Embossing equipment remote monitoring method and system based on Internet of Things

Abstract

The invention discloses a remote monitoring method and system for embossing equipment based on the Internet of Things, and relates to the technical field of the Internet of Things, and the method comprises the steps: determining a remote monitoring system of the embossing equipment according to a working data set of the embossing equipment, the form data of a product outputted by the embossing equipment, and an alarm signal of the embossing equipment; the accuracy of the remote monitoring system of the embossing equipment is ensured, and remote monitoring of the embossing equipment is realized. In the remote monitoring system of the embossing equipment, multiple pieces of corresponding abnormal data are determined according to the working data set of the embossing equipment, and a first abnormal event of the embossing equipment is determined according to the multiple pieces of abnormal data, the working video of the embossing equipment and the alarm signal of the embossing equipment. Therefore, the second abnormal event is determined according to the working image of the embossing equipment and the form data of the product, and the corresponding autonomous adjustment logic is matched according to the first abnormal event, the second abnormal event and the working state of the embossing equipment, so that autonomous adjustment of the embossing equipment is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a remote monitoring method and system for embossing equipment based on Internet of Things. Background Art

[0002] With the development of science and technology, the Internet of Things is gradually applied to people's lives. The Internet of Things is related to embossing equipment and controls the embossing equipment. In the existing technology, the embossing equipment outputs multiple working data during the working process, and determines the corresponding working status according to the multiple working data. However, remote monitoring relying solely on the working data of the embossing equipment cannot guarantee the accuracy of the remote monitoring system of the embossing equipment, nor can it achieve autonomous adjustment of the embossing equipment. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art. The present invention provides a remote monitoring method and system for embossing equipment based on the Internet of Things.

[0004] The embodiment of the present invention provides a remote monitoring method of an embossing device based on the Internet of Things, comprising: Collect the regional location of embossing equipment in the indoor factory; Determine the corresponding independent data space according to the location of the area, the network space of the indoor factory, and the interactive space of the embossing equipment; The independent data space collects the working data set of the embossing equipment via the corresponding Internet of Things; Determine a remote monitoring system for the embossing equipment based on a set of working data of the embossing equipment, morphological data of products output by the embossing equipment, and an alarm signal of the embossing equipment; In a remote monitoring system of an embossing device, a plurality of corresponding abnormal data are determined according to a set of working data of the embossing device, and a first abnormal event of the embossing device is determined according to the plurality of abnormal data, a working video of the embossing device, and an alarm signal of the embossing device; The second abnormal event is determined according to the working image of the embossing equipment and the morphological data of the product, and the corresponding autonomous adjustment logic is matched according to the first abnormal event, the second abnormal event and the working state of the embossing equipment.

[0005] The embodiment of the present invention provides a remote monitoring system for an embossing device based on the Internet of Things. The remote monitoring system for an embossing device based on the Internet of Things is applied to the remote monitoring method for an embossing device based on the Internet of Things. The remote monitoring system for an embossing device based on the Internet of Things includes: A collection module is used to collect the regional location of the embossing equipment in the indoor factory; An independent data space module is used to determine the corresponding independent data space according to the location of the area, the network space of the indoor factory and the interactive space of the embossing equipment; A working data module, used for collecting working data sets of embossing equipment via the corresponding Internet of Things in the independent data space; A remote monitoring module, used to determine a remote monitoring system of the embossing device according to a set of working data of the embossing device, morphological data of the product output by the embossing device, and an alarm signal of the embossing device; An abnormality module is used to determine a plurality of corresponding abnormal data according to a set of working data of the embossing device in a remote monitoring system of the embossing device, and to determine a first abnormal event of the embossing device according to the plurality of abnormal data, a working video of the embossing device and an alarm signal of the embossing device; The autonomous adjustment module is used to determine the second abnormal event according to the working image of the embossing equipment and the morphological data of the product, and match the corresponding autonomous adjustment logic according to the first abnormal event, the second abnormal event and the working state of the embossing equipment.

[0006] In an embodiment of the present invention, through the method in the embodiment of the present invention, the regional location of the embossing equipment in the indoor factory is collected; the corresponding independent data space is determined according to the regional location, the network space of the indoor factory and the interactive space of the embossing equipment; the independent data space collects the working data set of the embossing equipment through the corresponding Internet of Things; the remote monitoring system of the embossing equipment is determined according to the working data set of the embossing equipment, the morphological data of the product output by the embossing equipment and the alarm signal of the embossing equipment, which is compatible with the overall consideration of the working data set of the embossing equipment, the morphological data of the product output by the embossing equipment and the alarm signal of the embossing equipment, thereby ensuring the accuracy of the remote monitoring system of the embossing equipment and realizing remote monitoring of the embossing equipment.

[0007] Furthermore, in the remote monitoring system of the embossing equipment, a corresponding plurality of abnormal data are determined based on the working data set of the embossing equipment, and a first abnormal event of the embossing equipment is determined based on the plurality of abnormal data, the working video of the embossing equipment, and the alarm signal of the embossing equipment. The first abnormal event of the embossing equipment is introduced, and the abnormal data of the embossing equipment is managed and controlled.

[0008] Therefore, the second abnormal event is determined according to the working image of the embossing equipment and the morphological data of the product, and the corresponding autonomous adjustment logic is matched according to the first abnormal event, the second abnormal event and the working state of the embossing equipment. The overall consideration of the first abnormal event, the second abnormal event and the working state of the embossing equipment is compatible, the dynamic interaction of the first abnormal event and the second abnormal event is guaranteed, and the accuracy of the autonomous adjustment logic of the embossing equipment is guaranteed, so as to realize the autonomous adjustment of the embossing equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic diagram of an application scenario of a remote monitoring method for an embossing device based on the Internet of Things in one embodiment; Figure 2 is a flow chart of a remote monitoring method of an embossing device based on the Internet of Things in an embodiment of the present invention; Figure 3 Schematic diagram of the structure of a remote monitoring system for embossing equipment based on the Internet of Things in an embodiment of the present invention; Figure 4 The figure is a hardware diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0010] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Embodiment 1

[0011] The remote monitoring method of embossing equipment based on the Internet of Things provided in this application is applied to Figure 1 In the application environment shown, the computer 102 communicates with the server 104 through the network. The terminal 102 is but not limited to various personal computers, servers, and the Internet of Things, and the server 104 is implemented by an independent server or a server cluster composed of servers. Embodiment 2

[0012] See also Figures 1 to 4 , a remote monitoring method of an embossing device based on the Internet of Things, applied to a remote monitoring scenario of an embossing device based on the Internet of Things; the remote monitoring method of an embossing device based on the Internet of Things includes: Step S11: collecting the regional location of the embossing equipment in the indoor factory; Step S12: determining a corresponding independent data space according to the location of the area, the network space of the indoor factory, and the interactive space of the embossing equipment; Step S13: The independent data space collects the working data set of the embossing equipment via the corresponding Internet of Things; Step S14: determining a remote monitoring system of the embossing device according to a set of working data of the embossing device, morphological data of the product output by the embossing device, and an alarm signal of the embossing device; Step S15: in the remote monitoring system of the embossing device, a plurality of corresponding abnormal data are determined according to the working data set of the embossing device, and a first abnormal event of the embossing device is determined according to the plurality of abnormal data, the working video of the embossing device and the alarm signal of the embossing device; Step S16: determining a second abnormal event according to the working image of the embossing device and the morphological data of the product, and matching the corresponding autonomous adjustment logic according to the first abnormal event, the second abnormal event and the working state of the embossing device.

[0013] In step S11, the regional location of the embossing equipment in the indoor factory is collected; In the specific implementation process of the present invention, the specific steps are: S111: Collect the spatial model of the indoor factory; S112: Determine a monitoring area according to a spatial model of the indoor factory and a central position of the embossing equipment; S113: Determine the regional location of the embossing equipment in the indoor factory based on the monitoring area and the spatial model of the indoor factory.

[0014] In an embodiment of the present application, the spatial model of the indoor factory is collected; the monitoring area is determined based on the spatial model of the indoor factory and the central position of the embossing equipment, and the spatial model of the indoor factory and the central position of the embossing equipment are introduced to control the spatial model of the indoor factory and the central position of the embossing equipment as a whole, thereby ensuring the accuracy of the monitoring area.

[0015] At this point, accurate three-dimensional spatial information of the indoor factory is obtained to provide basic data for subsequent monitoring, layout optimization, etc. Use a three-dimensional laser scanner to scan the indoor factory to obtain high-precision point cloud data, which can be processed by software to generate a spatial model of the indoor factory.

[0016] Based on the spatial model and the actual location of the embossing equipment, a reasonable monitoring range is defined to effectively monitor the equipment status and prevent potential safety risks. First, the center position of the embossing equipment needs to be accurately marked in the acquired spatial model. This can be achieved by manually placing a three-dimensional icon representing the equipment in the model or directly marking the location of the equipment. According to the size, operating range and safety requirements of the embossing equipment, a suitable monitoring radius is set or a polygonal monitoring area is defined. The monitoring radius can be determined based on the range of activity of the equipment, while the polygonal monitoring area can be flexibly set based on the layout of the equipment and the surrounding environment. When setting the monitoring area, it is necessary to ensure that there are no obstructions in the monitoring area so that the camera or other sensors can clearly capture the equipment status. If there are obstructions, the monitoring area needs to be adjusted or monitoring equipment needs to be added to ensure unobstructed vision.

[0017] Specifically, suppose there is a factory workshop where embossing equipment is located. In order to collect the spatial model of the workshop, we chose the laser scanning method. The workshop was scanned in all directions using a 3D laser scanner to obtain a large amount of point cloud data. This data was imported into professional 3D modeling software. After processing, denoising, and registration, a high-precision 3D model was finally generated. The model accurately reflects the workshop's building structure, equipment layout, channel width and other information.

[0018] After acquiring the spatial model and marking the location of the embossing equipment, we began to set the monitoring area. Considering the operating range and safety requirements of the embossing equipment, we set a circular monitoring area with a radius of 5 meters and the center of the equipment. At the same time, we also checked whether there were any obstructions in the monitoring area and found that there was a row of shelves on the side close to the equipment that would block the view. Therefore, we adjusted the shape of the monitoring area and turned it into a polygonal area to ensure that the camera can clearly capture the status of the equipment. In addition, we also plan to install an additional camera above the shelf to monitor the top area of ​​the equipment.

[0019] Therefore, the regional position of the embossing equipment in the indoor factory is determined based on the monitoring area and the spatial model of the indoor factory, thereby ensuring the accuracy of the regional position of the embossing equipment in the indoor factory.

[0020] At this point, integrate the monitoring areas (such as circles, polygons, etc.) obtained in the previous steps with the three-dimensional spatial model of the indoor factory. Ensure that the monitoring area is accurately located in the model and matches the actual operating area of ​​the equipment. In the integrated spatial model, clearly mark the exact location of the embossing equipment, which is usually in the center of the monitoring area or the best observation point determined based on the operating characteristics of the equipment. Ensure that all relevant location information (including equipment location, monitoring area boundaries, etc.) uses a unified coordinate system, which helps to achieve accurate spatial positioning in subsequent data management and remote monitoring. If the embossing equipment or monitoring area changes over time (such as equipment movement, production line adjustment, etc.), you need to consider dynamically updating this information in the model.

[0021] In step S12, the corresponding independent data space is determined according to the location of the area, the network space of the indoor factory, and the interactive space of the embossing equipment; In the specific implementation process of the present invention, the specific steps are: S121: Get the location of the area; S122: Determine the network space of the indoor factory according to the location of the area and the spatial location of the network device in the indoor factory; S123: traverse the embossing equipment and collect the interaction space of the embossing equipment; S124: multiple interactions of the location of the area, the network space of the indoor factory, and the interactive space of the embossing equipment; S125: determining a first space parameter according to the location of the area and the network space of the indoor factory, and determining a second space parameter according to the network space of the indoor factory and the interactive space of the embossing equipment; S126: Determine a corresponding independent data space according to the embossing device, the first space parameter and the second space parameter, where the independent data space collects the working data output by the embossing device.

[0022] In an embodiment of the present application, the regional location is obtained; the network space of the indoor factory is determined according to the regional location and the spatial location of the network device in the indoor factory, thereby ensuring the matching of the network space of the indoor factory.

[0023] At this time, the regional location is obtained, and the network coverage and network topology of the entire factory are determined based on the regional location of the embossing equipment and the spatial location of the network devices (such as routers, switches, wireless access points, etc.) in the indoor factory.

[0024] First, it is necessary to collect information such as the type, location, performance parameters, etc. of all network devices in the indoor factory. Based on the collected information, a network topology diagram of the factory can be drawn to show the connection relationship and signal transmission path between each network device. Combining the regional location of the embossing equipment and the network topology diagram, the network coverage area where the equipment is located, as well as the signal blind spots or weak signal areas that exist, can be analyzed. Based on the analysis results, the configuration of the network device can be optimized to ensure that the embossing equipment can obtain a stable and efficient network connection.

[0025] Specifically, after determining the regional location of the embossing equipment, we began to collect information on all network devices in the factory and drew a network topology map. By analyzing the topology map, we found that the embossing equipment was within the coverage of a wireless access point, but it was far away from the access point and faced the problem of signal attenuation. To solve this problem, we adjusted the power and antenna direction of the wireless access point to ensure that the embossing equipment could obtain a stable wireless network connection. At the same time, we also optimized the configuration of other network devices in the factory to improve the network performance and stability of the entire factory.

[0026] Furthermore, the embossing equipment is traversed and the interactive space of the embossing equipment is collected; multiple interactions are performed on the location of the area, the network space of the indoor factory, and the interactive space of the embossing equipment, thereby realizing multiple interactions among the location of the area, the network space of the indoor factory, and the interactive space of the embossing equipment.

[0027] At this time, the embossing equipment is traversed, and the interactive space of the embossing equipment is collected during the traversal process. The interactive space of the collected embossing equipment is introduced, and then the location of the area, the network space of the indoor factory, and the interactive space of the embossing equipment are associated.

[0028] The information of the location of the area, the network space of the indoor factory, and the interactive space of the embossing equipment are integrated to form a unified spatial description. The interactive relationship between different spaces is analyzed, such as how the device location affects the quality of network communication, how the network configuration affects the real-time nature of device interaction, and how the device interaction rules are coordinated with the production process. Based on the above analysis, multiple interactive simulation experiments are conducted to verify the interactive effects under different configurations and conditions. Based on the results of the simulation experiments, optimization suggestions are put forward to improve the device interaction performance, increase production efficiency, or reduce network latency.

[0029] Specifically, we first integrated the information of device location, network configuration, and interaction rules. Then, we analyzed the impact of device location on network communication and found that some devices were located at the edge of the network access point and faced the problem of unstable signal. At the same time, we also found that certain parameters in the network configuration limited the real-time nature of device interaction. In order to improve these problems, we conducted multiple interaction simulation experiments, adjusted the location and power of the network access point, optimized the network configuration parameters, and adjusted the interaction rules of the equipment to better adapt to the production process. These optimization measures significantly improved the interactive performance and production efficiency of the equipment.

[0030] Therefore, the first space parameters are determined according to the location of the area and the network space of the indoor factory, and the second space parameters are determined according to the network space of the indoor factory and the interactive space of the embossing equipment; the corresponding independent data space is determined according to the embossing equipment, the first space parameters and the second space parameters. The independent data space collects the working data output by the embossing equipment, is compatible with the overall consideration of the embossing equipment, the first space parameters and the second space parameters, and ensures the precise control of the independent data space.

[0031] At this time, the first space parameter and the second space parameter are introduced; the first space parameter is mainly based on the regional location of the embossing equipment and the network space of the indoor factory to extract key spatial characteristics, which include the distance of the physical location of the equipment relative to the network access point, the signal strength and quality of the network coverage area where the equipment is located, network delay, etc. The first space parameter will directly affect the communication performance and stability between the equipment and the network.

[0032] The second space parameters are key parameters related to the equipment interaction capability extracted based on the network space of the indoor factory and the interactive space of the embossing equipment. These parameters include the communication protocol compatibility between the equipment and other equipment, data transmission rate, data synchronization mechanism, interaction frequency, etc. The second space parameters will determine the interaction efficiency and collaborative working ability of the equipment in the factory production process.

[0033] Create an independent data space for each embossing device to collect and analyze the working data output by the device. This data space will be customized based on the characteristics of the device (such as model, function, etc.), the first space parameters (such as network performance parameters), and the second space parameters (such as interaction capability parameters). When creating an independent data space, determine what types of data need to be collected, such as production quantity, quality indicators, fault logs, operation records, etc. Design a data storage solution to ensure the security, integrity, and accessibility of the data, which includes the use of technologies such as databases, data warehouses, or cloud storage. Based on the collected data, conduct real-time or offline analysis to extract useful information and insights.

[0034] Specifically, suppose we have an indoor factory with multiple embossing devices, where device A is located within the coverage of network access point B, but at a relatively long distance. In step S125, we first calculate the distance of device A relative to the access point based on the regional location of device A and the location of access point B, and measure the network signal strength and quality at the location of device A. These measurements constitute part of the first spatial parameters, which are used to evaluate the communication performance between device A and the network. We analyzed the interaction space of device A and found that it uses a specific communication protocol to exchange data with other devices and has a fixed data synchronization mechanism. In addition, we also determined the interaction frequency and data transmission rate of device A. These characteristics constitute the second spatial parameters, which are used to evaluate the interaction efficiency and collaborative working ability of device A in the factory production process. An independent data space is created for device A, which is customized based on the model, function, network performance parameters (such as signal strength, network delay) and interaction capability parameters (such as communication protocol, data transmission rate) of device A.

[0035] In terms of data collection, we determined that we needed to collect data such as the production quantity, quality indicators, fault logs, and operation records of equipment A, which were transmitted to the data space in real time through the built-in sensors or network interfaces of equipment A. In terms of data storage, we designed a secure database system to store this data and set appropriate access rights to ensure data security. In terms of data analysis, we used machine learning algorithms to analyze the collected data to predict equipment failures, optimize production processes, and improve product quality.

[0036] In step S13, the independent data space collects the working data set of the embossing equipment via the corresponding Internet of Things; In the specific implementation process of the present invention, the specific steps are: S131: Collect the independent data space; S132: Matching a corresponding Internet of Things based on the independent data space and the network space of the indoor factory; S133: Associating the embossing device, the independent data space, and the Internet of Things; S134: determining a data transmission path according to the embossing device, the independent data space, and the Internet of Things; S135: The independent data space collects a plurality of working data of the embossing device via the data transmission path; S136: Determine a working data set of the embossing equipment according to a plurality of working data of the embossing equipment.

[0037] In an embodiment of the present application, the independent data space is collected; the corresponding Internet of Things is matched based on the independent data space and the network space of the indoor factory, thereby achieving overall consideration of the independent data space and the network space of the indoor factory and ensuring the matching accuracy of the Internet of Things.

[0038] At this time, the independent data space is a space dedicated to storing, processing and analyzing data in a specific field. In IoT applications, this space is usually used to store data collected from IoT devices and supports functions such as data query, analysis and visualization. The independent data space and the network space of the indoor factory are introduced, and the independent data space and the network space of the indoor factory are considered as a whole. At the same time, the data collected by the IoT devices is transmitted to the independent data space through the network for storage and processing. IoT applications such as monitoring, early warning, optimization, etc. are developed based on the data in the independent data space.

[0039] Specifically, use cloud storage services or local databases as independent data spaces to store environmental parameters and production equipment status data collected from IoT devices. Plan a network topology based on industrial Ethernet to ensure that all IoT devices in the factory can be connected to the network. Select IoT devices such as temperature sensors, humidity sensors, and light sensors to monitor environmental parameters in the factory in real time. Select devices such as actuators and controllers to adjust the operating status of production equipment according to environmental parameters. At the same time, transmit the data collected by IoT devices to an independent data space through the network for storage and processing. Use data encryption technology to ensure security during data transmission. Adjust the data transmission strategy according to the actual situation of the network space, such as selecting a suitable communication protocol and optimizing the data transmission path. Develop an IoT application based on the data in the independent data space to monitor the environmental parameters and production equipment status in the factory in real time.

[0040] Furthermore, the embossing device, the independent data space and the Internet of Things are associated; the data transmission path is determined according to the embossing device, the independent data space and the Internet of Things, which is compatible with the overall consideration of the embossing device, the independent data space and the Internet of Things, and realizes the precise control of the data transmission path.

[0041] At this point, assign a unique identifier (such as device ID, MAC address, etc.) to each embossing device and register it in the IoT platform or data management system. The registration process includes recording the basic information of the device (such as model, production date, location, etc.) and configuring its communication parameters (such as IP address, port number, etc.). Create a dedicated data storage area or data table for each embossing device in an independent data space. Ensure that this storage area is associated with the device identifier on the IoT platform so that data can be correctly transferred from the device to the data space. Integrate the embossing device to the IoT platform through an appropriate communication protocol (such as MQTT, HTTP, CoAP, etc.). Configure the IoT platform to identify and process the data from the embossing device and forward it to the corresponding independent data space.

[0042] At the same time, set up appropriate security measures (such as data encryption, access control, etc.) to protect the communication between the embossing device and the IoT platform and independent data space. Configure permission management policies to ensure that only authorized personnel or systems can access and modify the data of the embossing device.

[0043] Therefore, the independent data space collects multiple working data of the embossing device via the data transmission path; the working data set of the embossing device is determined according to the multiple working data of the embossing device, thereby ensuring the accuracy of the working data set of the embossing device.

[0044] At this time, the independent data space is a virtual environment or platform dedicated to storing and processing data. In this scenario, it is used to collect and store the working data of the embossing equipment. The data transmission path is the channel for data to be transmitted from the embossing equipment to the independent data space. It includes various network connections, sensor interfaces, etc. to ensure that data can be transmitted to the data space accurately and in a timely manner. The embossing equipment will generate a variety of data during the working process, such as temperature, pressure, speed, output, etc. These data are collected and stored in the independent data space through the data transmission path for subsequent analysis and processing.

[0045] The working data set is a set of data sets with specific meanings or uses that are screened and sorted from multiple working data of the embossing equipment. These data sets are used for equipment performance analysis, fault diagnosis, production efficiency optimization, etc. This process requires the collected working data to be screened, classified and sorted. It is necessary to screen according to factors such as the nature, importance, and timestamp of the data to ensure the accuracy and usefulness of the data set.

[0046] Specifically, suppose that there is an embossing device that needs to monitor its temperature, pressure and output during the production process. These data are collected through the built-in sensors of the device and transmitted to an independent data space through a wireless network. The software system in the data space will automatically receive, store and process these data to provide support for subsequent analysis and decision-making. Suppose that we have collected the temperature, pressure and output data of the embossing equipment. In step S136, we need to further process and analyze these data. For example, we can average the temperature data according to the time period to obtain the average temperature in different time periods; correlate the pressure data with the output data to find out the relationship between pressure and output, etc. Through these processing and analysis, we can obtain a set of embossing equipment working data containing information such as average temperature, pressure range, and output statistics. These data sets can be used to evaluate the operating status of the equipment, optimize production parameters, and predict equipment failures.

[0047] Assume that the embossing equipment has a decrease in output during production. We can use the collected working data to analyze it through the following steps: Collect data: Collect temperature, pressure and output data of embossing equipment through data transmission path.

[0048] Determine the data set: Filter and organize the collected data to obtain a data set containing average temperature, pressure range and production statistics for different time periods.

[0049] Analyze the data: By comparing the data sets of different time periods, it is found that the temperature and pressure in a certain period of time are higher, and the output also decreases significantly. This indicates that the equipment is overheated or overloaded during this period of time, resulting in a decrease in production efficiency.

[0050] Optimize production: According to the analysis results, adjust the operating parameters of the equipment, such as lowering the temperature, adjusting the pressure, etc., to improve production efficiency.

[0051] In step S14, a remote monitoring system of the embossing device is determined according to the working data set of the embossing device, the morphological data of the product output by the embossing device, and the alarm signal of the embossing device; In the specific implementation process of the present invention, the specific steps are: S141: Acquire a working data set of the embossing equipment; S142: monitoring the embossing device in real time, and collecting images of products output by the embossing device; S143: determining the morphological data of the product output by the embossing device according to the recognition of the image of the product output by the embossing device; S144: Collecting alarm signals of embossing equipment; S145: performing multiple interactions on a work data set of the embossing device, morphological data of the product output by the embossing device, and an alarm signal of the embossing device; S146: Determine a remote monitoring system for the embossing equipment based on multiple interactions of a working data set of the embossing equipment, morphological data of the product output by the embossing equipment, and an alarm signal of the embossing equipment.

[0052] In an embodiment of the present application, a working data set of the embossing equipment is obtained; the embossing equipment is monitored in real time, and images of products output by the embossing equipment are collected; the morphological data of the products output by the embossing equipment are determined based on the recognition of the images of the products output by the embossing equipment, thereby realizing the recognition of the images of the products output by the embossing equipment and ensuring the accuracy of the morphological data of the products output by the embossing equipment.

[0053] At this time, the working data is extracted from the control system or data recording system of the embossing equipment. These data usually include the operating parameters of the equipment, such as temperature, pressure, speed, running time, etc., as well as production efficiency indicators such as output and yield rate, etc. These data are essential for understanding the operating status and production performance of the equipment. The working data comes from the built-in sensors of the equipment or is obtained through the interface between the equipment and the control system. The working data usually exists in digital form and is provided in the form of tables, database records or real-time data streams.

[0054] Use image acquisition devices (such as cameras) to monitor the output of the embossing equipment in real time and capture images of the product, which are used for subsequent product quality analysis and morphology recognition. High-resolution cameras are usually used to ensure that the details of the product can be clearly captured. The camera should be placed in a position where the product output can be clearly captured, such as at the exit of the equipment. The frequency of image acquisition depends on the production speed and quality control requirements, and needs to be acquired continuously or at fixed time intervals. The acquired images need to be stored in a safe location for subsequent analysis and processing.

[0055] The collected product images are analyzed using image processing techniques and algorithms to determine the product's morphological data, which includes the product's size, shape, edge definition, texture, etc. At the same time, the key morphological features of the product, such as length, width, area, etc., are extracted through image processing algorithms. The extracted morphological data is compared with the preset quality control standards to evaluate the quality of the product. The morphological data and quality control evaluation results are recorded for subsequent analysis and improvement.

[0056] Specifically, suppose there is an embossing plant that uses automated embossing equipment to produce embossed paper. To ensure product quality, operating data, including embossing temperature, pressure, and run time, are extracted from the control system of the embossing equipment, and these data are transmitted to the central monitoring system in real time through the equipment interface. Extract operating data from the control system of the embossing equipment, including embossing temperature, pressure, and run time, and these data are transmitted to the central monitoring system in real time through the equipment interface. Use image processing software to analyze the captured images. The software first uses edge detection technology to identify the outline of the embossed paper, and then measures its length, width, and area. The software then compares the measurement results with the preset quality control standards to evaluate the quality of the embossed paper. If the product quality does not meet the standards, the system will issue an alarm to prompt the operator to intervene.

[0057] Furthermore, the alarm signal of the embossing equipment is collected; multiple interactions are performed on the working data set of the embossing equipment, the morphological data of the product output by the embossing equipment, and the alarm signal of the embossing equipment, thereby realizing multiple interactions on the working data set of the embossing equipment, the morphological data of the product output by the embossing equipment, and the alarm signal of the embossing equipment.

[0058] At this point, the embossing equipment is usually equipped with sensors and alarm systems to monitor the operating status of the equipment and send out alarm signals when a fault or abnormal situation occurs. In this step, we collect these alarm signals so that we can understand the status of the equipment in a timely manner and take corresponding measures. Alarm signals include abnormal signals from temperature sensors, pressure sensors, motion sensors, etc. The alarm signals can be transmitted to the central monitoring system by wire or wireless means for real-time collection and processing.

[0059] The working data set of the embossing equipment, the morphological data of the product and the alarm signal are comprehensively analyzed and processed. Through multiple interactions, we can find the correlation and potential rules between the data, so as to have a more comprehensive understanding of the operating status of the equipment and the quality of the product. At the same time, different types of data are integrated to form a comprehensive data set. The data set is analyzed to find potential problems and trends. Based on the analysis results, decision support is provided for equipment maintenance, optimization and production process control.

[0060] Specifically, collect alarm signals: The embossing equipment is equipped with temperature sensors and pressure sensors to monitor the temperature and pressure of the equipment. When the temperature or pressure exceeds the preset threshold, the sensor will send an alarm signal, which is transmitted wirelessly to the central monitoring system for real-time collection and recording. The working data set of the embossing equipment (such as temperature, pressure, running time, etc.), the morphological data of the fabric and the alarm signal are comprehensively analyzed and processed. For example, if it is found that the texture clarity of the fabric decreases over a period of time, and the temperature alarm signal of the equipment appears frequently, it can be inferred that the equipment has an overheating problem, resulting in poor embossing effect. Based on this multiple interactive analysis, the embossing factory can take timely measures, such as adjusting equipment parameters, cleaning radiators, etc., to ensure the normal operation of the equipment and product quality.

[0061] Therefore, the remote monitoring system of the embossing equipment is determined based on the multiple interactions of the working data set of the embossing equipment, the morphological data of the product output by the embossing equipment, and the alarm signal of the embossing equipment, which is compatible with the overall consideration of the working data set of the embossing equipment, the morphological data of the product output by the embossing equipment, and the alarm signal of the embossing equipment, thereby ensuring the accuracy of the remote monitoring system of the embossing equipment.

[0062] At this point, build a comprehensive remote monitoring system. The remote monitoring system is designed to monitor the operating status of the embossing equipment, product quality, and any faults or abnormal conditions in real time, so as to ensure the stability of the production process and the consistency of product quality. Integrate the working data set of the embossing equipment (such as temperature, pressure, speed, running time, etc.) with the morphological data of the product (such as size, shape, texture, etc.) and the alarm signal of the equipment. Develop or select a suitable remote monitoring platform that should be able to receive, store, display and analyze data in real time. Configure the platform to display key indicators and alarm information so that operators can quickly identify and deal with potential problems.

[0063] At the same time, set thresholds and conditions to trigger alarms when data exceeds these thresholds or meets specific conditions. Configure notification mechanisms such as email, SMS or phone calls to ensure that operators or managers can receive alarm information immediately.

[0064] Specifically, when determining the remote monitoring system of the embossing equipment, first, we can create a matching table to record the matching between the working data set of the embossing equipment, the morphological data of the product, and the alarm signal of the equipment and the preset standards. Matching table example: In this matching table, we list some key parameters of the embossing equipment and the morphological data of the product, as well as their preset standards and actual data. By comparing the actual data with the preset standards, we can get the matching results and identify any abnormalities.

[0065] In order to more comprehensively evaluate the operating status of the embossing equipment and the product quality, we can assign a weight to each parameter and calculate the score based on how the actual data matches the preset criteria.

[0066] Weight and score calculation example: In this example, we assign a weight to each item, and the sum of the weights is 1. Then, we calculate a score for each item based on how well the actual data matches the preset criteria. If the actual data matches the preset criteria, it gets a full score; if it does not match, it gets 0 points. Finally, we add up the scores of all items to get the total score.

[0067] Total score calculation: Total score = 0.21 + 0.21 + 0.11 + 0.151 + 0.151 + 0.11 + 0.1*0 = 0.9 (points) Through the total score, we can evaluate the overall operating status of the embossing equipment and product quality. In this example, due to an alarm signal (pressure below the threshold), the total score is slightly lower than the full score, which reminds us that we need to pay attention to and handle this alarm signal to ensure the normal operation of the equipment and the consistency of product quality. Through the matching table, weight and scoring method, we can more comprehensively determine the remote monitoring system of the embossing equipment and promptly identify and handle any abnormal situations.

[0068] In step S15, in the remote monitoring system of the embossing device, a plurality of corresponding abnormal data are determined according to the working data set of the embossing device, and a first abnormal event of the embossing device is determined according to the plurality of abnormal data, the working video of the embossing device and the alarm signal of the embossing device; In the specific implementation process of the present invention, the specific steps are: S151: Remote monitoring system for data acquisition embossing equipment; S152: in the remote monitoring system of the embossing equipment, marking the working data set of the embossing equipment as abnormal; S153: determining a plurality of corresponding abnormal data according to the abnormal mark of the working data set of the embossing device; S154: collecting a working video of the embossing device, and determining a corresponding abnormal component according to dynamic recognition of the working video of the embossing device; S155: Associating multiple abnormal data, abnormal components and alarm signals of embossing equipment; S156: Determine the first abnormal event of the embossing equipment according to the plurality of abnormal data, the abnormal components and the alarm signal of the embossing equipment.

[0069] In an embodiment of the present application, a remote monitoring system for embossing equipment is collected; in the remote monitoring system for the embossing equipment, a working data set of the embossing equipment is marked as abnormal; corresponding multiple abnormal data are determined based on the abnormal marks of the working data set of the embossing equipment, thereby ensuring the accuracy of the multiple abnormal data.

[0070] Specifically, the remote monitoring system of the embossing equipment is collected. After obtaining the remote monitoring data of the embossing equipment, the next step is to analyze the data and mark the outliers. Outliers usually refer to data points that deviate significantly from the normal operating range or preset thresholds. These outliers may indicate that the equipment has problems such as failure, wear, or unstable operation. In order to mark anomalies, we need to set a reasonable threshold range. These thresholds can be determined based on the equipment's specifications, historical operating data, or industry standards. For example, for a temperature sensor, we can set a normal operating range of 150°C to 200°C, and any data points outside this range will be marked as abnormal.

[0071] After completing the anomaly marking, the next step is to identify all the data points marked as abnormal. These abnormal data points will serve as the focus of subsequent troubleshooting and maintenance work. In step S153, we need to carefully review the data records on the remote monitoring platform, find all the data points marked as abnormal, and record their specific values ​​and timestamps.

[0072] Specifically, we compare the temperature sensor data (200°C) with the preset threshold range (150°C to 200°C). Since 200°C is exactly within the threshold range, it will not be marked as abnormal. However, if the temperature sensor data suddenly increases to 250°C, it will exceed the threshold range and be marked as an abnormal value. Similarly, for pressure sensors and vibration sensors, we can also set corresponding threshold ranges and mark the data that exceeds the range as abnormal. Log in to the remote monitoring platform and view all data points marked as abnormal. Suppose we find that the temperature sensor data suddenly increases to 250°C at a certain moment, and this abnormal value lasts for 5 minutes. At the same time, we also find that the vibration sensor data also increased significantly during this period. These abnormal data points will serve as the focus of our subsequent troubleshooting. We can infer that the equipment may have problems with overheating or excessive vibration, and further inspection of the cooling system and mechanical components is required.

[0073] Furthermore, the working video of the embossing equipment is collected, and the corresponding abnormal parts are determined based on the dynamic recognition of the working video of the embossing equipment, thereby realizing the dynamic recognition of the working video of the embossing equipment and ensuring the accuracy of the abnormal parts.

[0074] At this time, use a high-definition camera or other video acquisition equipment to record the operating status of the embossing equipment in real time. Ensure the quality of the video recording, including clarity, frame rate, color reproduction, etc., for subsequent video analysis. Store the video data in a safe and reliable storage medium so that it can be called and analyzed at any time. Use video analysis technology to process and analyze the collected video data. Identify the embossing equipment and its various components in the video through algorithms such as image recognition and target detection. Compare the videos under normal and abnormal conditions to find out the changes in the operating status of the equipment, such as vibration, displacement, deformation, etc. Combined with the working principle and structural characteristics of the equipment, analyze the abnormal components that may be caused by these changes.

[0075] Specifically, taking an embossing machine as an example, high-definition cameras are installed at key locations of the embossing machine, such as the feed port, embossing area, and discharge port. The camera records the operating status of the embossing machine in real time and stores the video data in the hard disk of the server. Through the video analysis software, the working video of the embossing machine stored on the server is loaded. The software automatically processes the video, identifies the various components of the embossing machine, and marks them out in the video. Compared with the video under normal conditions, it is found that the vibration amplitude of the embossing area is significantly increased, accompanied by abnormal noise. Through further analysis, it is determined that the abnormal vibration and noise come from the bearings in the embossing area. The bearings may be worn, loose, or damaged, resulting in unstable operation of the embossing area.

[0076] Therefore, multiple abnormal data, abnormal components and alarm signals of the embossing equipment are associated; the first abnormal event of the embossing equipment is determined based on the multiple abnormal data, abnormal components and alarm signals of the embossing equipment, which is compatible with the overall consideration of multiple abnormal data, abnormal components and alarm signals of the embossing equipment, and realizes the precise control of the first abnormal event of the embossing equipment.

[0077] At this point, all abnormal data collected in the previous steps (such as abnormal readings from sensors such as temperature, pressure, vibration, etc.) are summarized and integrated. The identified abnormal components (such as worn bearings, clogged filters, etc.) are matched with the corresponding abnormal data. This usually involves a deep understanding of how the equipment works in order to accurately link data changes to specific component failures. At the same time, any signals from the alarm system of the embossing equipment are analyzed. These signals are usually associated with specific fault conditions, such as overheating, overvoltage, overcurrent, etc. Finally, a correlation analysis is performed to comprehensively consider multiple abnormal data, abnormal components, and alarm signals to reveal the potential connections and causal relationships between them.

[0078] Further comprehensive analysis of all collected information, including abnormal data, abnormal components, and alarm signals. Based on the results of the analysis, determine the primary abnormal event that caused the current equipment status. This event is usually the first or most influential failure, and it may be the root cause of other abnormal data or component failures. Once the first abnormal event is determined, a corresponding response strategy can be formulated, such as emergency shutdown, repair or replacement of faulty components.

[0079] When determining the first abnormal event of the embossing equipment based on multiple abnormal data, abnormal components and alarm signals of the embossing equipment, the definition of the "first abnormal event" is mainly based on comprehensive considerations of the following aspects: The first abnormal event is often the root cause of a series of subsequent chain reactions or secondary failures. This requires a deep understanding of the operating principles of the equipment, the interactions between components, and the potential connections between abnormal data. Through comprehensive analysis, it is possible to determine which abnormal event is a prerequisite or driving factor for the occurrence of other abnormalities.

[0080] The first abnormal event may not be the first to occur, but it is the most significant in terms of impact. It may pose the greatest threat to the performance, stability or safety of the equipment. When assessing the impact, it is necessary to consider the direct and indirect impact of the abnormal event on the operation of the equipment, as well as the possible consequences on production efficiency and product quality.

[0081] There may be consistency or correlation between multiple abnormal data, and these correlations may point to a common first abnormal event. For example, if multiple sensors detect abnormal data at the same time, and these data show consistency in the trend of change, then these abnormalities may be caused by a common cause. The alarm signal of the equipment is usually associated with a specific fault condition. If an alarm signal matches multiple abnormal data, and these data all point to the same component or system, then the failure of this component or system is likely to be the first abnormal event.

[0082] Specifically, suppose that during the monitoring process of the embossing equipment, the following abnormal data and alarm signals appear at the same time: The temperature sensor indicates that the temperature in the embossing area is too high.

[0083] The vibration sensor detected an abnormal increase in vibration amplitude in the embossed area.

[0084] The alarm system sends out an overheat alarm signal.

[0085] It was observed that the embossing effect decreased and the pattern was not clear.

[0086] When analyzing this information comprehensively, the following steps can be taken to determine the first abnormal event: Time sequence analysis: Check the timestamps of each abnormal data and alarm signal to determine the order in which they occurred.

[0087] Cause and effect analysis: Consider the potential links between temperature increase, increased vibration, overheat alarm, and reduced embossing effect. For example, temperature increase may lead to reduced embossing effect, while increased vibration may be a precursor to temperature increase or bearing wear.

[0088] Impact assessment: Assess the impact of each abnormality on equipment operation and production efficiency. For example, overheating may cause equipment shutdown, which will have a serious impact on production.

[0089] Data consistency and correlation check: Check the consistency between abnormal data (such as whether the temperature increase and vibration increase occur at the same time) and their correlation with the alarm signal (such as whether the overheating alarm matches the temperature increase).

[0090] Based on the above analysis, it can be determined that the first abnormal event is that the temperature of the embossing area is too high. This event may be caused by bearing wear, cooling system failure or embossing area blockage.

[0091] In step S16, a second abnormal event is determined according to the working image of the embossing device and the morphological data of the product, and a corresponding autonomous adjustment logic is matched according to the first abnormal event, the second abnormal event and the working state of the embossing device; In the specific implementation process of the present invention, the specific steps are: S161: monitoring the embossing equipment in real time, and collecting working images of the embossing equipment according to the real-time monitoring of the embossing equipment; S162: Associating the working image of the embossing device with the morphological data of the product; S163: Determine a second abnormal event based on the working image of the embossing device and the morphological data of the product; S164: determining a corresponding matching coefficient according to the matching between the second abnormal event and the first abnormal event, and collecting the working status of the embossing device; S165: Matching corresponding autonomous adjustment logic according to the first abnormal event, the second abnormal event, the matching coefficient and the working state of the embossing equipment.

[0092] In an embodiment of the present application, the embossing equipment is monitored in real time, and a working image of the embossing equipment is collected based on the real-time monitoring of the embossing equipment; the working image of the embossing equipment and the morphological data of the product are associated; a second abnormal event is determined based on the working image of the embossing equipment and the morphological data of the product, and the second abnormal event is introduced to facilitate overall control of the first abnormal event and the second abnormal event.

[0093] At this point, the operating status of the embossing equipment is monitored in real time to obtain the current working status of the equipment. To achieve this, it is necessary to install cameras or other image acquisition devices and ensure that their position and angle can clearly capture the key working areas of the embossing equipment. The real-time monitoring software should be able to capture images continuously and store these images in an easily accessible location for subsequent analysis and processing.

[0094] In actual operation, the camera is usually installed above or on the side of the embossing machine to ensure that the embossing die, embossing area and the status of the product during the embossing process can be captured. The monitoring software will set a certain image acquisition frequency to ensure that enough image data is captured to reflect the operating status of the embossing equipment. At the same time, in order to ensure the clarity and accuracy of the image, the camera needs to be regularly maintained and calibrated.

[0095] The working image of the embossing equipment is associated with the morphological data of the product. The morphological data usually includes the size, shape, texture and other characteristics of the product, which can be obtained through measuring tools or image analysis software. The purpose of the association is to analyze the relationship between the working state of the embossing equipment and the product quality, so as to find potential problems or anomalies. In actual operation, the morphological data of the embossed product will be measured using measuring tools (such as vernier calipers, microscopes, etc.). At the same time, the working image of the embossing equipment in the corresponding time period will be extracted from the monitoring system. Then, the image analysis software or data processing software will be used to associate these morphological data with the image for subsequent analysis and processing.

[0096] The second abnormal event is determined based on the working image of the embossing equipment and the morphological data of the product. An abnormal event usually refers to an equipment state or product quality problem that is not in line with expectations. By analyzing the working image and morphological data, problems such as equipment failure, wear, and improper adjustment can be found, thereby determining the second abnormal event. In actual operation, image processing software or data analysis software will be used to conduct in-depth analysis of the working image and morphological data. The product morphological data of different time periods or batches may be compared to find the changing trends or abnormal points. At the same time, the detailed features in the working image, such as the state of the embossing die, the texture of the embossing area, etc., will also be observed to find potential abnormalities or problems.

[0097] Specifically, suppose a diaper production company is using embossing equipment to emboss diapers. To ensure the quality of embossing, the company installed a high-definition camera to monitor the embossing equipment in real time. The camera is installed on the side of the embossing machine and can clearly capture the status of the embossing die and the embossing area. The monitoring software is set to capture one image per second and store these images in a specified folder on the server. In this way, these images can be accessed at any time to understand the operation of the embossing equipment. The morphological data of the embossed diapers were measured using a vernier caliper, including the width and length of the diapers, and the depth and width of the embossing pattern. At the same time, the working images of the embossing equipment in the corresponding time period were extracted from the monitoring system. Then, the morphological data were associated with the images using image analysis software. Through comparison and analysis, it was found that when a part of the embossing die was worn, the depth and width of the embossing pattern would change, thus affecting the embossing quality of the diapers.

[0098] By comparing the morphological data of different batches of diapers and the corresponding working images, it was found that when a part of the embossing die was severely worn, the depth and width of the embossing pattern would be significantly reduced, resulting in a decrease in the embossing quality of the diapers. Therefore, the second abnormal event was determined to be severe wear of the embossing die. In order to solve this problem, it was planned to stop the machine to replace the worn embossing die, and conduct a comprehensive inspection and adjustment of the embossing equipment.

[0099] Therefore, the corresponding matching coefficient is determined according to the matching of the second abnormal event with the first abnormal event, and the working status of the embossing equipment is collected; the corresponding autonomous adjustment logic is matched according to the first abnormal event, the second abnormal event, the matching coefficient and the working status of the embossing equipment, which is compatible with the overall consideration of the first abnormal event, the second abnormal event and the working status of the embossing equipment, ensures the dynamic interaction of the first abnormal event and the second abnormal event, and ensures the accuracy of the autonomous adjustment logic of the embossing equipment, so as to realize the autonomous adjustment of the embossing equipment.

[0100] At this point, the second abnormal event (the abnormality determined based on the working image of the embossing equipment and the product morphology data) is matched with the first abnormal event (the abnormality previously determined by other means, such as equipment vibration, temperature abnormality, etc.) to determine the degree of association between them. The matching coefficient is a quantitative indicator used to reflect the degree of association or similarity between two abnormal events. Generally speaking, the higher the matching coefficient, the closer the association between the two abnormal events. After determining the matching coefficient, it is also necessary to collect the current working status data of the embossing equipment, such as temperature, pressure, vibration, speed, etc. These data help to further understand the operating status of the equipment and provide a basis for subsequent analysis and adjustments.

[0101] According to the first abnormal event, the second abnormal event, the matching coefficient and the working status of the embossing equipment, an autonomous adjustment logic is formulated. Autonomous adjustment logic means that when an abnormality occurs, the equipment can automatically or according to the instructions of the operator to make corresponding adjustments to restore normal operation or reduce the impact of the abnormality on production. In actual operation, the formulation of autonomous adjustment logic needs to consider multiple factors, such as the type, severity, and frequency of abnormal events. At the same time, it is also necessary to formulate appropriate adjustment measures based on the specific situation of the equipment and production needs. These measures may include adjusting equipment parameters, shutting down for maintenance, replacing parts, etc.

[0102] Specifically, suppose a diaper production company's embossing equipment has two abnormal events: the first abnormal event is abnormal equipment vibration, and the second abnormal event is uneven embossing pattern depth. Through comparative analysis, it is found that there is a certain correlation between these two abnormal events. Specifically, when the equipment vibrates abnormally, the stability of the embossing mold is affected, resulting in uneven embossing pattern depth.

[0103] In order to quantify the degree of this correlation, a matching coefficient calculation method based on statistical analysis was used. Vibration data and embossing pattern depth data in multiple time periods were selected and correlation analysis was performed. The results showed that the matching coefficient between vibration anomalies and uneven embossing pattern depth was high (for example, 0.8), indicating that there was a strong correlation between them.

[0104] An autonomous adjustment logic was developed based on the first abnormal event (abnormal equipment vibration), the second abnormal event (uneven embossing pattern depth), the matching coefficient (0.8), and the working state of the embossing equipment (high load and large vibration amplitude).

[0105] Specifically, when the device vibrates abnormally and the matching coefficient of the uneven depth of the embossing pattern exceeds the preset threshold (such as 0.75), the system will automatically trigger the following adjustment logic: Reduce the load on the equipment to reduce the amplitude of vibration.

[0106] Adjust the position and stability of the embossing die to ensure uniformity of the embossing pattern.

[0107] If the vibration is still abnormal or the embossing pattern is still uneven after adjustment, the system will automatically stop and send out an alarm signal, prompting the operator to check and repair.

[0108] In addition, the matching table and weighted scoring method are used to illustrate the autonomous adjustment logic; the matching table is an intuitive and easy-to-understand method for matching the corresponding autonomous adjustment logic according to the first abnormal event, the second abnormal event, the matching coefficient, and the working state of the embossing equipment. The following is a simplified matching table example: Abnormal matching table of embossing equipment illustrate: The First Abnormal Event and Second Abnormal Event columns list the possible abnormality types.

[0109] The Match Coefficient Range column specifies the threshold range of the match coefficient between two anomaly events.

[0110] The embossing equipment working status column describes the current load status of the equipment or other relevant status information.

[0111] The Autonomous Adjustment Logic column gives the adjustment measures for specific abnormal combinations and matching coefficients.

[0112] Weight and score calculation method In addition to the matching table, you can also use the weight and score method to calculate the autonomous adjustment logic. This method is more flexible and can dynamically adjust the weight and score criteria according to actual conditions.

[0113] Assign a weight to each abnormal event, matching coefficient, and working state. For example, the weight of vibration abnormality is 0.3, the weight of uneven embossing pattern depth is 0.4, the weight of a matching coefficient of 0.8 is 0.2, and the weight of a high load state is 0.1.

[0114] The total score is calculated based on whether each abnormal event occurs, the actual value of the matching coefficient, and the working status of the equipment. For example, if vibration abnormality occurs (0.3 points), uneven embossing pattern depth also occurs (0.4 points), the matching coefficient is 0.85 (0.2*0.85=0.17 points), and the equipment is under high load (0.1 points), the total score is 0.3+0.4+0.17+0.1=1.07 points (the actual score may be normalized to ensure that the score is within a reasonable range).

[0115] Based on the total score, select the most appropriate adjustment measure from the preset adjustment logic library. For example, you can set a score threshold, and trigger a specific adjustment logic when the total score exceeds the threshold. Alternatively, you can use a more complex decision tree or machine learning model to select adjustment measures based on the score.

[0116] Suppose we have a simplified weighting and scoring system and we have the following results: Abnormal vibration occurred, score 0.3.

[0117] Uneven depth of the embossing pattern occurred, with a score of 0.4.

[0118] The matching coefficient is 0.8, and the score is 0.2*0.8=0.16.

[0119] The device is under high load and the score is 0.1.

[0120] The total score (after normalization) is 1.0 (assuming that the normalization process does not affect the relative size relationships).

[0121] Based on the preset score threshold and tuning logic library, we may get the following output: Self-adjusting logic output: Because the total score reaches or exceeds the preset threshold (for example, 0.8), the autonomous adjustment logic is triggered.

[0122] The specific adjustment measures are: reducing the equipment load to a medium level, adjusting the embossing die to improve stability, and continuously monitoring vibration and embossing quality.

[0123] If the problem is still not solved after adjustment, the system will automatically shut down and send out an alarm signal, prompting the operator to conduct further inspection and maintenance.

[0124] In an embodiment of the present invention, through the method in the embodiment of the present invention, the regional location of the embossing equipment in the indoor factory is collected; the corresponding independent data space is determined according to the regional location, the network space of the indoor factory and the interactive space of the embossing equipment; the independent data space collects the working data set of the embossing equipment through the corresponding Internet of Things; the remote monitoring system of the embossing equipment is determined according to the working data set of the embossing equipment, the morphological data of the product output by the embossing equipment and the alarm signal of the embossing equipment, which is compatible with the overall consideration of the working data set of the embossing equipment, the morphological data of the product output by the embossing equipment and the alarm signal of the embossing equipment, thereby ensuring the accuracy of the remote monitoring system of the embossing equipment and realizing remote monitoring of the embossing equipment.

[0125] Furthermore, in the remote monitoring system of the embossing equipment, a corresponding plurality of abnormal data are determined based on the working data set of the embossing equipment, and a first abnormal event of the embossing equipment is determined based on the plurality of abnormal data, the working video of the embossing equipment, and the alarm signal of the embossing equipment. The first abnormal event of the embossing equipment is introduced, and the abnormal data of the embossing equipment is managed and controlled.

[0126] Therefore, the second abnormal event is determined according to the working image of the embossing equipment and the morphological data of the product, and the corresponding autonomous adjustment logic is matched according to the first abnormal event, the second abnormal event and the working state of the embossing equipment. The overall consideration of the first abnormal event, the second abnormal event and the working state of the embossing equipment is compatible, the dynamic interaction of the first abnormal event and the second abnormal event is guaranteed, and the accuracy of the autonomous adjustment logic of the embossing equipment is guaranteed, so as to realize the autonomous adjustment of the embossing equipment. Embodiment 3

[0127] See also Figure 3 , Figure 3 It is a schematic diagram of the structural composition of a remote monitoring system for embossing equipment based on the Internet of Things in an embodiment of the present invention.

[0128] like Figure 3 As shown, a remote monitoring system for embossing equipment based on the Internet of Things, the remote monitoring system for embossing equipment based on the Internet of Things includes: A collection module 21 is used to collect the regional location of the embossing equipment in the indoor factory; An independent data space module 22 is used to determine the corresponding independent data space according to the location of the area, the network space of the indoor factory and the interactive space of the embossing equipment; A working data module 23, used for collecting working data sets of embossing equipment via the corresponding Internet of Things in the independent data space; A remote monitoring module 24, for determining a remote monitoring system of the embossing device according to a set of working data of the embossing device, morphological data of the product output by the embossing device, and an alarm signal of the embossing device; The abnormal module 25 is used to determine a plurality of corresponding abnormal data according to the working data set of the embossing device in the remote monitoring system of the embossing device, and determine a first abnormal event of the embossing device according to the plurality of abnormal data, the working video of the embossing device and the alarm signal of the embossing device; The autonomous adjustment module 26 is used to determine the second abnormal event according to the working image of the embossing device and the morphological data of the product, and match the corresponding autonomous adjustment logic according to the first abnormal event, the second abnormal event and the working state of the embossing device. Embodiment 4

[0129] In this embodiment, an electronic device is provided. The internal structure diagram is as follows: Figure 4 As shown, the electronic device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities, the memory of the electronic device includes a non-volatile storage medium and an internal memory, the non-volatile storage medium stores an operating system and a computer program, and the non-volatile storage medium is deployed with a database, the database is used to store user behavior data and user portraits, the internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium, the network interface of the electronic device is used to communicate with other electronic devices that deploy application software, and the computer program is executed by the processor to implement a low-altitude patrol method of a drone, the display screen of the electronic device is a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device is a touch layer covered on the display screen, a key, a trackball or a touchpad set on the housing of the electronic device, or an external keyboard, touchpad or mouse, etc.

[0130] The technical features of the above embodiments are arbitrarily combined. In order to make the description concise, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A remote monitoring method for embossing equipment based on the Internet of Things, characterized in that: include: Collect the regional location of embossing equipment in the indoor factory; Determine the corresponding independent data space according to the location of the area, the network space of the indoor factory, and the interactive space of the embossing equipment; The independent data space collects the working data set of the embossing equipment via the corresponding Internet of Things; Determine a remote monitoring system for the embossing equipment based on a set of working data of the embossing equipment, morphological data of products output by the embossing equipment, and an alarm signal of the embossing equipment; In a remote monitoring system of an embossing device, a plurality of corresponding abnormal data are determined according to a set of working data of the embossing device, and a first abnormal event of the embossing device is determined according to the plurality of abnormal data, a working video of the embossing device, and an alarm signal of the embossing device; The second abnormal event is determined according to the working image of the embossing equipment and the morphological data of the product, and the corresponding autonomous adjustment logic is matched according to the first abnormal event, the second abnormal event and the working state of the embossing equipment.

2. The remote monitoring method of embossing equipment based on the Internet of Things according to claim 1 is characterized in that: The regional location of the collection embossing equipment in the indoor factory includes: Collect spatial models of indoor factories; Determine the monitoring area based on the spatial model of the indoor factory and the central location of the embossing equipment; The regional location of the embossing equipment in the indoor factory is determined based on the monitoring area and the spatial model of the indoor factory.

3. The remote monitoring method of embossing equipment based on the Internet of Things according to claim 2 is characterized in that: Determining the corresponding independent data space according to the location of the area, the network space of the indoor factory, and the interactive space of the embossing equipment includes: Get the location of the area; Determine the network space of the indoor factory according to the location of the area and the spatial location of the network devices in the indoor factory; Traversing the embossing equipment and collecting the interactive space of the embossing equipment; Multiple interactions are made with the location of the area, the cyberspace of the indoor factory, and the interactive space of the embossing equipment; Determine the first space parameter according to the location of the area and the network space of the indoor factory, and determine the second space parameter according to the network space of the indoor factory and the interactive space of the embossing equipment; A corresponding independent data space is determined according to the embossing device, the first space parameter and the second space parameter, and the independent data space collects the working data output by the embossing device.

4. The remote monitoring method of embossing equipment based on the Internet of Things according to claim 3 is characterized in that: The independent data space collects the working data set of the embossing equipment through the corresponding Internet of Things, including: Collecting the independent data space; Match the corresponding Internet of Things based on the independent data space and the network space of the indoor factory; Associating the embossing device, the independent data space and the Internet of Things; Determine a data transmission path according to the embossing device, the independent data space, and the Internet of Things; The independent data space collects a plurality of working data of the embossing device via a data transmission path; A working data set of the embossing device is determined according to a plurality of working data of the embossing device.

5. The remote monitoring method of embossing equipment based on the Internet of Things according to any one of claims 1 to 4, characterized in that: The remote monitoring system of the embossing device is determined according to the working data set of the embossing device, the morphological data of the product output by the embossing device, and the alarm signal of the embossing device, including: Obtaining a working data set of the embossing equipment; Monitor the embossing equipment in real time and collect images of the products output by the embossing equipment; Determining morphological data of a product output by the embossing device based on recognition of an image of the product output by the embossing device; Collect alarm signals from embossing equipment; Multiple interactions are performed on the working data set of the embossing equipment, the morphological data of the product output by the embossing equipment, and the alarm signal of the embossing equipment; The remote monitoring system of the embossing equipment is determined based on multiple interactions of the working data set of the embossing equipment, the morphological data of the product output by the embossing equipment, and the alarm signal of the embossing equipment.

6. The remote monitoring method of embossing equipment based on the Internet of Things according to claim 5 is characterized in that: In the remote monitoring system of the embossing device, a plurality of corresponding abnormal data are determined according to the working data set of the embossing device, and a first abnormal event of the embossing device is determined according to the plurality of abnormal data, the working video of the embossing device and the alarm signal of the embossing device, including: Remote monitoring system for collection embossing equipment; In the remote monitoring system of the embossing equipment, the working data set of the embossing equipment is marked as abnormal; A plurality of corresponding abnormal data are determined according to the abnormal marks of the working data set of the embossing device.

7. The remote monitoring method of embossing equipment based on the Internet of Things according to claim 6 is characterized in that: In the remote monitoring system of the embossing device, a plurality of corresponding abnormal data are determined according to the working data set of the embossing device, and a first abnormal event of the embossing device is determined according to the plurality of abnormal data, the working video of the embossing device and the alarm signal of the embossing device, further comprising: Collecting the working video of the embossing equipment, and determining the corresponding abnormal parts according to the dynamic recognition of the working video of the embossing equipment; Correlate multiple abnormal data, abnormal parts and alarm signals of embossing equipment; A first abnormal event of the embossing equipment is determined according to the plurality of abnormal data, the abnormal components and the alarm signal of the embossing equipment.

8. The remote monitoring method of embossing equipment based on the Internet of Things according to claim 7 is characterized in that: The determining of the second abnormal event according to the working image of the embossing device and the morphological data of the product, and matching the corresponding autonomous adjustment logic according to the first abnormal event, the second abnormal event and the working state of the embossing device include: Conducting real-time monitoring of the embossing equipment and collecting working images of the embossing equipment according to the real-time monitoring of the embossing equipment; Correlate the working images of the embossing equipment and the morphological data of the product; The second abnormal event is determined based on the working image of the embossing equipment and the morphological data of the product.

9. The remote monitoring method of embossing equipment based on the Internet of Things according to claim 8, characterized in that: The second abnormal event is determined according to the working image of the embossing device and the morphological data of the product, and the corresponding autonomous adjustment logic is matched according to the first abnormal event, the second abnormal event and the working state of the embossing device, and further includes: Determine a corresponding matching coefficient according to the matching of the second abnormal event with the first abnormal event, and collect the working status of the embossing device; The corresponding autonomous adjustment logic is matched according to the first abnormal event, the second abnormal event, the matching coefficient and the working state of the embossing equipment.

10. A remote monitoring system for embossing equipment based on the Internet of Things, characterized in that: The remote monitoring system of the embossing equipment based on the Internet of Things is applied to the remote monitoring method of the embossing equipment based on the Internet of Things as claimed in any one of claims 1 to 9, and the remote monitoring system of the embossing equipment based on the Internet of Things comprises: A collection module is used to collect the regional location of the embossing equipment in the indoor factory; An independent data space module is used to determine the corresponding independent data space according to the location of the area, the network space of the indoor factory and the interactive space of the embossing equipment; A working data module, used for collecting working data sets of embossing equipment via the corresponding Internet of Things in the independent data space; A remote monitoring module, used to determine a remote monitoring system of the embossing device according to a set of working data of the embossing device, morphological data of the product output by the embossing device, and an alarm signal of the embossing device; An abnormality module is used to determine a plurality of corresponding abnormal data according to a set of working data of the embossing device in a remote monitoring system of the embossing device, and to determine a first abnormal event of the embossing device according to the plurality of abnormal data, a working video of the embossing device and an alarm signal of the embossing device; The autonomous adjustment module is used to determine the second abnormal event according to the working image of the embossing equipment and the morphological data of the product, and match the corresponding autonomous adjustment logic according to the first abnormal event, the second abnormal event and the working state of the embossing equipment.

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