Remote Monitoring Method and System for Embossing Equipment Based on Internet of Things

Abstract

The present invention discloses a remote monitoring method and system for an embossing device based on the Internet of Things. The present invention relates to the technical field of the Internet of Things. A remote monitoring system for the embossing device is determined according to the working data set of the embossing device, the morphological data of the products output by the embossing device, and the alarm signal of the embossing device, ensuring the accuracy of the remote monitoring system for the embossing device and realizing the remote monitoring of the embossing device. In the remote monitoring system of the embossing device, a corresponding plurality of 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. Therefore, a second abnormal event is determined according to the working image of the embossing device and the morphological data of the product, and corresponding autonomous adjustment logics are matched according to the first abnormal event, the second abnormal event, and the working state of the embossing device, so as to realize the autonomous adjustment of the embossing device.

Description

Technical Field

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

[0002] With the development of technology, the Internet of Things has gradually been applied to people's lives. The Internet of Things is associated with an embossing device and controls the embossing device. In the prior art, during the working process of the embossing device, multiple working data are output, and the corresponding working state is determined according to the multiple working data. However, relying solely on the working data of the embossing device for remote monitoring cannot ensure the accuracy of the remote monitoring system of the embossing device, nor can it achieve the autonomous adjustment of the embossing device. Summary of the Invention

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

[0004] An embodiment of the present invention provides a remote monitoring method for an embossing device based on the Internet of Things, including:

[0005] Collect the regional location of the embossing device in the indoor factory;

[0006] Determine the corresponding independent data space according to the regional location, the network space of the indoor factory, and the interaction space of the embossing device;

[0007] The independent data space collects the working data set of the embossing device through the corresponding Internet of Things;

[0008] Determine the remote monitoring system of the embossing device 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;

[0009] In the remote monitoring system of the embossing device, determine the corresponding multiple abnormal data according to the working data set of the embossing device, and determine the first abnormal event of the embossing device according to the multiple abnormal data, the working video of the embossing device, and the alarm signal of the embossing device;

[0010] 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.

[0011] An 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 above-mentioned 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:

[0012] A collection module, which is used to collect the regional location of the embossing device in the indoor factory;

[0013] An independent data space module, which is used to determine the corresponding independent data space according to the regional location, the network space of the indoor factory, and the interaction space of the embossing device;

[0014] A working data module, which is used to collect the working data set of the embossing device by the corresponding Internet of Things for the independent data space;

[0015] A remote monitoring module, which is used to determine the remote monitoring system of the embossing device 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;

[0016] An anomaly module, which is used to determine the corresponding multiple anomaly data according to the working data set of the embossing device in the remote monitoring system of the embossing device, and determine the first anomaly event of the embossing device according to the multiple anomaly data, the working video of the embossing device, and the alarm signal of the embossing device;

[0017] An autonomous adjustment module, which is used to determine the second anomaly 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 anomaly event, the second anomaly event, and the working state of the embossing device.

[0018] In the embodiment of the present invention, through the method in the embodiment of the present invention, the regional location of the embossing device 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 interaction space of the embossing device; the working data set of the embossing device is collected by the corresponding Internet of Things for the independent data space; 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, which takes into account the overall consideration of 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, ensures the accuracy of the remote monitoring system of the embossing device, and realizes the remote monitoring of the embossing device.

[0019] Furthermore, in the remote monitoring system of the embossing device, the corresponding multiple anomaly data are determined according to the working data set of the embossing device, and the first anomaly event of the embossing device is determined according to the multiple anomaly data, the working video of the embossing device, and the alarm signal of the embossing device, introducing the first anomaly event of the embossing device and controlling the anomaly data of the embossing device.

[0020] Therefore, the second abnormal event is determined based on 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, which takes into account the overall situation of the first abnormal event, the second abnormal event, and the working state of the embossing device, ensures the dynamic interaction between the first abnormal event and the second abnormal event, and ensures the accuracy of the autonomous adjustment logic of the embossing device, so as to facilitate the realization of the autonomous adjustment of the embossing device. Description of the Drawings

[0021] Figure 1 It is a schematic diagram of the application scenario of the remote monitoring method for an embossing device based on the Internet of Things in an embodiment;

[0022] Figure 2 It is a schematic flowchart of the remote monitoring method for an embossing device based on the Internet of Things in an embodiment of the present invention;

[0023] Figure 3 It is a schematic diagram of the structural composition of the remote monitoring system for an embossing device based on the Internet of Things in an embodiment of the present invention;

[0024] Figure 4 It is a hardware diagram of an electronic device shown according to an exemplary embodiment. Detailed Description of the Embodiments

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Embodiment 1

[0026] The remote monitoring method for an embossing device based on the Internet of Things provided in this application is applied to the application environment as Figure 1 shown. Among them, the computer 102 communicates with the server 104 through the network. Among them, 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

[0027] Please refer to Figures 1 to 4 , a remote monitoring method for an embossing device based on the Internet of Things, which is applied to the remote monitoring scenario of an embossing device based on the Internet of Things; the remote monitoring method for an embossing device based on the Internet of Things includes:

[0028] Step S11: Collect the regional location of the embossing device in the indoor factory;

[0029] Step S12: Determine the corresponding independent data space according to the regional location, the network space of the indoor factory, and the interaction space of the embossing device;

[0030] Step S13: The independent data space collects the working data set of the embossing device through the corresponding Internet of Things;

[0031] Step S14: Determine the remote monitoring system of the embossing device according to the working data set of the embossing device, the morphological data of the products output by the embossing device, and the alarm signals of the embossing device;

[0032] Step S15: In the remote monitoring system of the embossing device, determine corresponding multiple abnormal data according to the working data set of the embossing device, and determine the first abnormal event of the embossing device according to the multiple abnormal data, the working video of the embossing device, and the alarm signals of the embossing device;

[0033] Step S16: 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.

[0034] In step S11, collect the regional location of the embossing device in the indoor factory;

[0035] In the specific implementation process of the present invention, the specific steps are as follows:

[0036] S111: Collect the spatial model of the indoor factory;

[0037] S112: Determine the monitoring area according to the spatial model of the indoor factory and the central position of the embossing device;

[0038] S113: Determine the regional location of the embossing device in the indoor factory based on the monitoring area and the spatial model of the indoor factory.

[0039] In the embodiment of the present application, the spatial model of the indoor factory is collected; the monitoring area is determined according to the spatial model of the indoor factory and the central position of the embossing device. The spatial model of the indoor factory and the central position of the embossing device are introduced to overall control the spatial model of the indoor factory and the central position of the embossing device, ensuring the accuracy of the monitoring area.

[0040] At this time, obtain the accurate three-dimensional spatial information of the indoor factory 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, and these point cloud data can be processed by software to generate the spatial model of the indoor factory.

[0041] Based on the spatial model and the actual location of the embossing equipment, a reasonable monitoring range is delimited to effectively monitor the equipment status and prevent potential safety risks. First, the central position of the embossing equipment needs to be accurately marked in the collected spatial model, which can be achieved by manually placing a 3D icon representing the equipment in the model or directly marking at the location where the equipment is located. According to the size, operation 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 activity range of the equipment, while the polygonal monitoring area can be flexibly set according to the layout of the equipment and the surrounding environment. When setting the monitoring area, it is necessary to ensure that there are no obstacles within the monitoring area so that cameras or other sensors can clearly capture the equipment status. If there are obstacles, the monitoring area needs to be adjusted or additional monitoring equipment needs to be added to ensure unobstructed visibility.

[0042] Specifically, assume there is a factory workshop where the embossing equipment is located. To collect the spatial model of the workshop, we chose the method of laser scanning. The workshop was scanned comprehensively using a 3D laser scanner, obtaining a large amount of point cloud data. These data were imported into professional 3D modeling software and, after processing steps such as denoising and registration, a high-precision 3D model was finally generated. This model accurately reflects information such as the building structure, equipment layout, and aisle width of the workshop.

[0043] After collecting the spatial model and marking the location of the embossing equipment, we began to set the monitoring area. Considering the operation range and safety requirements of the embossing equipment, we set a circular monitoring area with a radius of 5 meters centered on the equipment center. At the same time, we also checked whether there were obstacles within the monitoring area and found that there was a row of shelves on one side close to the equipment that would block the line of sight. Therefore, we adjusted the shape of the monitoring area to a polygonal area to ensure that the camera can clearly capture the equipment status. In addition, we also plan to install an additional camera above the shelves to monitor the top area of the equipment.

[0044] Therefore, based on this monitoring area and the spatial model of the indoor factory, the regional position of the embossing equipment in the indoor factory is determined, ensuring the accuracy of the regional position of the embossing equipment in the indoor factory.

[0045] At this time, integrate the monitoring area (such as a circle, polygon, etc.) obtained in the previous steps with the three-dimensional space model of the indoor factory. Ensure that the monitoring area is accurately positioned in the model and matches the actual operating area of the equipment. In the integrated space model, clearly mark the exact position of the embossing equipment, which is usually at the center of the monitoring area or the best observation point determined according to the operating characteristics of the equipment. Ensure that all relevant position information (including equipment position, monitoring area boundary, 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 the monitoring area changes over time (such as equipment movement, production line adjustment, etc.), it is necessary to consider dynamically updating this information in the model.

[0046] In step S12, determine the corresponding independent data space according to the area position, the cyber space of the indoor factory, and the interaction space of the embossing equipment;

[0047] In the specific implementation process of the present invention, the specific steps are as follows:

[0048] S121: Obtain the area position;

[0049] S122: Determine the cyber space of the indoor factory according to the area position and the spatial position of the network devices in the indoor factory;

[0050] S123: Traverse the embossing equipment and collect the interaction space of the embossing equipment;

[0051] S124: Perform multiple interactions on the area position, the cyber space of the indoor factory, and the interaction space of the embossing equipment;

[0052] S125: Determine the first space parameter according to the area position and the cyber space of the indoor factory, and determine the second space parameter according to the cyber space of the indoor factory and the interaction space of the embossing equipment;

[0053] S126: Determine the corresponding independent data space according to the embossing equipment, the first space parameter, and the second space parameter, and this independent data space collects the working data output by the embossing equipment.

[0054] In the embodiment of the present application, obtaining the area position; determining the cyber space of the indoor factory according to the area position and the spatial position of the network devices in the indoor factory ensures the matching of the cyber space of the indoor factory.

[0055] At this time, obtain the area position, and determine the network coverage and network topology of the entire factory according to the area position of the embossing equipment and the spatial positions of the network devices (such as routers, switches, wireless access points, etc.) in the indoor factory.

[0056] First, it is necessary to collect information such as the types, locations, and performance parameters 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 relationships and signal transmission paths between network devices. 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 existing signal blind spots or weak signal areas, can be analyzed. According to the analysis results, the configuration of the network devices can be optimized to ensure that the embossing equipment can obtain a stable and efficient network connection.

[0057] Specifically, after determining the regional location of the embossing equipment, we started collecting information on all network devices in the factory and drew a network topology diagram. By analyzing the topology diagram, we found that the embossing equipment was within the coverage area of a wireless access point but was far from the access point, facing 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.

[0058] Furthermore, traverse the embossing equipment and collect the interaction space of the embossing equipment; perform multiple interactions on this regional location, the network space of the indoor factory, and the interaction space of the embossing equipment, achieving multiple interactions among this regional location, the network space of the indoor factory, and the interaction space of the embossing equipment.

[0059] At this time, traverse the embossing equipment, thereby collecting the interaction space of the embossing equipment during the traversal process, introducing the collection of the interaction space of the embossing equipment, and then correlating this regional location, the network space of the indoor factory, and the interaction space of the embossing equipment.

[0060] Integrate the information of this regional location, the network space of the indoor factory, and the interaction space of the embossing equipment to form a unified space description. Analyze the interaction relationships between different spaces, such as how the device location affects network communication quality, how the network configuration affects the real-time nature of device interactions, and how the device interaction rules coordinate with the production process. Based on the above analysis, conduct simulation experiments of multiple interactions to verify the interaction effects under different configurations and conditions. According to the results of the simulation experiments, put forward optimization suggestions to improve device interaction performance, increase production efficiency, or reduce network latency, etc.

[0061] Specifically, the information on device location, network configuration, and interaction rules was first integrated. Subsequently, we analyzed the impact of device location on network communication and found that some devices were located in the edge area of the network access point, facing the problem of unstable signals. At the same time, we also found that some parameters in the network configuration restricted the real-time performance of device interaction. To address these issues, we conducted simulation experiments on multiple interactions, adjusted the location and power of the network access point, optimized the network configuration parameters, and adjusted the device interaction rules to better adapt to the production process. These optimization measures significantly improved the interaction performance and production efficiency of the devices.

[0062] Therefore, the first spatial parameter is determined based on the regional location of this area and the network space of the indoor factory, and the second spatial parameter is determined based on the network space of the indoor factory and the interaction space of the embossing device; the corresponding independent data space is determined according to the embossing device, the first spatial parameter, and the second spatial parameter. This independent data space collects the working data output by the embossing device, taking into account the overall consideration of the embossing device, the first spatial parameter, and the second spatial parameter, ensuring the precise control of the independent data space.

[0063] At this time, the first spatial parameter and the second spatial parameter are introduced; the first spatial parameter mainly extracts key spatial characteristics based on the regional location of the embossing device and the network space of the indoor factory. These characteristics include the distance of the device's physical location relative to the network access point, the signal strength and quality of the network coverage area where the device is located, network latency, etc. The first spatial parameter will directly affect the communication performance and stability between the device and the network.

[0064] The second spatial parameter is to extract key parameters related to the device's interaction ability according to the network space of the indoor factory and the interaction space of the embossing device. These parameters include the communication protocol compatibility between the device and other devices, data transfer rate, data synchronization mechanism, interaction frequency, etc. The second spatial parameter will determine the interaction efficiency and collaborative working ability of the device in the factory production process.

[0065] 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 device's characteristics (such as model, function, etc.), the first spatial parameter (such as network performance parameters), and the second spatial parameter (such as interaction ability parameters). When creating the independent data space, determine which 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 using technologies such as databases, data warehouses, or cloud storage. According to the collected data, conduct real-time or offline analysis to extract useful information and insights.

[0066] Specifically, assume we have an indoor factory with multiple embossing devices. Device A is within the coverage area of network access point B but at a relatively far 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 where device A is located. These measurement values form part of the first spatial parameter for evaluating the communication performance between device A and the network. We analyze the interaction space of device A and find that it uses a specific communication protocol to exchange data with other devices and has a fixed data synchronization mechanism. In addition, we also determine the interaction frequency and data transmission rate of device A, and these characteristics form the second spatial parameter for evaluating the interaction efficiency and collaborative working ability of device A in the factory production process. An independent data space is created for device A, and this space is customized based on the model, function, network performance parameters (such as signal strength, network latency) and interaction ability parameters (such as communication protocol, data transmission rate) of device A.

[0067] In terms of data collection, we determine that data such as the production quantity, quality indicators, fault logs, and operation records of device A need to be collected, and these data are transmitted to the data space in real time through the built-in sensors or network interfaces of device A. In terms of data storage, we design a secure database system to store these data and set appropriate access permissions to ensure data security. In terms of data analysis, we use machine learning algorithms to analyze the collected data to predict device failures, optimize the production process, and improve product quality.

[0068] In step S13, the independent data space collects the working data set of the embossing device through the corresponding Internet of Things.

[0069] In the specific implementation process of the present invention, the specific steps are as follows:

[0070] S131: Collect the independent data space;

[0071] S132: Match the corresponding Internet of Things based on the independent data space and the network space of the indoor factory;

[0072] S133: Associate the embossing device, the independent data space, and the Internet of Things;

[0073] S134: Determine the data transmission path according to the embossing device, the independent data space, and the Internet of Things;

[0074] S135: The independent data space collects multiple working data of the embossing device through the data transmission path;

[0075] S136: Determine the working data set of the embossing device according to the multiple working data of the embossing device.

[0076] In an embodiment of the present application, the independent data space is collected; based on the independent data space and the network space of the indoor factory, the corresponding Internet of Things is matched, realizing the 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.

[0077] At this time, the independent data space is a space dedicated to storing, processing, and analyzing data in a specific field. In Internet of Things applications, this space is usually used to store data collected from Internet of Things devices and support 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 Internet of Things devices is transmitted through the network to the independent data space for storage and processing. Internet of Things applications, such as monitoring, warning, and optimization, are developed based on the data in the independent data space.

[0078] Specifically, a cloud storage service or a local database is used as the independent data space to store the environmental parameters and production equipment status data collected from the Internet of Things devices. A network topology based on industrial Ethernet is planned to ensure that all Internet of Things devices in the factory can be connected to the network. Internet of Things devices such as temperature sensors, humidity sensors, and light sensors are selected to monitor the environmental parameters in the factory in real time. Devices such as actuators and controllers are selected to adjust the operating state of the production equipment according to the environmental parameters. At the same time, the data collected by the Internet of Things devices is transmitted through the network to the independent data space for storage and processing. Data encryption technology is used to ensure the security of the data transmission process. The data transmission strategy is adjusted according to the actual situation of the network space, such as selecting the appropriate communication protocol and optimizing the data transmission path. An Internet of Things application is developed based on the data in the independent data space to monitor the environmental parameters and production equipment status in the factory in real time.

[0079] 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, accommodating the overall consideration of the embossing device, the independent data space, and the Internet of Things, and realizing the precise control of the data transmission path.

[0080] At this time, assign a unique identifier (such as device ID, MAC address, etc.) to each embossing device and register it in the Internet of Things 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 the independent data space. Ensure that this storage area is associated with the device identifier on the Internet of Things platform so that data can be correctly transferred from the device to the data space. Integrate the embossing device into the Internet of Things platform through an appropriate communication protocol (such as MQTT, HTTP, CoAP, etc.). Configure the Internet of Things platform to identify and process data from the embossing device and forward it to the corresponding independent data space.

[0081] Meanwhile, set appropriate security measures (such as data encryption, access control, etc.) to protect the communication between the embossing device, the Internet of Things platform, and the independent data space. Configure the permission management policy to ensure that only authorized personnel or systems can access and modify the data of the embossing device.

[0082] Therefore, the independent data space collects multiple working data of the embossing device through the data transmission path; determines the working data set of the embossing device based on the multiple working data of the embossing device, ensuring the accuracy of the working data set of the embossing device.

[0083] 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 device. The data transmission path is the channel for data to be transferred from the embossing device to the independent data space. It includes various network connections, sensor interfaces, etc., ensuring that data can be accurately and timely transferred to the data space. The embossing device generates various data during operation, such as temperature, pressure, speed, output, etc., and these data are collected and stored in the independent data space through the data transmission path for subsequent analysis and processing.

[0084] The working data set is a set of data with specific meanings or uses selected and sorted from the multiple working data of the embossing device. These data sets are used for device performance analysis, fault diagnosis, production efficiency optimization, etc. This process requires screening, classifying, and sorting the collected working data. 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.

[0085] Specifically, assume there is an embossing device that needs to monitor its temperature, pressure, and production output during the production process. These data are collected through sensors built into the device and transmitted to an independent data space via a wireless network. The software system in the data space automatically receives, stores, and processes these data to support subsequent analysis and decision-making. Assume we have already collected the temperature, pressure, and production output data of the embossing device. In step S136, we need to further process and analyze these data. For example, we can calculate the average temperature of the temperature data over time periods to obtain the average temperature for different time periods; perform correlation analysis on the pressure data and the production output data to find the relationship between pressure and production output, etc. Through these processes and analyses, we can obtain a set of working data for the embossing device that includes information such as average temperature, pressure range, and production output statistics. These data sets can be used to evaluate the operating status of the device, optimize production parameters, predict device failures, and other aspects.

[0086] Assume that the production output of the embossing device has decreased during the production process. We can analyze using the collected working data through the following steps:

[0087] Collect data: Collect the temperature, pressure, and production output data of the embossing device through the data transmission path.

[0088] Determine the data set: Screen and organize the collected data to obtain a data set that includes the average temperature for different time periods, pressure range, and production output statistics.

[0089] Analyze the data: Compare the data sets for different time periods and find that the temperature is relatively high and the pressure is relatively large during a certain time period, and at the same time the production output has also decreased significantly. This indicates that the device was in an overheated or overloaded state during that time period, resulting in a decrease in production efficiency.

[0090] Optimize production: According to the analysis results, adjust the operating parameters of the device, such as reducing the temperature, adjusting the pressure, etc., to improve production efficiency.

[0091] In step S14, determine the remote monitoring system of the embossing device based on the working data set of the embossing device, the morphological data of the products output by the embossing device, and the alarm signals of the embossing device;

[0092] In the specific implementation process of the present invention, the specific steps are as follows:

[0093] S141: Obtain the working data set of the embossing device;

[0094] S142: Monitor the embossing device in real time and collect images of the products output by the embossing device;

[0095] S143: Determine the morphological data of the products output by the embossing device based on the recognition of the images of the products output by the embossing device;

[0096] S144: Collect the alarm signals of the embossing device;

[0097] S145: Perform multiple interactions on the working data set of the embossing device, the morphological data of the products output by the embossing device, and the alarm signals of the embossing device;

[0098] S146: Determine the remote monitoring system of the embossing device based on the multiple interactions of the working data set of the embossing device, the morphological data of the products output by the embossing device, and the alarm signals of the embossing device.

[0099] In the embodiments of the present application, obtaining the working data set of the embossing device; monitoring the embossing device in real time, and collecting the images of the products output by the embossing device; determining the morphological data of the products output by the embossing device based on the recognition of the images of the products output by the embossing device realizes the recognition of the images of the products output by the embossing device and ensures the accuracy of the morphological data of the products output by the embossing device.

[0100] At this time, extract the working data from the control system or data recording system of the embossing device. These data usually include the operating parameters of the device, such as temperature, pressure, speed, operating time, etc., and the production efficiency indicators, such as output, yield, etc. These data are crucial for understanding the operating status and production performance of the device. The working data comes from the built-in sensors of the device or is obtained through the interface between the device 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.

[0101] Use an image acquisition device (such as a camera) to monitor the output of the embossing device in real time and capture the images of the products. These images are used for subsequent product quality analysis and morphological recognition. Usually, a high-resolution camera is used to ensure that the details of the products can be clearly captured. The camera should be placed at a position where the product output can be clearly photographed, such as the outlet of the device. The acquisition frequency of the images depends on the production speed and quality control requirements, and continuous acquisition or acquisition at fixed time intervals is required. The acquired images need to be stored in a safe location for subsequent analysis and processing.

[0102] Use image processing techniques and algorithms to analyze the acquired product images to determine the morphological data of the products. These data include the size, shape, edge sharpness, texture, etc. of the products. At the same time, extract the key morphological features of the products, such as length, width, area, etc. through image processing algorithms. Compare the extracted morphological data with the preset quality control standards to evaluate the quality of the products. Record the morphological data and the quality control evaluation results for subsequent analysis and improvement.

[0103] Specifically, assume there is an embossing factory using automated embossing equipment to produce embossed paper. To ensure product quality, working data is extracted from the control system of the embossing equipment, including embossing temperature, pressure, and running time. This data is transmitted in real time to the central monitoring system through the equipment interface. Working data is extracted from the control system of the embossing equipment, including embossing temperature, pressure, and running time. This data is transmitted in real time to the central monitoring system through the equipment interface. Image processing software is used to analyze the collected images. First, the software uses edge detection technology to identify the contour of the embossed paper, and then measures its length, width, and area. Next, the software 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.

[0104] Furthermore, alarm signals of the embossing equipment are collected; multiple interactions are performed on the working data set of the embossing equipment, the morphological data of the products output by the embossing equipment, and the alarm signals of the embossing equipment, realizing multiple interactions of the working data set of the embossing equipment, the morphological data of the products output by the embossing equipment, and the alarm signals of the embossing equipment.

[0105] At this time, the embossing equipment is usually equipped with sensors and an alarm system to monitor the operating status of the equipment and issue alarm signals when a fault or abnormal situation occurs. In this step, we collect these alarm signals to timely understand the status of the equipment and perform corresponding processing. Alarm signals include abnormal signals sent by temperature sensors, pressure sensors, motion sensors, etc. The alarm signals can be transmitted to the central monitoring system by wired or wireless means for real-time collection and processing.

[0106] The working data set of the embossing equipment, the morphological data of the products, and the alarm signals are comprehensively analyzed and processed. Through multiple interactions, we can discover the correlations and potential rules between the data, so as to more comprehensively understand the operating status of the equipment and product quality. At the same time, different types of data are fused to form a comprehensive data set. The data set is analyzed to discover potential problems and trends. Based on the analysis results, decision-making support is provided for equipment maintenance, optimization, and production process control.

[0107] 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 sensors will send out alarm signals, and these alarm signals are 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 signals are comprehensively analyzed and processed. For example, if it is found that the texture clarity of the fabric decreases during a certain period, and at the same time the equipment temperature alarm signals appear frequently, it can be inferred that the equipment has overheated, resulting in poor embossing effect. Based on this multiple interaction analysis, the embossing factory can take timely measures, such as adjusting equipment parameters, cleaning the radiator, etc., to ensure the normal operation of the equipment and product quality.

[0108] 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 products output by the embossing equipment, and the alarm signals of the embossing equipment, which incorporates the overall consideration of the working data set of the embossing equipment, the morphological data of the products output by the embossing equipment, and the alarm signals of the embossing equipment, ensuring the accuracy of the remote monitoring system of the embossing equipment.

[0109] At this time, a comprehensive remote monitoring system is constructed. The remote monitoring system aims to monitor the operating status of the embossing equipment, product quality, and any faults or abnormalities in real time, thereby ensuring 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 signals 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 handle potential problems.

[0110] At the same time, set thresholds and conditions, and when the data exceeds these thresholds or meets specific conditions, trigger an alarm. Configure a notification mechanism, such as email, text message, or phone call, to ensure that operators or managers can receive alarm information immediately.

[0111] Specifically, when determining the remote monitoring system of the embossing equipment, first, we can create a matching table to record the matching situations between the working data set of the embossing equipment, the morphological data of the product, and the alarm signals of the equipment and the preset standards. Matching table example:

[0112] 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 obtain the matching results and identify any abnormal situations.

[0113] 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 a score based on the matching situation between the actual data and the preset standards.

[0114] Example of weight and score calculation:

[0115] In this example, we assigned a weight to each item, and the sum of the weights is 1. Then, we calculated a score for each item based on the matching situation between the actual data and the preset standards. If the actual data matches the preset standards, the full score is obtained; if not, the score is 0. Finally, we added up the scores of all items to get the total score.

[0116] Total score calculation: Total score = 0.21 + 0.21 + 0.11 + 0.151 + 0.151 + 0.11 + 0.1 * 0 = 0.9 (points)

[0117] Through the total score, we can evaluate the overall operating status of the embossing equipment and the product quality. In this example, since there is an alarm signal (pressure below the threshold), the total score is slightly lower than the full score, which prompts us to pay attention to and handle this alarm signal to ensure the normal operation of the equipment and the consistency of the product quality. By means of the matching table, weights, and scores, we can more comprehensively determine the remote monitoring system of the embossing equipment and promptly identify and handle any abnormal situations.

[0118] In step S15, in the remote monitoring system of the embossing equipment, based on the set of working data of the embossing equipment, determine the corresponding multiple abnormal data, and determine the first abnormal event of the embossing equipment according to the multiple abnormal data, the working video of the embossing equipment, and the alarm signal of the embossing equipment;

[0119] In the specific implementation process of the present invention, the specific steps are as follows:

[0120] S151: Collect the remote monitoring system of the embossing equipment;

[0121] S152: In the remote monitoring system of the embossing equipment, perform abnormal marking on the set of working data of the embossing equipment;

[0122] S153: Determine the corresponding multiple abnormal data according to the abnormal marking of the set of working data of the embossing equipment;

[0123] S154: Collect the working video of the embossing equipment, and determine the corresponding abnormal components according to the dynamic recognition of the working video of the embossing equipment;

[0124] S155: Associate the multiple abnormal data, the abnormal components, and the alarm signal of the embossing equipment;

[0125] S156: Determine the first abnormal event of the embossing device based on multiple abnormal data, abnormal components, and the alarm signal of the embossing device.

[0126] In an embodiment of the present application, collect the remote monitoring system of the embossing device; in the remote monitoring system of the embossing device, perform abnormal marking on the working data set of the embossing device; determine the corresponding multiple abnormal data according to the abnormal marking of the working data set of the embossing device, ensuring the accuracy of the multiple abnormal data.

[0127] Specifically, collect the remote monitoring system of the embossing device. After obtaining the remote monitoring data of the embossing device, the next step is to analyze these data and mark the abnormal values. Abnormal values usually refer to those data points that deviate significantly from the normal operating range or preset threshold. These abnormal values may indicate problems such as device failure, wear, or unstable operation. To perform abnormal marking, we need to set a reasonable threshold range. These thresholds can be determined according to the device's specification manual, historical operation 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 point outside this range will be marked as abnormal.

[0128] After completing the abnormal marking, the next step is to determine all the data points marked as abnormal. These abnormal data points will be the focus of subsequent fault 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, etc.

[0129] Specifically, we compare the data of the temperature sensor (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 data of the temperature sensor suddenly increases to 250°C, then it will exceed the threshold range and be marked as an abnormal value. Similarly, for the pressure sensor and vibration sensor, we can also set the corresponding threshold ranges and perform abnormal marking on the data outside the range. Log in to the remote monitoring platform to view all the data points marked as abnormal. Suppose we find that the data of the temperature sensor 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 data of the vibration sensor has also increased significantly during this period. These abnormal data points will be the focus of our subsequent fault troubleshooting. We can infer that the device may have problems such as overheating or excessive vibration, and further inspections of the cooling system and mechanical components, etc. are required.

[0130] Furthermore, by collecting the working video of the embossing equipment and determining the corresponding abnormal components based on the dynamic recognition of the working video of the embossing equipment, the dynamic recognition of the working video of the embossing equipment is realized, ensuring the accuracy of the abnormal components.

[0131] At this time, use a high-definition camera or other video acquisition equipment to record the running state of the embossing equipment in real time. Ensure the quality of video recording, including clarity, frame rate, color restoration, etc., for subsequent video analysis. Store the video data in a safe and reliable storage medium for easy calling and analysis. Use video analysis technology to process and analyze the collected video data. Through algorithms such as image recognition and object detection, identify the embossing equipment and its various components in the video. Compare the videos in the normal state and the abnormal state to find the changes in the equipment running state, such as vibration, displacement, deformation, etc. Combine the working principle and structural characteristics of the equipment to analyze the abnormal components that may be caused by these changes.

[0132] Specifically, taking a certain embossing machine as an example, install high-definition cameras at key parts of the embossing machine, such as the feeding port, embossing area, discharging port, etc. The cameras record the running state of the embossing machine in real time and store the video data in the hard disk of the server. Through video analysis software, load the working video of the embossing machine stored on the server. The software automatically processes the video, identifies the various components of the embossing machine, and marks them on the video screen. Comparing with the video in the normal state, it is found that the vibration amplitude in the embossing area has increased significantly and is accompanied by abnormal noise. Through further analysis, it is determined that the abnormal vibration and noise originate from the bearing in the embossing area. The bearing may have problems such as wear, looseness or damage, resulting in unstable operation of the embossing area.

[0133] Therefore, associate multiple abnormal data, abnormal components, and the alarm signals of the embossing equipment; determine the first abnormal event of the embossing equipment based on the multiple abnormal data, abnormal components, and the alarm signals of the embossing equipment, taking into account the overall situation of the multiple abnormal data, abnormal components, and the alarm signals of the embossing equipment, and realizing the accurate control of the first abnormal event of the embossing equipment.

[0134] At this time, summarize and integrate all the abnormal data (such as abnormal readings of sensors such as temperature, pressure, and vibration) collected in the previous steps. Match the identified abnormal components (such as worn bearings, blocked filters, etc.) with the corresponding abnormal data. This usually involves a deep understanding of the equipment working principle to accurately link data changes with specific component failures. At the same time, analyze any signals issued by the alarm system of the embossing equipment. These signals are usually associated with specific fault conditions, such as overheating, overpressure, overcurrent, etc. Finally, conduct correlation analysis, considering multiple abnormal data, abnormal components, and alarm signals comprehensively to reveal the potential connections and causal relationships between them.

[0135] Further comprehensively analyze all the collected information, including abnormal data, abnormal components, and alarm signals. Based on the analysis results, determine the primary abnormal event that leads to the current device state. This event is usually the first-occurring or most influential fault, and it may be the root cause of other abnormal data or component failures. Once the first abnormal event is determined, corresponding countermeasures can be formulated, such as emergency shutdown, repair, or replacement of faulty components, etc.

[0136] When determining the first abnormal event of the embossing device based on multiple abnormal data, abnormal components, and the alarm signals of the embossing device, the definition of the "first abnormal event" is mainly based on the following comprehensive considerations:

[0137] The first abnormal event is often the root cause of a series of subsequent chain reactions or secondary faults. This requires an in-depth understanding of the device's operating principle, the interaction between components, and the potential connections between abnormal data. Through comprehensive analysis, it can be determined which abnormal event is the prerequisite or driving factor for other abnormalities.

[0138] The first abnormal event may not be the earliest-occurring, but it is the most significant in terms of the degree of impact. It may pose the greatest threat to the device's performance, stability, or safety. When evaluating the degree of impact, it is necessary to consider the direct and indirect impacts of the abnormal event on the device operation, as well as the possible consequences for production efficiency and product quality.

[0139] There may be consistency or relevance among multiple abnormal data, and these relevances may point to a common first abnormal event. For example, if multiple sensors simultaneously detect abnormal data and these data show consistency in the change trend, then these abnormalities may be caused by a common reason. The alarm signals of the device are usually associated with specific fault conditions. 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 very likely to be the first abnormal event.

[0140] Specifically, assume that during the monitoring of the embossing device, the following abnormal data and alarm signals occur simultaneously:

[0141] The temperature sensor shows that the temperature in the embossing area is too high.

[0142] The vibration sensor detects that the vibration amplitude in the embossing area increases abnormally.

[0143] The alarm system issues an overheat alarm signal.

[0144] It is observed that the embossing effect deteriorates and the pattern is unclear.

[0145] When comprehensively analyzing this information, the following steps can be taken to determine the first abnormal event:

[0146] Time-sequence analysis: Check the timestamps of each abnormal data and alarm signal to determine the order in which they occur.

[0147] Causality analysis: Consider the potential connections among increased temperature, increased vibration, overheat alarm, and decreased embossing effect. For example, an increase in temperature may lead to a decrease in the embossing effect, and an increase in vibration may be a precursor to an increase in temperature or bearing wear.

[0148] Impact assessment: Evaluate the impact of each abnormality on equipment operation and production efficiency. For example, overheating may cause the equipment to stop, thus having a serious impact on production.

[0149] Data consistency and relevance check: Check the consistency among abnormal data (such as whether an increase in temperature and an increase in vibration occur simultaneously) and their relevance to alarm signals (such as whether the overheat alarm matches the increase in temperature).

[0150] Based on the above analysis, it can be determined that the first abnormal event is the excessive temperature in the embossing area. This event may be caused by bearing wear, cooling system failure, or blockage in the embossing area, etc.

[0151] In step S16, determine the second abnormal event based on 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;

[0152] In the specific implementation process of the present invention, the specific steps are as follows:

[0153] S161: Monitor the embossing equipment in real time and collect the working image of the embossing equipment according to the real-time monitoring of the embossing equipment;

[0154] S162: Associate the working image of the embossing equipment and the morphological data of the product;

[0155] S163: Determine the second abnormal event based on the working image of the embossing equipment and the morphological data of the product;

[0156] S164: Determine the corresponding matching coefficient according to the matching between the second abnormal event and the first abnormal event, and collect the working state of the embossing equipment;

[0157] S165: Match 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.

[0158] In an embodiment of the present application, the embossing device is monitored in real time, and the working image of the embossing device is collected based on the real-time monitoring; the working image of the embossing device and the morphological data of the product are associated; a second abnormal event is determined based on the working image of the embossing device and the morphological data of the product, and the second abnormal event is introduced to facilitate the overall control of the first abnormal event and the second abnormal event.

[0159] At this time, the operating state of the embossing device is monitored in real time to obtain the current working condition of the device. To achieve this, it is necessary to install a camera or other image acquisition devices and ensure that their positions and angles can clearly capture the key working areas of the embossing device. The real-time monitoring software should be able to continuously capture images and store these images in an easily accessible location for subsequent analysis and processing.

[0160] In actual operation, the camera is usually installed above or on the side of the embossing machine to ensure that it can capture the embossing die, the embossing area, and the state of the product during the embossing process. The monitoring software will set a certain image acquisition frequency to ensure that sufficient image data is captured to reflect the operating state of the embossing device. At the same time, to ensure the clarity and accuracy of the images, the camera also needs to be regularly maintained and calibrated.

[0161] The working image of the embossing device is associated with the morphological data of the product. The morphological data usually includes features such as the size, shape, and texture of the product, and these data 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 device and the product quality, so as to discover potential problems or abnormalities. In actual operation, measuring tools (such as vernier calipers, microscopes, etc.) are used to measure the morphological data of the embossed product. At the same time, the working images of the embossing device during the corresponding time period are extracted from the monitoring system. Then, image analysis software or data processing software is used to associate these morphological data with the images for subsequent analysis and processing.

[0162] A second abnormal event is determined based on the working image of the embossing device and the morphological data of the product. Abnormal events usually refer to device states or product quality problems that do not conform to expectations. By analyzing the working images and morphological data, problems such as device failures, wear, and improper adjustment can be discovered, thereby determining the second abnormal event. In actual operation, image processing software or data analysis software is used to deeply analyze the working images and morphological data. It may compare the morphological data of products in different time periods or different batches to discover the changing trends or abnormal points. At the same time, the detailed features in the working images, such as the state of the embossing die and the texture of the embossing area, are also observed to discover potential abnormalities or problems.

[0163] Specifically, assume that a diaper manufacturing company is using an embossing device to emboss diapers. To ensure the embossing quality, the company installs a high-definition camera to monitor the embossing device 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 collect 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 device. A vernier caliper is used to measure the morphological data of the embossed diapers, including the width, length of the diapers, and the depth and width of the embossing pattern, etc. At the same time, the working images of the embossing device during the corresponding period are extracted from the monitoring system. Then, an image analysis software is used to associate these morphological data with the images. Through comparison and analysis, it is found that when a certain part of the embossing die wears, the depth and width of the embossing pattern will change, thus affecting the embossing quality of the diapers.

[0164] By comparing the morphological data and the corresponding working images of different batches of diapers, it is found that when a certain part of the embossing die wears severely, the depth and width of the embossing pattern will decrease significantly, resulting in a decline in the embossing quality of the diapers. Therefore, the second abnormal event is determined to be severe wear of the embossing die. To solve this problem, it is planned to stop the machine to replace the worn embossing die and conduct a comprehensive inspection and adjustment of the embossing device.

[0165] Therefore, the corresponding matching coefficient is determined according to the matching of the second abnormal event and the first abnormal event, and the working status of the embossing device is collected; according to the first abnormal event, the second abnormal event, the matching coefficient, and the working status of the embossing device, the corresponding self-adjustment logic is matched, which takes into account the overall situation of the first abnormal event, the second abnormal event, and the working status of the embossing device, ensures the dynamic interaction between the first abnormal event and the second abnormal event, and ensures the accuracy of the self-adjustment logic of the embossing device, so as to facilitate the realization of the self-adjustment of the embossing device.

[0166] At this time, it is required to match the second abnormal event (the abnormal determined based on the working images of the embossing device and the product morphological data) with the first abnormal event (the abnormal determined by other means before, such as equipment vibration, temperature abnormality, etc.) to determine the degree of association between them. The matching coefficient is a quantitative index 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 device, such as temperature, pressure, vibration, rotation speed, etc. These data help to further understand the operation status of the device and provide a basis for subsequent analysis and adjustment.

[0167] Based on 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. The autonomous adjustment logic means that when an abnormality occurs, the equipment can automatically or according to the operator's instructions make corresponding adjustments to resume normal operation or reduce the impact of the abnormality on production. In actual operation, formulating the autonomous adjustment logic requires considering multiple factors, such as the type, severity, and occurrence frequency of the abnormal event. At the same time, it is also necessary to combine the specific situation of the equipment and production requirements to formulate appropriate adjustment measures. These measures may include adjusting equipment parameters, shutting down for maintenance, replacing components, etc.

[0168] Specifically, assume that an embossing equipment of a diaper manufacturing company has experienced two abnormal events successively: the first abnormal event is abnormal vibration of the equipment, and the second abnormal event is uneven depth of the embossing pattern. 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 die is affected, resulting in uneven depth of the embossing pattern.

[0169] To quantify this degree of correlation, a matching coefficient calculation method based on statistical analysis is used. Vibration data and embossing pattern depth data in multiple time periods are selected for correlation analysis. The results show that the matching coefficient between abnormal vibration and uneven embossing pattern depth is relatively high (for example, 0.8), indicating a strong correlation between them.

[0170] Based on the first abnormal event (abnormal vibration of the equipment), the second abnormal event (uneven depth of the embossing pattern), the matching coefficient (0.8), and the working status of the embossing equipment (high load, large vibration amplitude), an autonomous adjustment logic is formulated.

[0171] Specifically, when the matching coefficient of abnormal equipment vibration and uneven embossing pattern depth exceeds a preset threshold (such as 0.75), the system will automatically trigger the following adjustment logic:

[0172] Reduce the equipment load to reduce the vibration amplitude.

[0173] Adjust the position and stability of the embossing die to ensure the uniformity of the embossing pattern.

[0174] If the vibration is still abnormal or the embossing pattern is still uneven after adjustment, the system will automatically shut down and send an alarm signal to prompt the operator to check and repair.

[0175] In addition, a matching table and a weight 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 status of the embossing equipment. The following is a simplified example of a matching table:

[0176] Abnormal Matching Table for Embossing Equipment

[0177] Description:

[0178] The first abnormal event and the second abnormal event columns list the possible types of abnormalities.

[0179] The matching coefficient range column specifies the threshold range of the matching coefficient between two abnormal events.

[0180] The working status column of the embossing equipment describes the current load status or other relevant status information of the equipment.

[0181] The self-adjustment logic column gives the adjustment measures for specific abnormal combinations and matching coefficients.

[0182] Weight and Score Calculation Method

[0183] In addition to the matching table, a method of weights and scores can also be used to calculate the self-adjustment logic. This method is more flexible and can dynamically adjust the weight and score criteria according to the actual situation.

[0184] Assign a weight to each abnormal event, matching coefficient, and working status. For example, the weight of vibration abnormality is 0.3, the weight of uneven embossing pattern depth is 0.4, the weight when the matching coefficient is 0.8 is 0.2, and the weight of the high-load status is 0.1.

[0185] Calculate the total score 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 (getting 0.3 points), uneven embossing pattern depth also occurs (getting 0.4 points), the matching coefficient is 0.85 (getting 0.2 * 0.85 = 0.17 points), and the equipment is in a high-load status (getting 0.1 points), then the total score is 0.3 + 0.4 + 0.17 + 0.1 = 1.07 points (the actual score may be normalized to ensure the score is within a reasonable range).

[0186] Based on the total score, select the most appropriate adjustment measure from the preset adjustment logic library. For example, a score threshold can be set, and when the total score exceeds this threshold, a specific adjustment logic is triggered. Or, a more complex decision tree or machine learning model can be used to select the adjustment measure according to the score.

[0187] Suppose we have a simplified weight and score system and get the following results:

[0188] Vibration abnormality occurs, score 0.3.

[0189] Uneven embossing pattern depth occurs, score 0.4.

[0190] The matching coefficient is 0.8, and the score is 0.2 * 0.8 = 0.16.

[0191] The device is in a high-load state, and the score is 0.1.

[0192] The total score (after normalization) is 1.0 (assuming the normalization process does not affect the relative magnitude relationship).

[0193] According to the preset score threshold and the adjustment logic library, we may obtain the following output:

[0194] Autonomous adjustment logic output:

[0195] Since the total score reaches or exceeds the preset threshold (e.g., 0.8), the autonomous adjustment logic is triggered.

[0196] The specific adjustment measures are as follows: reduce the device load to a medium level, adjust the embossing die to improve stability, and continuously monitor vibration and embossing quality.

[0197] If the problem is still not solved after the adjustment, the system will automatically shut down and send an alarm signal to prompt the operator to conduct further inspections and repairs.

[0198] In the embodiment of the present invention, through the method in the embodiment of the present invention, the regional position of the embossing device in the indoor factory is collected; according to the regional position, the network space of the indoor factory, and the interaction space of the embossing device, the corresponding independent data space is determined; the independent data space collects the working data set of the embossing device through the corresponding Internet of Things; according to the working data set of the embossing device, the morphological data of the products output by the embossing device, and the alarm signal of the embossing device, the remote monitoring system of the embossing device is determined, which takes into account the overall consideration of the working data set of the embossing device, the morphological data of the products output by the embossing device, and the alarm signal of the embossing device, ensures the accuracy of the remote monitoring system of the embossing device, and realizes the remote monitoring of the embossing device.

[0199] Furthermore, in the remote monitoring system of the embossing device, a corresponding plurality of 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. The first abnormal event of the embossing device is introduced, and the abnormal data of the embossing device are controlled.

[0200] Therefore, 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, which takes into account the first abnormal event, the second abnormal event and the working state of the embossing device as a whole, ensures the dynamic interaction between the first abnormal event and the second abnormal event, and ensures the accuracy of the autonomous adjustment logic of the embossing device, so as to realize the autonomous adjustment of the embossing device. Embodiment III

[0201] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of the remote monitoring system of the embossing device based on the Internet of Things in the embodiment of the present invention.

[0202] As Figure 3 shown, a remote monitoring system of an embossing device based on the Internet of Things, the remote monitoring system of the embossing device based on the Internet of Things includes:

[0203] An acquisition module 21, configured to acquire the regional location of the embossing device in the indoor factory;

[0204] An independent data space module 22, configured to determine a corresponding independent data space according to the regional location, the network space of the indoor factory, and the interaction space of the embossing device;

[0205] A working data module 23, configured to collect a working data set of the embossing device through the corresponding Internet of Things for the independent data space;

[0206] A remote monitoring module 24, configured to determine a remote monitoring system of the embossing device 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;

[0207] An abnormal module 25, configured to determine corresponding multiple abnormal data according to the working data set of the embossing device in the remote monitoring system of the embossing device, and determine the first abnormal event of the embossing device according to the multiple abnormal data, the working video of the embossing device, and the alarm signal of the embossing device;

[0208] An autonomous adjustment module 26, configured to determine a second abnormal event according to the working image of the embossing device and the morphological data of the product, and match a corresponding autonomous adjustment logic according to the first abnormal event, the second abnormal event, and the working state of the embossing device. Embodiment IV

[0209] In this embodiment, an electronic device is provided. The internal structure diagram is as Figure 4As 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 a database is deployed on the non-volatile storage medium. 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 the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with other electronic devices on which application software is deployed. When the computer program is executed by the processor, it implements a low-altitude patrol method for an unmanned aerial vehicle. The display screen of the electronic device is a liquid crystal display screen or an electronic ink display screen. The input device of the electronic device is a touch layer covering the display screen, and is also a button, a trackball, or a touchpad provided on the outer shell of the electronic device, or an external keyboard, touchpad, or mouse, etc.

[0210] For any combination of the technical features of the above embodiments, for the sake of brevity of description, 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, it should be considered to be within the scope described in this specification.

Claims

1. A remote monitoring method for embossing equipment based on the Internet of Things, characterized in that: include: Collecting the regional location of the embossing equipment in the indoor factory: Collecting the spatial model of the indoor factory; determining the monitoring area according to the spatial model of the indoor factory and the central location of the embossing equipment; determining the regional location of the embossing equipment in the indoor factory based on the monitoring area and the spatial model of the indoor factory; Determine the corresponding independent data space according to the regional location, the network space of the indoor factory and the interactive space of the embossing equipment: obtain the regional location; determine the network space of the indoor factory according to the regional location and the spatial location of the network device in the indoor factory; traverse the embossing equipment and collect the interactive space of the embossing equipment; perform multiple interactions on the regional location, the network space of the indoor factory and the interactive space of the embossing equipment; determine the first space parameter according to the regional location 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; determine the corresponding independent data space according to the embossing equipment, the first space parameter and the second space parameter, and the independent data space collects the working data output by the embossing equipment; The independent data space collects a set of working data of the embossing device via the corresponding Internet of Things: collecting the independent data space; matching 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; determining 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 the data transmission path; determining a set of working data of the embossing device according to the plurality of working data of the embossing device; Determine the remote monitoring system of the embossing equipment 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: obtain the working data set of the embossing equipment; monitor the embossing equipment in real time, and collect the image of the product output by the embossing equipment; determine the morphological data of the product output by the embossing equipment according to the recognition of the image of the product output by the embossing equipment; collect the alarm signal of the embossing equipment; perform 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; determine the remote monitoring system of the embossing equipment according to 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; In the remote monitoring system of the embossing equipment, a plurality 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 plurality of abnormal data, the working video of the embossing equipment and the alarm signal of the embossing equipment: the remote monitoring system of the embossing equipment is collected; in the remote monitoring system of the embossing equipment, the working data set of the embossing equipment is abnormally marked; the plurality of corresponding abnormal data are determined according to the abnormal marks of the working data set of the embossing equipment; the working video of the embossing equipment is collected, and the corresponding abnormal components are determined according to the dynamic recognition of the working video of the embossing equipment; the plurality of abnormal data, the abnormal components and the alarm signal of the embossing equipment are associated; the 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; 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 embossing equipment is monitored in real time, and the working image of the embossing equipment is collected according to the real-time monitoring of the embossing equipment; the working image of the embossing equipment and the morphological data of the product are associated; the second abnormal event is determined based on the working image of the embossing equipment and the morphological data of the product.

2. The remote monitoring method of embossing equipment based on the Internet of Things according to claim 1 is 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.

3. 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 2, and the remote monitoring system of the embossing equipment based on the Internet of Things comprises: The acquisition module is used to acquire the regional position of the embossing equipment in the indoor factory: acquire the spatial model of the indoor factory; determine the monitoring area according to the spatial model of the indoor factory and the central position of the embossing equipment; determine the regional position of the embossing equipment in the indoor factory based on the monitoring area and the spatial model of the indoor factory; The independent data space module is used to determine the corresponding independent data space according to the regional location, the network space of the indoor factory and the interactive space of the embossing equipment: obtain the regional location; determine the network space of the indoor factory according to the regional location and the spatial location of the network device in the indoor factory; traverse the embossing equipment and collect the interactive space of the embossing equipment; perform multiple interactions on the regional location, the network space of the indoor factory and the interactive space of the embossing equipment; determine the first space parameter according to the regional location 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; determine the corresponding independent data space according to the embossing equipment, the first space parameter and the second space parameter, and the independent data space collects the working data output by the embossing equipment; A working data module is used for the independent data space to collect a working data set of the embossing device via the corresponding Internet of Things: collecting the independent data space; matching 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; determining 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 the data transmission path; determining a working data set of the embossing device according to the plurality of working data of the embossing device; A remote monitoring module is used to determine the remote monitoring system of the embossing equipment 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: obtaining the working data set of the embossing equipment; monitoring the embossing equipment in real time, and collecting the image of the product output by the embossing equipment; determining the morphological data of the product output by the embossing equipment according to the recognition of the image of the product output by the embossing equipment; collecting the alarm signal of the embossing equipment; performing 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; determining the remote monitoring system of the embossing equipment according to 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; The abnormal module is used to determine the corresponding multiple abnormal data according to the working data set of the embossing equipment in the remote monitoring system of the embossing equipment, and determine the first abnormal event of the embossing equipment according to the multiple abnormal data, the working video of the embossing equipment and the alarm signal of the embossing equipment: collect the remote monitoring system of the embossing equipment; in the remote monitoring system of the embossing equipment, mark the working data set of the embossing equipment as abnormal; determine the corresponding multiple abnormal data according to the abnormal mark of the working data set of the embossing equipment; collect the working video of the embossing equipment, and determine the corresponding abnormal parts according to the dynamic recognition of the working video of the embossing equipment; associate the multiple abnormal data, abnormal parts and the alarm signal of the embossing equipment; determine the first abnormal event of the embossing equipment according to the multiple abnormal data, abnormal parts and the alarm signal of the embossing equipment; 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: monitor the embossing equipment in real time, and collect the working image of the embossing equipment according to the real-time monitoring of the embossing equipment; associate the working image of the embossing equipment and the morphological data of the product; and determine the second abnormal event based on the working image of the embossing equipment and the morphological data of the product.

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