Equipment remote monitoring method suitable for smart campus

Abstract

The invention relates to the technical field of intelligent campus equipment monitoring, in particular to an intelligent campus equipment remote monitoring method, which comprises the steps of data acquisition module construction, edge computing node deployment and data processing, cloud deep analysis and decision support, and dynamic routing protocol implementation and optimization. Equipment operation data is collected through a distributed sensor network, edge computing nodes are used for primary processing, deep analysis is completed in combination with cloud resources, and a data transmission path is optimized through a dynamic routing protocol. According to the application, unified management of various types of equipment can be realized, the data transmission efficiency and the intelligent analysis capability are improved, high-frequency and multi-dimensional data processing requirements are met, the operation stability and reliability of the system are remarkably improved, and comprehensive technical support is provided for an intelligent campus scene.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of information technology and intelligent monitoring technology, specifically a device remote monitoring method suitable for smart campus. With the continuous deepening of smart campus construction, device remote monitoring technology has gradually become an important means to ensure the efficient operation of campus facilities. However, the existing device remote monitoring method still has certain limitations in the application of smart campus scenarios, especially in multi-device collaborative management, data transmission efficiency and intelligent analysis, which cannot fully meet the diversified needs of smart campus.

[0002] After searching, a patent document with publication number CN110472749B discloses a "device remote monitoring method and monitoring device", with a publication date of January 9, 2024. This technical solution receives the real-time working parameters of the target device through the monitoring device, and judges whether the device has a fault by combining the normal state parameter range, so as to realize timely alarm and fault handling. However, this solution is mainly suitable for independent monitoring of single devices, and when facing multiple types of devices in smart campus (such as security devices, teaching devices, environmental monitoring devices, etc.), the support capability for multi-device collaborative work is limited, and the realization of unified management and efficient scheduling has certain challenges. In addition, this solution relies heavily on the real-time and reliability of data transmission, but in complex network environments there may be risks of data delay or loss, which will affect the monitoring effect.

[0003] Another patent document with publication number CN119629211B discloses a "water treatment device remote monitoring and operation system and method", with a publication date of April 18, 2025. This technical solution realizes the all-round monitoring and intelligent operation management of water treatment equipment through the use of super-sensitive composite sensor matrix module, intelligent edge collector module and hybrid ad hoc network transmission module. However, this solution mainly faces specific field devices (such as water treatment equipment), and its application scene is relatively limited, making it difficult to be directly applied to the diversified device types in smart campus. At the same time, although this solution emphasizes the comprehensiveness of data collection and transmission, it lacks support for high-frequency, multi-dimensional data processing capabilities in smart campus, which may lead to a decrease in system operation efficiency. In addition, this solution mainly relies on cloud computing in intelligent analysis, lacks support for edge computing, and may have response delay problems when the data volume is large.

[0004] The aforementioned problems indicate that existing remote equipment monitoring methods still have significant shortcomings in smart campus scenarios, particularly in areas such as multi-device collaborative management, data transmission stability in complex network environments, and intelligent analysis capabilities. Therefore, this invention provides a remote equipment monitoring method suitable for smart campuses, aiming to improve the intelligence level and operational efficiency of smart campus equipment monitoring through unified multi-device management, efficient data transmission mechanisms, and edge-cloud collaborative computing, thereby meeting the diverse needs of smart campuses. Summary of the Invention

[0005] One of the objectives of this invention is to overcome the shortcomings of existing technologies and provide a remote monitoring method for equipment in smart campuses, which can manage various types of equipment in a unified manner and improve data transmission efficiency and intelligent analysis capabilities.

[0006] The second objective of this invention is to achieve efficient data processing and real-time response in complex network environments through a multi-level computing architecture, thereby improving the operational stability of the smart campus equipment monitoring system.

[0007] The third objective of this invention is to provide a device status analysis mechanism based on edge computing and cloud collaboration, which supports rapid processing of high-frequency, multi-dimensional data and meets the diverse needs of smart campus scenarios.

[0008] To achieve the above objectives, the technical principle adopted in this invention is as follows: Data on device operation is collected through a distributed sensor network, and edge computing nodes are used to perform preliminary data processing to reduce data transmission volume. Simultaneously, cloud computing resources are combined to perform in-depth analysis of key data, forming a multi-layered device status assessment model. Furthermore, a dynamic routing protocol is used to optimize data transmission paths, reducing network latency and packet loss rate, thereby further improving system reliability.

[0009] Based on the above technical principles, the present invention adopts the following technical solution: a remote monitoring method for equipment in smart campuses, characterized in that the method includes the following steps: a. construction of a data acquisition module; b. deployment of edge computing nodes and data processing; c. in-depth analysis and decision support of cloud computing resources; d. implementation and optimization of dynamic routing protocols.

[0010] A method for remote monitoring of equipment in smart campuses is characterized by the following specific steps: a. Construction of a data acquisition module, specifically: a-1. Deploying a distributed sensor network to collect equipment operating parameters; a-2. Connecting sensor nodes to edge computing nodes via wireless communication protocols; a-3. Embedding an adaptive sampling algorithm in the sensor nodes to adjust the sampling frequency according to the equipment operating status; b. Deployment and data processing of edge computing nodes, specifically: b-1. Deploying edge computing nodes in multiple areas within the campus, with each node covering equipment within a certain range; b-2. Using edge computing nodes to perform preliminary processing on the collected data, including data cleaning, anomaly detection, and feature extraction; b-3. Classifying the processed data according to priority, with high-priority data directly uploaded to the cloud and low-priority data stored locally; c. In-depth analysis and decision support of cloud computing resources, specifically: c-1. Establishing an equipment status assessment model in the cloud and performing comprehensive analysis by combining historical and real-time data; c-2. Using machine learning algorithms to predict equipment operating trends and generate early warning information; c-3. The analysis results are fed back to the edge computing nodes to guide the optimization of device operation; d. Implementation and optimization of dynamic routing protocols, the specific steps of which are: d-1. Establish dynamic routing protocols between edge computing nodes and select the optimal path based on network load and link quality; d-2. Update the routing table regularly to ensure the real-time performance and reliability of data transmission paths; d-3. Activate backup links when the network is congested to avoid data loss.

[0011] The specific implementation method of step a-1 above is as follows: a-1-1. Select the sensor type suitable for the smart campus environment, including temperature sensor, humidity sensor, vibration sensor, etc.; a-1-2. Install the sensor node in the key parts of the device and connect it by bolt fixing or adhesive; a-1-3. Configure the communication module of the sensor node to support Zigbee, LoRa or Wi-Fi protocols to ensure a stable connection with the edge computing node.

[0012] The specific implementation of step b-2 above is as follows: b-2-1. Load a data cleaning algorithm into the edge computing node to remove noisy and duplicate data; b-2-2. Use statistical analysis methods to detect outliers in the data and mark them as potential fault points; b-2-3. Extract key characteristic parameters of equipment operation, such as temperature change rate and vibration frequency, as the basis for subsequent analysis.

[0013] The specific implementation of step c-2 above is as follows: c-2-1. Train a support vector machine (SVM) model in the cloud to identify abnormal patterns in the device's operating status; c-2-2. Combine time series analysis algorithms to predict the device's operating trend over a future period; c-2-3. Generate early warning information based on the prediction results and notify relevant personnel via SMS or email.

[0014] The specific implementation of step d-1 above is as follows: d-1-1. Embed dynamic routing protocol software in the edge computing node, supporting OSPF or RIP protocols; d-1-2. Define routing rules, prioritizing links with low latency and high bandwidth; d-1-3. Periodically measure link quality, including packet loss rate, latency, and jitter, and dynamically adjust the routing table.

[0015] The dynamic routing protocol of this invention reduces the probability of network congestion by periodically updating the routing table. In actual testing, the protocol maintains low latency and packet loss rate even under high network load. Furthermore, the collaborative working mode between edge computing nodes and cloud computing resources significantly improves the overall system performance. Preliminary data processing at the edge computing nodes reduces the computational burden on the cloud while improving data transmission efficiency. In a smart campus scenario, this method supports unified management of various types of devices, including security equipment, teaching equipment, and environmental monitoring equipment, meeting diverse needs. (See attached figures.)

[0016] Figure 1 This is a schematic diagram of the system architecture of the method of the present invention, showing the connection relationship and data flow direction between the data acquisition module, edge computing nodes, cloud computing resources and dynamic routing protocol.

[0017] Figure 2 The flowchart illustrates the data processing steps for edge computing nodes, detailing the specific steps and logical order of data cleaning, anomaly detection, and feature extraction.

[0018] Figure 3 This diagram illustrates the implementation process of a dynamic routing protocol, showing the execution flow of routing rules, link quality measurement, and routing table updates.

[0019] The attached diagram is labeled as follows: 1. Data acquisition module; 2. Edge computing node; 3. Cloud computing resources; 4. Dynamic routing protocol; 5. Data cleaning module; 6. Anomaly detection module; 7. Feature extraction module; 8. Routing selection rules; 9. Link quality measurement module; 10. Routing table update module. Detailed implementation method.

[0020] like Figure 1The system architecture diagram of the method of the present invention is shown. The data acquisition module 1 is connected to the edge computing node 2 via a wireless communication protocol. The edge computing node 2 and the cloud computing resource 3 transmit data via a network. The dynamic routing protocol 4 runs between the edge computing nodes 2 to optimize the data transmission path. The data acquisition module 1 consists of multiple sensors, including temperature sensors, humidity sensors, and vibration sensors, which are installed in key parts of the device and connected by bolts or adhesive. The sensor nodes embed communication modules supporting Zigbee, LoRa, or Wi-Fi protocols to ensure stable connection with the edge computing node 2. The edge computing nodes 2 are distributed in multiple areas of the campus, each covering devices within a certain range. Each node contains a data cleaning module 5, an anomaly detection module 6, and a feature extraction module 7 for preliminary processing of the collected data. The cloud computing resource 3 is responsible for deep analysis and decision support, combining historical and real-time data to build a device status assessment model and using machine learning algorithms to predict device operating trends. The dynamic routing protocol 4 periodically measures the link quality through the link quality measurement module 9 and transmits the results to the routing table update module 10, thereby dynamically adjusting the routing table.

[0021] In practical applications, the construction process of data acquisition module 1 is as follows: First, select sensor types suitable for the smart campus environment and install sensor nodes in key parts of the equipment. For example, install temperature sensors near the air outlet of air conditioning equipment and vibration sensors on the casing of motor equipment. The adaptive sampling algorithm in the sensor nodes adjusts the sampling frequency according to the equipment's operating status. When the equipment is under high load, the sampling frequency is increased, while when the equipment is under low load, the sampling frequency is decreased to reduce the amount of data. These sensor nodes send the collected equipment operating parameters to the nearby edge computing node 2 via a wireless communication protocol. After receiving the data, the edge computing node 2 processes it as follows: Figure 2 As shown, the data cleaning module 5 first loads a data cleaning algorithm to remove noisy and duplicate data. Then, the anomaly detection module 6 uses statistical analysis methods to detect outliers in the data and marks them as potential fault points. Finally, the feature extraction module 7 extracts key characteristic parameters of equipment operation, such as temperature change rate and vibration frequency. These processed data are classified according to priority. High-priority data is directly uploaded to cloud computing resource 3, while low-priority data is stored locally for later use.

[0022] The specific implementation process of cloud computing resource 3 is as follows: A device status assessment model is established in the cloud, which combines historical and real-time data for comprehensive analysis. The cloud uses a support vector machine model to identify abnormal patterns in device operation and combines time series analysis algorithms to predict the device's operating trend over a future period. For example, when the vibration frequency of a teaching device continues to rise, the cloud will generate an early warning message and notify relevant personnel via SMS or email. In addition, the cloud feeds back the analysis results to edge computing node 2 to guide the optimization of device operation. For example, if the temperature change rate of a device exceeds the normal range, the cloud will suggest adjusting the device's operating parameters to reduce the temperature.

[0023] The implementation process of Dynamic Routing Protocol 4 is as follows: Figure 3 As shown, edge computing node 2 embeds dynamic routing protocol software supporting OSPF or RIP, defining routing rules to prioritize links with low latency and high bandwidth. Link quality measurement module 9 periodically measures link quality, including packet loss rate, latency, and jitter, and transmits the measurement results to routing table update module 10. Routing table update module 10 dynamically adjusts the routing table based on the link quality measurement results, ensuring the real-time performance and reliability of data transmission paths. In case of network congestion, backup links are activated to prevent data loss. For example, when the packet loss rate of a link exceeds a preset threshold, dynamic routing protocol 4 automatically switches to a backup link to ensure the continuity of data transmission.

[0024] In a smart campus scenario, the method of this invention supports unified management of various types of devices, including security equipment, teaching equipment, and environmental monitoring equipment. For example, projectors and air conditioning units installed in classrooms collect operating parameters through sensor nodes and send them to edge computing node 2. Edge computing node 2 performs preliminary data processing and uploads the data to cloud computing resource 3 for in-depth analysis. The cloud generates optimization suggestions based on the analysis results and feeds them back to edge computing node 2, thereby guiding the optimization of device operation. Simultaneously, a dynamic routing protocol 4 ensures efficient data transmission in complex network environments, reducing the probability of network congestion. Through the above steps, the method of this invention enables remote monitoring of smart campus devices, meeting diverse needs and improving the overall performance of the system. To better enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention is further supplemented below with a specific application scenario.

[0025] In a smart campus scenario, taking a projector and air conditioning unit in a classroom as examples, the system first collects equipment operating parameters through temperature sensors, vibration sensors, and other sensors in data acquisition module 1. These sensor nodes are installed in key parts of the equipment; for example, the temperature sensor is fixed near the air conditioner vent using bolts to ensure its stability, while the vibration sensor is glued to the projector motor housing. The sensor nodes embed communication modules supporting the Zigbee protocol and dynamically adjust the sampling frequency according to the equipment's operating status. When the projector is under high load, the adaptive sampling algorithm increases the sampling frequency to capture high-frequency operating data; while under low load, the sampling frequency decreases to reduce the amount of data. These sensor nodes transmit the collected equipment operating parameters to a nearby edge computing node 2 via a wireless communication protocol.

[0026] After receiving the data, edge computing node 2 activates its internal data cleaning module 5, loading a data cleaning algorithm to process the raw data, removing noise and duplicate data. Subsequently, the anomaly detection module 6 uses statistical analysis methods to further process the cleaned data, identifying outliers and marking them as potential fault points. For example, when the projector's vibration frequency exceeds a preset threshold, the data is marked as an anomaly and recorded. Finally, the feature extraction module 7 extracts key feature parameters from the processed data, such as temperature change rate and vibration frequency. These feature parameters are divided into high-priority and low-priority categories. High-priority data is directly uploaded to cloud computing resource 3, while low-priority data is stored locally for later use.

[0027] After receiving high-priority data from edge computing node 2, cloud computing resource 3 combines historical and real-time data to build a device status assessment model. The cloud utilizes a Support Vector Machine (SVM) model to identify abnormal patterns in device operation. For example, when the temperature change rate of an air conditioner continues to rise and exceeds the normal range, the cloud generates an early warning message and notifies relevant personnel via SMS or email. Simultaneously, the cloud uses time-series analysis algorithms to predict the device's operating trend over a future period. For example, if the projector's vibration frequency shows a continuous upward trend, the cloud predicts a potential risk of mechanical component wear and issues maintenance recommendations in advance. Furthermore, the cloud feeds back the analysis results to edge computing node 2 to guide device operation optimization. For example, if the air conditioner's temperature change rate is too high, the cloud suggests adjusting its operating parameters to lower the temperature, thereby extending the device's lifespan.

[0028] Throughout the data transmission process, Dynamic Routing Protocol 4 ensures efficient data transmission in complex network environments. Edge computing node 2 embeds dynamic routing protocol software supporting OSPF, defining routing rules that prioritize links with low latency and high bandwidth. Link quality measurement module 9 periodically measures link quality, including packet loss rate, latency, and jitter, and transmits the measurement results to routing table update module 10. For example, when the packet loss rate of a link exceeds a preset threshold, routing table update module 10 dynamically adjusts the routing table based on the link quality measurement results, switching to a backup link to ensure data transmission continuity. In network congestion situations, Dynamic Routing Protocol 4 can automatically activate backup links to avoid data loss due to network congestion.

[0029] Through the above steps, the method of this invention enables remote monitoring of smart campus equipment. For example, in a classroom setting, the operating parameters of the projector and air conditioning equipment are collected by sensor nodes, preliminarily processed by edge computing node 2, and uploaded to cloud computing resource 3 for in-depth analysis. The cloud generates optimization suggestions based on the analysis results and feeds them back to edge computing node 2, thereby guiding the optimization of equipment operation. Simultaneously, the dynamic routing protocol 4 ensures efficient data transmission in complex network environments, reducing the probability of network congestion. Through the collaborative working mode of edge computing and cloud computing, the system significantly improves data processing efficiency and operational stability, meeting the diverse needs of smart campus scenarios.

[0030] All content not described in detail in this specification is prior art known to those skilled in the art, and the model parameters of each electrical appliance are not specifically limited; conventional equipment can be used. Electrical control components not mentioned in this technical solution are not shown in the figures because they are prior art, and will not be described further here.

[0031] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for remote monitoring of equipment in a smart campus, characterized in that... The method includes the following steps: a. Construction of the data acquisition module (1): The operating parameters of the device are collected by deploying a distributed sensor network and the sensor nodes are connected to the edge computing nodes (2). An adaptive sampling algorithm is embedded in the sensor nodes to adjust the sampling frequency according to the device operating status; b. Deployment and data processing of the edge computing nodes (2): The collected data is preliminarily processed, including data cleaning, anomaly detection and feature extraction. The processed data is classified according to priority. High-priority data is uploaded to the cloud computing resources (3) and low-priority data is stored locally; c. In-depth analysis and decision support of the cloud computing resources (3): The device status assessment model is established in the cloud. The historical data and real-time data are combined for comprehensive analysis. Machine learning algorithms are used to predict the device operating trend and generate early warning information; d. Implementation and optimization of the dynamic routing protocol (4): A dynamic routing protocol is established between the edge computing nodes (2). The optimal path is selected according to the network load and link quality, and the routing table is updated regularly.

2. The remote equipment monitoring method for smart campuses according to claim 1, characterized in that... The distributed sensor network described in step a includes temperature sensors, humidity sensors, and vibration sensors. These sensors are installed in key parts of the equipment and connected by bolts or adhesive. The communication modules of the sensor nodes support Zigbee, LoRa, or Wi-Fi protocols.

3. The remote monitoring method for equipment in a smart campus according to claim 1, characterized in that... The dynamic routing protocol (4) described in step d periodically measures the link quality, including packet loss rate, latency and jitter, through the link quality measurement module (9), and the routing table update module (10) dynamically adjusts the routing table according to the measurement results, and enables backup links to avoid data loss when the network is congested.

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