A voltage monitoring and early warning method for a vehicle-mounted charger based on an Internet of Things

Abstract

The application discloses a kind of voltage monitoring early warning methods of vehicle charger based on Internet of Things, it includes the following steps: the wireless sensor network of the core convergence node, relay forwarding node, terminal monitoring node is constructed;Terminal monitoring node gathers each vehicle charger data, and then is uploaded to core convergence node by relay forwarding node;Core convergence node receives the above-mentioned data and pre-processes, generates standardized voltage working condition data set and is transmitted to cloud Internet of Things platform, cloud Internet of Things platform analyzes the data, judges vehicle charger voltage abnormality grade;Cloud Internet of Things platform executes hierarchical early warning and safety protection according to vehicle charger voltage abnormality grade.The application realizes the full domain automation collection of vehicle charger voltage working condition, standardization processing, intelligent analysis and hierarchical protection, significantly improves the operation stability and safety of vehicle charging equipment, reduces equipment failure rate, operation and maintenance cost and charging safety accident incidence.

Description

Technical Field

[0001] This invention relates to Internet of Things (IoT) technology, and more particularly to a voltage monitoring and early warning method for an on-board charger based on IoT. Background Technology

[0002] With the rapid popularization of new energy vehicles and in-vehicle intelligent devices, the on-board charger, as a core component for vehicle power supply and device charging, directly affects the vehicle's electrical safety and the lifespan of in-vehicle equipment. During long-term operation, on-board chargers are susceptible to factors such as vehicle vibration, on-board voltage fluctuations, high-temperature environments, aging wiring, and sudden load changes, leading to faults such as overvoltage, undervoltage, sudden voltage changes, and abnormal voltage harmonics. If abnormal voltage conditions cannot be monitored and warned of in a timely manner, it can easily cause charger burnout, short circuits in the vehicle's electrical system, and battery bulging. In severe cases, it can cause vehicle fires, damage to precision on-board equipment, and other safety accidents, posing a significant safety hazard.

[0003] Existing on-board charger voltage monitoring technologies mostly adopt a local single-point wired detection mode, relying solely on a single built-in detection module within the charger to complete voltage acquisition. This approach suffers from the following technical problems: First, it has a limited monitoring dimension, only collecting voltage data from the charger's output terminal and failing to simultaneously collect input voltage, operating environment parameters, and line voltage drop data, making it difficult to accurately pinpoint the causes of voltage anomalies. Second, it lacks data storage and traceability capabilities, failing to achieve cloud storage and remote viewing of voltage operation data, thus posing significant challenges to fault tracing and maintenance analysis. To address the issues of low monitoring accuracy and unintelligent early warning in existing technologies, there is an urgent need to provide a voltage monitoring and early warning method for on-board chargers to resolve these technical problems. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a voltage monitoring and early warning method for vehicle chargers based on the Internet of Things, solving the problems of low voltage monitoring accuracy and unintelligent early warning in traditional vehicle chargers.

[0005] This invention discloses a voltage monitoring and early warning method for an on-board charger based on the Internet of Things, which includes the following steps:

[0006] S1. Build a wireless sensor network and complete node deployment and network initialization settings. The wireless sensor network includes a core aggregation node, several relay forwarding nodes and several terminal monitoring nodes. The terminal monitoring nodes are installed at the input and output sampling points of each vehicle charger in the monitoring area. The several relay forwarding nodes are randomly scattered in the monitoring area. The core aggregation node is located at the center of the monitoring area.

[0007] S2. Each of the terminal monitoring nodes synchronously collects data from each vehicle charger within the monitoring area according to a preset cycle, and transmits the collected data to the relay forwarding node. The relay forwarding node transmits the received data to the core aggregation node. The data includes the input bus voltage, output load voltage, voltage fluctuation amplitude during charging, line voltage loss, charger operating temperature, and device number of the vehicle charger.

[0008] S3. The core aggregation node preprocesses the received data to generate a standardized voltage condition dataset; and transmits the standardized voltage condition dataset to the cloud IoT platform, wherein the cloud IoT platform has a built-in voltage analysis model, which analyzes the received standardized voltage condition dataset to determine the voltage anomaly level of the vehicle charger.

[0009] S4. The cloud-based IoT platform performs graded early warning and safety protection based on the abnormal voltage level of the vehicle charger.

[0010] Preferably, the wireless sensor network is constructed according to the following method:

[0011] S11. Divide the monitoring area into multiple K×L monitoring sub-areas, randomly distribute several relay forwarding nodes in the monitoring area, and set the core aggregation node at the center of the monitoring area;

[0012] S12. After deployment, the core aggregation node broadcasts a clustering instruction to the entire network. After receiving the clustering instruction, each relay forwarding node sends a data packet carrying its own information to the core aggregation node. The data includes the energy information and location information of the relay forwarding node.

[0013] The core aggregation node determines whether each relay forwarding node is eligible to run for cluster head based on the received data packets, and adds the eligible relay forwarding nodes to the set Ω; wherein, the eligibility of a relay forwarding node to run for cluster head can be determined in the following manner:

[0014] The core aggregation node calculates the values ​​of each relay forwarding node using the following formula. The probability of being elected as the leader of the group:

[0015]

[0016] In the formula, For relay forwarding nodes Initial energy value, For relay forwarding nodes Current remaining energy value For relay forwarding nodes The remaining energy value after the last round of cluster leader election. For the estimated relay forwarding nodes before the next round of cluster head election The remaining energy value, For relay forwarding nodes Maximum load capacity, For relay forwarding nodes Historical average load capacity For relay forwarding nodes Reliability coefficient, The spatial influence factor represents the relay forwarding node. The degree to which spatial location influences the election of a candidate leader. Indicates relay forwarding node Distance from the core aggregation node Indicates relay forwarding node With relay forwarding nodes The distance between them, where n=1,2,..., , To monitor sub-region j excluding relay forwarding nodes Number of remaining relay forwarding nodes; Indicates relay forwarding node The distance between the monitoring sub-region j and the center location. Indicates relay forwarding node Communication coverage area This represents the area of ​​the monitored sub-region j; , , Let be the weight coefficient, and satisfy... It is used to adjust the proportion of energy, distance, and distribution uniformity in the election;

[0017] like This represents a relay forwarding node. A node that is eligible to run for cluster leader will be added to set Ω; otherwise, it represents a relay node. Those who are not eligible to run for the leadership position, among them, The threshold value is the probability threshold required for a relay forwarding node within the pre-defined monitoring sub-region j to act as a cluster head.

[0018] Based on the obtained set Ω, the core aggregation node further determines the true cluster head of each monitoring sub-region, and the remaining relay forwarding nodes in the monitoring sub-region, as cluster member nodes, join the true cluster head of their respective monitoring sub-region, becoming the cluster member nodes of the true cluster head, and finally obtain a wireless sensor network with a single cluster structure.

[0019] Preferably, the method further includes selecting a new cluster head from within the cluster at a first preset time interval.

[0020] Preferably, the terminal monitoring node is a sensor node.

[0021] Preferably, the voltage analysis model analyzes the received standardized voltage condition data to determine the voltage anomaly level of the on-board charger, specifically:

[0022] The voltage analysis model is set with preset graded voltage thresholds, which include normal voltage threshold range, slightly abnormal voltage threshold range, moderate fault voltage threshold range, and severely dangerous voltage threshold range. The voltage analysis model compares and analyzes the received standardized voltage condition data to determine which voltage threshold range it belongs to, and then determines whether the voltage is abnormal. If abnormal, it determines the voltage abnormality level. The slightly abnormal voltage threshold range, moderate fault voltage threshold range, and severely dangerous voltage threshold range correspond to the slightly abnormal level, moderate abnormal level, and severely abnormal level, respectively.

[0023] Preferably, the cloud-based IoT platform performs graded early warning and safety protection based on the abnormal voltage level of the vehicle charger, specifically:

[0024] If the voltage abnormality level of the vehicle charger is determined to be a minor abnormality, the corresponding vehicle indicator light will flash as a warning, and the abnormality log will be uploaded to the remote IoT platform simultaneously.

[0025] If the voltage abnormality level of the vehicle charger is determined to be moderate, the corresponding vehicle buzzer will be triggered to issue an audible and visual alarm, and a warning message will be pushed to the vehicle terminal and the user's APP at the same time.

[0026] If the on-board charger is determined to be at a severe abnormality level, a high-frequency audible and visual warning will be triggered for the corresponding vehicle, and a protection command will be issued simultaneously to control the on-board charger relay to disconnect the power supply circuit. At the same time, an emergency fault alarm will be sent to the remote IoT platform to promptly remind maintenance personnel to lock the corresponding vehicle and take repair measures.

[0027] Preferably, the method further includes the cloud-based IoT platform for permanently encrypting and storing all received standardized voltage condition data.

[0028] Preferably, the core aggregation node preprocesses the received data, specifically by using a Kalman filter algorithm to reduce noise in the received data.

[0029] This invention overcomes the technical bottlenecks of complex wiring and limited monitoring range in traditional vehicle charger voltage monitoring by constructing a hierarchical wireless sensor network including a core aggregation node, relay forwarding nodes, and terminal monitoring nodes. The terminal monitoring nodes accurately collect data on the input bus voltage, output load voltage, voltage fluctuation amplitude during charging, line voltage loss, charger operating temperature, and device serial number, enabling targeted monitoring of core voltage parameters. Randomly deployed relay forwarding nodes effectively expand the monitoring network's coverage, resolving issues such as signal obstruction and limited transmission distance within the monitoring area. The centrally located core aggregation node coordinates global data, enabling unified management of all vehicle chargers within the monitoring area. Based on IoT wireless sensor network technology, this invention achieves fully automated data acquisition, standardized processing, intelligent analysis, and hierarchical protection of vehicle charger voltage conditions, constructing a comprehensive, high-precision vehicle charger voltage monitoring and early warning system. This significantly improves the operational stability and safety of vehicle charging equipment, reducing equipment failure rates, maintenance costs, and the incidence of charging safety accidents. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the voltage monitoring and early warning system according to an embodiment of the present invention.

[0031] Attached label: Terminal monitoring node 1; Relay forwarding node 2; Core aggregation node 3; Cloud IoT platform 4. Detailed Implementation

[0032] To make the technical problems solved, the technical solutions, and the beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0033] In the description of this invention, it should be understood that the terms "longitudinal," "radial," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0034] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0035] See Figure 1 This invention provides a voltage monitoring and early warning system, comprising several terminal monitoring nodes 1, several relay forwarding nodes 2, a core aggregation node 3, and a cloud-based IoT platform 4. The terminal monitoring nodes 1 are installed at the input and output sampling points of each on-board charger within the monitoring area. The relay forwarding nodes 2 are randomly distributed within the monitoring area, and the core aggregation node 3 is positioned at the center of the monitoring area. Based on this system, this invention further provides an IoT-based voltage monitoring and early warning method for on-board chargers, comprising the following steps:

[0036] S1. Build a wireless sensor network and complete node deployment and network initialization settings. The wireless sensor network includes a core aggregation node 3, several relay forwarding nodes 2 and several terminal monitoring nodes 1. The terminal monitoring nodes 1 are installed at the input and output sampling points of each vehicle charger in the monitoring area. The several relay forwarding nodes 2 are randomly scattered in the monitoring area. The core aggregation node 3 is arranged at the center of the monitoring area.

[0037] S2. Each of the terminal monitoring nodes 1 synchronously collects data from each vehicle charger within the monitoring area according to a preset cycle, and transmits the collected data to the relay forwarding node 2. The relay forwarding node 2 transmits the received data to the core aggregation node 3. The data includes the input bus voltage of the vehicle charger, the output load voltage, the voltage fluctuation amplitude during charging, the line voltage loss, the charger operating temperature, and the device number.

[0038] S3. The core aggregation node 3 preprocesses the received data to generate a standardized voltage condition dataset; and transmits the standardized power condition dataset to the cloud IoT platform 4. The cloud IoT platform 4 has a built-in voltage analysis model, which analyzes the received standardized voltage condition dataset to determine the voltage anomaly level of the vehicle charger.

[0039] S4. The cloud-based IoT platform 4 performs graded early warning and safety protection based on the abnormal voltage level of the vehicle charger.

[0040] This invention overcomes the technical bottlenecks of complex wiring and limited monitoring range in traditional vehicle charger voltage monitoring by constructing a hierarchical wireless sensor network including a core aggregation node 3, relay forwarding nodes 2, and terminal monitoring nodes 1. The terminal monitoring nodes accurately collect data on the input bus voltage, output load voltage, voltage fluctuation amplitude during charging, line voltage loss, charger operating temperature, and device serial number, enabling targeted monitoring of core voltage parameters. Randomly deployed relay forwarding nodes 2 effectively expand the coverage of the monitoring network, solving problems such as signal obstruction and limited transmission distance within the monitoring area. The centrally located core aggregation node 3 coordinates global data, achieving unified management of all vehicle chargers within the monitoring area. Based on IoT wireless sensor network technology, this invention achieves fully automated data collection, standardized processing, intelligent analysis, and hierarchical protection of vehicle charger voltage conditions, constructing a full-process, high-precision vehicle charger voltage monitoring and early warning system. This significantly improves the operational stability and safety of vehicle charging equipment, reducing equipment failure rates, maintenance costs, and the incidence of charging safety accidents.

[0041] Furthermore, the wireless sensor network is constructed according to the following method:

[0042] S11. Divide the monitoring area into multiple K×L monitoring sub-areas, randomly distribute several relay forwarding nodes 2 in the monitoring area, and set the core aggregation node 3 at the center of the monitoring area;

[0043] S12. After deployment, the core aggregation node 3 broadcasts a clustering instruction to the entire network. After receiving the clustering instruction, each relay forwarding node 2 and terminal monitoring node 1 sends a data packet carrying its own information to the core aggregation node. The data includes the energy information and location information of the relay forwarding node 2 and the energy information and location information of the terminal monitoring node 1.

[0044] The core aggregation node 3 determines whether each relay forwarding node is eligible to compete for cluster head status based on the received data packets, and adds the eligible relay forwarding nodes 3 to the set Ω; wherein, the eligibility of the relay forwarding node 2 to compete for cluster head status can be determined in the following manner:

[0045] The core aggregation node calculates the values ​​of each relay forwarding node using the following formula. The probability of being elected as the leader of the group:

[0046]

[0047] In the formula, For relay forwarding nodes Initial energy value, For relay forwarding nodes Current remaining energy value For relay forwarding nodes The remaining energy value after the last round of cluster leader election. For the estimated relay forwarding nodes before the next round of cluster head election The remaining energy value, For relay forwarding nodes Maximum load capacity, For relay forwarding nodes Historical average load capacity For relay forwarding nodes Reliability coefficient, The spatial influence factor represents the relay forwarding node. The degree to which spatial location influences the election of a candidate leader. Indicates relay forwarding node Distance from the core aggregation node Indicates relay forwarding node With relay forwarding nodes The distance between them, where n=1,2,..., , To monitor sub-region j excluding relay forwarding nodes Number of remaining relay forwarding nodes; Indicates relay forwarding node The distance between the monitoring sub-region j and the center location. Indicates relay forwarding node Communication coverage area This represents the area of ​​the monitored sub-region j; , , Let be the weight coefficient, and satisfy... It is used to adjust the proportion of energy, distance, and distribution uniformity in the election;

[0048] like This represents a relay forwarding node. A node that is eligible to run for cluster leader will be added to set Ω; otherwise, it represents a relay node. Those who are not eligible to run for the leadership position, among them, The threshold value is the probability threshold required for a relay forwarding node within the pre-defined monitoring sub-region j to act as a cluster head.

[0049] Based on the obtained set Ω, the core aggregation node further determines the true cluster head of each monitoring sub-region, and the remaining relay forwarding nodes in the monitoring sub-region, as cluster member nodes, join the true cluster head of their respective monitoring sub-region, becoming the cluster member nodes of the true cluster head, and finally obtain a wireless sensor network with a single cluster structure.

[0050] In the above embodiment, the monitoring area is divided into multiple monitoring sub-areas. Then, the core aggregation node 3 broadcasts a cluster head election instruction to the entire network. Relay forwarding nodes 2 that receive the instruction send their own data packets to the core aggregation node 3. The core aggregation node 3 then confirms whether each relay forwarding node 2 is eligible to run for cluster head, thus obtaining a set Ω containing eligible relay forwarding nodes 2. In confirming whether a relay forwarding node 2 is eligible to run for cluster head, the influence of energy consumption and distance factors are fully considered. Not only are multiple energy factors such as the initial energy of the relay forwarding node, real-time remaining energy, remaining energy during the previous round of cluster head election, and remaining energy during the next round of cluster head election introduced, but also the distance between the relay forwarding node and the core aggregation node, as well as its distance from other relay forwarding nodes, are considered. This allows for the determination of whether a relay forwarding node is qualified to serve as a cluster head. In determining eligibility to run for cluster head, the applicant also innovatively introduces a spatial distribution equilibrium correction term. relay forwarding nodes Offset distance relative to the center of monitoring sub-region j, relay forwarding node The overlap between the effective communication coverage area and the area of ​​the monitored sub-region j is used as a constraint to dynamically adjust the relay forwarding nodes. Probability of cluster leader election. This is a load balancing correction term. The higher the historical load, the smaller this term's value. Setting this term can reduce the probability of high-load relay nodes being elected as cluster heads, preventing excessive cluster head load and transmission congestion. When relay nodes are distributed at the edge of the monitoring sub-area with low coverage overlap, their election weight is automatically reduced to suppress edge-disadvantaged nodes from being elected as cluster heads; when relay nodes... When a cluster head is located at the center of monitoring sub-region j and its communication coverage area is highly matched with that of monitoring sub-region j, its election priority is effectively increased. This mechanism fundamentally constrains the spatial distribution of the cluster head, achieving uniform and complete monitoring coverage in each monitoring sub-region, and significantly improving the overall monitoring accuracy and coverage performance.

[0051] In one alternative implementation, It can be calculated using the following formula:

[0052]

[0053] In the formula, The threshold value is the probability threshold required for a relay node within the pre-defined monitoring sub-region j to act as a cluster head. To monitor the highest energy value of relay forwarding nodes within sub-region j, To monitor the minimum energy value of relay forwarding nodes within sub-region j, The threshold value for the time influence factor is preferably 0.92. To monitor the number of relay forwarding nodes within sub-region j, To monitor relay forwarding nodes within sub-region j Distance from the core aggregation node The preset threshold for the spatial influence factor is, preferably, 0.18. To monitor relay forwarding nodes in sub-region j The sum of spatial distances between the node and other remaining relay nodes. The distance between the equivalent positions of all relay forwarding nodes in the monitored sub-region j (the equivalent position refers to the weighted centroid position of all relay forwarding nodes in the monitored sub-region j) and the center position of the monitored sub-region j; To monitor relay forwarding nodes in sub-region j Communication coverage area This represents the area of ​​the monitored sub-region j; , , Let be the weight coefficient, and satisfy... It is used to adjust the weight of energy, distance, and distribution uniformity in the election.

[0054] In the above implementation, the probability threshold required for a relay forwarding node in monitoring sub-region j to act as a cluster head is calculated using the formula above. This not only includes the influence of the relay forwarding node's energy, load capacity, and distribution uniformity within monitoring sub-region j, but also considers the influence of the distance between the relay forwarding node and the core aggregation node on its ability to act as a cluster head. This allows for a more accurate estimation of the probability threshold required for a relay forwarding node in monitoring sub-region j to act as a cluster head, thereby more accurately determining whether each relay forwarding node belongs to set Ω. This facilitates the subsequent selection of the optimal sensor node as the cluster head, achieving the goal of balancing the energy of the wireless sensor network and extending its lifespan.

[0055] In one implementation, based on the obtained set Ω, the core convergence node further determines the true cluster head of each monitoring sub-region, specifically:

[0056] If there is only one relay forwarding node 2 in the monitoring sub-region, then the relay forwarding node 2 directly becomes the true cluster head in the monitoring sub-region. Then, the other relay forwarding nodes 2 in the monitoring sub-region join the cluster head as cluster member nodes and become the cluster member nodes of the cluster head. At the same time, the terminal monitoring node 1 in its monitoring sub-region will first transmit the collected data to the nearest cluster member node, and then the cluster member node will forward the data to the cluster head.

[0057] If there are Q relay forwarding nodes 2 located in set Ω within the monitored sub-region, where Q≥2, then the probability value of the q-th relay forwarding node 2 being selected as the true cluster head is calculated using the following formula. The relay forwarding node with the highest probability value is selected as the true cluster head. Then, the other relay forwarding nodes 2 within the monitored sub-region join the cluster head as cluster member nodes, becoming its cluster member nodes. Simultaneously, the terminal monitoring nodes 1 within the monitored sub-region transmit the collected data to the nearest cluster member node, which then forwards the data to the cluster head. Wherein, the relay forwarding nodes within the monitored sub-region u... The probability of being elected as the true cluster leader is calculated using the following formula:

[0058]

[0059] In the formula, To monitor relay forwarding nodes within sub-region u The probability value of being elected as the true cluster leader. To monitor relay forwarding nodes within sub-region u Current remaining energy value To monitor relay forwarding nodes within sub-region u Current remaining energy value To monitor the number of relay nodes belonging to set Ω within sub-region u, To monitor relay forwarding nodes within sub-region u Spatial distance from the core convergence node To monitor relay forwarding nodes within sub-region u Spatial distance from the core convergence node To monitor the terminal monitoring nodes within the sub-region u Transmit unit data to the nearest relay forwarding node The amount of energy required, To monitor relay forwarding nodes within sub-region u All received data is transmitted to the relay forwarding node. The required energy consumption value, where C is the number of terminal monitoring nodes within the monitoring sub-region u; , These are the energy distance adjustment factor and the topology adjustment factor, respectively, and they satisfy... .

[0060] In the above implementation, for the scenario where there are at least two relay forwarding nodes in the monitoring sub-area located in set Ω, the above formula can select the relay forwarding node with the "most sufficient energy" and "optimal location" to serve as the cluster head. This can ensure the reliable transmission of data in the monitoring sub-area while minimizing energy consumption, effectively solving the problems of excessive cluster head load and short life cycle of wireless sensor networks in traditional clustering algorithms.

[0061] If no relay forwarding node is located in set Ω within the monitored sub-region, the probability value of each relay forwarding node being selected as the true cluster head is calculated using the following formula. The relay forwarding node with the highest probability value is then selected as the true cluster head. The other relay forwarding nodes within the monitored sub-region join the cluster head as cluster member nodes, becoming its cluster member nodes. Simultaneously, the terminal monitoring nodes within the monitored sub-region transmit the collected data to the nearest cluster member node, which then forwards the data to the cluster head. Wherein, the relay forwarding nodes within the monitored sub-region v... The probability of being elected as the true cluster leader is calculated using the following formula:

[0062]

[0063] In the formula, To monitor relay forwarding nodes within sub-region v The probability value of being elected as the true cluster leader. To monitor relay forwarding nodes within sub-region v Current remaining energy value To monitor relay forwarding nodes within sub-region v Current remaining energy value To monitor the number of relay forwarding nodes within sub-region v, To monitor relay forwarding nodes within sub-region v Spatial distance from the core convergence node To monitor relay forwarding nodes within sub-region v Spatial distance from the core convergence node To monitor terminal monitoring nodes within sub-region v Transmit unit data to the nearest relay forwarding node The amount of energy required, To monitor relay forwarding nodes within sub-region v All received data is transmitted to the relay forwarding node. The amount of energy required, To determine the number of terminal monitoring nodes within the monitoring sub-region v; , These are the energy distance adjustment factor and the topology adjustment factor, respectively, and they satisfy... ;

[0064] In the above implementation, for the scenario where no relay forwarding node is located in set Ω within the monitoring sub-area, the above formula can select the relay forwarding node with the "most sufficient energy" and "optimal location" to serve as the cluster head. This can ensure the reliable transmission of data within the monitoring sub-area while minimizing energy consumption, effectively solving the problems of excessive cluster head load and short life cycle of wireless sensor networks in traditional clustering algorithms.

[0065] Furthermore, the voltage monitoring and early warning method also includes selecting a new cluster head from within the cluster at a first preset time interval. This embodiment of the invention employs a cluster head rotation mechanism at a first preset time interval, periodically selecting a new cluster head node to take over the data aggregation and forwarding work. This avoids the problem of a single cluster head bearing the burden of receiving, aggregating, preprocessing, and forwarding massive amounts of data for an extended period, which can easily lead to rapid energy consumption and significantly extends the lifespan of the wireless sensor network.

[0066] Furthermore, the terminal monitoring node is a sensor node.

[0067] Furthermore, the voltage analysis model analyzes the received standardized voltage condition data to determine the voltage anomaly level of the on-board charger, specifically:

[0068] The voltage analysis model is set with preset graded voltage thresholds, which include normal voltage threshold ranges (e.g., 12V-14.5V), slightly abnormal voltage threshold ranges (e.g., 11V-12V or 14.5V-15V), moderate fault voltage threshold ranges (e.g., 10V-11V or 15V-16V), and severely dangerous voltage threshold ranges (e.g., less than 10V or greater than 16V). The voltage analysis model compares and analyzes the received standardized voltage condition data to determine which voltage threshold range it belongs to, and then determines whether the voltage is abnormal. If abnormal, it determines the voltage abnormality level. The slightly abnormal voltage threshold range, moderate fault voltage threshold range, and severely dangerous voltage threshold range correspond to the slightly abnormal level, moderate abnormal level, and severely abnormal level, respectively.

[0069] Compared to traditional voltage monitoring methods that can only simply determine whether the on-board charger voltage is normal or abnormal, this invention adopts a multi-gradient voltage threshold method, which can accurately distinguish between four states of normal operation, slight abnormality, moderate abnormality, and severe abnormality of the on-board charger voltage. This completely solves the problems of low accuracy and inability to quantify the degree of fault in traditional judgment methods. Moreover, the graded design of the on-board charger voltage fault provides a reliable basis for the subsequent graded early warning and differentiated safety protection strategies, making the voltage monitoring and early warning of the on-board charger more refined, standardized, and intelligent.

[0070] Furthermore, the cloud-based IoT platform performs tiered early warning and safety protection based on the abnormal voltage level of the vehicle charger, specifically:

[0071] If the voltage abnormality level of the vehicle charger is determined to be a minor abnormality, the corresponding vehicle indicator light will flash as a warning, and the abnormality log will be uploaded to the remote IoT platform simultaneously.

[0072] If the voltage abnormality level of the vehicle charger is determined to be moderate, the corresponding vehicle buzzer will be triggered to issue an audible and visual alarm. At the same time, a warning message will be pushed to the vehicle terminal and the user APP, and the abnormality log will be uploaded to the remote IoT platform.

[0073] If the on-board charger is determined to be at the severe abnormality level, a high-frequency audible and visual warning will be triggered for the corresponding vehicle, and a protection command will be issued simultaneously to control the on-board charger relay to disconnect the power supply circuit. At the same time, an emergency fault alarm will be sent to the cloud IoT platform to promptly remind maintenance personnel to lock the corresponding vehicle and take repair measures. The abnormality log will also be uploaded to the cloud IoT platform simultaneously.

[0074] This invention employs a three-tiered differentiated handling logic based on the severity of on-board charger voltage anomalies. Minor voltage deviations are only indicated by flashing vehicle indicator lights and log entries in the background, without generating loud buzzers that would disrupt normal vehicle use. Moderate faults are accompanied by audible and visual alarms and multi-device message pushes, allowing both the driver and mobile users to simultaneously perceive potential safety hazards from the on-board charger. Severe faults trigger high-frequency audible and visual alerts. The intensity of the three-tiered warning system increases in tandem with the severity of the fault, preventing excessive alarms for minor faults that could cause user panic, while providing sufficient warning for high-risk faults. This differentiated fault handling significantly reduces the command pressure on the cloud-based IoT platform, preventing congestion caused by a large number of minor fault reports simultaneously, ensuring the real-time issuance of warning and protection commands, and improving the overall stability of the IoT platform.

[0075] Furthermore, the method also includes the cloud-based IoT platform permanently encrypting and storing all received standardized voltage condition data. By permanently encrypting and storing all received standardized voltage condition data, on the one hand, the full-cycle operation data of the on-board charger is completely preserved, providing data support for subsequent on-board charger fault tracing and product performance iteration; on the other hand, encrypted storage prevents the leakage of user vehicle data and meets data security compliance requirements.

[0076] Furthermore, the core aggregation node preprocesses the received data, specifically by employing a Kalman filter algorithm to reduce noise. By introducing a Kalman filter algorithm to preprocess the raw collected data, the core aggregation node effectively filters out noise data caused by vehicle vibration, electromagnetic interference, etc., during the acquisition process, retaining valid data. This improves the accuracy of voltage sampling data, reduces the probability of false positives, and ensures the accuracy and reliability of subsequent graded early warning and fault determination results.

[0077] 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, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A voltage monitoring and early warning method for an on-board charger based on the Internet of Things, characterized in that, Includes the following steps: S1. Build a wireless sensor network and complete node deployment and network initialization settings. The wireless sensor network includes a core aggregation node, several relay forwarding nodes and several terminal monitoring nodes. The terminal monitoring nodes are installed at the input and output sampling points of each vehicle charger in the monitoring area. The several relay forwarding nodes are randomly scattered in the monitoring area. The core aggregation node is located at the center of the monitoring area. S2. Each of the terminal monitoring nodes synchronously collects data from each vehicle charger within the monitoring area according to a preset cycle, and transmits the collected data to the relay forwarding node. The relay forwarding node transmits the received data to the core aggregation node. The data includes the input bus voltage, output load voltage, voltage fluctuation amplitude during charging, line voltage loss, charger operating temperature, and device number of the vehicle charger. S3. The core aggregation node preprocesses the received data to generate a standardized voltage condition dataset; and transmits the standardized voltage condition dataset to the cloud IoT platform, wherein the cloud IoT platform has a built-in voltage analysis model, and the voltage analysis model analyzes the received standardized voltage condition dataset to determine the voltage anomaly level of the vehicle charger. S4. The cloud-based IoT platform performs graded early warning and safety protection based on the abnormal voltage level of the vehicle charger.

2. The voltage monitoring and early warning method according to claim 1, characterized in that, The wireless sensor network is constructed according to the following method: S11. Divide the monitoring area into multiple K×L monitoring sub-areas, randomly distribute several relay forwarding nodes in the monitoring area, and set the core aggregation node at the center of the monitoring area; S12. After deployment, the core aggregation node broadcasts a clustering instruction to the entire network. After receiving the clustering instruction, each relay forwarding node sends a data packet carrying its own information to the core aggregation node. The data includes the energy information and location information of the relay forwarding node. The core aggregation node determines whether each relay forwarding node is eligible to run for cluster head based on the received data packets, and adds the eligible relay forwarding nodes to the set Ω; wherein, the eligibility of a relay forwarding node to run for cluster head can be determined in the following manner: The core aggregation node calculates the values ​​of each relay forwarding node using the following formula. The probability of being elected as the leader of the group: In the formula, For relay forwarding nodes Initial energy value, For relay forwarding nodes Current remaining energy value For relay forwarding nodes The remaining energy value after the last round of cluster leader election. For the estimated relay forwarding nodes before the next round of cluster head election The remaining energy value, For relay forwarding nodes Maximum load capacity, For relay forwarding nodes Historical average load capacity For relay forwarding nodes Reliability coefficient The spatial influence factor represents the relay forwarding node. The degree to which spatial location influences the election of a candidate leader. Indicates relay forwarding node Distance from the core aggregation node Indicates relay forwarding node With relay forwarding nodes The distance between them, where n=1,2,..., , To monitor sub-region j excluding relay forwarding nodes Number of remaining external relay forwarding nodes; Indicates relay forwarding node The distance between the monitoring sub-region j and the center location. Indicates relay forwarding node Communication coverage area This represents the area of ​​the monitored sub-region j; , , Let be the weight coefficient, and satisfy... It is used to adjust the proportion of energy, distance, and distribution uniformity in the election; like This represents a relay forwarding node. A node that is eligible to run for cluster leader will be added to set Ω; otherwise, it represents a relay node. Those who are not eligible to run for the leadership position, among them, The threshold value is the probability threshold required for a relay forwarding node within the pre-defined monitoring sub-region j to act as a cluster head. Based on the obtained set Ω, the core aggregation node further determines the true cluster head of each monitoring sub-region, and the remaining relay forwarding nodes in the monitoring sub-region, as cluster member nodes, join the true cluster head of their respective monitoring sub-region, becoming the cluster member nodes of the true cluster head, and finally obtain a wireless sensor network with a single cluster structure.

3. The voltage monitoring and early warning method according to claim 2, characterized in that, It also includes selecting a new cluster head from within the cluster at a first preset time interval.

4. The voltage monitoring and early warning method according to claim 1, characterized in that, The terminal monitoring node is a sensor node.

5. The voltage monitoring and early warning method according to claim 1, characterized in that, The voltage analysis model analyzes the received standardized voltage operating condition data to determine the voltage anomaly level of the on-board charger, specifically: The voltage analysis model is set with preset graded voltage thresholds, which include normal voltage threshold range, slightly abnormal voltage threshold range, moderate fault voltage threshold range, and severely dangerous voltage threshold range. The voltage analysis model compares and analyzes the received standardized voltage condition data to determine which voltage threshold range it belongs to, and then determines whether the voltage is abnormal. If abnormal, it determines the voltage abnormality level. The slightly abnormal voltage threshold range, moderate fault voltage threshold range, and severely dangerous voltage threshold range correspond to the slightly abnormal level, moderate abnormal level, and severely abnormal level, respectively.

6. The voltage monitoring and early warning method according to claim 5, characterized in that, The cloud-based IoT platform performs tiered early warning and safety protection based on the abnormal voltage level of the vehicle charger, specifically: If the voltage abnormality level of the vehicle charger is determined to be a minor abnormality, the corresponding vehicle indicator light will flash as a warning, and the abnormality log will be uploaded to the remote IoT platform simultaneously. If the voltage abnormality level of the vehicle charger is determined to be moderate, the corresponding vehicle buzzer will be triggered to issue an audible and visual alarm, and a warning message will be pushed to the vehicle terminal and the user's APP at the same time. If the on-board charger is determined to be at a severe abnormality level, a high-frequency audible and visual warning will be triggered for the corresponding vehicle, and a protection command will be issued simultaneously to control the on-board charger relay to disconnect the power supply circuit. At the same time, an emergency fault alarm will be sent to the remote IoT platform to promptly remind maintenance personnel to lock the corresponding vehicle and take repair measures.

7. The voltage monitoring and early warning method according to claim 6, characterized in that, The method also includes the cloud-based IoT platform for permanently encrypting and storing all received standardized voltage condition datasets.

8. The voltage monitoring and early warning method according to claim 1, characterized in that, The core aggregation node preprocesses the received data, specifically by using a Kalman filter algorithm to reduce noise in the received data.

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