Remote Maintenance Guidance Method and System for Electric Chainsaw Guide Plate Based on Internet of Things

Abstract

The present application provides a method and system for remote maintenance guidance of an electric chainsaw guide plate based on the Internet of Things, which relates to the technical field of data processing and includes: connecting to an Internet of Things database and extracting target monitoring data; using the target monitoring data to align the acquisition time, performing monitoring data analysis to obtain an immediate analysis result and a time-series analysis result, where the immediate analysis result is an abnormal state that exists in real time and the time-series analysis result is a predicted abnormal state obtained by performing time-series analysis and prediction; parsing a maintenance strategy according to the immediate analysis result and the time-series analysis result, splitting the maintenance strategy into instructions according to the execution priority, generating maintenance guidance information, and performing remote maintenance guidance based on the maintenance guidance information. The present application solves the technical problem that the existing maintenance method cannot monitor and analyze the device operation data in real time, resulting in strong maintenance lag and lack of preventive maintenance, and achieves the technical effect of improving the maintenance efficiency of the electric chainsaw guide plate and the operation stability and safety of the device.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and specifically to a method and system for remote maintenance guidance of an electric chainsaw guide plate based on the Internet of Things. Background Art

[0002] An electric chainsaw is a common power tool widely used in fields such as logging, gardening, and construction. As one of the key components of an electric chainsaw, the guide plate is used to support and guide the operation of the chain, and its performance directly affects the working efficiency and service life of the electric chainsaw. Since the guide plate of the electric chainsaw bears a large amount of friction and tension during operation, the guide plate may experience wear, deformation, etc., resulting in a decline in its performance, and even causing failures, which in turn affects the normal operation of the entire electric chainsaw.

[0003] Currently, the maintenance of the electric chainsaw guide plate mainly relies on manual regular inspections or empirical judgments. Common maintenance methods include visual inspections, manually detecting the degree of wear, repairing after failures, or relying on the usage time of the equipment for regular replacements. On the one hand, relying on manual regular inspections is not only inefficient but also difficult to detect potential wear or failures in a timely manner, resulting in a lag in maintenance work. On the other hand, repairs after failures are often accompanied by unplanned downtime of the equipment, which not only affects the operation progress but also increases the maintenance cost. In addition, due to the lack of precise data monitoring and analysis of operating parameters, maintenance decisions overly rely on manual experience, resulting in inaccurate and unscientific maintenance strategies. Summary of the Invention

[0004] This application provides a method and system for remote maintenance guidance of an electric chainsaw guide plate based on the Internet of Things, which solves the technical problem that the existing maintenance methods cannot monitor and analyze the equipment operation data in real time, resulting in strong maintenance lag and lack of preventive maintenance, and achieves the technical effect of improving the maintenance efficiency of the electric chainsaw guide plate and the operation stability and safety of the equipment.

[0005] In view of the above problems, on the one hand, this application provides a method for remote maintenance guidance of an electric chainsaw guide plate based on the Internet of Things. The method includes: connecting to the Internet of Things database and extracting target monitoring data, where the target monitoring data includes at least one or more of temperature, vibration, tension, and wear data; using the target monitoring data to align the acquisition time, performing monitoring data analysis to obtain an immediate analysis result and a time series analysis result, where the immediate analysis result is an abnormal state that exists in real time, and the time series analysis result is a predicted abnormal state obtained by performing time series analysis and prediction; parsing a maintenance strategy according to the immediate analysis result and the time series analysis result, splitting the maintenance strategy into instructions according to the execution priority, generating maintenance guidance information, and performing remote maintenance guidance based on the maintenance guidance information.

[0006] On the other hand, the present application also provides a remote maintenance guidance system for an electric chainsaw guide plate based on the Internet of Things. The system includes: a monitoring data extraction module for connecting to an Internet of Things database to extract target monitoring data, where the target monitoring data includes at least one or more of temperature, vibration, tension, and wear data; a monitoring data analysis module for aligning the acquisition time using the target monitoring data and performing monitoring data analysis to obtain an immediate analysis result and a time-series analysis result. The immediate analysis result is an abnormal state that exists in real time, and the time-series analysis result is a predicted abnormal state obtained through time-series analysis and prediction; a maintenance guidance module for parsing a maintenance strategy based on the immediate analysis result and the time-series analysis result, splitting the maintenance strategy into instructions according to the execution priority, generating maintenance guidance information, and performing remote maintenance guidance based on the maintenance guidance information.

[0007] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0008] By connecting to an Internet of Things database, extracting target monitoring data, a foundation for data monitoring is established, providing accurate data support for subsequent analysis and maintenance to ensure the acquisition of real-time status information of the electric chainsaw guide plate. Using the target monitoring data for acquisition time alignment and performing monitoring data analysis to obtain an immediate analysis result and a time-series analysis result, the immediate analysis result helps identify abnormal states that exist in real time, while the time-series analysis result reveals potential abnormal states through predictive analysis, not only improving the accuracy of problem identification but also enhancing the ability of predictive maintenance to prevent potential failures in advance. Parsing the maintenance strategy based on the immediate analysis result and the time-series analysis result, splitting the maintenance strategy into instructions according to the execution priority, generating maintenance guidance information, realizes the personalization and automation of maintenance activities, ensuring the pertinence and effectiveness of maintenance activities. Performing remote maintenance guidance based on the maintenance guidance information, quickly responding to equipment requirements, avoiding unplanned downtime, and significantly improving maintenance efficiency and the operational stability of the equipment.

[0009] In summary, the present application combines Internet of Things technology, monitors the device status in real time, and through data analysis, provides a more objective basis for maintenance decisions, can timely detect and prevent potential failures, and provides remote maintenance guidance, thus realizing active monitoring and predictive maintenance, reducing the unplanned downtime of the device, not only improving the maintenance efficiency of the electric chainsaw guide plate, but also improving the reliability and service life of the device.

[0010] The above description is only an overview of the technical solution of this application. In order to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific implementation manners of this application are hereinafter given. Brief Description of the Drawings

[0011] Figure 1 It is a schematic flowchart of the method for remotely maintaining and guiding an electric chainsaw guide plate based on the Internet of Things provided by an embodiment of this application;

[0012] Figure 2 It is a schematic flowchart of generating maintenance guidance information in the method for remotely maintaining and guiding an electric chainsaw guide plate based on the Internet of Things provided by an embodiment of this application;

[0013] Figure 3 It is a schematic structural diagram of the system for remotely maintaining and guiding an electric chainsaw guide plate based on the Internet of Things provided by an embodiment of this application.

[0014] Description of the reference numerals: The monitoring data extraction module 10, the monitoring data analysis module 20, and the maintenance guidance module 30. Detailed Description of the Embodiment

[0015] By providing the method and system for remotely maintaining and guiding an electric chainsaw guide plate based on the Internet of Things in the embodiment of this application, the technical problem that the existing maintenance method cannot monitor and analyze the device operation data in real time, resulting in strong maintenance lag and lack of preventive maintenance, is solved, and the technical effects of improving the maintenance efficiency of the electric chainsaw guide plate and the operation stability and safety of the device are achieved.

[0016] Embodiment 1, as Figure 1 shown, the embodiment of this application provides a method for remotely maintaining and guiding an electric chainsaw guide plate based on the Internet of Things, and the method includes:

[0017] Connect to the Internet of Things database and extract target monitoring data, where the target monitoring data includes at least one or more of temperature, vibration, tension, and wear data.

[0018] Specifically, the Internet of Things database is a system that collects, stores, and manages a large amount of data generated by Internet of Things devices, and can efficiently process and store real-time data from various sensors, providing a data basis for data analysis and applications. The target monitoring data specifically refers to data closely related to the operation state of the electric chainsaw, including but not limited to temperature, vibration, tension, and wear degree. These data directly reflect the working state and health condition of the electric chainsaw guide plate.

[0019] The Internet of Things database collects key data on the operating status of the equipment in real time through a series of Internet of Things devices installed on the electric chain saw, such as temperature sensors, vibration sensors, tension sensors, and wear monitoring sensors. Taking temperature monitoring as an example, the temperature sensor installed on the electric chain saw can monitor its working temperature in real time, and the data is transmitted to the Internet of Things database through a wireless network (such as Wi-Fi or 4G / 5G). Similarly, the vibration sensor can monitor the vibration of the electric chain saw during operation; the tension sensor is responsible for monitoring the tension state of the chain; the wear monitoring sensor evaluates the degree of wear by detecting the surface condition, such as through capacitance change or optical measurement.

[0020] Establish a network connection with the Internet of Things database, extract the required monitoring data from the database according to the preset parameters or query conditions, and grasp the operating status of the electric chain saw in real time, providing reliable data support for subsequent analysis and maintenance. Among them, the temperature data can determine whether the equipment is overheated, and the vibration data can detect whether there are signs of imbalance or damage to the equipment; the tension and wear data can evaluate the state of the chain and the wear degree of the guide plate.

[0021] Use the target monitoring data to align the collection time, perform monitoring data analysis, and obtain immediate analysis results and time series analysis results. The immediate analysis result is the abnormal state that exists in real time, and the time series analysis result is the predicted abnormal state obtained by performing time series analysis and prediction.

[0022] Specifically, during the operation of the electric chain saw, multiple sensors (temperature, vibration, tension, wear) may collect data at different frequencies. It is necessary to align the collection time of the extracted monitoring data. The collection time alignment ensures the consistency of the data at the time point and provides an accurate time reference for subsequent analysis. The collection time alignment is usually completed through data preprocessing techniques, such as using timestamps to match and align data points. This process can utilize the time series processing functions provided in the Pandas library of Python or use a stream processing framework such as Apache Flink to process and align data in real time.

[0023] Perform immediate analysis and time series analysis on the aligned data. The immediate analysis is based on the real-time data stream, and uses a real-time stream processing framework combined with threshold detection and anomaly detection algorithms to identify the current abnormal state, such as overheating, abnormal vibration, etc., to obtain the immediate analysis result. For example, when the temperature of the electric chain saw exceeds the preset threshold, the immediate analysis will trigger an alarm to prompt the user to take cooling measures. The time series analysis uses historical data to predict future abnormal states. By analyzing past temperature, vibration and other data, some potential trends or patterns can be found. For example, if it is found that the temperature gradually rises after a certain period of time, it can be predicted that there is a risk of overheating of the equipment in the future.

[0024] Through data alignment and monitoring data analysis, it is possible to quickly determine whether the current state of the device is normal, and at the same time prospectively predict possible future failure risks, achieve immediate fault handling and preventive maintenance, thereby extending the device life and reducing downtime.

[0025] Analyze the maintenance strategy based on the immediate analysis result and the time-series analysis result, split the maintenance strategy into instructions according to the execution priority, generate maintenance guidance information, and conduct remote maintenance guidance based on the maintenance guidance information.

[0026] Specifically, based on the current and future device states of the electric saw chain guide described by the immediate analysis result and the time-series analysis result, a series of specific maintenance strategies are derived. After generating the maintenance strategy, the execution priority is assigned according to the urgency of the fault and its impact on the normal operation of the device. The most important maintenance strategy is arranged first to ensure that the most urgent and important problems are solved first. For example, overheating may immediately cause the device to stop, so the priority of this problem is higher than minor wear. Then, the specific tasks in the maintenance strategy are decomposed into executable instructions, each instruction clearly specifying the specific maintenance steps. Based on the split instructions, maintenance guidance information is generated, which is used to guide maintenance personnel or an automated system to perform maintenance operations. Based on Internet of Things technology, maintenance information or instructions are remotely transmitted through the network to directly guide device operators to perform maintenance work or remotely trigger automated maintenance tasks.

[0027] Exemplarily, it is analyzed that the chain tension of the electric chain saw is abnormal (immediate analysis result) and the guide plate is expected to reach the wear threshold in one week (time-series analysis result). According to the urgency and impact, the chain tension adjustment strategy is given the highest priority. First, the chain tension adjustment strategy is analyzed, and then the preventive maintenance strategy for guide plate wear. The chain tension adjustment strategy is broken down into steps such as "check the chain tensioner" and "adjust the tension to the recommended value", and a detailed operation guide is generated, including the usage instructions of the adjustment tool and safety operation specifications. For the preventive maintenance of guide plate wear, instructions such as "check the wear condition of the guide plate" and "replace the guide plate if necessary" are generated. Through remote connection, guidance information for immediately adjusting the chain tension is sent to the on-site operator, accompanied by a schematic diagram and operation steps of the adjustment tool, or the device self-check or adjustment operation is remotely triggered through the Internet of Things system. At the same time, according to the prediction of guide plate wear, a maintenance reminder is sent to the maintenance team one week in advance to ensure the smooth progress of the maintenance work.

[0028] Through the above steps, it is possible to intelligently generate maintenance strategies based on the immediate and future device states, and through hierarchical instructions and remote guidance, ensure that the maintenance work of the electric saw chain guide is completed in a timely and effective manner, thereby reducing fault downtime and improving operation efficiency.

[0029] Further, before connecting to the Internet of Things database in the embodiments of the present application, it includes:

[0030] Construct a multi-level sensing module, including a first-level temperature sensing and vibration sensing module, a second-level tension sensing module, and a third-level wear sensing module. There is an adaptive activation function between each level of sensing modules. When the adaptive activation function is satisfied, the higher-level sensing module is awakened to achieve step-by-step triggering; establish a data acquisition channel for the multi-level sensing module, send the sensed acquisition data through the data acquisition channel and generate an acquisition timestamp at the same time, and obtain the monitoring data of each level; establish a mapping association between the monitoring data of each level and the multi-level sensing module, and construct a multi-level Internet of Things database.

[0031] Specifically, the multi-level sensing module is a system composed of different sensors, which is divided into multiple levels according to the function or importance of the sensors, and the sensors at each level monitor different types of data. In the embodiments of the present application, the multi-level sensing module includes a first-level temperature sensing and vibration sensing module, a second-level tension sensing module, and a third-level wear sensing module. An adaptive activation function is set between each level of sensing modules, and this function will automatically judge whether it is necessary to enable the higher-level monitoring level.

[0032] Among them, the first-level temperature sensing and vibration sensing module is composed of several temperature sensors and vibration sensors, which are used to monitor the basic operating state of the electric chain saw, including temperature and vibration, and identify problems such as overheating, friction, and abnormal mechanical movement. When abnormalities are found in the first-level monitoring, such as when the temperature or vibration data reaches the abnormal threshold, the second-level tension sensing module is automatically activated through the adaptive activation function. The second-level tension sensing module is composed of several tension sensors, which are used to monitor the tension state of the chain and identify problems such as the chain being too loose or too tight. When the second-level tension sensing module detects abnormal chain tension or the data of the first-level sensing module continues to be abnormal, the adaptive activation function activates the third-level wear sensing module. The third-level wear sensing module is composed of several wear sensors, which are used to monitor the wear conditions of the guide plate and the chain and provide more detailed fault information. This design of step-by-step activation helps to save system resources. Only when the low-level sensors detect abnormalities will the high-level sensor modules be activated. If everything is normal, only the first-level sensors need to run to reduce energy consumption and data processing burden.

[0033] Adopt Internet of Things communication protocols such as MQTT or AMQP to establish a data acquisition channel for the multi-level sensing module. Each level of sensor module sends the data it acquires through this data acquisition channel and generates a timestamp to record the time of data acquisition. The role of the timestamp is to ensure that all data can be arranged in chronological order, ensuring that even if the data comes from sensors at different levels, it can be analyzed in the same time dimension.

[0034] To manage and analyze this data, it is necessary to map and associate the monitoring data at each level with the corresponding sensing modules to establish the correspondence between the data and the sensing modules. Through this mapping, the data source can be clearly understood, that is, which sensing module generates each piece of data, so as to improve the accuracy of data analysis.

[0035] Based on the monitoring data generated by the multi-level sensing modules and the correspondence between the data and the sensing modules, a multi-level Internet of Things database corresponding to the multi-level architecture of the sensors is established for storing and retrieving different types of data. NoSQL databases such as MongoDB and Cassandra, or the Hadoop distributed file system can be used to build the Internet of Things database to support the storage and rapid access of large-scale data.

[0036] Through this structured and hierarchical design of sensors and databases, it is possible to respond to the operating state of the electric chainsaw, gradually trigger sensor monitoring, capture anomalies in a timely manner, and provide real-time and accurate equipment monitoring and remote maintenance support.

[0037] Furthermore, the method described in the embodiment of the present application further includes:

[0038] Taking temperature, vibration, tension, and wear as variables respectively, fitting the abnormal state of the variables through historical abnormal data to determine the abnormal state threshold of each variable, where the abnormal state threshold is a correlation coefficient including the load; extracting the top abnormal events from the historical abnormal data, and evaluating the correlation of the temperature, vibration, tension, and wear respectively to determine the associated influence relationship between the top abnormal events and the temperature, vibration, tension, and wear; configuring the activation selection conditions according to the abnormal state threshold and the associated influence relationship to establish the adaptive activation function, and the activation selection conditions are independent conditions of the abnormal state threshold or the union conditions of the abnormal state threshold and the associated influence relationship.

[0039] Specifically, taking temperature, vibration, tension, and wear as variables, analyzing the historical abnormal data recorded in the past, and finding out the typical performance of each variable when it is in an abnormal state through statistical and fitting methods, and determining the abnormal state threshold of each variable according to different loads. For example, the normal upper limit of the temperature of the electric chainsaw under light load may be 80°C, but it may be allowed to reach 90°C under heavy load. This method of adjusting the threshold according to the load ensures the accuracy of anomaly detection.

[0040] Variable anomaly state fitting can be accomplished using regression analysis. Select an appropriate regression model, including linear regression, polynomial regression, logistic regression, etc. When establishing the regression model, particularly consider the impact of the change in the load on the anomaly state and introduce the load as an independent variable into the model. Train the regression model using historical data and evaluate the accuracy and generalization ability of the model through methods such as cross-validation.

[0041] Next, extract the top anomaly events from the historical data, that is, those events in the historical data that cause the device to stop running or seriously affect performance, such as overheating of the guide plate, severe vibration, chain breakage, etc. Extract the temperature, vibration, tension, and wear values when the top event occurs and record the variable state at that time. Use correlation analysis methods, such as Pearson correlation coefficient or multivariate regression, to evaluate the associated impact relationships between these top events and temperature, vibration, tension, and wear. These associated relationships help to understand the interaction between multiple variables. Through correlation analysis, calculate the correlation coefficient between each pair of variables, such as temperature and vibration, vibration and tension, to judge the intensity of the interaction between these variables in the top anomaly events. Exemplarily, it is analyzed that when the temperature rises during the top anomaly event, the vibration intensifies, indicating a strong association between temperature and vibration; by analyzing the chain tension and vibration data, it is found that the abnormal vibration is related to the deviation of the chain tension; by analyzing the relationship between the chain tension and wear data, it is found that when the tension is too loose or too tight, the wear rate of the guide plate increases significantly, indicating an association between the two.

[0042] According to the obtained anomaly state thresholds and associated impact relationships, configure the activation selection conditions. These activation selection conditions are the thresholds of single variables. For example, activating the high-level sensor only when the temperature exceeds the threshold, or they can be joint conditions of multiple variables. For example, activating the next-level sensor only when both the temperature and vibration exceed the threshold. Based on these activation selection conditions, establish an adaptive activation function through a programming language. This function determines whether to activate a higher-level monitoring system by judging the current device state in real time.

[0043] Exemplarily, taking the secondary tension sensing module as an example, when the activation selection condition is the threshold of a single variable, that is, activating the secondary tension sensing module only when the temperature exceeds the threshold or the vibration exceeds the threshold, the adaptive activation function can be set as:

[0044] , where T and V respectively represent the real-time data of temperature, vibration, tension, and wear, and T´ and V´ respectively represent the anomaly thresholds of temperature and vibration.

[0045] When the activation selection condition is the union condition of the associated impact relationships, that is, activating the secondary tension sensing module only when both the temperature and vibration exceed the threshold, the adaptive activation function can be set as:

[0046] where T and V respectively represent the real-time data of temperature, vibration, tension, and wear, and T' and V' respectively represent the abnormal thresholds of temperature and vibration.

[0047] The adaptive activation function can also be constructed by integrating independent conditions and union conditions. When any one of the variables of temperature and vibration exceeds the threshold or both temperature and vibration exceed the threshold simultaneously, the secondary tension sensing module is activated. At this time, the adaptive activation function can be set as:

[0048] where T and V respectively represent the real-time data of temperature, vibration, tension, and wear, and T' and V' respectively represent the abnormal thresholds of temperature and vibration.

[0049] By analyzing the historical abnormal data in detail, the activation selection conditions can be configured according to the abnormal state and associated impacts, and an adaptive activation function can be established to ensure that the adaptive activation function can automatically activate the higher-level sensor module at the appropriate time, realizing intelligent and accurate monitoring data acquisition.

[0050] Furthermore, the embodiment of the present application uses the target monitoring data to perform acquisition time alignment, analyzes the monitoring data, and obtains immediate analysis results and time series analysis results, including:

[0051] Generate the acquisition time according to the activation time of the sensing module, perform time alignment starting from the earliest acquisition time in the target monitoring data, and obtain the time series matrix of the target monitoring data, where the horizontal elements of the time series matrix are the data of different monitoring variables at the same acquisition time, and the vertical elements are the data of the same monitoring variable at different acquisition times; according to the time series matrix, perform horizontal data analysis and evaluation to obtain the immediate analysis results; perform vertical data analysis and prediction according to the time series matrix to obtain the time series analysis results.

[0052] Specifically, generate a list of acquisition timestamps according to the activation time of different sensing modules, select the earliest acquisition time as the benchmark, and then arrange the subsequent sensing data according to this benchmark time, and place the data collected by different sensors at different time points within the same time frame to construct a time series matrix. This time series matrix is a two-dimensional data structure used to store the monitoring data of different sensors at different time points. The horizontal axis represents the values collected by different sensors at the same moment, and the vertical axis represents the data collected by the same sensor at different times. For example, at a certain time point, the temperature is 70°C, the vibration is 3g, the tension is 12N, and the wear is 0.5mm, and these data will be placed in the same row. The acquisition values of the temperature at different times are 70°C, 72°C, 75°C, and are arranged in the same column in sequence.

[0053] Using this timing matrix, horizontal data analysis is first performed, that is, analyzing the data of different sensors at the same moment to judge the current operating state of the device. For example, if a sudden increase in temperature and an abnormal increase in vibration are detected at the same moment, it may be judged that there is a fault in the current device. This analysis will generate an immediate analysis result indicating whether there are problems that need to be solved immediately.

[0054] Then, vertical data analysis is carried out, that is, analyzing the change trend of the same variable over time. For example, by analyzing the change trend of temperature in the past few hours, the future temperature change can be predicted. The result of vertical analysis can discover potential problems, such as whether the rising speed of temperature exceeds the normal value and may exceed the safety threshold at a certain future time point. The timing analysis result generated by this analysis is used to predict the future state of the device, so as to achieve preventive maintenance.

[0055] This analysis method based on the timing matrix can comprehensively master the state of the device, not only discover problems at the current moment, but also give early warnings in advance to help formulate long-term maintenance strategies.

[0056] Furthermore, the embodiment of the present application performs horizontal data analysis and evaluation according to the timing matrix to obtain the immediate analysis result, including:

[0057] According to the association and influence relationship between the top abnormal event and temperature, vibration, tension, and wear, a discriminator is trained based on historical abnormal data. The temperature, vibration, tension, and wear monitoring data in the horizontal data are used as input variables of the discriminator. The top abnormal event is discriminated through the discriminator, and the identified abnormal event is output as the immediate analysis result. At the same time, the immediate analysis result is compared with an abnormal warning threshold. When the abnormal warning threshold is reached, a maintenance warning message is sent, and the maintenance warning message includes the abnormal event.

[0058] Specifically, a discriminator is trained based on historical abnormal data. The trained discriminator is a machine learning model, such as logistic regression, support vector machine, or neural network. The discriminator learns the association and influence relationship between the top abnormal event and temperature, vibration, tension, and wear monitoring data to judge whether a top abnormal event occurs.

[0059] After training is completed, the current monitoring data of temperature, vibration, tension, and wear in the horizontal data of the timing matrix are used as input variables and input into the discriminator. For each time point, the discriminator will receive a set of data points of temperature, vibration, tension, and wear as input. The discriminator will analyze these input data to judge whether the current device state conforms to the characteristics of the top abnormal event that occurred in history and output an immediate analysis result indicating whether there is an abnormality in the current state.

[0060] Then, compare the real-time analysis result with a pre-set abnormal warning threshold. This abnormal warning threshold is a pre-set standard used to judge the severity of an abnormality. If the abnormal event output by the discriminator reaches or exceeds the abnormal warning threshold, a maintenance warning message will be immediately sent. This warning message usually contains detailed information about the abnormal event, such as the type of abnormality, the occurrence time, etc.

[0061] By training the discriminator, it is possible to quickly and accurately identify abnormalities by monitoring the current health status of the data analysis device in real time. At the same time, the abnormal warning threshold mechanism ensures that a warning is only issued when there is indeed a risk in the device, avoiding unnecessary maintenance interference and improving the efficiency and safety of device management.

[0062] Furthermore, the embodiment of the present application performs longitudinal data analysis and prediction according to the timing matrix to obtain the timing analysis result, including:

[0063] According to the change trend of the variables fitted from the longitudinal data, obtain the change trend coefficient; based on the change trend coefficient, perform state change prediction according to the timing relationship to obtain the predicted timing variable; use the predicted timing variable as the input variable to perform top abnormal event discrimination through the discriminator, and output the predicted abnormal event as the timing analysis result. The timing analysis result includes the predicted abnormal state and its predicted time.

[0064] Specifically, according to the longitudinal data in the timing matrix, analyze the changes in temperature, vibration, tension, and wear at different time points respectively. By fitting these data, determine the change trend coefficient of each variable. This coefficient is a mathematical quantity that reflects the trend and rate of change of the monitored variable over time. For example, by fitting the temperature data, it is found that the temperature is rising at a rate of 2°C per hour, and this rate is the change trend coefficient. The change trend coefficient can be obtained by fitting through time series analysis, regression analysis, etc. For example, use linear regression to fit the change trend of temperature over time. Take time as the independent variable and temperature as the dependent variable to establish a linear regression model, observe the rising or falling rate of temperature, and predict whether overheating will occur in the future.

[0065] Use the calculated change trend coefficient to predict the values of the monitored variables (temperature, vibration, tension, wear) at future time points to obtain the predicted timing variable. Input the predicted timing variable into the previously trained discriminator to determine whether a top abnormal event is likely to occur at the predicted time point. If the prediction result indicates that an abnormality may occur, record the predicted abnormal state and its predicted occurrence time, and output it as the timing analysis result.

[0066] Exemplarily, perform a longitudinal analysis on the temperature monitoring data of an electric chain saw, use the ARIMA model to fit the temperature change trend, and calculate the change trend coefficient. Assume that the model predicts that the temperature will rise from 60°C to 65°C in the next 2 hours, approaching the abnormal warning threshold of 63°C. Input the predicted temperature value (65°C) into the discriminator for abnormal event prediction. If the result output by the discriminator indicates that a top abnormal event (such as equipment overheating) may occur after 2 hours, generate the time series analysis result: "It is predicted that the equipment may have an overheating abnormality after 2 hours."

[0067] Through longitudinal data analysis and prediction, it is possible to give early warnings of impending failures, greatly reducing sudden failures and downtime of equipment.

[0068] Furthermore, as Figure 2 shown, the embodiment of the present application analyzes the maintenance strategy according to the instant analysis result and the time series analysis result, divides the maintenance strategy into instructions according to the execution priority, and generates maintenance guidance information, including:

[0069] According to the instant analysis result and the time series analysis result, obtain the abnormal event and its abnormal state time; establish the mapping relationship between the abnormal event and the maintenance means, including the compensation efficiency of the maintenance means, obtain the maintenance compensation time limit according to the abnormal state time, and determine the maintenance strategy by using the maintenance compensation time limit and the compensation efficiency; perform a synchrony analysis on the maintenance strategy, and when the synchrony condition is met, generate the maintenance guidance information according to the maintenance strategy; when the maintenance strategy does not meet the synchrony condition, perform a priority analysis on the maintenance strategy to determine the strategy priority, where the strategy priority represents the risk degree of the abnormal event; sort the maintenance strategy according to the priority, and perform instruction segmentation according to the sorting result to generate the maintenance guidance information.

[0070] Specifically, according to the instant analysis result and the time series analysis result, identify the abnormal events and determine the exact time when these abnormal events may occur. Extract the maintenance plans pre-entered by the operator and establish the mapping relationship between the abnormal events and the maintenance means. This mapping relationship indicates the maintenance means that can be adopted for specific abnormal events (such as too high temperature, excessive vibration). For example, too high temperature may require equipment cooling or coolant replacement, and excessive vibration may require equipment calibration. For each maintenance means, evaluate its compensation efficiency. The compensation efficiency refers to the efficiency of the maintenance means to effectively repair or improve the abnormal state within a certain time. This efficiency can be determined according to historical data or equipment specifications. For example, lubricating oil may have an efficiency of 60% in reducing temperature within 10 minutes.

[0071] Determine the maintenance compensation time limit for each abnormal state based on the compensation efficiency of the maintenance means and the abnormal state time. This time limit is the acceptable maintenance time window for the equipment after the occurrence of the abnormal state to avoid further damage or failure. For example, if it is predicted that the tension will exceed the standard in 2 hours, then the maintenance compensation time limit is within 2 hours. The comprehensively determined compensation efficiency and maintenance compensation time limit generate maintenance strategies. There can be one or more maintenance strategies, and each maintenance strategy corresponds to an abnormal state.

[0072] Then, conduct a synchrony analysis on the maintenance strategies to determine whether there are interferences and execution sequences among multiple maintenance strategies and whether they can be executed simultaneously. If the synchrony conditions are met, that is, multiple maintenance strategies can be carried out simultaneously, then generate maintenance guidance information according to the synchronized maintenance strategies to remind the operator or the automatic operating system to execute these maintenance strategies step by step.

[0073] If the synchrony conditions are not met, then priority parsing is required. By analyzing the risk levels of each abnormal event, determine which tasks should be executed first. For example, an over-high temperature may be more urgent than minor wear, so the temperature issue will be processed first. Determine the strategy priorities through priority parsing, sort the maintenance strategies according to the priorities, and perform instruction splitting according to the sorting results to generate maintenance guidance information.

[0074] Exemplarily, the real-time analysis result indicates a temperature abnormality, and the time-series analysis result predicts a future vibration abnormality. Determine the maintenance means and maintenance compensation time limit according to the analysis results. For the current temperature abnormality, immediate cooling is required; for the predicted vibration abnormality, the connection components need to be checked and tightened in advance. The acceptable maintenance time window for the electric chain saw is 1 hour to avoid production stoppage. Determine the corresponding maintenance strategies as "Immediately start the cooling system" and "Check and tighten the equipment 1.5 hours later". Since cooling and checking cannot be synchronized, priority needs to be parsed. The temperature abnormality is more urgent, so cooling is prioritized; the vibration abnormality has a lower risk, so checking is carried out later. Finally, generate the maintenance guidance information of "Immediately start the cooling system" and "Check the connection components of the equipment 1.5 hours later".

[0075] Through the above steps, it is possible to formulate and execute maintenance strategies efficiently and orderly based on real-time monitoring and predictive analysis, ensure the normal operation of the equipment and production efficiency, and reduce the risk of sudden failures.

[0076] Furthermore, before sorting the maintenance strategies according to the priorities and performing instruction splitting according to the sorting results in the embodiment of the present application, it also includes:

[0077] Determine whether there is a shutdown strategy in the maintenance strategy. When there is one, parse the compensation strategy for the corresponding abnormal event based on the timing relationship of the corresponding abnormal event; match the compensation strategy with maintenance strategies of other priorities. When the match is successful, configure the matched maintenance strategy with the highest priority, and at the same time use the maximum value among the maintenance strategy and the compensation strategy as the execution strategy to reset the priority.

[0078] Specifically, before sorting the maintenance strategies and splitting the instructions, it is first necessary to determine whether there is a shutdown strategy. Since shutdown means a complete interruption of production, it usually brings huge production losses, delay costs, and additional restart time costs. Therefore, shutdown is usually the last choice and has the lowest priority, and it is only selected when serious failures cannot be avoided by other maintenance means.

[0079] Analyze whether there is a strategy that requires the equipment to be shut down in the maintenance strategy. When there is a shutdown strategy, further parse the compensation strategy for this strategy. The compensation strategy refers to the measures taken to reduce the shutdown time or mitigate the impact of abnormal events. For example, the equipment needs to be shut down for 2 hours to repair the tension, but the equipment running time can be extended through short-term cooling, and the cooling is the compensation strategy.

[0080] Match the compensation strategy with maintenance strategies of other priorities to find a matched maintenance strategy that can be coordinated or executed synchronously. If the match is successful, configure the matched maintenance strategy with the highest priority to ensure that equipment abnormalities can be handled without shutting down the equipment. At the same time, use the maximum value among the maintenance strategy and the compensation strategy as the execution strategy, that is, select the strategy that can solve the problem to the greatest extent, and reorder the priorities of the maintenance strategies to ensure that the compensation strategy can be executed in a timely manner.

[0081] Exemplarily, there is a shutdown strategy of "wear limit exceeded, replace the guide plate" in the maintenance strategy. After parsing, it is found that "lubrication" can reduce wear. Therefore, "lubrication" is used as the compensation strategy and matched with maintenance strategies of other priorities. It is found that "lubrication" and "tension adjustment" can be carried out simultaneously. Therefore, "lubrication" and "tension adjustment" are used as the matched maintenance strategies, set to the highest priority, and the lubrication amount is set to be the largest.

[0082] If the match is not successful, that is, there is no maintenance strategy that can be executed simultaneously with the current compensation strategy, a new compensation strategy needs to be added at this time. The introduction of the compensation strategy is to delay the occurrence of problems or mitigate the impact of problems. For example, if there is a risk of excessive tension in the equipment but it cannot be shut down immediately for repair, the process of equipment damage can be delayed by temporarily reducing the load, adjusting the running speed, or short-term adjusting the tension parameters, etc.

[0083] After adding the compensation strategy, it is necessary to re-evaluate the priorities of all strategies and reset the priorities of the maintenance strategies to ensure that the compensation strategy can be executed in a timely manner. According to the reset priorities, evaluate whether the current maintenance strategies meet the maintenance requirements of all abnormal events, including evaluating whether each abnormal event has corresponding handling measures, whether the handling time is reasonable, and whether the minimum impact on production is caused. If the evaluation results show that some strategies fail to meet the maintenance requirements or production goals, additional maintenance steps need to be added, the maintenance schedule needs to be adjusted, or the compensation strategy needs to be optimized.

[0084] Through the compensation strategy and priority reset, it can be ensured that the equipment operates normally in the short term, the downtime is postponed to the greatest extent, and the continuity of production is guaranteed.

[0085] In summary, the method for remotely guiding the maintenance of the electric chain saw guide plate based on the Internet of Things provided by the embodiments of the present application has the following technical effects:

[0086] Construct a multi-level sensing module and an Internet of Things database that trigger step by step to monitor the operating status of the equipment in real time and accurately, while reducing resource consumption; connect to the Internet of Things database, extract the target monitoring data, establish a basis for data monitoring, and provide accurate data support for subsequent analysis and maintenance to ensure that the real-time status information of the electric chain saw guide plate can be obtained. Use the target monitoring data to align the acquisition time, obtain a time series matrix for monitoring data analysis, train a discriminator through historical data, obtain immediate analysis results and time series analysis results. The immediate analysis results help identify abnormal states that exist in real time, while the time series analysis results reveal potential abnormal states through predictive analysis, which not only improves the accuracy of problem identification but also enhances the ability of predictive maintenance, thereby preventing potential failures in advance. Analyze the maintenance strategy according to the immediate analysis result and the time series analysis result, divide the maintenance strategy into instructions according to the execution priority, generate maintenance guidance information, realize the personalization and automation of maintenance activities, and ensure the pertinence and effectiveness of maintenance activities. Carry out remote maintenance guidance based on the maintenance guidance information, quickly respond to the equipment requirements, avoid unplanned downtime, and significantly improve the maintenance efficiency and the operating stability of the equipment.

[0087] Generally speaking, the embodiments of the present application combine Internet of Things technology to monitor the equipment status in real time, and through data analysis, provide a more objective basis for maintenance decision-making, can timely discover and prevent potential failures, and provide remote maintenance guidance, thereby realizing active monitoring and predictive maintenance, reducing the unplanned downtime of the equipment, not only improving the maintenance efficiency of the electric chain saw guide plate, but also improving the reliability and service life of the equipment.

[0088] Embodiment 2, as Figure 3As shown in the figure, the embodiment of the present application provides a remote maintenance guidance system for an electric chainsaw guide plate based on the Internet of Things. The system includes:

[0089] A monitoring data extraction module 10, which is used to connect to the Internet of Things database and extract target monitoring data. The target monitoring data includes at least one or more of temperature, vibration, tension, and wear data.

[0090] A monitoring data analysis module 20, which is used to align the acquisition time using the target monitoring data, analyze the monitoring data, and obtain an immediate analysis result and a time-series analysis result. The immediate analysis result is an abnormal state that exists in real time, and the time-series analysis result is a predicted abnormal state obtained by performing time-series analysis and prediction.

[0091] A maintenance guidance module 30, which is used to analyze the maintenance strategy according to the immediate analysis result and the time-series analysis result, split the maintenance strategy into instructions according to the execution priority, generate maintenance guidance information, and perform remote maintenance guidance based on the maintenance guidance information.

[0092] Furthermore, the system in the embodiment of the present application is also used to perform the following steps:

[0093] Construct a multi-level sensing module, including a first-level temperature and vibration sensing module, a second-level tension sensing module, and a third-level wear sensing module. There is an adaptive activation function between each level of sensing modules. When the adaptive activation function is satisfied, the higher-level sensing module is awakened to achieve step-by-step triggering; establish a data acquisition channel for the multi-level sensing module, send the sensed acquisition data through the data acquisition channel and generate an acquisition timestamp at the same time, and obtain the monitoring data of each level; establish a mapping association between the monitoring data of each level and the multi-level sensing module, and construct a multi-level Internet of Things database.

[0094] Furthermore, the system in the embodiment of the present application is also used to perform the following steps:

[0095] Taking temperature, vibration, tension, and wear as variables respectively, fitting the abnormal state of the variables through historical abnormal data, and determining the abnormal state threshold of each variable. The abnormal state threshold is a relationship coefficient including the load; extracting the top abnormal events according to the historical abnormal data, respectively evaluating the correlation of the temperature, vibration, tension, and wear, and determining the associated influence relationship between the top abnormal events and the temperature, vibration, tension, and wear; configuring activation selection conditions according to the abnormal state threshold and the associated influence relationship, and establishing the adaptive activation function. The activation selection condition is an independent condition of the abnormal state threshold or a union condition of the abnormal state threshold and the associated influence relationship.

[0096] Further, the monitoring data analysis module 20 of the embodiment of the present application is further configured to perform the following steps:

[0097] Generate an acquisition time according to the activation time of the sensing module, perform time alignment starting from the earliest acquisition time in the target monitoring data to obtain a time series matrix of the target monitoring data, where the horizontal elements of the time series matrix are data of different monitoring variables at the same acquisition time, and the vertical elements are data of the same monitoring variable at different acquisition times; according to the time series matrix, perform horizontal data analysis and evaluation to obtain the immediate analysis result; perform vertical data analysis and prediction according to the time series matrix to obtain the time series analysis result.

[0098] Further, the monitoring data analysis module 20 of the embodiment of the present application is further configured to perform the following steps:

[0099] According to the associated influence relationship between the top abnormal event and the temperature, vibration, tension, and wear, train a discriminator based on historical abnormal data, use the temperature, vibration, tension, and wear monitoring data in the horizontal data as the input variables of the discriminator, perform top abnormal event discrimination through the discriminator, output the identified abnormal event as the immediate analysis result, and at the same time compare the immediate analysis result with an abnormal warning threshold. When the abnormal warning threshold is reached, send a maintenance warning message, and the maintenance warning message includes the abnormal event.

[0100] Further, the monitoring data analysis module 20 of the embodiment of the present application is further configured to perform the following steps:

[0101] Fit the change trend of the variables in the vertical data to obtain a change trend coefficient; perform state change prediction according to the time series relationship based on the change trend coefficient to obtain a predicted time series variable; use the predicted time series variable as an input variable to perform top abnormal event discrimination through the discriminator, and output the predicted abnormal event as the time series analysis result, and the time series analysis result includes the predicted abnormal state and its predicted time.

[0102] Further, the maintenance guidance module 30 of the embodiment of the present application is further configured to perform the following steps:

[0103] Based on the instant analysis result and the timing analysis result, obtain the abnormal event and its abnormal status time; establish the mapping relationship between the abnormal event and the maintenance means, including the compensation efficiency of the maintenance means, obtain the maintenance compensation time limit according to the abnormal status time, and determine the maintenance strategy by using the maintenance compensation time limit and the compensation efficiency; perform a synchronization analysis on the maintenance strategy. When the synchronization condition is met, generate the maintenance guidance information according to the maintenance strategy; when the maintenance strategy does not meet the synchronization condition, perform a priority analysis on the maintenance strategy to determine the strategy priority, where the strategy priority represents the risk degree of the abnormal event; sort the maintenance strategies according to the priority, and perform instruction segmentation according to the sorting result to generate the maintenance guidance information.

[0104] Further, the maintenance guidance module 30 in the embodiment of the present application is further configured to perform the following steps:

[0105] Judge whether there is a shutdown strategy in the maintenance strategy. When there is one, analyze the compensation strategy of the corresponding abnormal event based on the timing relationship of the corresponding abnormal event; match the compensation strategy with the maintenance strategies of other priorities. When the match is successful, configure the matched maintenance strategy with the highest priority, and at the same time use the maximum of the maintenance strategy and the compensation strategy as the execution strategy, and reset the priority.

[0106] Through the foregoing detailed description of the method for remotely maintaining and guiding the chain saw guide plate based on the Internet of Things in this specification, those skilled in the art can clearly know the system for remotely maintaining and guiding the chain saw guide plate based on the Internet of Things in this embodiment. For the system disclosed in Embodiment 2, since it corresponds to the method disclosed in Embodiment 1, it has corresponding functional modules and beneficial effects. For the relevant parts, refer to the description in the method part.

[0107] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A remote maintenance guidance method for an electric chain saw guide plate based on the Internet of Things, characterized in that: include: Connecting to an Internet of Things database to extract target monitoring data, wherein the target monitoring data includes at least one or more of temperature, vibration, tension, and wear data; Using the target monitoring data to perform acquisition time alignment, perform monitoring data analysis, and obtain instant analysis results and time series analysis results, wherein the instant analysis results are abnormal states existing in real time, and the time series analysis results are predicted abnormal states obtained by performing time series analysis prediction; Analyze the maintenance strategy according to the instant analysis results and the timing analysis results, divide the maintenance strategy into instructions according to the execution priority, generate maintenance guidance information, and perform remote maintenance guidance based on the maintenance guidance information; Wherein, the connecting to the Internet of Things database previously includes: Construct a multi-level sensor module, including a first-level temperature sensor and a vibration sensor module, a second-level tension sensor module, and a third-level wear sensor module, wherein each level of the sensor module has an adaptive activation function, and when the adaptive activation function is satisfied, the higher-level sensor module is awakened to achieve step-by-step triggering; Establish a data acquisition channel for multi-level sensor modules, send sensor acquisition data through the data acquisition channel and generate acquisition timestamps at the same time, and obtain monitoring data at each level; Establish mapping associations between monitoring data at each level and multi-level sensor modules, and build a multi-level Internet of Things database; Wherein, the method further comprises: Taking temperature, vibration, tension and wear as variables respectively, fitting the abnormal state of the variables is performed through historical abnormal data to determine the abnormal state threshold of each variable, wherein the abnormal state threshold is a relationship coefficient including the load amount; Extracting top abnormal events according to the historical abnormal data, respectively evaluating the correlation of the temperature, vibration, tension, and wear, and determining the correlation and influence relationship between the top abnormal events and the temperature, vibration, tension, and wear; An activation selection condition is configured according to the abnormal state threshold and the associated influence relationship to establish the adaptive activation function, wherein the activation selection condition is an independent condition of the abnormal state threshold or a union condition of the abnormal state threshold and the associated influence relationship.

2. The method for remote maintenance guidance of an electric chain saw guide plate based on the Internet of Things as claimed in claim 1, characterized in that: The target monitoring data is used to align the acquisition time, perform monitoring data analysis, and obtain instant analysis results and time series analysis results, including: Generate the acquisition time according to the activation time of the sensor module, and perform time alignment with the earliest acquisition time in the target monitoring data as the starting point to obtain a time series matrix of the target monitoring data, wherein the horizontal elements of the time series matrix are data of different monitoring variables at the same acquisition time, and the vertical elements are data of different acquisition times of the same monitoring variable; According to the time series matrix, horizontal data analysis and evaluation are performed to obtain the instant analysis results; The longitudinal data analysis and prediction are performed according to the time series matrix to obtain the time series analysis result.

3. The method for remote maintenance guidance of an electric chain saw guide plate based on the Internet of Things as claimed in claim 2, characterized in that: According to the time series matrix, horizontal data analysis and evaluation are performed to obtain the instant analysis results, including: According to the correlation and influence relationship between the top abnormal event and the temperature, vibration, tension and wear, a discriminator is generated based on historical abnormal data training, and the temperature, vibration, tension and wear monitoring data in the lateral data are used as input variables of the discriminator. The top abnormal event is discriminated by the discriminator, and the identified abnormal event is output as the instant analysis result. At the same time, the instant analysis result is compared with the abnormal warning threshold. When the abnormal warning threshold is reached, maintenance warning information is sent, and the maintenance warning information contains the abnormal event.

4. The method for remote maintenance guidance of an electric chain saw guide plate based on the Internet of Things as claimed in claim 3, characterized in that: Performing longitudinal data analysis and prediction according to the time series matrix to obtain the time series analysis result includes: Fitting the changing trend of the variable according to the longitudinal data to obtain the changing trend coefficient; Based on the change trend coefficient, a state change prediction is performed according to a time series relationship to obtain a predicted time series variable; The predicted time series variable is used as an input variable to perform top abnormal event discrimination through the discriminator, and the predicted abnormal event is output as the time series analysis result, and the time series analysis result includes the predicted abnormal state and its predicted time.

5. The remote maintenance guidance method for electric chain saw guide plate based on Internet of Things as claimed in claim 1, characterized in that: The maintenance strategy is parsed according to the instant analysis result and the timing analysis result, and the maintenance strategy is divided into instructions according to the execution priority to generate maintenance guidance information, including: Obtaining abnormal events and their abnormal state time according to the instant analysis results and the time series analysis results; Establishing a mapping relationship between the abnormal event and the maintenance means, including the compensation efficiency of the maintenance means, obtaining a maintenance compensation time limit according to the abnormal state time, and determining the maintenance strategy using the maintenance compensation time limit and the compensation efficiency; Performing synchronization analysis on the maintenance strategy, and when a synchronization condition is met, generating the maintenance guidance information according to the maintenance strategy; When the maintenance strategy does not meet the synchronization condition, the maintenance strategy is prioritized to determine the strategy priority, wherein the strategy priority represents the risk level of the abnormal event; The maintenance strategies are sorted according to the priorities, instructions are segmented according to the sorting results, and the maintenance guidance information is generated.

6. The method for remote maintenance guidance of an electric chain saw guide plate based on the Internet of Things as claimed in claim 5, characterized in that: The maintenance strategies are sorted according to the priorities, and instructions are segmented according to the sorting results, and the method also includes: Determine whether the maintenance strategy has a shutdown strategy, and if so, analyze the compensation strategy of the corresponding abnormal event based on the time sequence relationship of the corresponding abnormal event; The compensation strategy is used to match maintenance strategies of other priorities. When the match is successful, the matching maintenance strategy is configured with the highest priority, and the maximum amount of the maintenance strategy and the compensation strategy is used as the execution strategy to reset the priority.

7. The electric chain saw guide plate remote maintenance guidance system based on the Internet of Things is characterized by: The system is used to execute the remote maintenance guidance method for the electric chain saw guide plate based on the Internet of Things according to any one of claims 1 to 6, comprising: A monitoring data extraction module, the monitoring data extraction module is used to connect to the Internet of Things database to extract target monitoring data, the target monitoring data at least includes one or more of temperature, vibration, tension, and wear data; A monitoring data analysis module, wherein the monitoring data analysis module is used to use the target monitoring data to perform acquisition time alignment, perform monitoring data analysis, and obtain instant analysis results and time series analysis results, wherein the instant analysis results are abnormal states existing in real time, and the time series analysis results are predicted abnormal states obtained by performing time series analysis prediction; A maintenance guidance module is used to analyze the maintenance strategy according to the instant analysis results and the timing analysis results, divide the maintenance strategy into instructions according to the execution priority, generate maintenance guidance information, and perform remote maintenance guidance based on the maintenance guidance information.

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