Intelligent vacuum forming machine data monitoring system based on Internet of Things

Abstract

The invention provides a vacuum forming machine data intelligent monitoring system based on the Internet of Things, and the system comprises a data collection module which is configured to obtain target multi-dimensional data generated by a plurality of vacuum forming machines in the operation process based on the Internet of Things; the fault analysis and prediction module is configured to perform fault real-time analysis and / or fault prediction according to the target multi-dimensional data of the corresponding plastic vacuum forming machine, and generate a fault early warning instruction or operation parameter optimization information; the operation optimization module is configured to perform operation optimization on the corresponding plastic vacuum forming machine according to the operation parameter optimization information; and the fault early warning module is configured to perform fault early warning on the corresponding plastic vacuum forming machine according to the fault early warning instruction. According to the system, the problem of low efficiency of data management and plastic vacuum forming machine control of an existing plastic vacuum forming machine data management scheme is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of blister machine control, and in particular to an intelligent monitoring system for blister machine data based on the Internet of Things. Background Art

[0002] In the field of industrial manufacturing, especially in the blister molding process, data collection and processing of production equipment are key links in ensuring product quality, improving production efficiency and realizing intelligent management. The currently widely used blister machine equipment data management system has significant technical bottlenecks and cannot meet the stringent requirements of modern intelligent manufacturing for data real-time, interoperability and intelligence. The main technical defects are as follows:

[0003] Low data collection efficiency and insufficient accuracy: The existing system relies heavily on manual recording of key production data (such as temperature, pressure, cycle time, and finished product quantity) at fixed times and locations. This method is not only extremely inefficient and consumes valuable human resources, but also easily leads to omissions, errors, or distortion of critical data due to human factors such as operator negligence, fatigue, and discrepancies in recording standards. This traditional data acquisition model has become an obstacle to refined management and scientific decision-making, and fails to provide an accurate and reliable foundation for traceability and optimization of the production process.

[0004] Data silos are serious and processing standards are lacking: Existing factory environments typically deploy blister machines of varying models and batches. The underlying control systems (such as PLCs) or upper computer monitoring systems of these devices typically operate independently, with data stored in separate local databases. There is a lack of unified, standardized communication protocols and access interfaces between devices, and data formats vary. This heterogeneous environment prevents the effective interconnection of massive amounts of data generated by different devices, making it impossible to form an integrated data view at the factory level, making it difficult to conduct global production status analysis, collaborative optimization, and unified scheduling management across devices.

[0005] Equipment status monitoring lacks timeliness: Existing data collection solutions generally utilize offline storage or periodic polling and reporting mechanisms (e.g., batch export daily or at the end of a shift). This prevents managers from continuously and in real time access to the equipment's instantaneous operating parameters and key status information (e.g., start / stop, fault alarms). The significant lag in monitoring inevitably leads to inability to immediately detect equipment anomalies, long fault response times, and inability to promptly intervene and correct minor fluctuations in the production process. This significantly increases the risk of wasted resources due to unplanned equipment downtime and increased defective product rates, and may even lead to potential production safety or quality incidents.

[0006] Lack of intelligence at the data analysis layer: Traditional blister machine data management systems are often only capable of basic data collection, storage, and simple visualization, remaining at the passive recording level. Existing technology systems generally fail to effectively integrate the Internet of Things (IoT) edge-sensing architecture with advanced data analysis algorithms (such as edge computing-based preprocessing, machine learning-driven pattern recognition, and predictive analysis). Consequently, intelligent diagnosis of equipment operating status and early failure prediction are impossible, and deep mining of production potential based on historical and real-time data is impossible. For example, this involves achieving online adaptive optimization of processing parameters, predicting equipment lifespan / failure rate, or identifying process bottlenecks to improve overall efficiency. The level of intelligence is seriously insufficient. Summary of the Invention

[0007] The present application provides an intelligent monitoring system for blister machine data based on the Internet of Things to solve the problem of low efficiency in data management and blister machine control in existing blister machine data management solutions.

[0008] The system comprises:

[0009] A data acquisition module, wherein the data acquisition module is configured to acquire target multi-dimensional data generated by a plurality of blister machines during operation based on the Internet of Things;

[0010] A fault analysis and prediction module, wherein the fault analysis and prediction module is configured to perform real-time fault analysis and / or fault prediction based on the target multi-dimensional data of the corresponding blister machine, and generate fault warning instructions or operating parameter optimization information;

[0011] An operation optimization module, wherein the operation optimization module is configured to optimize the operation of the corresponding blister machine according to the operation parameter optimization information;

[0012] A fault warning module is configured to provide a fault warning to the corresponding blister machine according to the fault warning instruction.

[0013] Preferably, the data acquisition module includes:

[0014] A raw data acquisition unit, wherein the raw data acquisition unit is configured to acquire multi-dimensional data of all blister machines;

[0015] A data filtering unit is configured to preprocess the multidimensional data to obtain the target multidimensional data; the preprocessing includes extreme data screening and missing value filling.

[0016] Preferably, the target multi-dimensional data includes operating process parameters, blister machine setting parameters and operating environment parameters;

[0017] The original data acquisition unit includes:

[0018] a process parameter monitor, wherein the process parameter monitor is configured to obtain the process parameter;

[0019] A blister machine parameter monitor, wherein the blister machine parameter monitor is configured to obtain setting parameters of the blister machine;

[0020] an environmental parameter monitor, the environmental parameter monitor being configured to obtain the operating environment parameter;

[0021] A data mapper is configured to construct a data mapping between the process parameters, the blister machine setting parameters, and the operating environment parameters of the same blister machine and the corresponding blister machine.

[0022] Preferably, the fault analysis and prediction module includes:

[0023] A device selection unit, the device selection unit being configured to select the corresponding target multi-dimensional data according to the data mapping corresponding to the blister machine;

[0024] A fault model unit is configured to perform real-time fault analysis and / or fault prediction based on the target multi-dimensional data selected by the equipment selection unit, and generate fault warning instructions or operating parameter optimization information.

[0025] Preferably, the fault model unit includes a real-time fault analysis model and a fault prediction model;

[0026] The real-time fault analysis model is used to:

[0027] Performing real-time fault analysis based on the target multi-dimensional data and generating the fault warning instruction; the fault warning instruction is used to indicate that a fault has occurred in the corresponding blister machine;

[0028] When the blister machine undergoing fault monitoring operates normally, an equipment optimization instruction is generated;

[0029] The fault prediction model is used to:

[0030] When receiving the equipment optimization instruction, performing fault prediction based on the target multi-dimensional data to generate fault prediction data;

[0031] The equipment operation data is reversed based on the fault prediction data to obtain the operation parameter optimization information.

[0032] Preferably, the operation optimization module includes:

[0033] an optimization positioning unit, the optimization positioning unit being configured to extract parameter settings and / or component positions to be optimized based on the operating parameter optimization information; the component positions being positions of components on the corresponding blister machine; the parameter settings to be optimized including blister machine setting parameters, operating process parameters, and / or operating environment parameters to be optimized;

[0034] An optimization processing unit is configured to perform operation optimization on the parameter settings to be optimized and / or the component positions according to the operation parameter optimization information.

[0035] Preferably, the fault warning module includes:

[0036] a fault locating unit, the fault locating unit being configured to extract fault parameter settings and / or fault component locations according to the fault warning instruction; the fault component locations being locations of components corresponding to the blister machine where the fault occurs; the fault parameter settings including blister machine setting parameters, operating process parameters, and / or operating environment parameters where the fault occurs;

[0037] A fault warning unit is configured to provide a fault warning for the fault parameter setting and / or the fault component position according to the fault warning instruction.

[0038] Preferably, the system further comprises:

[0039] The display module is configured to visually display all data and operating conditions of the system.

[0040] Preferably, the system further comprises:

[0041] A storage module is configured to store all data acquired and generated by the data acquisition module, the fault analysis and prediction module, the operation optimization module, the fault warning module and the display module.

[0042] Preferably, the system further comprises:

[0043] A cloud server is configured to share the data stored in the storage module with all blister machines.

[0044] As can be seen from the above content, the present application provides an intelligent monitoring system for blister machine data based on the Internet of Things. The system includes a data acquisition module, which is configured to obtain target multi-dimensional data generated by several blister machines during operation based on the Internet of Things; a fault analysis and prediction module, which is configured to perform real-time fault analysis and / or fault prediction based on the target multi-dimensional data of the corresponding blister machine, and generate fault warning instructions or operating parameter optimization information; an operation optimization module, which is configured to optimize the operation of the corresponding blister machine based on the operating parameter optimization information; and a fault warning module, which is configured to provide fault warnings for the corresponding blister machine based on the fault warning instructions. Through the above system, the present application solves the problem of low efficiency of data management and blister machine control in existing blister machine data management solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0046] Figure 1 This is a schematic diagram of an intelligent monitoring system for blister machine data based on the Internet of Things for this application;

[0047] Figure 2 This is a schematic diagram of a data acquisition module in an intelligent monitoring system for blister machine data based on the Internet of Things for this application;

[0048] Figure 3 This is a schematic diagram of a fault analysis and prediction module in an intelligent monitoring system for blister machine data based on the Internet of Things for this application;

[0049] Figure 4 This is a schematic diagram of an operation optimization module in an intelligent monitoring system for blister machine data based on the Internet of Things for this application;

[0050] Figure 5 This is a schematic diagram of a fault warning module in an intelligent monitoring system for blister machine data based on the Internet of Things. DETAILED DESCRIPTION

[0051] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0052] It should be noted that the brief descriptions of terms in this application are only for the purpose of facilitating the understanding of the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise specified, these terms should be understood according to their ordinary and usual meanings.

[0053] It should be noted that, in this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described in this application as "exemplary" or "for example" should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0054] Figure 1 This is a schematic diagram of an intelligent monitoring system for blister machine data based on the Internet of Things.

[0055] See also Figure 1 It can be seen that this embodiment provides an intelligent monitoring system for blister machine data based on the Internet of Things, and the system includes:

[0056] The data acquisition module 100 is configured to acquire target multi-dimensional data generated by a plurality of blister machines during operation based on the Internet of Things.

[0057] Specifically, in this embodiment, the data acquisition module 100 is used to obtain all data generated during the operation of the blister machine. Since the operating conditions of the blister machine and the parameters of the environment in which the blister machine is located are not single, it is necessary to obtain all required data, namely the target dimensional data.

[0058] The system further comprises:

[0059] The fault analysis and prediction module 200 is configured to perform real-time fault analysis and / or fault prediction based on the target multi-dimensional data of the corresponding blister machine, and generate fault warning instructions or operating parameter optimization information.

[0060] Specifically, in this embodiment, the purpose of acquiring the target multi-dimensional data is to analyze the operation status of the blister machine according to the target multi-dimensional data, so as to achieve the best maintenance of the operation of the blister machine.

[0061] The process of performing operation analysis on the blister machine according to the target multi-dimensional data is divided into two steps.

[0062] The first step is to analyze whether the current blister machine has an operating failure based on the target multi-dimensional data.

[0063] The second step is to predict whether the blister machine will fail within a certain period of time based on the target multi-dimensional data when the blister machine is currently operating normally, and generate the operating parameter optimization information to optimize and adjust the blister machine, so as to achieve long-term maintenance of the blister machine.

[0064] It should be noted that the second step is performed when the blister machine is detected to be in normal operation in the first step, that is, if the blister machine fails, it will not be optimized, and the failure will be solved first.

[0065] The system further comprises:

[0066] The operation optimization module 300 is configured to optimize the operation of the corresponding blister machine according to the operation parameter optimization information.

[0067] Specifically, in this embodiment, the optimization of the blister machine is performed through the operation optimization module 300 .

[0068] The system further comprises:

[0069] The fault warning module 400 is configured to provide a fault warning to the corresponding blister machine according to the fault warning instruction.

[0070] Specifically, in this embodiment, the fault warning module 400 is used to perform a fault warning of the blister machine.

[0071] Figure 2 This is a schematic diagram of a data acquisition module in an intelligent monitoring system for blister machine data based on the Internet of Things.

[0072] See also Figure 2 It can be seen that, further, in some embodiments, the data acquisition module 100 includes:

[0073] The original data acquisition unit 110 is configured to acquire multi-dimensional data of all blister machines.

[0074] Specifically, in this embodiment, the original multi-dimensional data of the blister machine is acquired through the original data acquisition unit 110 .

[0075] The data acquisition module 100 further includes:

[0076] The data filtering unit 120 is configured to pre-process the multi-dimensional data to obtain the target multi-dimensional data; the pre-processing includes extreme data screening and missing value filling.

[0077] Specifically, in this embodiment, after the data filtering unit 120 obtains the original multi-dimensional data of the blister machine, since the multi-dimensional data often contains data non-compliance, it is necessary to filter the multi-dimensional data. The multi-dimensional data is pre-processed by the data filtering unit 120 to filter out the non-compliant data.

[0078] The preprocessing mainly includes the extreme data screening process and the missing value filling process.

[0079] The extreme data screening process can be understood as filtering out extremely large or extremely small data in the multi-dimensional data.

[0080] The missing value filling process can be understood as using the median or mean in the multidimensional data to fill in the missing data in the multidimensional data or the missing data caused by data filtering.

[0081] Furthermore, in some embodiments, the target multi-dimensional data includes operating process parameters, blister machine setting parameters, and operating environment parameters.

[0082] Specifically, in this embodiment, the data monitoring of the blister machine is mainly divided into the operating process parameters, the blister machine setting parameters and the operating environment parameters.

[0083] The operating process parameters can be understood as the relevant parameters for the raw material processing of the blister machine, the blister machine setting parameters can be understood as the settings of the blister machine entity, and the operating environment parameters can be understood as the relevant data of the environment where the blister machine is located.

[0084] The raw data acquisition unit 110 includes:

[0085] a process parameter monitor 111, wherein the process parameter monitor 111 is configured to obtain the operating process parameters;

[0086] A blister machine parameter monitor 112, wherein the blister machine parameter monitor 112 is configured to obtain setting parameters of the blister machine;

[0087] An environmental parameter monitor 113, wherein the environmental parameter monitor 113 is configured to obtain the operating environment parameters;

[0088] The data mapper 114 is configured to construct a data mapping between the process parameters, the blister machine setting parameters, and the operating environment parameters of the same blister machine and the corresponding blister machine.

[0089] Specifically, in this embodiment, the operating process parameters, the blister machine setting parameters and the operating environment parameters are obtained respectively through the process parameter monitor 111, the blister machine parameter monitor 112 and the environmental parameter monitor 113, and the process parameter monitor 111, the blister machine parameter monitor 112 and the environmental parameter monitor 113 are connected through the Internet of Things.

[0090] Among them, considering that the system does not monitor only one blister machine in most cases, the target multi-dimensional data obtained is also obtained from several blister machines. If these data are not effectively planned, it will inevitably lead to data confusion. Therefore, this embodiment further provides the data mapper 114, and uses the data mapper 114 to map the data generated by each blister machine, so that it can be clearly known which data comes from which blister machine based on the data mapping.

[0091] Figure 3 This is a schematic diagram of a fault analysis and prediction module in an intelligent monitoring system for blister machine data based on the Internet of Things.

[0092] See also Figure 3 It can be seen that, further, in some embodiments, the fault analysis and prediction module 200 includes:

[0093] The device selection unit 210 is configured to select the corresponding target multi-dimensional data according to the data mapping corresponding to the blister machine.

[0094] Specifically, in this embodiment, after receiving the target multi-dimensional data, it is necessary to perform separate fault analysis on each blister machine, so it is necessary to select the ownership of the data. By setting the device selection unit 210 to select the corresponding target multi-dimensional data according to the data mapping, the data of the same blister machine can be gathered together, so as to perform subsequent fault analysis corresponding to each blister machine.

[0095] The fault analysis and prediction module 200 further includes:

[0096] The fault model unit 220 is configured to perform real-time fault analysis and / or fault prediction based on the target multi-dimensional data selected by the equipment selection unit 210, and generate fault warning instructions or operating parameter optimization information.

[0097] Specifically, in this embodiment, the fault model unit 220 is used to perform real-time fault analysis and / or fault prediction of the blister machine, and thereby generate corresponding fault warning instructions or operating parameter optimization information.

[0098] Wherein, the fault model unit 220 includes a real-time fault analysis model and a fault prediction model;

[0099] The real-time fault analysis model is used to:

[0100] Performing real-time fault analysis based on the target multi-dimensional data and generating the fault warning instruction; the fault warning instruction is used to indicate that a corresponding blister machine has a fault; when the blister machine undergoing fault monitoring is operating normally, generating an equipment optimization instruction;

[0101] The fault prediction model is used to:

[0102] When the equipment optimization instruction is received, fault prediction is performed based on the target multi-dimensional data to generate fault prediction data; and equipment operation data is reversed based on the fault prediction data to obtain the operation parameter optimization information.

[0103] Figure 4 This is a schematic diagram of an operation optimization module in an intelligent monitoring system for blister machine data based on the Internet of Things for this application.

[0104] See also Figure 4 It can be seen that, further, in some embodiments, the operation optimization module 300 includes:

[0105] The optimization positioning unit 310 is configured to extract the parameter settings and / or component positions to be optimized based on the operating parameter optimization information; the component positions are the positions of the components on the corresponding blister machine; the parameter settings to be optimized include the blister machine setting parameters, operating process parameters and / or operating environment parameters to be optimized.

[0106] Specifically, in this embodiment, since the part that needs to be optimized in the blister machine is very complex, if it is only prompted that optimization is needed without informing the specific optimization position, it will inevitably lead to optimization failure. Therefore, the optimization positioning unit 310 is set, and the optimization positioning unit 310 extracts the parameter settings to be optimized and / or the component positions that need to be optimized according to the operating parameter optimization information.

[0107] The parameter settings to be optimized can be understood as the process parameters of the blister machine or the setting parameters of the blister machine itself, and the component positions are the physical components of the blister machine.

[0108] The operation optimization module 300 further includes:

[0109] The optimization processing unit 320 is configured to perform operation optimization on the parameter settings to be optimized and / or the component positions according to the operation parameter optimization information.

[0110] Specifically, in this embodiment, when specific locations that need to be optimized are located, the optimization processing unit 320 performs operation optimization on these locations according to the operation parameter optimization information.

[0111] Figure 5 This is a schematic diagram of a fault warning module in an intelligent monitoring system for blister machine data based on the Internet of Things.

[0112] See also Figure 5 It can be seen that, further, in some embodiments, the fault warning module 400 includes:

[0113] The fault location unit 410 is configured to extract the fault parameter setting and / or the fault component position according to the fault warning instruction; the fault component position is the position of the component on the corresponding blister machine where the fault occurs; the fault parameter setting includes the blister machine setting parameters, operating process parameters and / or operating environment parameters where the fault occurs.

[0114] Specifically, in this embodiment, since the part of the blister machine that needs to be optimized is very complex, if only a warning is issued without informing the specific location of the fault, it will inevitably increase the difficulty of subsequent troubleshooting. Therefore, the fault location unit 410 is set, and the fault location unit 410 extracts the fault parameter settings and / or the fault component location that need to be optimized according to the fault warning instruction.

[0115] The fault parameter setting can be understood as the process parameter of the blister machine or the setting parameter of the blister machine itself, and the fault component position is the physical component of the blister machine.

[0116] The fault warning module 400 further includes:

[0117] The fault warning unit 420 is configured to perform a fault warning on the fault parameter setting and / or the fault component location according to the fault warning instruction.

[0118] Specifically, in this embodiment, when locations that specifically require fault warning are located, the fault warning unit 420 performs operation fault warning on these locations according to the fault warning instruction.

[0119] See also Figure 1 It is also known that, further, in some embodiments, the system further includes:

[0120] The display module 500 is configured to visually display all data and operating conditions of the system.

[0121] Specifically, in this embodiment, the entire process of monitoring the blister machine is visually displayed through the display module 500, so that the operation and maintenance personnel can more intuitively understand the operation status of the blister machine.

[0122] See also Figure 1 It is also known that, further, in some embodiments, the system further includes:

[0123] The storage module 600 is configured to store all data acquired and generated by the data acquisition module 100 , the fault analysis and prediction module 200 , the operation optimization module 300 , the fault warning module 400 and the display module 500 .

[0124] Specifically, in this embodiment, considering that the operation and maintenance of the blister machine and subsequent overhaul require historical data of the blister machine, the storage module 600 is set to store the data generated in the entire system, thereby providing a data basis for subsequent data tracing.

[0125] See also Figure 1 It is also known that, further, in some embodiments, the system further includes:

[0126] The cloud server 700 is configured to share the data stored in the storage module 600 with all blister machines.

[0127] Specifically, in this embodiment, based on the purpose of data sharing, the cloud server 700 is set to share the data and related control logic generated in this system with all blister machines, thereby realizing unified deployment and unified management of blister machine data in multiple locations.

[0128] This embodiment has the following advantages:

[0129] The IoT architecture enables unified data collection and management across multiple devices, resolving the data fragmentation issues inherent in traditional systems. The application of intelligent analytical models transforms raw data into directly executable optimization instructions, shortening the decision-making process from problem discovery to resolution. Predictive maintenance reduces unplanned downtime, and precise positioning eliminates unnecessary component replacements. The cloud-based server design supports data sharing and collaboration across multiple devices, laying the foundation for large-scale applications. Continuous operational optimization ensures consistent process parameters, thereby guaranteeing stable product quality.

[0130] For ease of explanation, the above description has been made in conjunction with specific embodiments. However, the above discussion of some embodiments is not intended to be exhaustive or to limit the embodiments to the specific forms disclosed above. Based on the above teachings, various modifications and variations can be obtained. The above embodiments are selected and described to better explain the content of this disclosure, thereby enabling those skilled in the art to better use the embodiments.

Claims

1. An intelligent monitoring system for blister machine data based on the Internet of Things, characterized in that: The system comprises: A data acquisition module (100), wherein the data acquisition module (100) is configured to acquire target multi-dimensional data generated by a plurality of blister machines during operation based on the Internet of Things; A fault analysis and prediction module (200), the fault analysis and prediction module (200) being configured to perform real-time fault analysis and / or fault prediction based on the target multi-dimensional data corresponding to the blister machine, and generate fault warning instructions or operating parameter optimization information; An operation optimization module (300), wherein the operation optimization module (300) is configured to optimize the operation of the corresponding blister machine according to the operation parameter optimization information; A fault warning module (400) is configured to provide a fault warning to the corresponding blister machine according to the fault warning instruction.

2. The intelligent monitoring system for blister machine data based on the Internet of Things according to claim 1 is characterized in that: The data acquisition module (100) comprises: A raw data acquisition unit (110), wherein the raw data acquisition unit (110) is configured to acquire multi-dimensional data of all blister machines; A data filtering unit (120) is configured to pre-process the multi-dimensional data to obtain the target multi-dimensional data; the pre-processing includes extreme data screening processing and missing value filling processing.

3. The intelligent monitoring system for blister machine data based on the Internet of Things according to claim 2 is characterized in that: The target multi-dimensional data includes operating process parameters, blister machine setting parameters and operating environment parameters; The original data acquisition unit (110) comprises: a process parameter monitor (111), wherein the process parameter monitor (111) is configured to obtain the operating process parameter; A blister machine parameter monitor (112), the blister machine parameter monitor (112) being configured to obtain setting parameters of the blister machine; An environmental parameter monitor (113), the environmental parameter monitor (113) being configured to obtain the operating environment parameter; A data mapper (114) is configured to construct a data mapping between the process parameters, the blister machine setting parameters, and the operating environment parameters of the same blister machine and the corresponding blister machine.

4. The intelligent monitoring system for blister machine data based on the Internet of Things according to claim 3 is characterized in that: The fault analysis and prediction module (200) comprises: A device selection unit (210), the device selection unit (210) being configured to select the corresponding target multi-dimensional data according to the data mapping corresponding to the blister machine; A fault model unit (220) is configured to perform real-time fault analysis and / or fault prediction based on the target multi-dimensional data selected by the equipment selection unit (210), and generate fault warning instructions or operating parameter optimization information.

5. The intelligent monitoring system for blister machine data based on Internet of Things according to claim 4 is characterized in that: The fault model unit (220) includes a real-time fault analysis model and a fault prediction model; The real-time fault analysis model is used to: Performing real-time fault analysis based on the target multi-dimensional data and generating the fault warning instruction; the fault warning instruction is used to indicate that a fault has occurred in the corresponding blister machine; When the blister machine undergoing fault monitoring operates normally, an equipment optimization instruction is generated; The fault prediction model is used to: When receiving the equipment optimization instruction, performing fault prediction based on the target multi-dimensional data to generate fault prediction data; The equipment operation data is reversed based on the fault prediction data to obtain the operation parameter optimization information.

6. The data intelligent monitoring system for blister machines based on the Internet of Things according to claim 1 is characterized in that: The operation optimization module (300) includes: An optimization positioning unit (310), the optimization positioning unit (310) being configured to extract parameter settings to be optimized and / or component positions according to the operating parameter optimization information; the component positions are positions of components on the corresponding blister machine; the parameter settings to be optimized include blister machine setting parameters, operating process parameters, and / or operating environment parameters to be optimized; An optimization processing unit (320) is configured to perform operation optimization on the parameter settings to be optimized and / or the component positions according to the operation parameter optimization information.

7. The intelligent monitoring system for blister machine data based on the Internet of Things according to claim 1 is characterized in that: The fault warning module (400) comprises: A fault location unit (410), the fault location unit (410) being configured to extract fault parameter settings and / or fault component locations according to the fault warning instruction; the fault component locations being locations of components corresponding to the blister machine where the fault occurs; the fault parameter settings including blister machine setting parameters, operating process parameters, and / or operating environment parameters where the fault occurs; A fault warning unit (420) is configured to provide a fault warning for the fault parameter setting and / or the fault component position according to the fault warning instruction.

8. The intelligent monitoring system for blister machine data based on Internet of Things according to claim 1 is characterized in that: The system further comprises: A display module (500) is configured to visually display all data and operating conditions of the system.

9. The intelligent monitoring system for blister machine data based on Internet of Things according to claim 8, characterized in that: The system further comprises: A storage module (600) is configured to store all data acquired and generated by the data acquisition module (100), the fault analysis and prediction module (200), the operation optimization module (300), the fault warning module (400), and the display module (500).

10. The intelligent monitoring system for blister machine data based on Internet of Things according to claim 9, characterized in that: The system further comprises: A cloud server (700), wherein the cloud server (700) is configured to share the data stored in the storage module (600) with all blister machines.

Images