An online monitoring system for the operating status of a switch

Abstract

The present invention relates to the technical field of switch monitoring. The present invention relates to an online monitoring system for the operating state of a switch. It includes a switch network establishment unit, a device classification unit, an operation analysis unit, an operation prediction unit, and a parameter adjustment unit; the switch network establishment unit is used to establish a switch group network and connect the switches of factory equipment to the switch group network; by automatically performing repetitive tasks and processes, it reduces manual intervention and processing time, thereby improving the overall operation efficiency. At the same time, when changing work tasks, by establishing a fluctuation prediction model, it compares the real-time operating state with the predicted fluctuating operating state, so as to avoid the situation of failure not being reported or misreported during the process of adjusting work tasks, reduce the possibility of operation errors in fault detection, and improve the safety and stability of production operations.

Description

Technical Field

[0001] The present invention relates to the technical field of switch monitoring, and more specifically, to an online monitoring system for the operating status of switches. Background Art

[0002] During the production and manufacturing process in a factory, the factory monitors the operation of factory equipment based on the operating status of switches. Sometimes, it is necessary to control the equipment to adjust the operating status according to the orders of the purchasers, so as to make the production capacity meet the requirements of completing the orders.

[0003] Currently, during the process of work task adjustment, factory equipment needs to adjust work parameters according to work tasks. It takes time for the equipment to reach a stable operating state during the adjustment process. However, since the intelligent fault detection in the factory determines faults based on work tasks combined with the operating status, it is easy to cause misjudgments.

[0004] If fault detection is not performed during the adjustment process, when a fault occurs during the equipment adjustment process and cannot be autonomously recovered, it will cause delays in production time, and the staff needs to monitor and adjust in real time according to the operating status of the switch, with a low degree of automation. To reduce this situation, an online monitoring system for the operating status of switches is proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide an online monitoring system for the operating status of switches to solve the problems raised in the above background art.

[0006] To achieve the above purpose, an online monitoring system for the operating status of switches is provided, including a switch network establishment unit, an equipment classification unit, an operation analysis unit, an operation prediction unit, and a parameter adjustment unit.

[0007] The switch network establishment unit is used to establish a switch group network and connect the switches of factory equipment to the switch group network.

[0008] The equipment classification unit is used to perform pipeline cleaning and classification on the operating status uploaded by the switches, classify the operating status of factory equipment in the same area according to the same pipeline, and simultaneously obtain the work tasks of each pipeline.

[0009] The operation analysis unit is used to perform stability analysis based on the historical operating status of the switches combined with the work tasks, obtain the stable operating status of the switch group corresponding to each work task, and then extract other operating statuses as the fluctuating operating statuses during work task switching.

[0010] The operation prediction unit is used to establish a fluctuation prediction model according to the task differences in combination with the fluctuating operation state, input the real-time work task and the target work task, output the predicted fluctuating operation state through the fluctuation prediction model, and then compare the difference between the real-time operation state and the predicted fluctuating operation state;

[0011] The parameter adjustment unit is used to send the real-time operation state with differences to the fluctuation prediction model for adjustment parameter analysis, obtain the adjustment parameters of all factory equipment in the same area, and send the adjustment parameters of each factory equipment to the switch through the switch group network for parameter adjustment.

[0012] As a further improvement of this technical solution, the switch network establishment unit installs a switch for each factory equipment in the pipeline as the device switch;

[0013] By installing a switch at the factory equipment management end as the core switch, a switch group network is established using the core switch;

[0014] A virtual local area network (VLAN) is set up. Each device switch represents a VLAN. The operation state collected at the factory equipment is uploaded to the factory equipment management end through the switch group network by connecting the device switch to the switch group network.

[0015] As a further improvement of this technical solution, the device classification unit includes a status classification module and a work task acquisition module;

[0016] The status classification module is used to obtain the equipment information equipped on each pipeline through the factory equipment management end, establish a data storage library for the pipeline separately according to the number of pipelines, then clean and classify the operation states uploaded by the switch according to the equipment information, classify the operation states corresponding to the equipment belonging to the same pipeline into the same area, and then input them into the data storage library of the corresponding pipeline;

[0017] The work task acquisition module is used to obtain the work tasks of each pipeline at the factory equipment management end, and record the release time, deadline and content of each work task.

[0018] As a further improvement of this technical solution, the operation analysis unit includes a status correspondence module and a stability analysis module;

[0019] The status correspondence module is used to extract the historical work tasks of each pipeline, and combine the obtained historical operation states according to the time period of the historical work tasks, so as to obtain the historical operation state corresponding to each historical work task;

[0020] The stability analysis module is used to perform stability analysis on the historical operating status and historical work tasks, detect the stability of the operating status in the historical work tasks, generally taking the operating status with a high proportion of running time and small fluctuation range as the stable operation, and then summarize the stable operating status of the pipeline to obtain the stable operating status of the switch group corresponding to each work task.

[0021] As a further improvement of this technical solution, the operation analysis unit includes a fluctuation status extraction module;

[0022] The fluctuation status extraction module is used to delete the stable operating status in each work task and take the remaining operating status as the fluctuation status when the work task is switched;

[0023] The above work tasks belong to the work tasks without faults during the operation when they are analyzed and used.

[0024] As a further improvement of this technical solution, the formula of the operation analysis unit is as follows:

[0025] ;

[0026] Among them, A is the stability evaluation index, R represents the proportion of the normal operating status during the overall work task, CV is the coefficient of variation, used to measure the volatility of the operating status in different work tasks, and K is a regulation coefficient, used to balance the influence of the running time proportion and volatility.

[0027] As a further improvement of this technical solution, the operation prediction unit obtains the task difference between adjacent historical work tasks by simultaneously extracting the work differences between adjacent historical work tasks.

[0028] As a further improvement of this technical solution, the formula of the operation prediction unit is as follows:

[0029] ;

[0030] Among them, is the metric of the t-th task, such as the completion time, resource usage, is the same metric of the (t - 1)-th task, and M is the task difference metric;

[0031] ;

[0032] Among them, is the predicted fluctuation operating status at time t, is the task metric at time t, is the smoothing coefficient, usually taking values between 0 and 1, representing the weight of the current task difference metric, is the predicted fluctuation running state at the previous time point t-1;

[0033] ;

[0034] Among them, Z is the difference metric, C is the actual fluctuation state, and B is the predicted fluctuation state.

[0035] As a further improvement of this technical solution, the calculation formula of the parameter adjustment unit is as follows:

[0036]

[0037] Among them, is the predicted value at the next moment, is the historical data;

[0038] ;

[0039] Among them, is the new proportional gain, is the current proportional gain, is the adjustment amount recommended according to the fluctuation prediction model.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] In the online monitoring system for the operating state of a switch, repetitive tasks and processes can be automatically executed, reducing manual intervention and processing time, thereby improving the overall operation efficiency. At the same time, when changing work tasks, by establishing a fluctuation prediction model, the real-time operating state and the predicted fluctuation operating state are compared for differences, thereby avoiding the situation of undetected or misreported faults during the process of adjusting work tasks, reducing the possibility of operation errors in fault detection, enhancing the safety and stability of production operations, and through the parameter adjustment unit, adjusting the faulty factory equipment and connecting all factory equipment in the same area for adjustment, avoiding low adjustment efficiency of factory equipment, and bringing significant improvement to the overall equipment operation management and production efficiency of the factory. Brief Description of the Drawings

[0042] Figure 1 is the overall structural schematic diagram of the present invention.

[0043] The meanings of each label in the figure are as follows:

[0044] 10. Switch network establishment unit; 20. Equipment classification unit; 30. Operation analysis unit; 40. Operation prediction unit; 50. Parameter adjustment unit. Detailed Embodiment

[0045] 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. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0046] Please refer to Figure 1 As shown, the purpose of this embodiment is to provide an online monitoring system for the operating status of switches, including a switch network establishment unit 10, a device classification unit 20, an operation analysis unit 30, an operation prediction unit 40, and a parameter adjustment unit 50;

[0047] The switch network establishment unit 10 is used to establish a switch group network and connect the switches of factory equipment to the switch group network;

[0048] The switch network establishment unit 10 installs a switch for each factory equipment in the pipeline as the device switch;

[0049] By installing a switch at the factory equipment management end as the core switch, the core switch is used to establish a switch group network;

[0050] Set up a virtual local area network VLAN. Each device switch represents a VLAN. The operation status collected at the factory equipment is uploaded to the factory equipment management end through the switch group network by connecting the device switch to the switch group network. The working steps are as follows:

[0051] Installation of device switches: Install a switch for each factory equipment as the device switch. The switch is used to connect the factory equipment itself and is responsible for data communication and management at the device level;

[0052] Setting of core switches: Install one or more core switches at the factory equipment management end. The core switch is the center of the entire switch group network and is responsible for connecting all device switches and communicating with the factory equipment management end;

[0053] Establishing a switch group network: Use the core switch to connect all device switches to build a unified switch group network. This network can adopt a logical topology structure such as a star or a ring to ensure effective communication between devices and between the device management end;

[0054] Setting VLAN: For each device switch, set the corresponding virtual local area network VLAN. Each VLAN represents the logical network of a device, enabling logical isolation of data between devices. At the same time, data exchange as needed is carried out through the core switch.

[0055] Data collection and upload: Configure data collection devices such as sensors and monitoring devices on the device switch to collect the operating status of factory equipment. These data are connected to the corresponding VLAN through the device switch and uploaded to the factory equipment management end through the core switch. The core switch needs to be configured with routing and security policies to ensure that the data can be transmitted securely and received by the management end.

[0056] The device classification unit 20 is used to perform pipeline cleaning and classification on the operating status uploaded by the switch, classify the operating status of factory equipment in the same area according to the same pipeline, and obtain the work tasks of each pipeline at the same time.

[0057] The device classification unit 20 includes a status classification module and a work task acquisition module.

[0058] The status classification module is used to obtain the device information equipped for each pipeline through the factory equipment management end, establish a data storage repository for each pipeline separately according to the number of pipelines, and then clean and classify the operating status uploaded by the switch according to the device information, classify the operating status corresponding to the devices belonging to the same pipeline in the same area, and then input it into the data storage repository corresponding to the pipeline. The working steps are as follows:

[0059] Obtain device information: Obtain the device information equipped for each pipeline from the factory equipment management end.

[0060] Establish a data storage repository: Establish a data storage repository for each pipeline separately, which is used to store the operating status data of the devices in the pipeline. Each data storage repository should be designed to store the operating status data of the devices of a specific pipeline.

[0061] Clean and classify the operating status: Clean and classify the operating status data uploaded from the switch according to the device information to ensure the accuracy and consistency of the data.

[0062] Classify the data of the same pipeline: Classify the operating status data corresponding to the devices belonging to the same pipeline to ensure that the data is correctly input into the data storage repository of the corresponding pipeline.

[0063] The work task acquisition module is used to obtain the work tasks of each pipeline in the factory equipment management end, and record the release time, deadline and work task content of each work task. The working steps are as follows:

[0064] Task management module: Create a task management module in the factory equipment management system, which is used to store and process the information of work tasks.

[0065] Work task content: Record the specific content of each work task, which can be a text description or a task number, the timestamp or date-time when the task is issued, and the deadline timestamp or date-time when the task is completed;

[0066] Pipeline association: Ensure that each work task can be associated with the corresponding pipeline for tracking and managing the execution of tasks on different pipelines.

[0067] The operation analysis unit 30 is used to perform a stability analysis based on the historical operation status of the switch in combination with the work tasks, obtain the stable operation status of the switch group corresponding to each work task, and then extract other operation statuses as the fluctuating operation status when the work task is switched;

[0068] The operation analysis unit 30 includes a status correspondence module and a stability analysis module;

[0069] The status correspondence module is used to extract the historical work tasks of each pipeline, and combine the obtained historical operation status according to the time period of the historical work tasks, so as to obtain the historical operation status corresponding to each historical work task. The work steps are as follows:

[0070] Historical work task data: Extract the historical work task data of each pipeline from the device management system. These data should include information such as the start time, end time, and task content of the work task;

[0071] Historical operation status data: Collect the historical operation status data of each pipeline, which includes the operation time of the device, fault records, maintenance conditions, etc.;

[0072] Time period matching: For each historical work task, determine its corresponding time period, which is from the start time to the end time of the task;

[0073] Historical operation status acquisition: Extract or calculate the historical operation status data within the determined time period, which includes the operation time of the device and the production quantity.

[0074] The stability analysis module is used to perform a stability analysis on the historical operation status and historical work tasks, perform a stability detection on the operation status in the historical work tasks. Generally, the operation status with a relatively high operation time percentage and a small fluctuation range is regarded as a stable operation, and then the stable operation status of the pipeline is summarized to obtain the stable operation status of the switch group corresponding to each work task.

[0075] The operation analysis unit 30 includes a fluctuation state extraction module;

[0076] The fluctuation state extraction module is used to delete the stable operation status in each work task, and regard the remaining operation status as the fluctuation state when the work task is switched;

[0077] When the above work tasks are analyzed and used, they are work tasks without faults during the operation process. The work steps are as follows:

[0078] Define the stable operation state: Usually, the stable operation state is manifested as a state with a relatively high proportion of operation time and a small fluctuation range. The following indicators can be considered:

[0079] Proportion of operation time: Calculate the proportion of normal operation during each work task;

[0080] Fluctuation range: For normal operation, calculate its fluctuation degree in different work tasks, such as the standard deviation or coefficient of variation;

[0081] Stable state identification: According to the defined stability indicators of high proportion and low fluctuation range, identify the stable operation state during each work task. For example, select the operation state with the highest proportion of operation time and a relatively small fluctuation range as the stable state;

[0082] Summarize the stable operation state: Summarize the operation states identified as stable in each work task to form a list of stable operation states of this pipeline;

[0083] Fluctuation state analysis: Consider the remaining operation states in each work task that are not classified as stable states as fluctuation states. These states include the adaptation situation of the work task to the change of equipment parameters. The formula is as follows:

[0084] ;

[0085] Among them, A is the stability evaluation index, R is the proportion of the normal operation state during the overall work task, CV is the coefficient of variation, which is used to measure the volatility of the operation state in different work tasks, and K is a regulation coefficient, which is used to balance the influence of the proportion of operation time and volatility;

[0086] In this formula, the larger the CV, the higher the volatility of the operation state; the larger the R, the higher the proportion of the operation state in the overall work task. Therefore, through calculation, the stability of each section of the operation state can be comprehensively evaluated;

[0087] The specific value of k needs to be determined according to the specific situation and data characteristics. Usually, a suitable proportional coefficient can be selected through actual data analysis or empirical values to reflect different degrees of emphasis on the proportion of operation time and volatility.

[0088] The operation prediction unit 40 is used to establish a fluctuation prediction model according to the task difference and the fluctuating operation state, input the real-time work task and the target work task, output the predicted fluctuating operation state through the fluctuation prediction model, and then compare the difference between the real-time operation state and the predicted fluctuating operation state;

[0089] The operation prediction unit 40 extracts the task differences between adjacent historical work tasks simultaneously to obtain the task differences between adjacent historical work tasks. The work steps are as follows:

[0090] Data preparation: Obtain the relevant data of adjacent historical work tasks, including task completion time, task execution status, etc.;

[0091] Define the task difference measurement index. For example, the difference in task completion time, the difference in task execution resources, the difference in task success rate, etc. can be selected. The specific selection depends on the nature of the task and the available data;

[0092] Calculate the task difference: For the task difference measurement index, the following formula can be used for calculation:

[0093]

[0094] where, is the measurement index of the t-th task, such as completion time, resource usage, is the same measurement index of the (t - 1)-th task, and M is the task difference measurement;

[0095] The relative difference between the current task and the previous task calculated by this formula can help better understand the trend of task changes because the baseline of the previous task is considered;

[0096] The calculation formula of the operation prediction unit 40 is as follows:

[0097] ;

[0098] where, is the predicted fluctuation operation state at time t, is the task measurement at time t, is the smoothing coefficient, usually taking values between 0 and 1, indicating the weight of the current task difference measurement, is the predicted fluctuation operation state at the previous time point t - 1;

[0099] When the model becomes a simple moving average model, only depending on the current observation value , ;

[0100] When the model does not consider the current observation value , only depending on the historical predicted value , ;

[0101] At In the case of, the model calculates the new predicted value by weighted averaging the current observation and the previous prediction This method allows the model to smooth out the fluctuations in the data to a certain extent, thus better capturing long-term and short-term trend changes.

[0102] ;

[0103] where Z is the difference measure, C is the actual fluctuation state, and B is the predicted fluctuation state;

[0104] First, calculate , that is, calculate the absolute difference between the actual fluctuation state and the predicted fluctuation state, and then divide this absolute difference by B to obtain a proportional value of the relative difference.

[0105] The parameter adjustment unit 50 is used to send the real-time operating state with differences to the fluctuation prediction model for adjustment parameter analysis, obtain the adjustment parameters of all factory equipment in the same area, and send the adjustment parameters of each factory equipment to the switch through the switch group network for parameter adjustment. The working steps are as follows:

[0106] Difference detection and signal analysis: Compare the real-time collected data with the expected operating mode or benchmark to detect whether there are abnormalities or differences, which is achieved through statistical methods, machine learning models or rule engines;

[0107] Fluctuation prediction model: Design or select an appropriate fluctuation prediction model to predict the future equipment operating state or parameters;

[0108] Adjustment parameter acquisition: According to the output of the fluctuation prediction model, obtain the parameters that need to be adjusted for each factory equipment, which involves parameter adjustment, optimization or setting new operating points;

[0109] Parameter transmission and adjustment: Send the adjustment parameters obtained from the fluctuation prediction model to the switch, and then transmit these parameters to each factory equipment through the switch network. These parameters include control instructions for the equipment, set points for adjustment, etc.; The formula is as follows:

[0110] ;

[0111] where is the predicted value at the next moment, is historical data;

[0112] are the observed values of the time series y at different time points. These values can be various indicators of equipment operation, such as productivity, energy consumption, temperature, etc. f is a function that receives the past time series data​ as input and generate predicted values as output, the specific form of the function f can be various prediction models, such as linear regression, time series models like ARIMA, Given past data, the value of the future time further t + 1 predicted by the model, and the accuracy of the prediction depends on the selection of the model f and the accuracy of parameter adjustment;

[0113] ;

[0114] wherein, is the new proportional gain, is the current proportional gain, is the adjustment amount recommended by the fluctuation prediction model;

[0115] First, an initial value is required, and usually this value is determined when the system starts running or at a certain time point, which represents the changes that occur during the update process, which can be a positive increase or a negative decrease, depending on external or internal factors in the system or process. By adding the old value and the change amount the new value can be calculated, and this process is a simple addition operation used to update the status or attribute;

[0116] This formula describes how to calculate the new value based on the old value and the change amount, thereby reflecting the changes in the process.

[0117] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. An online monitoring system for the operating status of a switch, characterized in that: It includes a switch network establishment unit (10), a device classification unit (20), an operation analysis unit (30), an operation prediction unit (40), and a parameter adjustment unit (50); The switch network establishment unit (10) is used to establish a switch group network and connect the switches of factory equipment to the switch group network; The device classification unit (20) is used to perform pipeline cleaning and classification on the operation status uploaded by the switches, classify the operation status of factory equipment in the same area according to the same pipeline, and simultaneously obtain the work tasks of each pipeline; The operation analysis unit (30) is used to perform stability analysis based on the historical operation status of the switches in combination with the work tasks, obtain the stable operation status of the switch group corresponding to each work task, and then extract other operation statuses as the fluctuating operation status when the work task is switched; The operation analysis unit (30) includes a status correspondence module and a stability analysis module; The status correspondence module is used to extract the historical work tasks of each pipeline, and combine the obtained historical operation status according to the time period of the historical work tasks, so as to obtain the historical operation status corresponding to each historical work task; The stability analysis module is used to perform stability analysis on the historical operation status and historical work tasks, perform stability detection on the operation status in the historical work tasks, use the operation status with a relatively high operation time percentage and a small fluctuation amplitude as the stable operation, and then summarize the stable operation status of this pipeline, so as to obtain the stable operation status of the switch group corresponding to each work task; The operation prediction unit (40) is used to establish a fluctuation prediction model based on the task difference in combination with the fluctuating operation status, input the real-time work task and the target work task, output the predicted fluctuating operation status through the fluctuation prediction model, and then compare the difference between the real-time operation status and the predicted fluctuating operation status; The parameter adjustment unit (50) is used to send the real-time operation status with differences to the fluctuation prediction model for adjustment parameter analysis, obtain the adjustment parameters of all factory equipment in the same area, and send the adjustment parameters of each factory equipment to the switch through the switch group network for parameter adjustment.

2. The online monitoring system for the operating state of a switch according to claim 1, wherein: The switch network establishment unit (10) installs a switch for each factory equipment in the pipeline as the device switch; Install a switch at the factory equipment management end as the core switch, and use the core switch to establish a switch group network; Set up a virtual local area network VLAN. Each device switch represents a VLAN. Connect to the switch group network through the device switch, and upload the operation status collected at the factory equipment to the factory equipment management end through the switch group network.

3. An online monitoring system for the operating status of a switch according to claim 1, characterized in that: The device classification unit (20) includes a status classification module and a work task acquisition module; The state classification module is used to obtain the device information equipped for each production line through the factory equipment management terminal, establish a data repository for each production line separately according to the number of production lines, then clean and classify the operating states uploaded by the switch according to the device information, classify the operating states corresponding to the devices belonging to the same production line into the same area, and then input them into the data repository of the corresponding production line; The work task acquisition module is used to obtain the work tasks of each production line at the factory equipment management terminal, and record the release time and deadline of each work task, as well as the work task content.

4. An online monitoring system for the operating status of a switch according to claim 1, characterized in that: The operation analysis unit (30) includes a fluctuation state extraction module; The fluctuation state extraction module is used to delete the stable operation states in each work task, and regard the remaining operation states as the fluctuation states when the work task is switched; The above work tasks are work tasks without faults during the operation analysis.

5. An online monitoring system for the operating state of a switch according to claim 1, characterized in that: The formula of the operation analysis unit (30) is as follows: ； Among them, A is the stability evaluation index, R is the proportion of the normal operation state during the overall work task period, CV is the coefficient of variation, which is used to measure the volatility of the operation state in different work tasks, and K is an adjustment coefficient, which is used to balance the influence of the operation time proportion and volatility.

6. An online monitoring system for the operating status of a switch according to claim 1, characterized in that: The operation prediction unit (40) extracts the work differences between adjacent historical work tasks at the same time to obtain the task differences between adjacent historical work tasks.

Images