current operating condition of the processing equipment as adjustment in response to a situation where the mold identity of the processing mold on the processing equipment is the same as the historical identity corresponding to a historical mold on the processing equipment, and the time interval between the current receipt of the status parameters and the previous receipt of the status parameters is greater than an interval threshold.

2. The monitoring system according to claim 1, characterized in that, The current operating condition of the processing equipment also includes normal production; the operating condition identification device is used to determine the current operating condition of the processing equipment based on the status parameters of the processing equipment during the processing process and the mold identity of the processing mold on the processing equipment, including: in response to the time interval between the current time of receiving the status parameter and the previous time of receiving the status parameter being equal to an interval threshold, or receiving a new status parameter within a preset time period after the previous time of receiving the status parameter, and the mold identity of the processing mold on the processing equipment being the same as the historical identity corresponding to the historical mold on the processing equipment, determining that the current operating condition of the processing equipment is normal production.

3. The monitoring system according to claim 1, characterized in that, The monitoring system also includes a data analysis device and a control device. The control device is connected to the working condition identification device and the data analysis device, respectively. The data acquisition device is connected to the data analysis device. The data analysis device is used to determine at least the processing mode of the processing mold. The control device is used to suspend the operation of the data analysis device in response to the current working condition of the processing equipment being abnormal production.

4. The monitoring system according to claim 3, characterized in that, The data analysis device includes a parameter learning module connected to the data acquisition device. The parameter learning module is used to learn and generate a parameter range corresponding to the normal processing mode based on the state parameters of the processing equipment during the processing process. The control device is also used to restart the operation of the data analysis device in response to the current working condition of the processing equipment returning to normal production. The data analysis device is also used to relearn and generate a new parameter range based on the new state parameters received after restarting.

5. The monitoring system according to claim 3, characterized in that, The data analysis device includes a quality verification module, a cleaning module, a feature extraction module, a parameter learning module, and a processing pattern recognition module. The quality verification module is connected to the data acquisition device. The quality verification module, the cleaning module, and the feature extraction module are connected in sequence. The feature extraction module is also connected to the parameter learning module and the processing pattern recognition module, respectively. The parameter learning module and the processing pattern recognition module are connected.

6. The monitoring system according to claim 3, characterized in that, The monitoring system also includes an abnormal execution device, which is connected to the data analysis device and the control device respectively. The abnormal execution device is at least used to adjust the processing mode of the processing equipment according to the processing mode of the processing mold. The control device is also used to suspend the operation of the abnormal execution device in response to the current working condition of the processing equipment being abnormal production.

7. The monitoring system according to claim 1, characterized in that, The monitoring system further includes a data analysis device; the data acquisition device includes a sensor, a data acquisition card, and an edge control computer. The data acquisition card is connected to the sensor and the edge control computer respectively. The sensor is installed on the processing equipment, and the edge control computer is connected to the data analysis device and the working condition identification device. The sensor converts the state parameters of the processing equipment during the processing process into analog electrical signals and sends the analog electrical signals to the data acquisition card. The data acquisition card converts the analog electrical signals into digital signals and sends them to the edge control computer. The edge control computer performs data reconstruction based on the digital signals to obtain the state parameters.

8. The monitoring system according to claim 7, characterized in that, The sensor includes at least one of a strain sensor, an acceleration sensor, an acoustic emission sensor, a force sensor, and a displacement sensor; wherein the strain sensor is disposed on the unloading plate of the processing mold, the acceleration sensor and the acoustic emission sensor are disposed on the machine base of the processing equipment, the force sensor is disposed on the column of the processing equipment, and the displacement sensor is disposed between the upper and lower templates of the processing mold.

9. A stamping monitoring system, characterized in that, The stamping monitoring system includes: A mold identification device is used to connect to a stamping machine to identify the mold identity of a stamping mold on the stamping machine; A data acquisition device is used to connect to the stamping machine to collect the status parameters of the stamping process of the stamping machine; The working condition identification device is connected to the data acquisition device and the mold identification device respectively, and is used to determine the current working condition of the stamping machine based on the state parameters of the stamping process and the mold identification of the stamping mold on the stamping machine; wherein, the current working condition of the stamping machine includes abnormal production, and the abnormal production includes machine stoppage, machine adjustment and mold change; The operating condition identification device is used to determine the current operating condition of the stamping machine based on the state parameters during the stamping process and the mold identity of the stamping die on the stamping machine. This includes: the operating condition identification device determining the current operating condition of the stamping machine as "die change" in response to a difference between the mold identity of the stamping die on the stamping machine and the historical identity corresponding to a historical mold on the stamping machine; determining the current operating condition of the stamping machine as "stop" in response to a difference between the mold identity of the stamping die on the stamping machine and the historical identity corresponding to a historical mold on the stamping machine, and no new state parameters being received within a preset time period after the previous receipt of the state parameters; and determining the current operating condition of the stamping machine as "machine adjustment" in response to a difference between the mold identity of the stamping die on the stamping machine and the historical identity corresponding to a historical mold on the stamping machine, and the time interval between the current receipt of the state parameters and the previous receipt of the state parameters being greater than an interval threshold.

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