Method, device and equipment for monitoring production state of rolling and connecting equipment and medium

Abstract

The invention provides a method, a device and equipment for monitoring the production state of rolling and connecting equipment and a medium. The method comprises the following steps: acquiring operation data of the rolling and connecting equipment; inputting the operation data into a preset machine fingerprint model for processing to obtain a current operation state of the rolling and connecting equipment; and according to the obtained operation state, generating corresponding early warning information through predefined logical judgment and early warning rules. By constructing the machine fingerprint model, the running state of the equipment can be accurately monitored in real time according to the actual running data of the rolling and connecting equipment. And the limitation of the traditional monitoring means, such as dependence on manual inspection and limited sensor data, is overcome, so that the accuracy of fault early warning is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of rolling equipment, and in particular to a method, device, equipment and medium for monitoring the production status of rolling equipment. Background Art

[0002] In the field of tobacco manufacturing, the cigarette rolling machine is the core equipment for producing cigarettes, and its operating stability and reliability are directly related to the quality of the product and the continuity of production. However, traditional cigarette rolling equipment monitoring methods mainly rely on regular manual inspections and basic sensor monitoring, which have significant limitations. Manual inspections cannot provide continuous monitoring, resulting in the inability to capture subtle changes and minor faults in equipment operation in real time, which may miss the best time for timely intervention, causing small problems to gradually accumulate into major faults, thereby affecting production efficiency and equipment life. In addition, basic sensors can usually only monitor a limited number of parameters, such as temperature and pressure, and cannot fully reflect the actual operating status of the equipment, which limits the accuracy and reliability of fault warnings.

[0003] Furthermore, traditional monitoring methods lack in-depth analysis and mining of equipment operation data, and are unable to optimize warning rules and maintenance strategies based on the actual operating characteristics of the equipment. In addition, the non-standardization of fault warning and processing procedures has led to the efficiency and effectiveness of processing being highly dependent on the experience and judgment of maintenance personnel, which may vary significantly between different operators, affecting the consistency and timeliness of fault response. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a method, device, equipment and medium for monitoring the production status of a winding and splicing device, so as to solve the problems in the related art.

[0005] A first aspect of the present disclosure provides a method for monitoring the production status of a rolling device, comprising:

[0006] Obtain the operating data of the winding equipment;

[0007] Inputting the operation data into a preset machine fingerprint model for processing to obtain the current operation status of the winding and splicing device;

[0008] According to the obtained operating status, the corresponding warning information is generated through predefined logical judgment and warning rules.

[0009] In an embodiment of the first aspect, the warning information is divided into general warning information and high-level warning information according to the severity of the equipment operation status; wherein,

[0010] The general warning information indicates that although the operation status of the winding and splicing equipment is abnormal, it does not affect the continuation of the production process;

[0011] The high-level warning information indicates that the operating state of the winding and splicing equipment is abnormally serious and sufficient to affect the normal operation of the production process.

[0012] In an embodiment of the first aspect, the general warning information is input into a preset maintenance assistance model for diagnosis to obtain a diagnosis result;

[0013] The diagnostic results are transmitted to the event module, and specific processing suggestions are generated according to the preset event classification processing rules;

[0014] Assigning the generated processing suggestions to corresponding roto-roller groups so as to perform maintenance operations;

[0015] After the maintenance operation is completed, the operation result is fed back to the event module to update the operating status and warning information of the winding and splicing equipment to achieve closed-loop control.

[0016] In an embodiment of the first aspect, the high-level warning information is sent directly to designated management personnel so that corresponding processing measures can be taken quickly.

[0017] In an embodiment of the first aspect, the construction of the machine fingerprint model includes:

[0018] Obtain historical data of multiple operating indicators of the coiling equipment;

[0019] Slice the historical data of the operation index according to different stages of the operation of the winder and splicer, wherein the stages include an initial start-up period, a stable operation period, and a long-term operation period;

[0020] Calculating the average value and variance of the historical operation data in each stage;

[0021] Based on the calculated mean and variance, determine the reasonable threshold value of each operating indicator in each stage;

[0022] Based on reasonable threshold data of all stages, a machine fingerprint model of the winding equipment is formed.

[0023] In an embodiment of the first aspect, the inputting the operation data into a preset machine fingerprint model for processing to obtain the current operation state of the winding device includes:

[0024] identifying the operation phase to which the operation data belongs;

[0025] Based on the operation stage, comparing the operation data with a reasonable threshold of the corresponding stage in the machine fingerprint model;

[0026] When the operating data exceeds a reasonable threshold in the machine fingerprint model, it is determined that the winding device is in an abnormal operating state.

[0027] In an embodiment of the first aspect, further comprising:

[0028] Based on the acquired operation data of the winding and splicing equipment, the machine fingerprint model is regularly updated, and a reasonable threshold value of each operation indicator in each operation stage is recalculated.

[0029] A second aspect of the present disclosure provides a device for monitoring the production status of a rolling device, comprising:

[0030] An acquisition module, used to acquire the operation data of the winding and splicing equipment;

[0031] A processing module, used for inputting the operation data into a preset machine fingerprint model for processing, and obtaining the current operation status of the winding and splicing device;

[0032] The warning generation module is used to generate corresponding warning information according to the obtained operating status through predefined logical judgment and warning rules.

[0033] The third aspect of the present disclosure provides an electronic device, comprising: a processor and a memory; wherein the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory, so that the electronic device executes the method for monitoring the production status of the winding equipment described in the first aspect.

[0034] A fourth aspect of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by an electronic device, implements the method for monitoring the production status of a winding device as described in the first aspect.

[0035] As described above, the embodiments of the present disclosure provide a method, device, equipment and medium for monitoring the production status of a rolling device, which have at least the following technical effects:

[0036] (1) By building a machine fingerprint model, the operating status of the equipment can be accurately monitored in real time based on the actual operating data of the winding equipment. This overcomes the limitations of traditional monitoring methods, such as reliance on manual inspection and limited sensor data, thereby improving the accuracy of fault warning.

[0037] (2) By classifying warning information into general warnings and high-level warnings, maintenance personnel can take appropriate maintenance measures according to the severity of the warning. For general warnings, diagnosis and suggestions are provided through the maintenance assistance model, while for high-level warnings, management personnel are directly notified, ensuring the timeliness and effectiveness of maintenance response.

[0038] (3) By feeding back the results of maintenance operations to the event module and updating the machine fingerprint model, a closed-loop control of monitoring and maintenance is achieved. This closed-loop mechanism not only improves the stability of equipment operation, but also enables the monitoring system to continuously learn and adapt, thereby continuously optimizing early warning and maintenance strategies.

[0039] (4) The machine fingerprint model provides a unique "fingerprint" for each winding device, making equipment management more refined. This refined management helps to better understand the operating characteristics of each device and provides strong data support for equipment maintenance, upgrades and life prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A schematic flow chart showing a method for monitoring the production status of a roll-to-roll device in one embodiment of the present disclosure.

[0041] Figure 2 A schematic diagram showing the process of constructing a machine fingerprint model in an embodiment of the present disclosure.

[0042] Figure 3 A schematic diagram showing the flow of generating the running status in one embodiment of the present disclosure.

[0043] Figure 4 A schematic diagram showing the process of general warning information collaboration in one embodiment of the present disclosure.

[0044] Figure 5 A schematic flow chart showing a method for monitoring the production status of a rolling device in another embodiment of the present disclosure.

[0045] Figure 6 A schematic diagram of a module showing a device for monitoring the production status of a rolling device in one embodiment of the present disclosure.

[0046] Figure 7 A schematic diagram of the circuit structure of an electronic device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0047] The following is an explanation of the embodiments of the present disclosure by specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the information disclosed by the present disclosure. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in the present disclosure can also be modified or changed in various ways according to different viewpoints and application modules without departing from the spirit of the present disclosure. It should be noted that the embodiments in the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0048] The following is a detailed description of the embodiments of the present disclosure with reference to the accompanying drawings so that those skilled in the art can easily implement the present disclosure. The present disclosure can be embodied in many different forms and is not limited to the embodiments described herein.

[0049] A first aspect of the present disclosure provides a method for monitoring the production status of a rolling device.

[0050] like Figure 1 As shown, a flow chart of a method for monitoring the production status of a rolling device in an embodiment of the present disclosure is shown, the method comprising steps S11 to S13, wherein:

[0051] Step S11, obtaining the operation data of the winding and splicing equipment.

[0052] Specifically, in order to ensure the efficient and stable operation of the tobacco rolling and splicing equipment in the tobacco manufacturing process, the key is to obtain and analyze its operating data in real time. The acquisition of these data involves multiple aspects, including but not limited to image data, roll and package data, downtime data, appearance defect rejection data, cigarette weight data, etc.

[0053] Image data, which uses a high-precision camera to capture the appearance of the cigarette in order to detect and record any possible defects, such as irregular shapes or uneven distribution of tobacco.

[0054] The data collected from the pack count provides statistics on the number of cigarettes produced in a specific period of time, which not only reflects the production efficiency but also helps monitor the output quality of the production line.

[0055] Downtime data records events where equipment stops operating, including the time, duration, and cause of the downtime, which is crucial for identifying production bottlenecks and equipment maintenance.

[0056] The appearance defect rejection data records the number of cigarettes rejected from the production line due to appearance problems, which helps us understand the product qualification rate and the process links that need to be improved.

[0057] By precisely measuring the weight of each cigarette, we ensure that they meet predetermined quality standards. Any weight fluctuations outside the tolerance range may point to problems in the filling or compression process.

[0058] Step S12: input the operation data into a preset machine fingerprint model for processing to obtain the current operation status of the winding device.

[0059] Specifically, the machine fingerprint model is a model built on a machine learning algorithm that can identify and learn the behavior patterns of devices in normal and abnormal states. By training on historical data, the model can establish a unique "fingerprint" for the device, which represents the standard state of the device under different operating conditions. When real-time data is input into the model, it is compared with this preset "fingerprint" to evaluate whether the current operating state meets expectations.

[0060] In some embodiments, Figure 2 As shown, a schematic diagram of the construction process of the machine fingerprint model is shown, including steps S21-S25, wherein:

[0061] Step S21, obtaining historical data of multiple operating indicators of the winding and splicing equipment.

[0062] Specifically, in the initial stage of building the machine fingerprint model, it is necessary to first collect historical data of multiple operating indicators of the cigarette-making equipment under normal and abnormal conditions. These data may include key parameters such as image data, packaging data, downtime data, appearance defect rejection data, cigarette weight data, etc., which provide a basis for subsequent analysis.

[0063] Step S22, segmenting the historical data of the operation index according to different operation stages of the winder-winding device, wherein the stages include an initial startup period, a stable operation period, and a long operation period.

[0064] Specifically, the collected historical operation data is then processed in slices according to the different stages of equipment operation. These stages usually include the initial startup, stable operation and long-term operation, and each stage reflects the characteristics of the equipment under different working conditions. The purpose of slicing is to more accurately capture and understand the behavior patterns of the equipment at each stage.

[0065] Step S23: Calculate the average value and variance of the historical operation data in each stage.

[0066] Specifically, for the operation data in each stage, statistical analysis is performed to calculate the mean and variance of each operation indicator. The mean provides the central trend of the indicator, while the variance reflects the degree of fluctuation of the data. These statistics are the key to determining reasonable thresholds in the future.

[0067] Step S24: Based on the calculated average value and variance, determine a reasonable threshold value for each operating indicator in each stage.

[0068] Specifically, the average value and variance calculated in step S23 are used to determine the reasonable thresholds of each operating indicator in each stage. These thresholds are the criteria for evaluating whether the equipment is operating normally, and they define the acceptable range of equipment operating parameters. The setting of reasonable thresholds is based on a deep understanding of the historical performance of the equipment, ensuring the accuracy and practicality of the model.

[0069] Step S25: forming a machine fingerprint model of the winding device based on reasonable threshold data of all stages.

[0070] Specifically, finally, based on the reasonable threshold data of all stages, a machine fingerprint model of the winding equipment is formed. This model is a comprehensive representation of the equipment's operating status. It integrates the equipment's operating characteristics at different stages and provides a reliable benchmark for real-time monitoring and early warning. The construction of the machine fingerprint model enables the equipment maintenance team to quickly identify and respond to potential operating problems, thereby improving production efficiency and equipment reliability.

[0071] Take the two operating indicators of number of shutdowns and average cigarette weight as examples.

[0072] First, collect relevant operating data of the cigarette rolling equipment within a week, including daily downtime flow data and average cigarette weight data. The downtime data records in detail the start time and reason of each downtime, while the weight average data provides the average weight of cigarettes at each time point.

[0073] Before analysis, we need to clean the collected data and remove data points recorded during non-production periods to ensure the accuracy of the analysis.

[0074] Then the statistical time span (AS) is set to 30 minutes, which means that every 30 minutes is an analysis segment. The time period (fb) of each production shift is set to 8 hours. There are two shifts in a day, so each shift can be divided into 16 analysis segments.

[0075] For each 30-minute analysis segment, two features were extracted: downtime reasons and mean cigarette weight. Downtime reasons were categorized according to the specific reason for each downtime, while mean cigarette weight was the average of all measurements within that period.

[0076] For each analysis segment, an ordered array with two dimensions is constructed, one dimension is the number of downtimes and the other dimension is the average cigarette weight. In this way, each analysis segment can be represented by a two-dimensional array.

[0077] By analyzing the analysis segments with the same sequence number within a week, we calculate the average value of each feature in these analysis segments, and this average value is used as the machine fingerprint threshold of the analysis segment.

[0078] Finally, a three-dimensional array is formed to represent the machine fingerprint, where the three dimensions are shift, analysis segment, and feature. For this example, there are two features (L=2), so the machine fingerprint model can be represented as a 2*16*2 three-dimensional array, which contains the thresholds for each shift, each analysis segment, and each feature.

[0079] In some embodiments, Figure 3 As shown, step S12 specifically includes steps S121-S123, wherein:

[0080] Step S121: Identify the operation phase to which the operation data belongs.

[0081] Specifically, it is first necessary to determine which operating stage of the winder equipment the collected operating data belongs to. This usually involves analyzing the timestamps and characteristic attributes of the data. The operating stages may include the initial startup, the stable operation period, and the long-term operation period, etc. Each stage has its own specific operating characteristics and expected performance. By analyzing the time series and characteristic patterns of the operating data, the system is able to classify the data into the corresponding stage. For example, the current stage can be determined by comparing the similarity of current data with historical data, or by using preset logical rules (such as time, output or other trigger conditions).

[0082] Step S122: Based on the operation stage, compare the operation data with a reasonable threshold of the corresponding stage in the machine fingerprint model.

[0083] Specifically, when the operation data is identified and classified into a specific operation stage, it is then compared with the reasonable threshold set for that stage in the machine fingerprint model. The machine fingerprint model contains the expected range of each feature in each stage, which is based on historical data and statistical analysis and can represent the healthy operation status of the device.

[0084] Step S123: When the operating data exceeds a reasonable threshold in the machine fingerprint model, it is determined that the winding device is in an abnormal operating state.

[0085] Specifically, if the operating data exceeds the preset reasonable threshold on any one or more characteristics, the system will determine that the equipment may be in an abnormal operating state. This exceeding the threshold may indicate a potential failure or performance degradation that requires immediate attention. For example, if the measurement value of a certain characteristic is continuously above or below the threshold, it may mean that the equipment parts are worn, failed, or the operating conditions are not ideal.

[0086] Step S13: Generate corresponding warning information according to the obtained operating status through predefined logical judgment and warning rules.

[0087] Specifically, by comparing the operating data with the thresholds in the machine fingerprint model, the abnormalities in the equipment operating status are identified, and corresponding warning information will be generated according to the predefined logical judgment and warning rules. These warning rules are formulated based on the historical operating data, failure modes and maintenance experience of the equipment, and they define the warning levels and response measures corresponding to different types and degrees of abnormalities.

[0088] In some implementations, in order to more effectively manage warnings and take appropriate response measures, warning information is divided into general warning information and high-level warning information according to the severity of the equipment operation status. This classification helps the maintenance team quickly identify which issues require immediate attention and which issues can be arranged for subsequent processing.

[0089] The general warning information is used to indicate that although the winder has detected abnormalities in the operating status, these abnormalities are not enough to affect the continuation of the production process. Such warnings may involve minor parameter deviations, occasional minor failures, or expected maintenance needs. For example, if the temperature reading of the equipment is slightly higher than the normal range but not reaching a dangerous level, a general warning may be issued to prompt the maintenance team to check the relevant parts at the next scheduled maintenance.

[0090] Further, such as Figure 4 As shown, the collaborative process of displaying the general warning information includes steps S41-S44, wherein:

[0091] Step S41: input the general warning information into a preset maintenance auxiliary model for diagnosis to obtain a diagnosis result.

[0092] Specifically, when general warning information is generated, it will be input into a preset maintenance assistance model. This model is a tool based on an expert system or machine learning algorithm, which can perform in-depth diagnostic analysis based on the input warning information, combined with the equipment's historical maintenance records and fault case database.

[0093] Step S42: delivering the diagnosis result to the event module, and generating specific processing suggestions according to preset event classification processing rules.

[0094] Specifically, the diagnostic results of the maintenance assistance model will be passed to the event module. In this module, the diagnostic results will be evaluated according to the preset event classification processing rules. These rules define the processing flow and priority corresponding to warnings of different types and severity. Based on the evaluation results, the event module will generate specific processing suggestions, including recommended maintenance measures, required resources and expected completion time.

[0095] Step S43: assign the generated processing suggestion to the corresponding winding and splicing machine group so as to perform the maintenance operation.

[0096] Specifically, the generated processing suggestions will be assigned to the corresponding winder group. This may involve automatically sending work orders or maintenance tasks to the maintenance team, or creating new maintenance events in the maintenance management system. The purpose of the assignment is to ensure that the maintenance team understands the specific actions that need to be performed and can respond in time to avoid the early warning problem from escalating into a more serious failure.

[0097] Step S44: After the maintenance operation is completed, the operation result is fed back to the event module to update the operating status and warning information of the winding and splicing equipment to achieve closed-loop control.

[0098] Specifically, after the maintenance operation is completed, the maintenance team will feed back the results of the operation to the event module. This includes the details of the maintenance activities, the problems solved, and any new situations observed. The event module uses this information to update the operating status and warning information of the winding equipment to achieve closed-loop control. This update helps the system to more accurately monitor the equipment status, evaluate the effectiveness of maintenance activities, and provide data support for future warnings and maintenance decisions.

[0099] The high-level warning information refers to those alarms that indicate serious abnormalities in the operation of the winder equipment, which, if not handled immediately, may lead to production interruptions, equipment damage, or product quality degradation. For example, such warnings may involve sudden failure of key components, a sharp drop in key performance indicators, or an increase in safety risks.

[0100] In some implementations, the high-level warning information is sent directly to designated management personnel so that corresponding processing measures can be taken promptly.

[0101] Specifically, after a high-level warning message is generated, it will be immediately sent directly to designated managers through preset communication channels. These channels may include email, SMS, mobile app push notifications or other instant messaging tools to ensure that the information can be conveyed quickly and accurately.

[0102] Managers who receive high-level warning information need to quickly assess the situation and initiate the appropriate emergency response process. This may include immediately stopping the affected production line, mobilizing emergency maintenance teams, adjusting production plans, or notifying other relevant departments. Managers decide on specific measures to take based on the nature and severity of the warning information. These measures may include emergency repairs, equipment replacement, adjustment of process parameters, safety inspections, or other necessary risk mitigation actions.

[0103] like Figure 5 As shown, the complete process of the monitoring method of the production status of the above-mentioned winding equipment is demonstrated.

[0104] In order to ensure that the machine fingerprint model of the roll-to-roll device can accurately reflect the actual operating status of the device and adapt to possible changes in the device over time, in some embodiments, it also includes:

[0105] Based on the acquired operation data of the winding and splicing equipment, the machine fingerprint model is regularly updated, and a reasonable threshold value of each operation indicator in each operation stage is recalculated.

[0106] Specifically, based on the latest operating data, the system will re-evaluate and calculate the reasonable thresholds of each operating indicator at different operating stages (such as the initial startup, stable operation period, and long-term operation period). Using the latest data and recalculated thresholds, the machine fingerprint model is retrained and optimized. This includes adjusting the model's algorithm parameters, redefining feature weights, or introducing new data features. The purpose of optimization is to improve the model's predictive accuracy and robustness. The updated and optimized machine fingerprint model will be deployed to the production environment, replacing the original model. This ensures that the monitoring system can use the latest model for real-time equipment status monitoring and early warning.

[0107] like Figure 6 As shown, a schematic diagram of a module of a monitoring device for the production status of a roll-to-roll device according to the second aspect of the present disclosure is shown. It should be noted that the principle and technical implementation of the monitoring device for the production status of a roll-to-roll device can refer to the monitoring method for the production status of a roll-to-roll device in the previous embodiment (e.g. Figure 1 ), so it will not be repeated in this embodiment.

[0108] exist Figure 6 In the embodiment, the monitoring device 600 for the production status of the rolling equipment includes:

[0109] An acquisition module 601 is used to acquire operation data of a rolling device;

[0110] The processing module 602 is used to input the operation data into a preset machine fingerprint model for processing to obtain the current operation status of the winding device;

[0111] The warning generation module 603 is used to generate corresponding warning information according to the obtained operating status through predefined logical judgment and warning rules.

[0112] It should be noted that Figure 6Each functional module in the embodiment can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a program instruction product. The program instruction product includes one or a group of program instructions. When the program instruction is loaded and executed on a computer, the process or function according to the present disclosure is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The program instruction can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0113] and, Figure 6 The device disclosed in the embodiment can be implemented by other module division methods. The device embodiments shown above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as a group of modules or modules can be combined or dynamically added to another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or modules can be electrical or other forms.

[0114] in addition, Figure 6 Each functional module and submodule in the embodiment can be dynamically in a processing component, or each module can exist physically separately, or two or more modules can be dynamically in one component. The above-mentioned dynamic components can be implemented in the form of hardware or in the form of software functional modules. If the above-mentioned dynamic components are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a disk or an optical disk, etc.

[0115] like Figure 7 As shown, a schematic diagram of the structure of an electronic device in an embodiment of the third aspect of the present disclosure is shown.

[0116] The electronic device can execute the following steps by running the computer program instructions: Figure 1 The method in any one of the items. Exemplarily, the electronic device may be a server group / server, a desktop computer, a laptop computer, etc., or a server / server group in the cloud that communicates remotely with a local terminal, a distributed computing node system, etc.

[0117] The electronic device 70 includes a bus 71, a processor 72, and a memory 73. The processor 72 and the memory 73 can communicate with each other via a bus 81. The memory 83 can store program instructions. The processor 72 implements the method steps in the previous embodiment by running the program instructions in the memory 73, such as Figure 1 Any method.

[0118] The bus 71 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, although only one thick line is used in the figure, it does not mean that there is only one bus or one type of bus.

[0119] In some embodiments, the processor 72 may be implemented as a central processing unit (CPU), a microprocessing unit (MCU), a system on chip (System On Chip), or a field programmable logic array (FPGA). The memory 83 may include a volatile memory (Volatile Memory) for temporary storage of data when running a program, such as a random access memory (Random Access Memory, RAM).

[0120] The memory 73 may also include a non-volatile memory (non-volatile memory) for data storage, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD) or a solid-state disk (SSD).

[0121] In some embodiments, the electronic device 70 may further include a communicator 74. The communicator 74 is used to communicate with the outside. In a specific example, the communicator 74 may include one or a group of wired and / or wireless communication circuit modules. For example, the communicator 74 may include one or more of a wired network card, a USB module, a serial interface module, etc. The wireless communication protocols followed by the wireless communication module include: for example, near field wireless communication (NFC) technology, infrared (IR) technology, global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), time division code division multiple access (TD-SCDMA), long term evolution (LTE), Bluetooth (BT), global navigation satellite system (GNSS), etc. One or more of the above.

[0122] A fourth aspect of the embodiments of the present disclosure provides a computer-readable storage medium, characterized in that program instructions are stored therein, and the program instructions are executed by running, for example Figure 1 A method for monitoring the production status of a rolling device in an embodiment.

[0123] That is, the method steps in the above embodiments are implemented as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or are implemented as computer code originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded via a network and to be stored in a local recording medium, so that the method represented herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA).

[0124] In summary, the production status monitoring method, device, equipment and medium of the winding equipment disclosed in the present invention obtain the operating data of the equipment in real time and input it into the preset machine fingerprint model to evaluate the current status of the equipment. The method divides the warning information into two categories: general and high-level, which correspond to different degrees of operating abnormalities. General warnings are diagnosed and processing suggestions are generated through the maintenance auxiliary model, while high-level warnings directly notify management personnel. The machine fingerprint model is built based on the historical operating data of the equipment and can determine reasonable thresholds based on statistical analysis of different operating stages. In addition, the model supports regular updates to adapt to changes in the operating status of the equipment to ensure the accuracy of monitoring and the timeliness of warnings. This method improves the operating efficiency and reliability of the winding equipment and reduces the risk of production interruptions.

[0125] The above embodiments are merely illustrative of the principles and effects of the present disclosure, and are not intended to limit the present disclosure. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present disclosure. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present disclosure shall still be covered by the claims of the present disclosure.

Claims

1. A method for monitoring the production status of a coiling device, characterized in that: include: Obtain the operating data of the winding equipment; Inputting the operation data into a preset machine fingerprint model for processing to obtain the current operation status of the winding and splicing device; According to the obtained operating status, the corresponding warning information is generated through predefined logical judgment and warning rules.

2. The method for monitoring the production status of a rolling equipment according to claim 1, characterized in that: According to the severity of the equipment operation status, the warning information is divided into general warning information and high-level warning information; wherein the general warning information indicates that although the operation status of the winding equipment is abnormal, it does not affect the continuation of the production process; The high-level warning information indicates that the operating state of the winding and splicing equipment is abnormally serious and sufficient to affect the normal operation of the production process.

3. The method for monitoring the production status of a rolling equipment according to claim 2, characterized in that: Inputting the general warning information into a preset maintenance auxiliary model for diagnosis to obtain a diagnosis result; The diagnostic results are transmitted to the event module, and specific processing suggestions are generated according to the preset event classification processing rules; Assigning the generated processing suggestions to corresponding roto-roller groups so as to perform maintenance operations; After the maintenance operation is completed, the operation result is fed back to the event module to update the operating status and warning information of the winding and splicing equipment to achieve closed-loop control.

4. The method for monitoring the production status of a rolling equipment according to claim 2, characterized in that: The high-level warning information is sent directly to the designated management personnel so that appropriate processing measures can be taken promptly.

5. The method for monitoring the production status of a rolling equipment according to claim 1, characterized in that: The construction of the machine fingerprint model includes: Obtain historical data of multiple operating indicators of the coiling equipment; Slice the historical data of the operation index according to different stages of the operation of the winder and splicer, wherein the stages include an initial start-up period, a stable operation period, and a long-term operation period; Calculating the average value and variance of the historical operation data in each stage; Based on the calculated mean and variance, determine the reasonable threshold value of each operating indicator in each stage; Based on reasonable threshold data of all stages, a machine fingerprint model of the winding equipment is formed.

6. The method for monitoring the production status of a rolling equipment according to claim 5, characterized in that: The step of inputting the operation data into a preset machine fingerprint model for processing to obtain the current operation status of the winding device includes: identifying the operation phase to which the operation data belongs; Based on the operation stage, comparing the operation data with a reasonable threshold of the corresponding stage in the machine fingerprint model; When the operating data exceeds a reasonable threshold in the machine fingerprint model, it is determined that the winding device is in an abnormal operating state.

7. The method for monitoring the production status of a rolling equipment according to claim 1, characterized in that: Also includes: Based on the acquired operation data of the winding and splicing equipment, the machine fingerprint model is regularly updated, and a reasonable threshold value of each operation indicator in each operation stage is recalculated.

8. A monitoring device for the production status of a rolling equipment, characterized in that: include: An acquisition module, used to acquire the operation data of the winding and splicing equipment; A processing module, used for inputting the operation data into a preset machine fingerprint model for processing, and obtaining the current operation status of the winding and splicing device; The warning generation module is used to generate corresponding warning information according to the obtained operating status through predefined logical judgment and warning rules.

9. An electronic device, characterized in that: The electronic device comprises: Processor and memory; Wherein, the memory is used to store computer programs; The processor is used to execute the computer program stored in the memory so that the electronic device executes the method for monitoring the production status of the winding equipment according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by an electronic device, the method for monitoring the production status of the winding equipment described in any one of claims 1 to 7 is implemented.

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