Airport self-service luggage check-in remote monitoring and maintenance system

Abstract

The invention relates to the technical field of airport automatic service, and discloses an airport self-service luggage check-in remote monitoring and maintenance system, which comprises a data acquisition module, a data transmission module, a data analysis module, an intelligent scheduling module, a remote maintenance module and a remote control module, the data acquisition module is used for acquiring equipment operation state data in real time; the data transmission module is used for transmitting the acquired equipment operation state data to a cloud end; the data analysis module is used for carrying out health assessment and fault prediction on the equipment based on the acquired equipment operation state data; the intelligent scheduling module is used for optimizing response time of maintenance tasks and scheduling of equipment maintenance personnel according to health assessment and fault prediction results; and the remote maintenance module is used for carrying out remote diagnosis and maintenance guidance on equipment faults. Through real-time monitoring, fault early warning and optimized maintenance task scheduling, the availability and maintenance efficiency of the equipment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of airport automation services, and specifically to an airport self-service baggage check-in remote monitoring and maintenance system. Background Art

[0002] Currently, airport self-service baggage check-in systems are widely used to improve baggage handling efficiency and passenger satisfaction. However, under the traditional operation and maintenance mode, the regular inspection and on-site repair strategies, although ensuring the basic operation of the equipment to a certain extent, are slow to respond to sudden failures and have low resource allocation efficiency, seriously affecting the airport operation efficiency.

[0003] In the existing self-service baggage check-in systems, the maintenance of equipment usually relies on manual inspection and regular overhaul, which makes the discovery and repair of equipment failures often lag behind. When the equipment fails, maintenance personnel need to detect problems on-site and repair them, resulting in a long downtime and unnecessary operation losses.

[0004] Currently, many systems lack effective means for predicting equipment failures and rely on manual experience for maintenance. When problems occur with the equipment, they are often discovered only after the failure has occurred, which not only increases the maintenance cost but also prolongs the equipment downtime. The lack of predictive data analysis and failure prediction leads to the suddenness of failures and the ineffectiveness of handling. The inefficiency of maintenance task scheduling.

[0005] In the prior art, the maintenance tasks of equipment are usually manually assigned, lacking intelligent scheduling. The priority of maintenance tasks and the allocation of personnel do not fully consider the health status of the equipment and the workload of maintenance personnel, resulting in uneven resource allocation or untimely task response sometimes. The manual scheduling method cannot flexibly respond to changes in the health status of the equipment, resulting in a long maintenance response time and affecting the overall availability of the equipment.

[0006] In the existing self-service baggage check-in systems, the function of remote maintenance is relatively weak. Maintenance personnel usually need to go to the equipment site for fault troubleshooting and repair, which not only consumes a lot of time but also may lead to low repair efficiency due to unskilled operation or insufficient remote support. On-site operation is restricted by the environment, personnel, and equipment, increasing the maintenance cost and reducing the timeliness of equipment repair.

[0007] Traditional self-service baggage check-in systems lack comprehensive monitoring and data analysis of equipment health. The operation data of the equipment is usually only used for basic operation records and is not fully utilized to predict and prevent the occurrence of failures. The equipment status monitoring in the prior art is usually relatively single, making it difficult to achieve comprehensive cross-equipment analysis and long-term failure prediction, and unable to effectively identify potential risks in advance, resulting in the suddenness and ineffectiveness of equipment failures.

[0008] Therefore, the present invention proposes an airport self-service baggage check-in remote monitoring and maintenance system to solve the deficiencies of the prior art. Summary of the Invention

[0009] In view of the deficiencies of the prior art, the present invention provides an airport self-service baggage check-in remote monitoring and maintenance system, which solves the problems that the self-service baggage check-in system lacks effective means for predicting equipment failures, lacks intelligent scheduling, and has relatively weak remote maintenance functions.

[0010] To achieve the above objectives, the present invention is realized through the following technical solutions: An airport self-service baggage check-in remote monitoring and maintenance system includes the following modules: A data acquisition module for collecting device operation status data in real time; A data transmission module for transmitting the collected device operation status data to the cloud; A data analysis module for performing health assessment and fault prediction on the device based on the collected device operation status data; An intelligent scheduling module for optimizing the response time of maintenance tasks and the scheduling of device maintenance personnel according to the health assessment and fault prediction results; A remote maintenance module for remote diagnosis and maintenance guidance of device failures; A remote control module for optimizing the interaction between maintenance personnel and the system.

[0011] Preferably, the data acquisition module includes: A temperature and humidity sensor for collecting temperature and humidity data of the device working environment; A vibration sensor for collecting the running vibration data of the device; A current sensor for collecting the current data of the device; A device status sensor for collecting the operation status data of the device.

[0012] Preferably, the data transmission module performs data transmission through a wireless communication protocol to ensure the real-time and stability of data transmission.

[0013] Preferably, the data analysis module analyzes the collected device status data based on the time series analysis method, uses the autoregressive integrated moving average model for fault prediction, and the autoregressive integrated moving average model predicts the fault trend through the combination of autoregression and moving average.

[0014] Preferably, the intelligent scheduling module uses the genetic algorithm to optimize the scheduling of maintenance tasks to minimize the response time of maintenance tasks and the device downtime.

[0015] Preferably, the remote maintenance module includes: An auxiliary diagnosis module for intelligently diagnosing equipment failures through a machine learning model and generating a fault diagnosis report; a remote guidance module for remotely guiding on-site maintenance personnel to troubleshoot and repair equipment failures through virtual reality and augmented reality technologies.

[0016] Preferably, the remote control module recognizes the voice commands of maintenance personnel through natural language processing technology, optimizes the interaction with the system based on semantic understanding technology, and controls the remote operation of the equipment.

[0017] Preferably, the data analysis module includes an equipment health assessment module for calculating the health index of the equipment based on the historical data and real-time data of the equipment.

[0018] Preferably, the remote maintenance module provides a virtual environment of the equipment through virtual reality technology for remote experts to diagnose equipment failures, and on-site maintenance personnel obtain real-time operation guidance through augmented reality devices.

[0019] The present invention also provides a fault warning method for an airport self-service baggage handling remote monitoring and maintenance system, including the following steps: Real-time data collection: Collect the operation data of the self-service baggage handling equipment through sensors; Data analysis and fault prediction: Based on the collected data, use a fault prediction model to evaluate the health of the equipment; Fault warning generation: When the health index of the equipment is lower than a preset threshold, generate a fault warning notice; Warning notice sending: Send a fault warning notice to maintenance personnel through the system; Maintenance task scheduling: Optimize the scheduling of maintenance tasks according to the warning notice and the health status of the equipment.

[0020] The present invention provides an airport self-service baggage handling remote monitoring and maintenance system. It has the following beneficial effects: 1. By adopting the technical solution of combining an intelligent scheduling module with equipment health assessment and fault prediction information, the present invention achieves the technical effect of automatically optimizing the scheduling of maintenance tasks and resource allocation. Compared with the technical solution of manually allocating maintenance tasks in the prior art, it solves the problems of inaccurate task scheduling and slow maintenance response speed, thus significantly improving the equipment maintenance efficiency and reducing the equipment downtime.

[0021] 2. By introducing the technical solution of combining natural language processing technology with the remote control module, the present invention achieves the technical effect of efficiently controlling the equipment through voice commands. Compared with the technical solution in the prior art that only relies on manual operation or remote operation interfaces, it solves the problems of complex operation steps and slow response speed, making the equipment fault repair faster and more intuitive, and improving the convenience and accuracy of remote operation.

[0022] 3. Through the collaboration of combining virtual reality and augmented reality technologies with the remote maintenance module, the present invention achieves efficient remote fault diagnosis and maintenance guidance effects. Compared with the existing method that only relies on text or video guidance, it solves the problem that on-site maintenance personnel cannot intuitively understand the location of faulty components and the repair steps, making the equipment fault repair process more accurate and reliable, thereby improving the maintenance quality and efficiency.

[0023] 4. By adopting a device health monitoring and fault prediction system based on big data analysis, the present invention achieves the technical effects of timely warning and early fault prevention. Compared with the existing solutions that cannot conduct comprehensive health assessment and early fault prediction, it solves the problems of sudden equipment faults and lagging maintenance response, improves the reliability of the equipment, and reduces the risk of equipment shutdown caused by sudden faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a schematic diagram of the system architecture of the remote monitoring and maintenance system of the present invention; Figure 2 is a flowchart of the steps of the fault warning method of the remote monitoring and maintenance system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings 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.

[0026] Please refer to the attached Figure 1 , the embodiment of the present invention provides an airport self-service baggage check-in remote monitoring and maintenance system, including a data acquisition module, a data transmission module, a data analysis module, an intelligent scheduling module, a remote maintenance module, and a remote control module.

[0027] The following will respectively describe each module of the system of the present invention in detail.

[0028] Data Acquisition Module In this embodiment, in order to achieve comprehensive monitoring and efficient fault prediction of self-service baggage handling equipment, the data acquisition module is designed as a multi-sensor integrated system, which can real-time monitor multiple key parameters of the equipment. The data acquisition module transmits the collected data to the cloud for subsequent analysis by the health assessment and fault prediction modules. Through multi-dimensional monitoring, the system can timely capture abnormalities in the equipment operation, thereby taking effective preventive measures.

[0029] In this embodiment, the data acquisition module includes multiple sensor units for collecting the operating status data of the device. Specifically, it includes: Temperature and humidity sensor: It is used to monitor the temperature and humidity of the environment around the device in real time. Temperature and humidity changes have a significant impact on the operation of the device. For example, high temperature may cause overheating of electrical components, while high humidity may trigger faults such as short circuits in the circuit. By monitoring the environmental conditions in real time, the system can promptly identify the potential impact of environmental factors on the device performance.

[0030] Vibration sensor: It is used to monitor the vibration data during the operation of the device in real time. The normal operation of the device should maintain a certain range of vibration frequency and amplitude. When the vibration exceeds the predetermined threshold, it usually indicates that there is wear or fault in the mechanical components of the device (such as motors, drive shafts, etc.). The vibration sensor can accurately record the vibration data, thus providing a basis for the early diagnosis of faults.

[0031] Current sensor: It is used to monitor the current load of the device. Abnormal changes in current are often precursors to device faults. For example, excessive current may cause circuit overload and may burn out the device; too small current indicates that the device cannot start or operate normally. The current sensor can monitor the current changes of the device in real time, and combined with the actual operating status of the load, it provides data support for the health assessment of the device.

[0032] Device status sensor: It is used to monitor the working status of the device, such as whether the device is in normal working condition, whether a fault or shutdown occurs, etc. Through these sensors, the system can promptly identify the abnormal status of the device, which serves as the basis for device fault prediction.

[0033] During the operation of the device, the data acquisition module will collect the operating data of the device in real time through sensors. The process of data acquisition includes the following steps: Real-time monitoring: Various sensors continuously monitor the operating data of the device, including temperature and humidity, vibration, current, and device status data, and transmit these data to the cloud server in real time.

[0034] Data transmission: The collected data is transmitted through wireless communication technologies (such as Wi-Fi, LoRa, 5G, etc.) to ensure the real-time and stability of the data. Through data transmission, all device status information can reach the data analysis module in a timely manner for further health assessment and fault prediction.

[0035] Data integration: The data transmitted to the cloud will be unified and integrated for the data analysis module to process. The data analysis module will combine the data of various sensors, comprehensively evaluate the health status of the device, and determine whether there is a potential fault risk.

[0036] To evaluate the health status of the device and conduct fault prediction, the output of the data acquisition module will serve as the basis for subsequent analysis. The health index H of the device is defined as the weighted average based on various sensor data. In this way, the system can comprehensively reflect the health status of the device. The calculation formula for the device health index is as follows: H = w 1 I t + w 2 V t + w 3 T t + w 4 H t + w 5 S t where H is the device health index at time t, representing the current health status of the device. w 1 , w 2 , w 3 , w 4 , w 5 are the weight coefficients for each item of data, representing the degree of influence of different data on the device health. The setting of the weight coefficients is based on the device type and the importance of each item of data for device fault prediction. For example, current and vibration data may have a greater impact on the device health, so the corresponding weight coefficients are higher.

[0037] I t is the current data at time t, representing the current load condition of the device, V t is the vibration data at time t, representing the vibration frequency or amplitude of the device, T t is the temperature data at time t, reflecting the temperature of the device operating environment. H t is the humidity data at time t, representing the humidity of the device operating environment, S t : the device status data at time t, usually indicating whether the device is in a normal working state.

[0038] According to the above formula, the device health index H is the weighted average of all sensor data. This health index can reflect whether the device is in a healthy state. If the device health index is lower than the preset threshold, it indicates that the device may have a fault or is about to have a fault, and the system will generate a fault warning in a timely manner and trigger relevant responses.

[0039] The work of the data acquisition module does not rely solely on the output of a single sensor, but through multi-sensor data fusion, combining the monitoring results of different sensors to comprehensively evaluate the health status of the device.

[0040] For example, the vibration sensor may detect abnormal vibrations within a certain frequency range, while the current sensor may capture current changes during the same period. The comprehensive analysis of these sensor data can provide more accurate fault prediction information.

[0041] Suppose at a certain moment, the calculated result of the health index H of the device is 0.65. The system determines that the current health status of the device is poor based on this value. Next, according to the health assessment of the device, various sensor data is further analyzed. For example: Current data I t = 5A, which is within the normal range.

[0042] Vibration data V t = 12mm / s, exceeding the normal vibration range, indicating that there is a mechanical abnormality in the device.

[0043] Temperature data T t = 50°C, exceeding the normal operating temperature. The device may malfunction due to overheating.

[0044] Humidity data H t = 90%, belonging to the too-high humidity range, which may cause a short-circuit problem in the device's circuit.

[0045] After comprehensive evaluation by the system, a fault warning is obtained based on the above data, instructing the maintenance personnel to check the vibration condition of the device and check whether the heat dissipation system of the device is working properly.

[0046] In addition to collecting basic temperature, humidity, vibration, current, and device status data, the data collection module can also expand other types of sensors as needed.

[0047] For example, adding a light sensor to monitor changes in external light of the device, or adding a gas sensor to monitor the concentration of harmful gases in the surrounding environment of the device. These additional data will further enrich the status evaluation of the device and improve the accuracy of fault prediction.

[0048] The data collection module in this embodiment comprehensively monitors the operating status of the device by integrating multiple sensors and transmits real-time data to the cloud. The system calculates the health index of the device based on various sensor data and conducts a comprehensive evaluation of the data through the weighted average method. These data can not only provide support for the health evaluation of the device, but also lay a foundation for subsequent fault prediction, intelligent scheduling, and remote maintenance.

[0049] Through this module, the system can achieve efficient and accurate fault diagnosis, and can give early warnings of possible device failures, avoid device downtime, and reduce maintenance costs.

[0050] Data transmission module The data transmission module of the present invention is a key component in the self-service baggage check-in remote monitoring and maintenance system, responsible for efficiently and stably transmitting various real-time data collected from the devices to the cloud to support subsequent data analysis and fault prediction. This module is closely integrated with the data acquisition module and the data analysis module to ensure that the operation data of the devices can be quickly and accurately uploaded to the cloud for processing.

[0051] In this embodiment, the data transmission module uses a wireless communication protocol to transmit the collected data. The wireless communication technology ensures the smooth cooperation of data among various modules by guaranteeing the efficient and stable transmission of device data.

[0052] The data transmission module supports the flexible selection of appropriate wireless communication protocols in different application scenarios of the device, such as Wi-Fi, LoRa, and 5G.

[0053] Wi-Fi communication protocol: Generally, when the communication distance between devices is short and the network environment is relatively dense, the Wi-Fi communication protocol is applicable. Wi-Fi provides high bandwidth and low-latency data transmission capabilities, enabling the efficient transmission of large amounts of data.

[0054] In an environment where devices are relatively concentrated, Wi-Fi can ensure the efficient transmission of data and guarantee data integrity during the transmission process.

[0055] LoRa communication protocol: As a low-power, long-distance wireless communication technology, LoRa is suitable for large-scale device monitoring.

[0056] In some embodiments, when the self-service baggage check-in devices are widely distributed and low-power, long-distance transmission is required, the LoRa communication protocol can effectively meet these needs. The characteristics of the LoRa protocol are long-distance transmission, low power consumption, and high coverage ability, which are particularly suitable for environments with a large number of devices and wide distribution.

[0057] 5G communication protocol: In some embodiments, when the number of devices is large and high-bandwidth, high-real-time data transmission is required, 5G communication technology provides a very efficient transmission solution.

[0058] Specifically, 5G supports low latency and high data transmission rates, and can support application scenarios with a large number of devices and data-intensive, ensuring the real-time response ability of the system.

[0059] The data transmission module realizes the real-time transmission of device-collected data through multiple wireless communication protocols. The workflow of data transmission is as follows: Data reception: Each sensor collects the real-time operation data of the device through the data acquisition module and performs preliminary preprocessing on the data. For example, the data obtained by temperature and humidity sensors, vibration sensors, current sensors, etc. through real-time monitoring is first preprocessed by the processing unit of the data acquisition module.

[0060] Data transmission: The preprocessed data is transmitted to the cloud through a wireless communication protocol (such as Wi-Fi, LoRa, or 5G). According to the device's deployment environment and transmission requirements, the system can automatically select the optimal communication protocol based on the current communication conditions. If the transmission distance is short and the network bandwidth requirement is large, the system may select the Wi-Fi protocol; if the device is deployed at a long distance or in a wide area, the LoRa protocol may be selected; if the network has high requirements for real-time performance and data bandwidth, the 5G protocol is used for transmission.

[0061] Data storage: After the data is transmitted to the cloud, the system stores the data in a distributed database for subsequent data analysis and fault prediction modules to use.

[0062] During the data transmission process, latency is an important indicator to measure data transmission performance. The data transmission latency Δt is determined by the transmission time t transmit , network latency t delay and queue latency t queue jointly. Therefore, the total data transmission latency can be expressed by the following formula: Δt = t transmit + t delay + t queue where Δt represents the total data transmission latency, t transmit represents the transmission time of the data packet from the sender to the receiver, and t delay represents the network latency, which is usually determined by factors such as network topology, router load, and signal interference.

[0063] The network latency varies under different communication protocols. For example, the Wi-Fi network latency is relatively low, while LoRa may have a longer transmission latency. t queue represents the queuing waiting time caused by heavy network load during the data packet transmission process. When the network is congested, the data packet will wait in the transmission queue, increasing the transmission latency.

[0064] t transmit is related to the size D of the data packet and the bandwidth B of the communication protocol. Specifically, t transmit can be calculated by the following formula: where D is the size of the data packet and B is the bandwidth of the communication protocol.

[0065] Suppose in a specific embodiment, the size of the data packet collected by the device is 500 Bytes, and Wi-Fi protocol is used for transmission. Suppose the bandwidth of Wi-Fi is 10 Mbps. According to the above formula, the data transmission time t transmit is calculated as follows: On this basis, suppose the network delay t delay = 2 ms, and the queuing time t queue = 1 ms. Then the total delay Δt is: Δt = 0.4 ms + 2 ms + 1 ms = 3.4 ms In this case, the total delay of data transmission is relatively low, which is suitable for application requirements with high real-time performance.

[0066] During the data transmission process, to ensure data security, all data transmitted through wireless communication protocols are encrypted using the Advanced Encryption Standard. Specifically, the system uses the AES-256 encryption algorithm to encrypt the data to ensure that the data during the transmission process will not be illegally intercepted or tampered with.

[0067] In addition, the data transmission module is also equipped with a CRC check mechanism. During the data transmission process, the data packet will go through CRC check to ensure that all data received at the receiving end has not been lost or damaged. If the data check fails, the system will automatically request retransmission.

[0068] According to different requirements, the data transmission module can also expand other communication technologies to improve transmission efficiency and stability. For example, in the case of a large number of devices and high bandwidth requirements, the MIMO (Multiple Input Multiple Output) technology can be used to improve the data transmission rate; when the device network environment is poor, the system can automatically switch to a low-power wide-area network technology (such as LoRa) to ensure reliable data transmission.

[0069] The data transmission module in this embodiment can flexibly select a suitable communication method according to the specific application scenario by adopting multiple wireless communication protocols (such as Wi-Fi, LoRa, 5G). It ensures the security and integrity of data through encryption and check mechanisms, and improves the reliability of data transmission through real-time monitoring and optimization of transmission delay. During the transmission process, the system can monitor the network status in real time to ensure the stability and efficiency of data, thus providing reliable support for subsequent data analysis and fault prediction.

[0070] Data Analysis Module In the self-service baggage check-in remote monitoring and maintenance system of the present invention, the data analysis module is responsible for real-time processing and analysis of the device operation data from the data acquisition module. Through the assessment of the device health status and fault prediction, this module can provide data-driven decision support, thereby optimizing the device maintenance strategy, reducing the device failure rate, and extending the device service life. The data analysis module is one of the cores of the entire system, and it is closely connected with the data acquisition module and the data transmission module to ensure the timely and effective health monitoring of the device operation.

[0071] In this embodiment, the data analysis module adopts a variety of analysis methods, including time series analysis, health assessment, and fault prediction, etc. Specifically, the data analysis module processes the device operation data obtained from the data acquisition module through an algorithm model, and then evaluates the current health status of the device and predicts the future fault risk of the device. The working principle and implementation method of the data analysis module will be described in detail below.

[0072] Generally, after the data collected by the data acquisition module is transmitted to the cloud through a wireless communication protocol, the data analysis module will first perform data preprocessing. The purpose of data preprocessing is to ensure data quality, remove noise and outliers, so as to provide high-quality input data for subsequent health assessment and fault prediction. Data preprocessing includes the following steps: Outlier removal: By performing statistical analysis on the data set, identify and remove outliers caused by sensor failures or environmental interferences. The existence of outliers will affect the accuracy and reliability of the data, so they need to be removed before analysis.

[0073] Missing value filling: Missing values may occur during data transmission or acquisition. To ensure data integrity, the data analysis module uses interpolation methods or other algorithms to fill in the missing values to ensure the continuity of the data set.

[0074] Data standardization: In order to enable the data of different sensors to be compared and fused, the data analysis module performs standardization processing on the data collected by different sensors. This step can unify the data dimension and ensure that the data can be calculated and analyzed on the same scale.

[0075] Through these preprocessing steps, the data analysis module can ensure that the processed data meets the requirements of subsequent analysis.

[0076] Specifically, the data analysis module will calculate the health index H of the device according to the real-time data of the device. This health index reflects the current health status of the device. The calculation method of the device health index is usually based on the data of multiple sensors, and the influence of various types of data is comprehensively evaluated by the weighted average method to evaluate the overall health status of the device. The specific calculation formula is as follows: H = w1 I t + w 2 V t + w 3 T t + w 4 H t + w 5 S t Under normal circumstances, the health index H is used to indicate the overall health status of the device. When H is lower than the set health threshold, the system will generate a fault warning to prompt the maintenance personnel to conduct necessary inspections and repairs.

[0077] As an option, the data analysis module also uses time series analysis methods to model the operating data of the device and predict possible future faults of the device.

[0078] Specifically, the data analysis module uses an autoregressive integrated moving average model to model the historical state data of the device and uses this model to predict the future operating state of the device. By capturing the patterns in the device's operating data, time series analysis can predict the fault trend of the device in advance.

[0079] The core of time series analysis is to model the relationship between the historical data y t of the device and the future predicted value y t+k . The time series of the device state can be represented by the following formula: y t = φ 1 y t-1 + φ 2 y t-2 + … + φ p y t-p + ∈ t + θ 1 ∈ t-1 + … + θ q ∈ t-q where y t is the device state data representing the moment t, p is the order of the autoregressive term, indicating the influence of past data on the current data, q is the order of the moving average term, indicating the influence of past errors on the current data, φ 1 , …, φ p are the coefficients of the autoregressive part, reflecting the influence of past device states on the current device state, θ 1 , …, θ q are the coefficients of the moving average part, indicating the influence of past prediction errors on the current device state, and ∈ t is the error term representing the moment t, usually representing random disturbances.

[0080] Through this model, the system can predict the future state of the device, thus giving early warnings of potential faults and helping maintenance personnel to intervene before the faults occur.

[0081] Specifically, after completing the health assessment and fault prediction, the data analysis module will generate corresponding fault warnings, which usually include the following information: Fault type: The system determines the fault type of the device through the fault prediction model, such as mechanical faults, electrical faults, etc.

[0082] Fault occurrence time: The system calculates the time range when the fault may occur according to the fault prediction model.

[0083] Maintenance suggestions: The system combines the historical data of the device, the fault type and the working state of the device to give maintenance suggestions to the maintenance personnel.

[0084] In a possible implementation, when the health index H of the device is lower than the set threshold, the system will automatically generate a fault warning notice in combination with the fault prediction result of the device. After receiving the notice, the maintenance personnel can conduct device inspections and maintenance according to the information in the warning.

[0085] In some embodiments, the data analysis module is not limited to using time series analysis and autoregressive integrated moving average models, but can also combine deep learning methods, such as neural networks, to further improve the accuracy of fault prediction.

[0086] The deep learning model can process more complex device operation data. Especially when the fault mode of the device is relatively complex and it is difficult for traditional statistical methods to accurately predict, machine learning algorithms such as neural networks can improve the accuracy of fault prediction by automatically learning fault characteristics.

[0087] Specifically, the neural network model can automatically learn the health mode of the device by training a large amount of historical device fault data. During the training process, the model will optimize the network weights according to the input-output relationship of the historical data, thereby improving the prediction ability of the device state.

[0088] The data analysis module in this embodiment realizes a comprehensive assessment of the device health status and fault prediction through a variety of technical means (including time series analysis, machine learning algorithms, deep learning methods, etc.). Through the calculation of the health index and the fault prediction model, the system can generate device fault warnings in a timely manner and provide necessary maintenance suggestions for the maintenance personnel. This module not only improves the reliability of device operation, but also provides strong decision-making support for the maintenance personnel, thus ensuring the continuous and stable operation of the self-service baggage handling equipment.

[0089] Intelligent scheduling module In the self-service baggage check-in remote monitoring and maintenance system of the present invention, as one of the core components, the intelligent scheduling module is responsible for automatically scheduling and optimizing the execution of maintenance tasks according to the health status of the equipment and the fault prediction information. This module can intelligently allocate maintenance tasks based on the real-time health data of the equipment, equipment fault prediction, and the status of maintenance personnel, ensuring that the equipment can be repaired in the shortest time, thereby improving the operation efficiency and availability of the equipment. Through algorithm optimization, the intelligent scheduling module reduces the intervention of manual operations and improves the automation and response speed of the entire system.

[0090] In this embodiment, the intelligent scheduling module depends on the equipment health index H and fault prediction information obtained from the data analysis module, and automatically generates and optimizes the maintenance task plan through algorithms. This module uses advanced scheduling algorithms to reasonably arrange and optimize the execution order of tasks according to factors such as the current state of the equipment, historical maintenance records, predicted fault time, availability of maintenance personnel, and skill matching degree. Through the scheduling function of this module, the downtime of the equipment can be minimized to the greatest extent, and the maintenance efficiency of the equipment can be improved.

[0091] Generally, the input data of the intelligent scheduling module comes from the data analysis module, which provides the equipment health index H and fault prediction information. Specifically, the equipment health assessment data and fault prediction data serve as the main basis for scheduling. At the same time, the system will also obtain information about maintenance personnel, including their skills, idle time, current task load, etc.

[0092] In some embodiments, the input data of the intelligent scheduling module includes: Health index data: The equipment health index H calculated by the data analysis module is used to determine whether the equipment needs maintenance.

[0093] Fault prediction data: The equipment fault prediction time provided by the data analysis module is used to predict the time when the equipment fails.

[0094] Maintenance personnel information: Information such as the availability of maintenance personnel, skill matching degree, and current workload.

[0095] Historical maintenance records: Used to evaluate the historical fault frequency and maintenance cycle of the equipment to help the system make more reasonable scheduling decisions.

[0096] Specifically, the intelligent scheduling module uses a genetic algorithm to automatically schedule maintenance tasks. The goal of this module is to minimize the downtime of the equipment and evenly distribute the workload of maintenance personnel as much as possible. The optimization goals are usually: Minimize response time: Try to reduce the time from the occurrence of a fault to the start of repair by maintenance personnel.

[0097] Balance the workload: Reasonably allocate maintenance tasks to avoid some maintenance personnel being overly busy while others are idle.

[0098] In a possible implementation, the optimization objective can be expressed by the following formula: where T i is the response time of the i-th maintenance task, that is, the time difference from task assignment to the start of maintenance, and N is the total number of all maintenance tasks.

[0099] In addition, the intelligent scheduling module also needs to consider the skill matching degree of maintenance personnel. Assuming the matching degree between the skills of maintenance personnel and the equipment type is σ i,j , the objective function of task scheduling can be expressed as: where σ i,j is the skill matching degree of the maintenance personnel for equipment j. The higher the skill matching degree, the higher the repair efficiency. T i,j represents the maintenance time of the maintenance personnel for equipment j.

[0100] Through this optimization algorithm, the system can achieve efficient scheduling of maintenance tasks, reduce the downtime of equipment, and optimize the workload of maintenance personnel.

[0101] As an option, the intelligent scheduling module supports dynamic adjustment of task priorities. The equipment health index H and the fault prediction data are used as the basis for priority adjustment. When the health index of the equipment is lower than the set threshold, or the predicted time of the fault is approaching, the intelligent scheduling module will automatically increase the priority of this task.

[0102] Specifically, the priority P t can be calculated by the following formula: P t = αH t + β·urgency(t failure ) where P t is the priority of task t, H is the health index of the equipment. The lower the health index, the higher the priority. urgency(t failure ) is the urgency of the fault, usually the difference between the fault prediction time and the current time. The smaller the difference, the higher the urgency. α and β are the weight coefficients of the health index and the urgency respectively, used to balance their influences.

[0103] Through this priority adjustment strategy, the system can automatically adjust the priority processing order of tasks according to the health status of the equipment and the urgency of the faults, ensuring that the most urgent faults are handled in a timely manner.

[0104] Specifically, the output tasks of the intelligent scheduling module include the following: Task assignment: The system assigns appropriate maintenance personnel to each maintenance task, matching according to personnel skills and workload.

[0105] Task priority: Based on the health status of the equipment and the results of fault prediction, the system assigns a priority to each task to ensure that urgent tasks are processed first.

[0106] Task schedule: The start time and completion time of each task are automatically arranged by the system to ensure the orderly progress of maintenance work and avoid time conflicts.

[0107] Suppose the health index H of the equipment is 0.4, lower than the set threshold of 0.5, and it is predicted that the equipment will fail within 3 hours. The intelligent scheduling module increases the priority of the maintenance task for this equipment and assigns the task to the most suitable maintenance personnel. Considering the idle time and skill level of the maintenance personnel, the system will also reasonably arrange tasks according to the workload of the maintenance personnel to ensure that the equipment can be repaired before it fails.

[0108] As an option, the intelligent scheduling module can also be integrated with other systems, such as the work order management system and the equipment management platform. Through integration with these systems, the intelligent scheduling module can provide more refined maintenance task management.

[0109] For example, when the system detects a device failure, it can automatically generate a work order and assign it to the relevant maintenance personnel, thus realizing an automated fault handling process.

[0110] In addition, the intelligent scheduling module can also continuously optimize the scheduling algorithm by learning historical maintenance data and equipment failure patterns to improve the efficiency and accuracy of scheduling.

[0111] The intelligent scheduling module in this embodiment can efficiently schedule and allocate maintenance tasks through an optimized algorithm and a priority adjustment mechanism. Through comprehensive analysis of factors such as the equipment health index, fault prediction information, maintenance personnel skills, and workload, the intelligent scheduling module ensures that the most urgent equipment problems can be processed in a timely manner, thereby reducing the equipment downtime and improving the overall operation and maintenance efficiency. While improving the operation efficiency of the self-service baggage handling equipment, this module reduces the need for manual intervention and enhances the automation and intelligence level of the system.

[0112] Remote maintenance module In the self-service baggage check-in remote monitoring and maintenance system of the present invention, the remote maintenance module provides remote fault diagnosis and repair guidance for equipment through advanced technical means. Through this module, the system can provide professional support to on-site maintenance personnel in a timely manner when equipment failures occur, thereby improving maintenance efficiency and reducing equipment downtime. The remote maintenance module works closely with the data analysis module and the data transmission module to ensure that the system can monitor and diagnose equipment in real time and accurately.

[0113] In this embodiment, the remote maintenance module utilizes artificial intelligence-assisted diagnosis, virtual reality, and augmented reality technologies to achieve remote fault troubleshooting and repair guidance for equipment. These technologies enable remote experts to guide on-site maintenance personnel to operate through a virtual environment or real-time video when equipment failures occur. The remote maintenance module can not only reduce the fault troubleshooting time but also improve the accuracy of equipment repair and avoid human errors.

[0114] Generally, when the data analysis module discovers equipment anomalies through the calculation of the equipment health index H, the remote maintenance module will initiate a fault diagnosis program.

[0115] The AI-assisted diagnosis module plays a key role in this process. Through machine learning algorithms, the AI-assisted diagnosis module can identify potential fault patterns from the historical data and real-time data of the equipment.

[0116] Specifically, the system will analyze the equipment status through an algorithm model based on the equipment health assessment results received from the data analysis module and automatically identify the fault type of the equipment.

[0117] For example, assume that the data analysis module analyzes the vibration data V t and current data I t of the equipment and finds that the vibration of the equipment exceeds the normal range. The AI-assisted diagnosis module will use the model trained with historical fault data to determine whether it is a mechanical fault or an electrical fault. Once the fault type is determined, the system will generate a fault report to help maintenance personnel quickly locate the problem and perform corresponding repairs.

[0118] As an option, the remote maintenance module utilizes virtual reality and augmented reality technologies to provide real-time equipment operation guidance for on-site maintenance personnel.

[0119] In this way, remote experts can view the detailed structure and fault location of the equipment in a virtual environment and mark on the virtual image of the equipment to instruct maintenance personnel to perform specific operations.

[0120] In a possible implementation, after on-site maintenance personnel wear AR glasses, remote experts can see the actual state of the equipment through real-time video and image transmission, and mark the fault location and maintenance steps on the equipment. This technology can reduce the operation errors of maintenance personnel and ensure the accurate and efficient completion of maintenance work.

[0121] For example, assume that the vibration sensor V of the equipment t shows abnormal vibration. The remote expert guides the maintenance personnel to disassemble the equipment through AR technology, check the motor components, and provide specific maintenance steps and precautions. In the AR glasses, the maintenance personnel will see the real-time markings and operation prompts of the expert. This method significantly improves the efficiency of fault handling.

[0122] Specifically, the remote maintenance module also optimizes the interaction between on-site personnel and the system through natural language processing technology. In this case, on-site maintenance personnel can input fault information or operation instructions by voice. The system will parse the instructions through voice recognition technology and give corresponding processing suggestions or operation steps according to the equipment status.

[0123] For example, on-site maintenance personnel may input by voice: "The vibration of the equipment exceeds the warning value. Is it necessary to stop the machine?" The system will automatically judge whether it is necessary to stop the machine and provide maintenance operation guidelines according to the real-time vibration data V t of the equipment and the health index H. Through natural language processing technology, the interaction between on-site personnel and the system is more efficient, reducing the need for manual input and improving the speed and accuracy of fault response.

[0124] In some embodiments, the remote maintenance module can also provide real-time feedback to help on-site maintenance personnel ensure the accuracy of maintenance work. When maintenance personnel perform equipment maintenance according to the guidance, the system will provide timely feedback according to the real-time status data of the equipment (such as temperature, vibration, current, etc.). For example, the system will verify whether the maintenance operation is correct according to the data feedback by the sensor to ensure that no new problems will occur during the equipment repair work.

[0125] Specifically, after on-site maintenance personnel disassemble the equipment according to the AR prompt, the system will monitor the change of the equipment status in real time and feedback whether the equipment repair is successful. If the equipment still has a fault, the system will prompt the maintenance personnel to conduct further inspections or re-operations to avoid omissions.

[0126] In this embodiment, there is a close cooperation relationship between the remote maintenance module and the data analysis module. The data analysis module generates the health index H and the fault prediction result through real-time analysis of the health status of the equipment, timely identifies potential faults of the equipment, and provides necessary support for the remote maintenance module. Through this cooperation, the remote maintenance module can provide more accurate fault diagnosis and repair guidance according to the data analysis results.

[0127] For example, after the health index H calculated by the data analysis module is lower than the threshold, the system will combine historical data and health assessment results to provide detailed fault diagnosis information for maintenance personnel. Based on this information, the remote maintenance module will guide the maintenance personnel to select the correct troubleshooting steps according to the equipment status to ensure that the equipment is repaired in a timely and effective manner.

[0128] Suppose the equipment has abnormal vibration data (V t > 15 mm / s), and the health index calculated by the data analysis module is H t = 0.3, which is lower than the set threshold H thresehold = 0.5. The data analysis module predicts that the equipment may have a mechanical failure within the next 3 hours. The system then activates the remote maintenance module. Through virtual reality and augmented reality technologies, remote experts provide on-site maintenance personnel with equipment disassembly steps and operation guidelines, and finally successfully troubleshoot the problem and restore the normal operation of the equipment.

[0129] As an option, the remote maintenance module can also be integrated with the equipment management platform and the work order management system. By automatically generating maintenance work orders and assigning them to on-site maintenance personnel, the remote maintenance module can provide better support for maintenance work. Through the work order management system, maintenance personnel can view the status of maintenance tasks in real time to ensure that all maintenance tasks are completed on time, further improving the operation and maintenance efficiency of the system.

[0130] The remote maintenance module in this embodiment provides remote fault diagnosis, repair guidance, and operation support for self-service baggage handling equipment through artificial intelligence, virtual reality, augmented reality, and natural language processing technologies. Through close cooperation with the data analysis module, the remote maintenance module can provide accurate troubleshooting suggestions based on the real-time health status of the equipment, reduce fault downtime, and improve the availability and operation and maintenance efficiency of the equipment.

[0131] This module provides efficient and accurate technical support for on-site maintenance personnel to ensure that the equipment can quickly resume normal operation.

[0132] Remote control module In the self-service baggage handling remote monitoring and maintenance system of the present invention, the remote control module is a key component to ensure that equipment failures can be quickly responded to and repaired. This module enables maintenance personnel to remotely control the equipment and quickly perform necessary operations to repair equipment failures, thereby minimizing equipment downtime and improving maintenance efficiency.

[0133] Through close cooperation with the data analysis module, the remote maintenance module, and the intelligent scheduling module, the remote control module can automatically generate control instructions based on the real-time health data and fault prediction information of the equipment to help maintenance personnel perform remote operations.

[0134] In this embodiment, the remote control module realizes the remote control of the self-service baggage check-in equipment through natural language processing technology, the equipment control interface and the intelligent control platform. Specifically, the remote control module allows on-site maintenance personnel to interact with the system through voice input or other remote operation interfaces, and regulates the equipment status through the system's instructions to assist in fault diagnosis, execute shutdown or startup operations, etc.

[0135] Through this module, maintenance personnel can control the equipment without being on-site, effectively improving the fault response speed and reducing the equipment downtime.

[0136] Generally, one of the core technologies of the remote control module is the voice command recognition and parsing based on natural language processing. On-site maintenance personnel can input fault information or control commands through voice. The system converts the voice information into text through voice recognition technology and further performs semantic analysis. Natural language processing technology can parse the input voice commands, understand their meanings, and perform appropriate operations according to the current status of the equipment.

[0137] Specifically, when the equipment fails, on-site maintenance personnel may say through voice: "The equipment vibration exceeds the standard. Is it necessary to shut down?" The remote control module first converts this voice command into text and extracts the key information in the command (such as "equipment vibration exceeds the standard" and "shutdown") through natural language processing technology. Then, the system will judge whether to shut down according to the vibration sensor data V t and the health index H of the equipment. If shutdown is required, the system will automatically generate a control command to remotely control the equipment to execute the shutdown operation.

[0138] As an option, the remote control module directly controls the various functions of the equipment by docking with the interface of the equipment control platform.

[0139] For example, on-site maintenance personnel can control the startup, stop, restart or adjustment of operation parameters of the equipment through the remote control module. When the equipment fails, the system will automatically or manually generate a control command based on the fault prediction information provided by the data analysis module to guide the equipment to execute relevant operations.

[0140] Specifically, the operations of the equipment can include: Shutdown operation: When the data analysis module detects a fault risk in the equipment, the remote control module will automatically issue a shutdown command to stop the operation of the equipment.

[0141] Restart operation: If the equipment has a system error or a temporary fault, the remote control module can command the equipment to restart to resume normal operation.

[0142] Adjust operating parameters: For some minor faults, the remote control module can adjust the operating parameters of the device (such as current, voltage, etc.) according to the device status to resume normal operation.

[0143] Through these control instructions, the remote control module can effectively handle device faults and ensure that the device can quickly resume operation.

[0144] Specifically, the remote control module not only supports manual operation, but also can automatically generate control instructions based on the results of fault prediction and device health assessment. When the device health index H is lower than the set threshold, and the fault prediction result shows that the device will fail in the short term, the system will automatically generate control instructions and execute corresponding operations through the remote control module.

[0145] For example, when the calculated result of the device health index is H = 0.4, lower than the preset threshold of 0.5, and the predicted fault occurrence time is within 3 hours, the remote control module will generate a shutdown instruction and execute it immediately. The automatic generation of control instructions greatly improves the response speed and reduces the time for device fault handling.

[0146] During the remote control process, the real-time feedback of the device status is very important. Through the real-time data connection with the device monitoring system, the remote control module can continuously track the device status and dynamically adjust the control instructions according to the operating status of the device.

[0147] Specifically, after the on-site maintenance personnel execute the control operation of the device through the remote control module, the system will monitor the change of the device status in real time and optimize the control strategy according to the information feedback by the device.

[0148] For example, if the remote control module instructs the device to shut down, the system will monitor the stop status of the device in real time to confirm whether the device has completely stopped running. If the device has not completely stopped, the system will automatically reissue the control instruction to ensure the device shuts down.

[0149] Specifically, the remote control module works closely with the remote maintenance module. The remote maintenance module provides operation guidance for on-site maintenance personnel through artificial intelligence-assisted diagnosis and virtual reality technology, while the remote control module is responsible for converting these guides into actual operation instructions and controlling the device to execute related tasks.

[0150] For example, when a fault occurs in the mechanical components of the device, the remote maintenance module guides the maintenance personnel to perform disassembly operations through virtual reality technology. The on-site maintenance personnel control the device through the remote control module, such as power off, disassemble the motor, etc., and complete the repair work according to the guidance of experts. Through this collaboration, the system can ensure the efficiency and accuracy of the repair work, and avoid operation errors and device damage.

[0151] Suppose the vibration data of the self-service baggage handling equipment is abnormal (V t = 12 mm / s), and the health index calculated by the data analysis module is H = 0.3, which is lower than the set threshold of 0.5. The system automatically generates a shutdown instruction and executes the equipment shutdown operation through the remote control module.

[0152] Meanwhile, the remote maintenance module provides troubleshooting guidance for maintenance personnel through virtual reality technology. The maintenance personnel execute relevant operations on the equipment through the remote control module and complete the repair task, finally successfully restoring the normal operation of the equipment.

[0153] As an option, the remote control module can also support more complex control operations. For example, the system can be extended to control advanced functions of the equipment through the network, such as the network settings of the equipment, data transmission rate, etc. Through these extensions, the remote control module can handle more complex fault and equipment operation requirements.

[0154] The remote control module in this embodiment realizes the remote control of the self-service baggage handling equipment by combining natural language processing, remote control interface and intelligent control platform. When the equipment fails, the system can automatically generate control instructions and execute them based on the health assessment and fault prediction results, helping on-site maintenance personnel quickly diagnose and repair equipment problems. The remote control module and the remote maintenance module work together to provide accurate troubleshooting instructions and operation steps, ensuring the efficient repair of the equipment, reducing the equipment downtime, and improving the operation and maintenance efficiency.

[0155] Please refer to Figure 2 The present invention also provides a fault warning method for an airport self-service baggage handling remote monitoring and maintenance system, including the following steps: S1. Real-time data acquisition: Collect the operation data of the self-service baggage handling equipment through sensors; S2. Data analysis and fault prediction: Based on the collected data, use a fault prediction model to evaluate the health of the equipment; S3. Fault warning generation: When the health index of the equipment is lower than the preset threshold, generate a fault warning notice; S4. Warning notice sending: Send the fault warning notice to the maintenance personnel through the system; S5. Maintenance task scheduling: Optimize and schedule maintenance tasks according to the warning notice and the health status of the equipment.

[0156] The method of this embodiment is executed based on the above system module embodiment, and its principle and technical effects are similar, so they will not be elaborated here.

[0157] In summary, the present invention provides a self-service baggage check-in remote monitoring and maintenance system. By integrating data acquisition, data analysis, intelligent scheduling, remote control, and remote maintenance modules, it realizes real-time monitoring, fault prediction, intelligent scheduling, and remote operation of the equipment. The system can automatically schedule maintenance tasks based on the health assessment and fault prediction information of the equipment, quickly respond to equipment failures, and achieve remote control of the equipment through the remote control module.

[0158] In addition, the system also combines artificial intelligence, virtual reality, and natural language processing technologies to optimize the operation efficiency and accuracy of maintenance personnel, reduce equipment downtime, and improve overall operation efficiency.

[0159] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An airport self-service baggage check-in remote monitoring and maintenance system, characterized in that: Includes the following modules: Data acquisition module, used to collect equipment operation status data in real time; A data transmission module, used to transmit the collected device operation status data to the cloud; A data analysis module, which performs health assessment and fault prediction on the equipment based on the collected equipment operation status data; Intelligent scheduling module, which optimizes the response time of maintenance tasks and the scheduling of equipment maintenance personnel based on health assessment and fault prediction results; Remote maintenance module, remote diagnosis and repair guidance of equipment failures; Remote control module to optimize the interaction between maintenance personnel and the system.

2. The airport self-service baggage check-in remote monitoring and maintenance system according to claim 1 is characterized in that: The data acquisition module comprises: Temperature and humidity sensor, used to collect temperature and humidity data of the equipment working environment; Vibration sensor, used to collect operating vibration data of the equipment; Current sensor, used to collect current data of the equipment; Equipment status sensor, used to collect equipment operating status data.

3. The airport self-service baggage check-in remote monitoring and maintenance system according to claim 1 is characterized in that: The data transmission module performs data transmission through a wireless communication protocol to ensure the real-time and stability of data transmission.

4. The airport self-service baggage check-in remote monitoring and maintenance system according to claim 1 is characterized in that: The data analysis module analyzes the collected equipment status data based on a time series analysis method, and uses an autoregressive integral moving average model to predict faults. The autoregressive integral moving average model predicts fault trends through a combination of autoregression and moving average.

5. The airport self-service baggage check-in remote monitoring and maintenance system according to claim 1 is characterized in that: The intelligent scheduling module uses a genetic algorithm to optimize the scheduling of maintenance tasks to minimize the response time of maintenance tasks and the equipment downtime.

6. The airport self-service baggage check-in remote monitoring and maintenance system according to claim 1 is characterized in that: The remote maintenance module comprises: Auxiliary diagnosis module, which is used to intelligently diagnose equipment faults through machine learning models and generate fault diagnosis reports; The remote guidance module is used to remotely guide on-site maintenance personnel to troubleshoot and repair equipment failures through virtual reality and augmented reality technologies.

7. The airport self-service baggage check-in remote monitoring and maintenance system according to claim 1 is characterized in that: The remote control module recognizes the voice commands of maintenance personnel through natural language processing technology, optimizes the interaction with the system based on semantic understanding technology, and controls the remote operation of the equipment.

8. The airport self-service baggage check-in remote monitoring and maintenance system according to claim 1 is characterized in that: The data analysis module includes an equipment health assessment module, which is used to calculate the health index of the equipment based on the historical data and real-time data of the equipment.

9. The airport self-service baggage check-in remote monitoring and maintenance system according to claim 1, characterized in that: The remote maintenance module provides a virtual environment for the equipment through virtual reality technology, allowing remote experts to diagnose equipment failures, and on-site maintenance personnel to obtain real-time operation guidance through augmented reality devices.

10. A fault warning method for an airport self-service baggage check-in remote monitoring and maintenance system, according to any one of claims 1 to 9, characterized in that: The following steps are involved: Real-time data collection: collect the operating data of self-service baggage check-in equipment through sensors; Data analysis and fault prediction: Based on the collected data, the fault prediction model is used to conduct health assessment of the equipment; Fault warning generation: When the health index of the device is lower than the preset threshold, a fault warning notification is generated; Early warning notification sending: send fault early warning notification to maintenance personnel through the system; Maintenance task scheduling: Optimize and schedule maintenance tasks based on early warning notifications and equipment health status.

Images