Dumb resource health monitoring method based on low-power RFID sensors

Abstract

The present invention discloses a dumb resource health monitoring method based on low-power RFID sensors, which relates to the field of data processing technology. The method includes: configuring intelligent RFID tags on dumb resources; obtaining resource location coordinates and constructing a topological network, performing node clustering and density clustering analysis, and optimizing the distribution and location of enhanced readers; by configuring regional trigger thresholds and setting step sampling frequencies, the intelligent RFID tags sample data at appropriate times and transmit it to the enhanced readers, perform sleep-wake analysis, and generate collaborative warnings. The present invention solves the technical problem that existing technologies cannot perform intelligent real-time health monitoring of dumb resources, resulting in high monitoring energy consumption, low accuracy, and poor response speed. The present invention achieves the technical effect of realizing low-power real-time monitoring of dumb resources by integrating low-power RFID sensors, improving monitoring accuracy and response speed, and reducing energy consumption.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a dumb resource health monitoring method based on a low-power RFID sensor. Background Art

[0002] With the rapid development of the Internet of Things (IoT) technology, intelligent monitoring systems have been widely used in the management of various resources. However, many traditionally dumb resources, such as manhole covers, drainage pipes, and power facilities, cannot provide real-time status monitoring and fault warnings, resulting in high maintenance costs and potential safety hazards. These resources often lack built-in intelligent sensors and are unable to automatically report their operating status. While existing technologies offer some sensor-based monitoring methods, they often face challenges such as high data collection frequency, high energy consumption, and limited monitoring coverage, making it difficult to provide timely and comprehensive information on resource health. Summary of the Invention

[0003] This application provides a dumb resource health monitoring method based on low-power RFID sensors, which is used to solve the technical problems that the existing technology cannot perform intelligent real-time health monitoring of dumb resources, resulting in high monitoring energy consumption, low accuracy and poor response speed.

[0004] The present application provides a dumb resource health monitoring method based on low-power RFID sensors, the method comprising: configuring a smart RFID tag on the dumb resource, the smart RFID tag integrating a sensor group, the sensor group comprising a MEMS tilt sensor, a three-axis vibration sensor, a magnetic switch, and a water level sensor; obtaining the resource location coordinates of the dumb resource, constructing a topological network based on the resource location coordinates, and using the topological network to cluster nodes and establish adjacent nodes; performing density clustering analysis on the topological network, initially distributing enhanced readers based on the density clustering analysis results, optimizing the positions of the enhanced readers through the adjacent nodes, and executing communication binding between the adjacent node smart RFID tags and the enhanced readers based on the position optimization results; configuring the regional trigger threshold of the smart RFID tag using the enhanced reader and setting a step sampling frequency; after communicating the sampling data of the smart RFID tag to the enhanced reader, performing sleep and wake-up analysis using the enhanced reader to generate a collaborative warning.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0006] The dumb resource health monitoring method provided by the present application, which relies on low-power RFID sensors, relates to the field of data processing technology. By configuring intelligent RFID tags integrating multiple sensors on dumb resources, the resource location is obtained and a topological network is constructed, the position of the enhanced reader is optimized, and by setting the trigger threshold and sampling frequency, the intelligent RFID tags collect data at the appropriate time and transmit it to the reader, perform sleep and wake-up analysis, and generate collaborative early warnings. This solves the technical problem that the existing technology cannot perform intelligent real-time health monitoring of dumb resources, resulting in high monitoring energy consumption, low accuracy and poor response speed. It realizes the technical effect of low-power real-time monitoring of dumb resources by integrating low-power RFID sensors, improving monitoring accuracy and response speed and reducing energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A flowchart of a dumb resource health monitoring method based on a low-power RFID sensor provided in an embodiment of the present application;

[0009] Figure 2 A schematic diagram of the process of performing sleep and wake-up analysis in the dumb resource health monitoring method based on a low-power RFID sensor provided in an embodiment of the present application;

[0010] Figure 3 This is a schematic diagram of the structure of a dumb resource health monitoring device based on a low-power RFID sensor provided in an embodiment of the present application.

[0011] Description of the accompanying drawings: smart RFID tag configuration module 11, node clustering module 12, reader / writer communication binding module 13, step sampling frequency setting module 14, sleep / wake-up analysis module 15. DETAILED DESCRIPTION

[0012] This application provides a dumb resource health monitoring method based on low-power RFID sensors, which is used to solve the technical problems that the existing technology cannot perform intelligent real-time health monitoring of dumb resources, resulting in high monitoring energy consumption, low accuracy and poor response speed.

[0013] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0014] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0015] Example 1, as Figure 1 As shown, the present application provides a dumb resource health monitoring method based on a low-power RFID sensor, the method comprising:

[0016] P10: Configure a smart RFID tag on the dumb resource. The smart RFID tag is integrated with a sensor group, which includes a MEMS tilt sensor, a three-axis vibration sensor, a magnetic switch, and a water level sensor.

[0017] Specifically, smart RFID tags are deployed on dumb resources (such as manhole covers). These tags integrate multiple sensor groups to provide real-time monitoring and feedback on the resource's status. Smart RFID tags combine radio frequency identification technology with multiple sensor functions. They not only store and transmit data but also interact with the surrounding environment to continuously monitor the health of the resource.

[0018] Among them, the sensor group includes four main sensors: MEMS tilt sensor, three-axis vibration sensor, magnetic switch and water level sensor. MEMS tilt sensor is a high-precision sensor that can detect the tilt angle of an object. For example, in manhole cover applications, when the manhole cover is displaced or deformed due to external forces, the tilt sensor can monitor and report abnormalities in real time, helping managers to determine whether the manhole cover is in the normal position, thereby avoiding potential safety hazards. The three-axis vibration sensor is responsible for monitoring the vibration of the manhole cover. It can measure the intensity and frequency of vibration in three directions and capture any abnormal vibration sources in time. For example, the manhole cover may be subjected to impact from vehicle running, earthquakes or other external forces. The three-axis vibration sensor can effectively sense this abnormal vibration and trigger an alarm mechanism to alert managers.

[0019] The magnetic switch detects changes in the magnetic field to determine whether the manhole cover is open or closed. If the cover is illegally opened or tampered with, the magnetic switch detects this change and transmits a signal to the smart RFID tag, providing safety monitoring. Furthermore, the water level sensor monitors water level fluctuations around the manhole cover. During rainy seasons or other unusual environmental conditions, the water level sensor can effectively detect rising water levels, preventing flooding or leaks and ensuring the normal operation of the manhole cover area.

[0020] By integrating these four sensors, smart RFID tags can provide multi-dimensional data support for the daily monitoring of facilities such as manhole covers. They can not only monitor their location, vibration, open and closed status, and water level changes, but also issue timely warnings when these factors exceed normal ranges. This sensor array configuration not only enhances the intelligent management of resources, but also provides more accurate and timely information for equipment maintenance and troubleshooting, thereby improving the efficiency and safety of facility management. Through continuous monitoring and real-time data transmission, smart RFID tags make these traditionally dumb resources more intelligent and flexible.

[0021] P20: Obtain the resource location coordinates of the dummy resource, construct a topological network based on the resource location coordinates, and use the topological network to cluster nodes and establish adjacent nodes.

[0022] It should be understood that in this step, the location coordinates of the dummy resources can be obtained using GPS or other positioning technologies. The goal is to assign precise geographic coordinates to each resource (such as a manhole cover). Once these coordinates are obtained, a topological network can be constructed using this information. A topological network is a network structure with interconnected nodes, constructed based on the geographic location of resources and their relationships. Each node represents a resource, and the connections between nodes represent their physical or logical proximity.

[0023] The process of building a topological network can begin by determining the spatial relationships between resources using their coordinate information. These resources may not be evenly distributed, so when building a topological network, it is necessary to analyze resource locations to determine which resources have strong connections and which resources may be remote or independent. The structure of a topological network not only considers the physical location of resources but also their communication requirements, monitoring scope, and management requirements.

[0024] Once the topology network is initially constructed, it can be used to perform node clustering. Node clustering involves dividing network nodes into groups based on distances or other similarity metrics. Nodes within each group are close to each other, while nodes between groups are more distant or less connected. This clustering analysis ensures that nodes in the same area or interconnected nodes are grouped together, enabling more efficient resource management and data transmission. For example, adjacent manhole covers might be grouped together, allowing for more efficient communication paths and reducing data transmission latency and energy consumption.

[0025] Finally, the results of the cluster analysis can be used to establish neighboring nodes. This process not only effectively categorizes nodes within the topological network, but also identifies which nodes are adjacent and can communicate directly or share data. This information is crucial for the subsequent deployment and location optimization of enhanced readers, as it determines which nodes can share information through short-range communication and which nodes require long-range communication to maintain contact. By clarifying the relationships between neighboring nodes, the overall performance of the network can be ensured, improving the responsiveness and accuracy of the resource monitoring system.

[0026] In general, the core of the P20 step is to accurately obtain the geographic coordinates of resources and use this information to build a reasonable topological network. By clustering nodes and establishing adjacent nodes in the topological network, a clear framework is provided for subsequent data transmission, information sharing, and equipment maintenance, helping to improve system efficiency and reliability.

[0027] P30: Perform density clustering analysis on the topological network, initially distribute enhanced readers based on the density clustering analysis results, optimize the positions of enhanced readers through the adjacent nodes, and perform communication binding between the adjacent node smart RFID tags and the enhanced readers based on the position optimization results.

[0028] Specifically, we first perform density cluster analysis on the topological network. Density cluster analysis assesses the density of nodes in the network to determine which nodes have high connectivity or data traffic requirements. In practice, high-density areas may represent close connections between multiple resource nodes, which typically require more efficient data transmission and stronger communication support. Through density cluster analysis, nodes in the topological network can be divided into different density levels, ensuring that high-density and low-density areas receive different resource allocation and management.

[0029] Next, based on the results of the density cluster analysis, the initial distribution of enhanced readers is determined. Enhanced readers are devices with enhanced performance and wider coverage, capable of interacting with and processing data from smart RFID tags. In a topological network, the initial distribution of enhanced readers typically depends on the density of nodes within the network. In high-density areas, enhanced readers need to be deployed more frequently or more efficiently to ensure smooth communication between all nodes and improve data transmission reliability and efficiency. In low-density areas, enhanced readers can be distributed more sparsely, reducing resource waste and system burden.

[0030] Next, the relationships between adjacent nodes in the topological network are used to optimize the locations of the enhanced readers. Position optimization involves optimizing the positions of the enhanced readers to ensure coverage of more adjacent nodes while ensuring stable and efficient data transmission. This process involves further analysis of the network topology, specifically considering factors such as the distance between nodes, communication signal strength, and data transmission path optimization. The goal of this optimization is to ensure that each enhanced reader is optimally positioned to minimize signal interference, reduce latency, and improve overall system responsiveness.

[0031] Finally, based on the location optimization results, communication binding is performed between adjacent smart RFID tags and enhanced readers. The core of this step is to ensure that each smart RFID tag establishes a stable communication connection with its adjacent enhanced reader. Through this communication binding, the enhanced reader can exchange data with adjacent smart RFID tags and perform remote monitoring and management when necessary. The communication binding process may also include technical measures such as communication channel selection and encryption security settings to ensure information security and transmission reliability.

[0032] In summary, the core of step P30 is to rationally partition the network topology through density clustering analysis, deploy enhanced readers based on the analysis results, and optimize their positioning by analyzing the relationships between adjacent nodes. Ultimately, by integrating the optimized reader layout with communication between smart RFID tags, efficient and stable resource monitoring and data transmission can be achieved, thereby improving the overall performance and management effectiveness of the system.

[0033] Furthermore, density cluster analysis is performed on the topological network. Step P30 in the embodiment of the present application further includes:

[0034] P31: Establish a spatiotemporal density function and use it to perform density cluster analysis:

[0035] ;

[0036] in, is the space-time density function value, representing the position time The comprehensive monitoring demand intensity, Represents the total number of dummy resources, is the dummy resource index number, is the spatial distance weight coefficient, is the static risk weight coefficient, is the time decay weight coefficient, is the dynamic risk weight coefficient, Characterization The spatial coordinates of a dummy resource, Represents the spatial coordinates of the point to be evaluated, Characterizes the spatial influence radius, Characterization The static risk coefficient of a dumb resource, Characterizes the maximum static risk value of the system, Characterize the time impact window, Characterization The time when the most recent event of a dumb resource occurred, Characterize the time-dynamic risk function, Characterizes the maximum dynamic risk value of the system.

[0037] Optionally, a spatiotemporal density function can be constructed based on the spatial location and event time of dummy resources. This function combines multiple factors, including spatial, static risk, time decay, and dynamic risk, to calculate the comprehensive monitoring demand intensity for each resource point in a specific time and space. The values ​​of this spatiotemporal density function can then be used to perform density clustering analysis on nodes (dummy resources) across the entire network. By comparing the spatiotemporal density of each node, it is possible to identify areas or nodes with higher risk density and areas with more dispersed resources, thereby optimizing resource deployment and data transmission paths.

[0038] This function not only assesses static risk but also takes into account dynamic changes over time, comprehensively evaluating the risk of each resource in the system. Analysis of the spatiotemporal density function can provide a basis for subsequent enhanced reader deployment, node optimization, and resource monitoring, enabling more efficient resource management and fault warning.

[0039] This spatiotemporal density function provides a method for comprehensively evaluating spatiotemporal risks and resource demand intensity. It can effectively cluster resources based on spatial and temporal factors, helping the system to more accurately identify high-risk areas and take corresponding resource allocation and management measures, thereby optimizing the operating efficiency of the entire monitoring network.

[0040] Furthermore, based on the density cluster analysis results, the enhanced readers are initially distributed. In this embodiment of the application, step P30 further includes:

[0041] P32: Obtaining the location attributes of the dummy resource, the location attributes including street location attributes and in-park location attributes; P33: Generating cross-region clustering constraints based on the location attributes; P34: Adjusting the density clustering analysis result constraints based on the cross-region clustering constraints.

[0042] In a possible embodiment of the present application, to ensure optimal configuration of the network structure and data transmission, the initial distribution of the enhanced readers and writers and the constraint adjustment of the density cluster analysis may be further refined.

[0043] First, the location attributes of dumb resources are acquired. This process involves comprehensively acquiring the spatial location information of dumb resources (such as manhole covers and sensors), including street location attributes and in-park location attributes. Street location attributes refer to the specific location of the resource within a city's streets or road network, typically determined through a geographic information system (GIS). In-park location attributes refer to the specific coordinates of the resource within a specific park or enclosed area. In different application scenarios, these two location attributes will affect resource management methods and information transmission paths. Street locations may face more complex traffic or environmental impacts, while in-park resources may have higher integration and communication requirements.

[0044] After obtaining the location attributes of dummy resources, cross-region clustering constraints can be generated based on these location attributes. Because different regions (such as streets and campuses) may have different communication requirements, resource management, and environmental conditions, density clustering analysis requires appropriate constraints on cross-region clustering. For example, resources within street areas may require more frequent monitoring and data updates due to high traffic flow, while resources within campuses may require more centralized management and data processing. By using cross-region clustering constraints, the system ensures that resources within different regions are appropriately allocated and clustered according to their actual needs, thereby optimizing data transmission efficiency and reducing system burden.

[0045] Next, the constraints of the density clustering analysis results are adjusted according to the cross-region clustering constraints. The purpose of this step is to further consider the cross-regional characteristics and adjust the clustering results on the basis of the original density clustering analysis. By introducing cross-region clustering constraints, the results of the density clustering analysis are not only based on the spatial distribution density of resources, but also take into account the special needs of the region. For example, high-density areas in street areas may require more reader support, while low-density areas in the park can reduce the number of readers, thereby achieving reasonable allocation and optimization of resources. Through this constraint adjustment, the system can effectively avoid unreasonable excessive concentration or dispersion of resources and improve the performance and response speed of the entire network.

[0046] Overall, by conducting in-depth analysis of resource location information and optimizing density clustering results based on cross-region clustering constraints, we ensured the proper distribution of enhanced readers across different regions. This approach not only improved system resource utilization but also ensured efficient and stable data transmission and device management in various scenarios.

[0047] P40: Use the enhanced reader to configure the area trigger threshold of the smart RFID tag and set the step sampling frequency.

[0048] Specifically, the enhanced reader is used to configure the area trigger threshold of the smart RFID tag and set the step sampling frequency of the tag.

[0049] First, the regional trigger threshold is a key parameter that controls when smart RFID tags begin collecting data. When certain environmental parameters monitored by the smart RFID tag (such as vibration, temperature, and humidity) reach the preset regional trigger threshold, the tag activates and begins sampling. These trigger thresholds can be adjusted based on the specific application scenario. For example, when the vibration intensity of a manhole cover exceeds a certain value, the smart RFID tag will trigger and begin collecting data for real-time status monitoring. This effectively avoids frequent sampling when no abnormalities occur, saving energy and reducing the burden of data processing. By controlling these thresholds, the enhanced reader ensures that the smart RFID tag initiates monitoring at the appropriate time.

[0050] The configuration of regional trigger thresholds typically requires consideration of resource characteristics and environmental variations. For example, certain areas may be subject to frequent external impacts (such as streets with heavy traffic), so the trigger thresholds in these areas may be set lower. In contrast, in more static environments (such as campuses), the trigger thresholds can be set higher to avoid false triggers.

[0051] Secondly, a stepped sampling frequency strategy refers to a strategy in which smart RFID tags adjust their sampling frequency based on different environmental conditions or time periods. In practical applications, the sampling frequency does not need to remain constant but can be adjusted dynamically based on specific needs. For example, under normal circumstances, when no anomalies occur with a manhole cover, the RFID tag's sampling frequency can be set low to reduce energy consumption and storage burden. However, when a trigger threshold is activated (such as when significant vibration or other anomalies occur), the sampling frequency can be increased to collect more data in real time for detailed analysis and early warning.

[0052] The stepped sampling frequency can be adjusted based on both time of day and environmental changes. For example, a higher sampling frequency may be required during peak daytime hours, when resources are more frequently used; whereas, the frequency can be lowered at night. When the external environment changes (such as increased vibration, rising temperature, or fluctuating water levels), the RFID tag's sampling frequency should automatically increase to ensure that important information is captured promptly and to avoid omissions.

[0053] By configuring regional trigger thresholds and stepped sampling frequencies, the enhanced reader can flexibly manage the operating status of smart RFID tags, optimizing the timing and frequency of data collection. This ensures that accurate data is provided at critical moments while maximizing energy and resource savings. This not only improves the system's intelligence but also enhances the device's adaptive capabilities, enabling it to dynamically adjust its operating mode based on actual needs, thereby achieving more efficient monitoring and data transmission.

[0054] P50: After the sampling data of the smart RFID tag is communicated to the enhanced reader, the enhanced reader is used to perform sleep and wake-up analysis to generate a collaborative warning.

[0055] Further, such as Figure 2 As shown, step P50 in the embodiment of the present application also includes:

[0056] P51: Use the enhanced reader to perform trigger analysis of abnormal events and generate the trigger probability of abnormal events; P52: Establish a wake-up strategy based on the event attributes of the current abnormal event and the trigger probability; P53: Synchronize the wake-up strategy to the smart RFID tag that is connected to the enhanced reader, and perform wake-up monitoring of the smart RFID tag using the wake-up strategy to establish the wake-up monitoring result; P54: Use the wake-up monitoring result to perform regional collaborative early warning identification and generate the collaborative early warning.

[0057] It should be understood that step P50 processes the sampled data of the smart RFID tag through the enhanced reader, and then performs sleep and wake-up analysis and generates a collaborative warning.

[0058] First, the sampled data from the smart RFID tag is transmitted to the enhanced reader via communication. This data may include different types of sensor output, such as vibration, inclination, and water level data. After the data is transmitted, the enhanced reader performs sleep-wakeup analysis to determine whether the RFID tag needs to be awakened for more frequent sampling or data processing. This process aims to ensure that the smart RFID tag operates only when necessary, thereby saving energy and avoiding unnecessary frequent data collection. For example, the enhanced reader performs trigger analysis for abnormal events and generates trigger probabilities based on the analysis results. For example, if the system detects vibration signals from heavy machinery operating or the inclination of a manhole cover exceeds a preset threshold, the enhanced reader will perform trigger analysis for these abnormal conditions and assess the probability of these events occurring. This trigger probability calculation is typically based on historical data, real-time sensor data, and its changing trends to determine the risk of the abnormal event occurring. Based on these trigger probabilities, the system can effectively predict potential risks and respond when necessary.

[0059] Next, based on the attributes of the current abnormal event (such as vibration caused by illegal heavy machinery construction, theft, manhole cover overturning, or abnormal water levels), as well as the corresponding trigger probability, the system establishes a wake-up strategy. A wake-up strategy determines how to wake up the RFID tag and change its sampling frequency when a potential anomaly is detected. For example, if the trigger probability is high, the system might set a lower trigger threshold and increase the sampling frequency based on the specific characteristics of the abnormal event. If the probability of an abnormal event is low, the system might delay wake-up to reduce unnecessary data transmission and energy consumption.

[0060] Furthermore, the wake-up policy is synchronized with the smart RFID tag connected to the enhanced reader, and wake-up monitoring of the smart RFID tag is performed according to the policy. Specifically, the enhanced reader transmits the wake-up policy to the smart RFID tag, enabling the tag to perform sampling tasks according to the policy. This policy dynamically adjusts the operating state of the smart RFID tag, reducing the sampling frequency during normal conditions and increasing the data collection frequency or performing more detailed monitoring tasks when potential anomalies arise. This helps improve resource management efficiency while ensuring that the system can provide accurate data at critical moments.

[0061] Finally, the wake-up monitoring results are used to identify and generate coordinated regional early warnings. Using the wake-up monitoring results of smart RFID tags, enhanced interrogators can identify abnormal conditions within a specific area and, by analyzing data from multiple adjacent nodes, determine whether there are systemic risks. For example, abnormal vibration or changes in the tilt of multiple manhole covers could indicate a potential problem (such as ground collapse, theft, or facility damage). In this case, the enhanced interrogator can comprehensively analyze data from different nodes, identify potential risks, and generate coordinated early warnings, prompting relevant managers to take timely action.

[0062] Through these steps, the system intelligently adjusts RFID tag monitoring frequency through precise wake-up strategies and analysis of abnormal events, ensuring timely acquisition of detailed data and accurate early warning when potential anomalies occur. This dynamic monitoring and early warning system not only reduces resource waste but also provides timely information at critical moments, effectively preventing facility damage or other safety issues, ensuring efficient and safe system operation.

[0063] Furthermore, step P52 of the embodiment of the present application further includes:

[0064] P52-1a: Determine whether the current abnormal event is a vibration abnormal event; P52-2a: If the current abnormal event is a vibration abnormal event, establish a vibration fingerprint library based on the sampling data; P52-3a: Use the vibration fingerprint library as the event's own attribute, and combine the trigger probability to establish a vibration threshold, vibration sampling frequency, and vibration response sensitivity coefficient, and use the vibration threshold, vibration sampling frequency, and vibration response sensitivity coefficient as a wake-up strategy.

[0065] Optionally, in the further refinement process, it is first possible to determine whether the current abnormal event is a vibration abnormal event. If the abnormal event is confirmed to be related to vibration after analysis of sensor data, the subsequent operation will be processed according to the vibration characteristics. If the current abnormal event is determined to be a vibration abnormality, the system will establish a vibration fingerprint library based on the collected vibration data. The vibration fingerprint library analyzes the characteristics of different vibration events and stores these characteristics in the form of "fingerprints", which is convenient for subsequent comparison with newly collected vibration data to determine whether the event belongs to the expected abnormal type. For example, through the vibration fingerprint library, the system can identify different vibration patterns and distinguish whether it is a device failure, illegal operation or environmental interference.

[0066] Next, the vibration fingerprint library is used as the inherent attribute of the vibration anomaly event, combined with the previously calculated trigger probability, to develop a wake-up strategy. These strategies specifically include setting a vibration threshold, a vibration sampling frequency, and a vibration response sensitivity coefficient. The vibration threshold determines when the vibration intensity exceeds a certain value, at which point the RFID tag will be activated and begin more frequent monitoring. This threshold must be appropriately set based on the type of event to ensure that the system does not frequently wake the tag up for minor vibrations. The vibration sampling frequency determines how frequently the RFID tag collects data during a vibration event. In the event of severe vibration, the system needs to increase the sampling frequency to obtain more detailed data, while in the event of minor vibration, the system can reduce the sampling frequency to save energy and reduce data volume. Furthermore, the vibration response sensitivity coefficient measures the system's sensitivity to vibration anomalies. A higher sensitivity allows the system to respond to even minor vibration changes, while a lower sensitivity triggers a response only in the event of stronger vibrations. The system adjusts these parameters based on the specific situation, ensuring that vibration anomalies are monitored with the appropriate sensitivity and frequency when necessary.

[0067] By synchronizing these parameters as wake-up strategies to the smart RFID tags communicating with the enhanced reader, the system can flexibly manage the working status of the RFID tags, ensuring timely data collection when abnormal vibration events occur, and adjusting the data collection frequency and sensitivity according to the severity of the event to improve the accuracy and efficiency of anomaly detection.

[0068] Furthermore, step P52 of the embodiment of the present application further includes:

[0069] P52-1b: Determine whether the current abnormal event is an abnormal tilt event; P52-2b: If the current abnormal event is an abnormal tilt event, perform additional judgment on whether there is displacement linkage; P52-3b: After redefining the attributes of the event itself according to the additional judgment results, use the trigger probability to establish a wake-up strategy.

[0070] In a possible embodiment of the present application, it is also necessary to determine whether the current abnormal event is an abnormal tilt event. If the current abnormal event is determined to be an abnormal tilt event, the system will then perform an additional displacement linkage additional judgment. The purpose of this additional judgment is to confirm whether the change in tilt is accompanied by the displacement of the equipment. For example, when the tilt of a manhole cover changes, it may not only be due to some slight external force, but also accompanied by displacement or deformation of the manhole cover. Through this judgment, the system can more accurately determine whether there is a potential displacement problem, and then decide whether a more stringent response to the event is required.

[0071] Once the additional discrimination of displacement linkage is completed, the attributes of the event itself will be redefined based on the discrimination results. This process involves re-evaluating the event, taking into account the nature of the tilt change and whether it is related to displacement, and then adjusting the attributes of the event to ensure that subsequent responses are more accurate and targeted. Based on this redefined event attribute, a new wake-up strategy is established in combination with the previously calculated trigger probability. This wake-up strategy sets the corresponding threshold, sampling frequency, and response sensitivity based on the attributes of the specific event and its triggering possibility. For example, if the abnormal tilt event is accompanied by a significant displacement change, a lower trigger threshold is set, and the sampling frequency and sensitivity are increased to ensure that these potential safety hazards can be detected and responded to in a timely manner.

[0072] This approach allows for flexible handling of tilt anomaly events, with additional discrimination further refining the event attributes and response strategies. This not only allows for efficient identification and handling of tilt anomaly events, but also allows for tailored monitoring and response strategies to ensure greater accuracy and optimal resource utilization.

[0073] Furthermore, step P52 of the embodiment of the present application further includes:

[0074] P52-1c: Determine whether the current abnormal event is a water level abnormal event; P52-2c: If the current abnormal event is a water level abnormal event, establish a wake-up strategy according to the water level timing signal.

[0075] Optionally, during further refinement, it's necessary to determine whether the current abnormal event is a water level abnormality. Water level abnormalities often occur in areas involving water bodies, such as drainage systems near culverts and manhole covers. If the water level exceeds the normal range, it can cause equipment damage or other potential risks. When a water level abnormality is detected, a corresponding wake-up strategy is established based on the water level timing signal.

[0076] Water level time series signals are data sequences that show water level changes over time. These data can reflect water level fluctuations, rising rates, and potential trends. Based on these signals, the severity of the current water level anomaly can be determined, and whether immediate action is needed. If the water level anomaly is severe, or if the trend indicates further rise, monitoring sensitivity can be increased by increasing the sampling frequency or lowering the trigger threshold, ensuring timely detection of water level changes and effective early warning.

[0077] In this way, a wake-up strategy for water level anomalies can be established based on water level timing signals, precisely controlling the sampling frequency and response mode of the smart RFID tags. When water level anomalies occur, a sensitive trigger mechanism and appropriate response strategy ensure timely detection of potential hazards and provide accurate data support to management personnel, allowing for rapid intervention and ensuring the safe operation of equipment and facilities.

[0078] Furthermore, to generate a collaborative warning, step P50 in the embodiment of the present application further includes:

[0079] P51s: Determine whether the warning level of the collaborative warning is higher than the autonomous trigger threshold; P52s: If the warning level is higher than the autonomous trigger threshold, generate a real-time response instruction; P53s: According to the real-time response instruction, transmit the collaborative warning to the data center in real time through the enhanced reader / writer to execute the early warning alarm.

[0080] Specifically, the collaborative warning generation process can be further refined to establish a collaborative warning generation and response mechanism. First, determine whether the collaborative warning level is higher than the autonomous trigger threshold. The collaborative warning level is typically determined by the system based on a comprehensive analysis of data from multiple monitoring nodes, indicating the urgency or risk level of the event. The autonomous trigger threshold is a set critical value that represents the standard for triggering a response when the system makes an autonomous judgment. If the warning level exceeds this threshold, it indicates that the current abnormal event or risk has reached a level of severity that requires an immediate response.

[0081] Next, if the warning level is determined to be higher than the autonomous trigger threshold, a real-time response instruction is generated. The generation of this instruction signifies that the system has identified an abnormal situation and decided to take appropriate action to address the risk or potential harm. This real-time response instruction may include a series of control operations, such as mobilizing emergency equipment, activating backup systems, or notifying management for on-site handling. This generation of an instruction ensures a timely response to high-risk events.

[0082] Finally, based on the generated real-time response instructions, the enhanced reader transmits the coordinated warning to the data center in real time, and a warning is issued. The enhanced reader sends detailed information about the coordinated warning (including the specific location, time, and warning level of the event) to the data center in real time, allowing relevant managers or monitoring systems to receive and make prompt decisions. The data center can then perform further processing based on the received warning information, such as initiating emergency response procedures, conducting subsequent data analysis, or issuing an alert. This ensures the rapid flow of warning information, allowing relevant personnel to obtain the most accurate risk information in a timely manner and take necessary countermeasures to prevent further deterioration.

[0083] Through these steps, when high-risk events are identified, early warning information can be quickly generated and transmitted, ensuring efficient emergency response and decision support. This mechanism not only improves the response speed of dumb resource health monitoring, but also strengthens the ability to predict and handle emergencies, ensuring a quick and accurate response to various emergencies.

[0084] Furthermore, step P51s of the embodiment of the present application further includes:

[0085] P51-1s: If the warning level is not higher than the autonomous trigger threshold, a retention instruction is generated; P51-2s: The collaborative warning is stored in the enhanced reader according to the retention instruction; P51-3s: When the enhanced reader is connected to the handheld device at any time point, the collaborative warning is transmitted to the handheld device and a pre-alarm is issued.

[0086] Optionally, the process for handling situations where the warning level is not higher than the autonomous trigger threshold can be further refined. First, determine whether the warning level is not higher than the autonomous trigger threshold. If the warning level is lower than or equal to the set autonomous trigger threshold, the current abnormal event or risk is deemed not to require an immediate response, and therefore the emergency handling process will not be triggered. Accordingly, a retention instruction can be generated, instructing the system to save the current collaborative warning information for further observation and evaluation during subsequent monitoring.

[0087] Next, the coordinated warning information is stored in the enhanced reader according to the retention instructions. This storage process ensures that even when the warning level is low, the relevant data is not lost and is temporarily retained in the system. As the core device for information processing and communication, the enhanced reader can store this warning information until it can be further processed or transmitted through appropriate channels.

[0088] Finally, when the enhanced reader establishes a connection with the handheld device at any point in time, the coordinated warning is transmitted to the handheld device and a pre-alarm is issued. This mechanism ensures that warning information is delivered to the operator via the handheld device at the appropriate time, especially when the operator is close to the relevant equipment. The handheld device receives the latest coordinated warning information and displays it on its interface, providing real-time alerts, helping personnel understand the current risk situation and take appropriate countermeasures.

[0089] This process is designed to ensure that all events are recorded and stored, even at low alert levels, and that this information can be promptly transmitted to relevant personnel at a later time when appropriate. This flexible storage and transmission mechanism ensures that the system maintains a high level of emergency response capabilities across a wide range of urgency levels, maximizing the safety of facilities and personnel.

[0090] Furthermore, after generating the collaborative warning, the embodiment of the present application further includes step P60, which further includes:

[0091] P61: Interactively obtain monitoring data of the smart RFID tag and use the monitoring data to build a health database of the dumb resource; P62: Perform dumb resource health management based on the health database.

[0092] In a possible embodiment of the present application, after the collaborative warning is generated, subsequent data management and health monitoring can be further performed. A health database is constructed through monitoring data of the smart RFID tags, and health management of dumb resources is performed based on the database.

[0093] First, monitoring data from smart RFID tags is interactively acquired. This data may include outputs from multiple sensors, such as temperature, humidity, vibration, and tilt, reflecting the health of dumb resources (such as manhole covers, equipment, and pipelines) over time and under different conditions. Smart RFID tags, integrated with sensors, collect various health data from these resources in real time and transmit this data to enhanced readers or data centers. This process allows for the acquisition of all relevant monitoring data, ensuring its integrity and accuracy through data interaction.

[0094] Next, we use this monitoring data to build a health database for dumb resources. This database is a comprehensive, centralized repository containing health status data for all dumb resources, including historical data, real-time monitoring data, and abnormal event records. By integrating this data, the health database provides fundamental support for subsequent resource health management.

[0095] Furthermore, health management of dumb resources is performed based on the health database. The goal of health management is to ensure that all dumb resources remain in good working condition throughout their service life and to reduce safety hazards and operational interruptions caused by resource failures. By analyzing the data stored in the health database, we can identify which resources have health risks and require maintenance or inspection. We can also schedule maintenance plans based on the health of the resources, perform preventive maintenance, and predict the probability of resource failures, allowing proactive action. This health management mechanism optimizes resource lifespan, reduces maintenance costs, and improves the efficiency and reliability of the entire monitoring system.

[0096] In summary, the embodiments of the present application have at least the following technical effects:

[0097] This application configures smart RFID tags with integrated multiple sensors on dumb resources to obtain resource locations and build a topological network, optimize the location of enhanced readers, set trigger thresholds and sampling frequencies, and enable smart RFID tags to collect data at appropriate times and transmit it to the reader, perform sleep-wake-up analysis, generate collaborative warnings, and achieve intelligent monitoring and real-time response to the health of dumb resources.

[0098] The technical effect of achieving low-power real-time monitoring of dumb resources by integrating low-power RFID sensors, improving monitoring accuracy and response speed and reducing energy consumption is achieved.

[0099] The second embodiment is based on the same inventive concept as the dumb resource health monitoring method relying on low-power RFID sensors in the previous embodiment. Figure 3 As shown, the present application provides a dumb resource health monitoring device based on a low-power RFID sensor. The device and method embodiments in the present application are based on the same inventive concept. The device includes:

[0100] The smart RFID tag configuration module 11 is used to configure a smart RFID tag on a dumb resource. The smart RFID tag is integrated with a sensor group, which includes a MEMS tilt sensor, a three-axis vibration sensor, a magnetic switch, and a water level sensor.

[0101] The node clustering module 12 is used to obtain the resource location coordinates of the dummy resources, construct a topological network according to the resource location coordinates, and perform node clustering using the topological network to establish adjacent nodes.

[0102] The reader-writer communication binding module 13 is used to perform density clustering analysis on the topological network, initially distribute the enhanced readers according to the density clustering analysis results, optimize the position of the enhanced readers through the adjacent nodes, and execute communication binding between the adjacent node smart RFID tags and the enhanced readers according to the position optimization results.

[0103] The step sampling frequency setting module 14 is used to configure the area trigger threshold of the smart RFID tag using the enhanced reader and set the step sampling frequency.

[0104] The sleep-wake-up analysis module 15 is used to communicate the sampled data of the smart RFID tag to the enhanced reader, and then use the enhanced reader to perform sleep-wake-up analysis to generate a collaborative warning.

[0105] Furthermore, the reader-writer communication binding module 13 is further configured to perform the following steps:

[0106] A spatiotemporal density function is established and density cluster analysis is performed using the spatiotemporal density function:

[0107] ;

[0108] in, is the space-time density function value, representing the position time The comprehensive monitoring demand intensity, Represents the total number of dummy resources, is the dummy resource index number, is the spatial distance weight coefficient, is the static risk weight coefficient, is the time decay weight coefficient, is the dynamic risk weight coefficient, Characterization The spatial coordinates of a dummy resource, Represents the spatial coordinates of the point to be evaluated, Characterizes the spatial influence radius, Characterization The static risk coefficient of a dumb resource, Characterizes the maximum static risk value of the system, Characterize the time impact window, Characterization The time when the most recent event of a dumb resource occurred, Characterize the time-dynamic risk function, Characterizes the maximum dynamic risk value of the system.

[0109] Furthermore, the reader-writer communication binding module 13 is further configured to perform the following steps:

[0110] Acquire location attributes of dummy resources, the location attributes including street location attributes and park location attributes; generate cross-region clustering constraints based on the location attributes; and perform constraint adjustment on the density clustering analysis result based on the cross-region clustering constraints.

[0111] Furthermore, the sleep / wake-up analysis module 15 is further configured to perform the following steps:

[0112] The enhanced reader is used to perform trigger analysis of abnormal events and generate a trigger probability of the abnormal event; a wake-up strategy is established based on the event attributes of the current abnormal event and the trigger probability; the wake-up strategy is synchronized to the smart RFID tag that is communicatively connected to the enhanced reader, and the wake-up monitoring of the smart RFID tag is performed using the wake-up strategy to establish a wake-up monitoring result; the wake-up monitoring result is used to perform regional collaborative early warning identification and generate the collaborative early warning.

[0113] Furthermore, the sleep / wake-up analysis module 15 is further configured to perform the following steps:

[0114] Determine whether the current abnormal event is a vibration abnormal event; if the current abnormal event is a vibration abnormal event, establish a vibration fingerprint library based on the sampled data; use the vibration fingerprint library as the event's own attribute, and combine the trigger probability to establish a vibration threshold, vibration sampling frequency, and vibration response sensitivity coefficient, and use the vibration threshold, vibration sampling frequency, and vibration response sensitivity coefficient as a wake-up strategy.

[0115] Furthermore, the sleep / wake-up analysis module 15 is further configured to perform the following steps:

[0116] Determine whether the current abnormal event is an abnormal tilt event; if the current abnormal event is an abnormal tilt event, perform additional judgment on whether there is displacement linkage; redefine the event attributes according to the additional judgment results, and establish a wake-up strategy using the trigger probability.

[0117] Furthermore, the sleep / wake-up analysis module 15 is further configured to perform the following steps:

[0118] Determine whether the current abnormal event is a water level abnormal event; if the current abnormal event is a water level abnormal event, establish a wake-up strategy according to the water level timing signal.

[0119] Furthermore, the sleep / wake-up analysis module 15 is further configured to perform the following steps:

[0120] Determine whether the warning level of the collaborative warning is higher than the autonomous trigger threshold; if the warning level is higher than the autonomous trigger threshold, generate a real-time response instruction; according to the real-time response instruction, transmit the collaborative warning to the data center in real time through the enhanced reader / writer to execute the early warning alarm.

[0121] Furthermore, the sleep / wake-up analysis module 15 is further configured to perform the following steps:

[0122] If the warning level is not higher than the autonomous trigger threshold, a retention instruction is generated; the collaborative warning is stored in the enhanced reader according to the retention instruction; when the enhanced reader is connected to the handheld device at any time point, the collaborative warning is transmitted to the handheld device and a pre-alarm is issued.

[0123] Furthermore, the system also includes a health management module for performing the following steps:

[0124] Interactively obtain monitoring data of the smart RFID tag, and use the monitoring data to build a health database of the dumb resource; and perform dumb resource health management based on the health database.

[0125] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0126] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0127] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A dumb resource health monitoring method based on a low-power RFID sensor is characterized in that: The method comprises: A smart RFID tag is configured on the dumb resource. The smart RFID tag is integrated with a sensor group, which includes a MEMS tilt sensor, a three-axis vibration sensor, a magnetic switch, and a water level sensor. Obtaining the resource location coordinates of the dummy resource, constructing a topological network based on the resource location coordinates, and using the topological network to cluster nodes and establish adjacent nodes; Performing density cluster analysis on the topological network, initially distributing enhanced readers based on the density cluster analysis results, optimizing the positions of the enhanced readers through the adjacent nodes, and performing communication binding between the smart RFID tags of the adjacent nodes and the enhanced readers based on the position optimization results; Using the enhanced reader to configure the area trigger threshold of the smart RFID tag and set the step sampling frequency; After the sampled data of the smart RFID tag is transmitted to the enhanced reader, the enhanced reader is used to perform sleep and wake-up analysis to generate a collaborative warning; The method of transmitting the sampled data of the smart RFID tag to the enhanced reader is as follows: performing sleep and wake-up analysis using the enhanced reader to generate a collaborative warning, including: Using the enhanced reader to perform trigger analysis of abnormal events and generate a trigger probability of the abnormal events; Establishing a wake-up strategy based on the event attributes of the current abnormal event and the trigger probability; Synchronizing the wake-up strategy to a smart RFID tag that is in communication with the enhanced reader, performing wake-up monitoring of the smart RFID tag using the wake-up strategy, and establishing a wake-up monitoring result; The wake-up monitoring result is used to perform regional collaborative early warning identification and generate the collaborative early warning.

2. The dumb resource health monitoring method based on a low-power RFID sensor according to claim 1, characterized in that: The step of establishing a wake-up strategy based on the event attributes of the current abnormal event and the trigger probability includes: Determine whether the current abnormal event is a vibration abnormal event; If the current abnormal event is a vibration abnormal event, a vibration fingerprint library is established according to the sampled data; The vibration fingerprint library is used as the event's own attribute, and the vibration threshold, vibration sampling frequency, and vibration response sensitivity coefficient are established in combination with the trigger probability. The vibration threshold, vibration sampling frequency, and vibration response sensitivity coefficient are used as the wake-up strategy.

3. The dumb resource health monitoring method based on a low-power RFID sensor according to claim 2, characterized in that: The step of establishing a wake-up strategy based on the event attributes of the current abnormal event and the trigger probability further includes: Determine whether the current abnormal event is an abnormal tilt event; If the current abnormal event is an abnormal tilt event, an additional determination is made as to whether there is displacement linkage; After redefining the attributes of the event itself according to the additional discrimination results, the trigger probability is used to establish a wake-up strategy.

4. The dumb resource health monitoring method based on a low-power RFID sensor according to claim 3, characterized in that: The step of establishing a wake-up strategy based on the event attributes of the current abnormal event and the trigger probability further includes: Determine whether the current abnormal event is a water level abnormal event; If the current abnormal event is a water level abnormal event, a wake-up strategy is established according to the water level timing signal.

5. The dumb resource health monitoring method based on a low-power RFID sensor according to claim 1, wherein: The performing density cluster analysis on the topological network includes: A spatiotemporal density function is established and density cluster analysis is performed using the spatiotemporal density function: ； in, is the space-time density function value, representing the position time The comprehensive monitoring demand intensity, Represents the total number of dummy resources, is the dummy resource index number, is the spatial distance weight coefficient, is the static risk weight coefficient, is the time decay weight coefficient, is the dynamic risk weight coefficient, Characterization The spatial coordinates of a dummy resource, Represents the spatial coordinates of the point to be evaluated, Characterizes the spatial influence radius, Characterization The static risk coefficient of a dumb resource, Characterizes the maximum static risk value of the system, Characterize the time impact window, Characterization The time when the most recent event of a dumb resource occurred, Characterize the time-dynamic risk function, Characterizes the maximum dynamic risk value of the system.

6. The dumb resource health monitoring method based on a low-power RFID sensor according to claim 1, characterized in that: The initial distribution of enhanced readers according to the density cluster analysis results includes: Obtaining location attributes of a dummy resource, wherein the location attributes include street location attributes and campus location attributes; generating cross-region clustering constraints based on the location attributes; The density cluster analysis result constraint adjustment is performed according to the cross-region clustering constraint.

7. The dumb resource health monitoring method based on a low-power RFID sensor according to claim 1, wherein: The generating of the collaborative warning further includes: Determining whether the warning level of the collaborative warning is higher than the autonomous triggering threshold; If the warning level is higher than the autonomous trigger threshold, generating a real-time response instruction; According to the real-time response instruction, the collaborative warning is transmitted to the data center in real time through the enhanced reader / writer to execute the early warning output.

8. The dumb resource health monitoring method based on a low-power RFID sensor according to claim 7, characterized in that: The determining whether the warning level of the collaborative warning is higher than the autonomous triggering threshold includes: If the warning level is not higher than the autonomous trigger threshold, generating a retention instruction; storing the collaborative warning in the enhanced reader according to the retention instruction; When the enhanced reader is connected to the handheld device at any time point, the collaborative warning is transmitted to the handheld device and the early warning is issued.

9. The dumb resource health monitoring method based on a low-power RFID sensor according to claim 1, wherein: After generating the collaborative warning, the following steps are included: Interactively obtain monitoring data of the smart RFID tag, and use the monitoring data to build a health database of the dumb resource; The health management of dumb resources is performed according to the health database.

10. A dumb resource health monitoring device based on a low-power RFID sensor, characterized in that: The device comprises: An intelligent RFID tag configuration module, the intelligent RFID tag configuration module is used to configure an intelligent RFID tag on a dumb resource, the intelligent RFID tag is integrated with a sensor group, the sensor group includes a MEMS tilt sensor, a three-axis vibration sensor, a magnetic switch, and a water level sensor; A node clustering module, which is used to obtain the resource location coordinates of the dummy resources, construct a topological network based on the resource location coordinates, and perform node clustering using the topological network to establish adjacent nodes; A reader / writer communication binding module is used to perform density cluster analysis on the topological network, initially distribute enhanced readers based on the density cluster analysis results, optimize the positions of enhanced readers through the adjacent nodes, and perform communication binding between the adjacent node smart RFID tags and the enhanced readers based on the position optimization results; A step sampling frequency setting module, which is used to configure the regional trigger threshold of the smart RFID tag using the enhanced reader and set the step sampling frequency; A sleep-wake-up analysis module is configured to transmit the sampled data of the smart RFID tag to the enhanced reader, perform sleep-wake-up analysis using the enhanced reader, and generate a collaborative warning; The sleep-wake analysis module is further configured to perform: Using the enhanced reader to perform trigger analysis of abnormal events and generate a trigger probability of the abnormal events; Establishing a wake-up strategy based on the event attributes of the current abnormal event and the trigger probability; Synchronizing the wake-up strategy to a smart RFID tag that is in communication with the enhanced reader, performing wake-up monitoring of the smart RFID tag using the wake-up strategy, and establishing a wake-up monitoring result; The wake-up monitoring result is used to perform regional collaborative early warning identification and generate the collaborative early warning.

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