Guardrail tube lamp remote monitoring and intelligent regulation and control system based on Internet of Things

Abstract

The invention discloses a guardrail tube lamp remote monitoring and intelligent regulation and control system based on the Internet of Things, particularly relates to the technical field of guardrail tube lamp remote monitoring, and aims to obtain two key performance indexes, namely a brightness control response delay coefficient and an instruction execution offset coefficient in real time, and perform continuous time sequence modeling and normalization processing to obtain a brightness control response delay coefficient and an instruction execution offset coefficient; early symptoms of the soft fault are accurately separated from the smooth flow; the response time delay and the execution deviation are comprehensively considered, a potential soft fault node evaluation index is constructed, and high-sensitivity capture of hidden degradation nodes is achieved; on the basis of physical distance and state collaboration degree two-dimensional information, a high-precision propagation sensitive weight matrix is constructed, a disturbed weight index is calculated based on a potential fault source and a neighborhood coupling relation, and accurate positioning of a fault diffusion path and an influence range is achieved; the node degradation identification module maps multi-source fuzzy input into a node propagation risk probability through a fuzzy inference rule, and accurately identifies a high propagation risk node on the premise of ensuring a low false alarm rate.

Description

Technical Field

[0001] This invention relates to the field of remote monitoring technology for guardrail lights, and more specifically, to a remote monitoring and intelligent control system for guardrail lights based on the Internet of Things. Background Technology

[0002] With the rapid development of smart cities and smart transportation systems, remote monitoring and intelligent control systems for guardrail lights based on the Internet of Things (IoT) architecture have been widely deployed in urban roads, bridges, highway guardrails, and other scenarios. These systems use wireless communication networks to remotely control the switching, brightness adjustment, status reporting, and alarm functions of lighting equipment. They feature automatic sensing, adaptive linkage, and cluster control, playing a crucial role in improving energy efficiency management and road safety.

[0003] However, in actual system operation, in addition to traditional explicit faults (such as equipment offline, communication interruption, power instability, etc.), there exists a more insidious but highly dangerous operational anomaly phenomenon: the "ghost effect of soft faults." This type of fault is not caused by node hardware failure or communication disconnection, but rather stems from the "slight degradation" behavior of individual nodes at the logic or control response levels, such as brightness adjustment delays, periodic output jitter, and asynchronous status feedback. These "soft anomalies" typically do not trigger system alarm mechanisms and are difficult to identify using conventional indicators. Even more challenging is that these anomalous nodes are often still online, possess communication capabilities, and can execute some control commands, making their abnormal behavior easily masked by normal fluctuations. However, in group control or multi-node linkage modes, the unstable control behavior of these nodes may gradually interfere with the operating status of surrounding lights, inducing system resonance or regional coordination imbalances, ultimately triggering a systemic degradation chain. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a remote monitoring and intelligent control system for guardrail lights based on the Internet of Things, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: The IoT-based remote monitoring and intelligent control system for guardrail lights includes a node status acquisition module, a soft fault node identification module, a propagation chain area perception module, and a node degradation identification module. The node status acquisition module is used to mark each guardrail tube light as a guardrail tube light node and acquire the brightness control response delay information and instruction execution offset information of the guardrail tube light. The brightness control response delay information includes a brightness control response delay coefficient, and the instruction execution offset information includes an instruction execution offset coefficient. The soft fault node identification module is used to construct a potential soft fault node evaluation model based on the brightness control response delay coefficient and the instruction execution offset coefficient, output the potential soft fault node evaluation index, and identify the guardrail tube light node that triggers potential soft faults. The formula used in the potential soft failure node assessment model is as follows: In the formula As an evaluation index for potential soft failure nodes, The brightness control response delay coefficient, The instruction execution offset coefficient. These represent the preset proportional coefficients for the brightness control response delay coefficient and the command execution offset coefficient, respectively. All greater than 0 The propagation chain area perception module is used to construct the propagation sensitivity weight matrix of the guardrail light nodes based on the physical distance and state coordination between the guardrail light nodes, and to calculate the disturbance weight index of each guardrail light node to determine whether the guardrail light node is in the propagation chain influence zone. The node degradation identification module is used to identify high-risk nodes for propagation by using fuzzy reasoning for guardrail light nodes that are triggered by soft faults within the propagation chain's influence zone.

[0006] In a preferred embodiment, by acquiring the brightness control response delay information of the guardrail tube light, analyzing the brightness control response delay of the guardrail tube light, and calculating the brightness control response delay coefficient, the degree of brightness control response delay of the guardrail tube light is measured. The logic for obtaining the brightness control response delay coefficient is as follows: The periodic data collected for each guardrail light node includes: command issuance time. Actual brightness change start time Target brightness value Actual brightness change curve ,in This is the time offset after the instruction is issued; Calculate the time difference of delay: ,in For the time difference of delay; Calculate the average response latency: ,in For the guardrail tube light nodes in the control cycle Average response latency within, The time difference of delay calculated at time k; Calculate the periodic response delay fluctuation value: ,in This represents the periodic response delay fluctuation value. Calculate the actual luminance response deviation rate: ,in This represents the actual brightness response deviation rate. Control cycle The time difference data within the interval is divided into For each of the three non-overlapping equal-width sub-intervals, count the number of delay time differences exceeding a preset delay time difference threshold, and calculate the delay probability. ,in For the first The delay probability of each sub-interval, For the first The number of sub-intervals with a delay time difference greater than the delay time difference threshold. , The total number of sub-intervals; calculate the information entropy of the delayed response: ,in For delayed response information entropy; Calculate the brightness control response delay factor: ,in The brightness control response delay coefficient, These represent the preset proportional coefficients for the average response delay rate, periodic response delay fluctuation value, actual brightness response deviation rate, and delay response information entropy, respectively. All are greater than 0.

[0007] In a preferred embodiment, the command execution offset information of the guardrail tube light is used to analyze the command execution offset of the guardrail tube light and calculate the command execution offset coefficient to measure the degree of command execution offset of the guardrail tube light. The logic for obtaining the instruction execution offset coefficient is as follows: If the guardrail light node receives a brightness control command at time t, the control target will be adjusted according to the time interval. The changing function is expressed as: ,in Indicates the relative time after the control command is executed. To control the brightness value of the target; Using the empirical parameter library Establish the family of expected response functions: ,in Indicates the parameter index of the empirical response function. Indicates in the parameter The expected response function generated below, This represents the set of expected response functions; Construct the desired trajectory function: ,in Represents the desired brightness trajectory. This indicates the target brightness change range of the control command. , The optimal matching parameters are those that best match the actual behavior of the guardrail lamp nodes in the expected response function family. For optimal matching parameters The expected response function generated; Obtain the actual brightness trajectory and compare it with the expected trajectory to construct the offset residual function: ,in For offset residual function, For the actual brightness trajectory; generate an offset residual vector sequence: ,in For the d-th discrete sampling point, , This represents the total number of sampling points; Define a set of multi-scale sliding window sizes: ,in Indicates the first Various scales of sliding window size, This represents the total number of scale layers; Calculate the instruction execution offset coefficient: ,in Indicates the instruction execution offset coefficient. Indicates the number of sliding windows. , .

[0008] In a preferred embodiment, the potential soft fault node evaluation index of each guardrail tube light node is compared with a preset potential soft fault node evaluation index threshold to identify the guardrail tube light node that triggers a potential soft fault, as follows: If the evaluation index of a potential soft fault node is greater than the threshold of the evaluation index of a potential soft fault node, then the current guardrail tube light node is marked as a guardrail tube light node that is triggered by a potential soft fault. If the evaluation index of a potential soft fault node is less than or equal to the threshold of the evaluation index of a potential soft fault node, then the guardrail tube light node is regarded as a normal node and the potential soft fault flag is not triggered.

[0009] In a preferred embodiment, a spatial topology map of the guardrail light nodes is constructed based on potential soft fault triggering of the guardrail light nodes. ,in This is a collection of guardrail tube light nodes. Given an edge set, the propagation sensitivity weight matrix is ​​obtained by calculating the propagation sensitivity weights of guardrail light nodes u and v based on the physical distance and state synergy between them. : ,in This represents the propagation sensitivity weights of guardrail light nodes u and v. Let u be the physical distance between guardrail tube light node and v. This is to prevent division by zero by a very small constant. The state coordination degree between guardrail tube light node u and guardrail tube light node v; Calculate the disturbance weight index for each guardrail light node: ,in This is the perturbation weighting index, used to comprehensively reflect the propagation impact of the failure risk of its neighboring nodes on this node. This represents the potential soft-fault node evaluation index for the guardrail tube light node u. Let v be the propagation sensitivity weight for guardrail light nodes u and v, where v = {1, 2, ..., V} and V is a positive integer.

[0010] In a preferred embodiment, the disturbance weight index of each guardrail lamp node is compared with a preset disturbance weight index threshold to determine whether the guardrail lamp node is in the propagation chain influence zone, as follows: If the disturbance weight index is greater than the disturbance weight index threshold, it means that the current guardrail tube light node is in the propagation chain influence zone. If the disturbance weight index is less than or equal to the disturbance weight index threshold, it means that the current guardrail tube light node is not in the influence zone of the propagation chain.

[0011] In a preferred embodiment, high-risk nodes for propagation are identified by fuzzy inference for soft-fault-triggered guardrail light nodes located within the propagation chain's influence zone. The specific process is as follows: Step A1: Obtain the potential soft fault node evaluation index and the disturbed weight index of the soft fault-triggered guardrail tube light node within the influence zone of the propagation chain. Define the potential soft fault node evaluation index and the disturbed weight index of the soft fault-triggered guardrail tube light node as input variables and divide them into different fuzzy sets respectively. Step A2: Define the node propagation risk probability as an output variable and partition it into a fuzzy set. Step A3: Develop a set of fuzzy rules to describe the impact of different input variables on the output variable; Step A4: Perform fuzzy reasoning based on fuzzy rules to identify nodes with high propagation risk.

[0012] The technical effects and advantages of this invention are as follows: 1. This invention significantly improves the adaptive perception capability and fault defense level of the IoT-based remote monitoring and intelligent control system for guardrail lights by introducing multi-dimensional dynamic response features and a networked propagation modeling mechanism. The node status acquisition module can not only acquire two key performance indicators in real time—brightness control response delay coefficient and instruction execution offset coefficient—but also accurately separate early signs of soft faults from smoothed traffic through continuous time-series modeling and normalization processing. The soft fault node identification module, based on a comprehensive consideration of response delay and execution deviation, constructs a potential soft fault node evaluation index, achieving high-sensitivity capture of "hidden degradation" nodes and enabling pre-emptive detection before the hardware alarm mechanism fails. The system employs a multi-dimensional approach: the propagation chain area perception module, based on physical distance and state coordination, constructs a high-precision propagation-sensitive weight matrix and calculates the disturbance weight index based on the coupling relationship between potential fault sources and their neighborhoods, enabling precise location of fault propagation paths and impact ranges. The node degradation identification module uses fuzzy inference rules to map multi-source fuzzy inputs to node propagation risk probabilities, accurately identifying high-risk nodes while maintaining a low false alarm rate. This significantly improves the system's ability to detect and prevent soft-fault ghost effects, while also possessing scalability and engineering feasibility, significantly enhancing system reliability, reducing maintenance costs, and improving the safety and energy efficiency of smart city lighting systems. Attached Figure Description

[0013] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a flowchart of the system according to an embodiment of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Example: The present invention provides, as follows Figure 1 The IoT-based remote monitoring and intelligent control system for guardrail lights shown includes a node status acquisition module, a soft fault node identification module, a propagation chain area perception module, and a node degradation identification module. The node status acquisition module is used to mark each guardrail tube light as a guardrail tube light node and acquire the brightness control response delay information and instruction execution offset information of the guardrail tube light. The brightness control response delay information includes a brightness control response delay coefficient, and the instruction execution offset information includes an instruction execution offset coefficient. The soft fault node identification module is used to construct a potential soft fault node evaluation model based on the brightness control response delay coefficient and the instruction execution offset coefficient, output the potential soft fault node evaluation index, and identify the guardrail tube light node that triggers potential soft faults. The propagation chain area perception module is used to construct the propagation sensitivity weight matrix of the guardrail light nodes based on the physical distance and state coordination between the guardrail light nodes, and to calculate the disturbance weight index of each guardrail light node to determine whether the guardrail light node is in the propagation chain influence zone. The node degradation identification module is used to identify high-risk nodes for propagation by fuzzy reasoning of guardrail tube light nodes that are triggered by soft faults within the propagation chain's influence zone. The node status acquisition module is used to mark each guardrail tube light as a guardrail tube light node and acquire the brightness control response delay information and instruction execution offset information of the guardrail tube light. The brightness control response delay information includes a brightness control response delay coefficient, and the instruction execution offset information includes an instruction execution offset coefficient. The brightness control response delay coefficient in this invention is a key dynamic indicator used to measure the difference between the actual response time and the expected response time of a guardrail tube light after receiving a brightness adjustment command. It reflects the temporal stability and execution accuracy of the guardrail tube light node's response efficiency in the control link. This coefficient can effectively capture the implicit performance degradation trend of nodes under non-hard fault conditions, and is particularly suitable for identifying "soft faults" in large-scale lighting networks in the Internet of Things environment that are difficult to detect by conventional electrical diagnostic methods. Under ideal working conditions, the guardrail tube light should be able to respond quickly and complete the brightness adjustment within a set time after the control system issues a brightness command, forming a highly consistent control-execution-feedback closed loop. However, in practical applications, due to non-fatal issues such as communication delay, aging of node control chips, power supply ripple interference, ambient temperature fluctuations, or poor local electrical contact, the brightness response of some nodes will exhibit varying degrees of "hysteresis". A large brightness control response delay coefficient indicates a significant time lag between command reception and brightness change at the guardrail light node, meaning the brightness adjustment response is slow or even briefly erratic. The root cause is likely not a typical fault such as an electrical circuit break or communication interruption, but rather a slight performance drift during system operation, which often cannot be detected by traditional state variables or alarm signals. Conversely, a small brightness control response delay coefficient indicates that the node can complete the brightness adjustment task within a near real-time time window, demonstrating good control response synchronization and drive consistency, meeting the basic requirements of dynamic coordination for IoT distributed control systems. It is worth noting that this continuous, weak but clearly trending control delay change, falling between "normal" and "faulty," constitutes an important criterion for identifying soft faults. In this invention, identifying potential soft fault triggering nodes based on the brightness control response delay coefficient has several beneficial effects: First, it realizes the transformation from "static state recognition" to "dynamic behavior feature recognition," breaking through the limitations of traditional methods that rely on threshold alarms or single signal monitoring, enabling the system to detect the performance boundary degradation trend of nodes in advance before irreversible faults occur; Second, the response delay coefficient has good time traceability and statistical quantifiability, enabling difference analysis with historical normal operating baselines, supporting online sliding evaluation, abnormal integral accumulation, and trend prediction modeling, which is conducive to building an intelligent fault-tolerant control mechanism; Third, in a networked system structure, this coefficient can also serve as the original disturbance signal for propagation chain modeling, providing a scientific basis for subsequent propagation chain identification.

[0016] Therefore, by obtaining the brightness control response delay information of the guardrail tube lights, analyzing the brightness control response delay of the guardrail tube lights, and calculating the brightness control response delay coefficient, the degree of brightness control response delay of the guardrail tube lights can be measured. The logic for obtaining the brightness control response delay coefficient is as follows: The periodic data collected for each guardrail light node includes: command issuance time. Actual brightness change start time Target brightness value Actual brightness change curve ,in It is the time offset after the instruction is issued, i.e. ,in This is the current sampling time; Calculate the time difference of delay: ,in For the time difference of delay; Calculate the average response latency: ,in For the guardrail tube light nodes in the control cycle Average response latency within, The time difference of delay calculated at time k; Calculate the periodic response delay fluctuation value: ,in This represents the periodic response delay fluctuation value. Calculate the actual luminance response deviation rate: ,in This represents the actual brightness response deviation rate. Control cycle The time difference data within the interval is divided into For each of the three non-overlapping equal-width sub-intervals, count the number of delay time differences exceeding a preset delay time difference threshold, and calculate the delay probability. ,in For the first The delay probability of each sub-interval, For the first The number of sub-intervals with a delay time difference greater than the delay time difference threshold. , The total number of sub-intervals; calculate the information entropy of the delayed response: ,in For delayed response information entropy; Calculate the brightness control response delay factor: ,in The brightness control response delay coefficient, These represent the preset proportional coefficients for the average response delay rate, periodic response delay fluctuation value, actual brightness response deviation rate, and delay response information entropy, respectively. All are greater than 0; It should be noted that the above formulas are all dimensionless calculations. Commonly used methods for removing dimensions include Min-Max normalization and Z-Score standardization, which will not be elaborated here. The settings should be tailored to the specific circumstances. For example, an expert-empowered approach could be adopted, where experts in relevant fields are invited to determine the pre-defined proportions for each indicator through professional opinion surveys and comprehensive evaluations. It can be 0.2, 0.3, 0.3, or 0.2; In this invention, the command execution offset coefficient is a core parameter used to measure the degree of deviation of the actual brightness adjustment behavior of the guardrail tube light from the expected adjustment behavior after receiving a central control command. This coefficient not only reflects the accuracy of the light fixture in executing control commands, but also reveals potential signs of "action deviation," "feedback lag," or "system misfollowing" during the execution process. By constructing an execution trajectory offset function and comprehensively considering the dynamic differences between the target curve of the control command and the actual brightness change trajectory, the command execution offset coefficient can quantitatively characterize weak execution anomalies at the node level. Under normal operating conditions, after receiving a brightness adjustment command, the actual brightness output trajectory of the guardrail tube light node should highly coincide with the expected brightness change trajectory, with only slight deviations within a tolerable range. However, if affected by factors such as aging of the internal drive module, response loop lag, firmware control misalignment, or power supply noise interference, its brightness adjustment behavior may gradually deviate from the expected target curve and exhibit a "chronic drift" trend that cannot be immediately corrected. In this case, the command execution offset coefficient can be calculated for early identification. A large instruction execution offset coefficient indicates that the node's brightness adjustment behavior frequently deviates from the target trajectory in multiple control cycles, potentially indicating functional degradation at the hardware and software levels. This makes it a potential "soft fault triggering source" node. While such nodes can maintain basic on / off functionality in the short term, the uncertainty in their brightness adjustment can potentially disrupt overall lighting coordination and timing consistency. More importantly, these offsets often lack system-level alarm mechanisms; simply judging the "on or off" status is insufficient to trigger maintenance responses, easily leading to ghosting effects and inducing resonant degradation propagation in localized areas. Conversely, a small instruction execution offset coefficient indicates that the node's brightness adjustment behavior closely follows the expected instruction trajectory, exhibiting good determinism, controllability, and coordination. This can be considered a "non-soft fault sensitive node" or a "stable node." Maintaining a stable proportion of such nodes in a distributed lighting network contributes to the system's overall anti-interference, synchronization, and long-term stable operation. Identifying potential soft fault triggering nodes based on instruction execution offset coefficients offers significant engineering and system benefits. On the one hand, it breaks through the traditional discrete judgment method based on "whether it responds" and "whether it is turned off", and turns to the continuous representation of "behavioral trajectory deviation trend", which can effectively capture edge abnormal nodes missed by traditional monitoring logic. On the other hand, as a core sub-indicator in the soft fault identification model, it can form a three-element dynamic analysis framework together with the brightness response delay coefficient and the state feedback asynchronous coefficient, so as to realize a more accurate multi-factor fusion judgment mechanism.

[0017] Therefore, by analyzing the command execution offset information of the guardrail tube lights, the command execution offset situation of the guardrail tube lights is analyzed, and the command execution offset coefficient is calculated to measure the degree of command execution offset of the guardrail tube lights. The logic for obtaining the instruction execution offset coefficient is as follows: If the guardrail light node receives a brightness control command at time t, the control target will be adjusted according to the time interval. The changing function is expressed as: ,in Indicates the relative time after the control command is executed. To control the brightness value of the target; Using the empirical parameter library Establish the family of expected response functions: ,in Indicates the parameter index of the empirical response function. Indicates in the parameter The expected response function generated below, This represents the set of expected response functions; Construct the desired trajectory function: ,in Represents the desired brightness trajectory. This indicates the target brightness change range of the control command. , The optimal matching parameters are those that best match the actual behavior of the guardrail lamp nodes in the expected response function family. For optimal matching parameters The expected response function generated; Obtain the actual brightness trajectory and compare it with the expected trajectory to construct the offset residual function: ,in For offset residual function, For the actual brightness trajectory; generate an offset residual vector sequence: ,in For the d-th discrete sampling point, , This represents the total number of sampling points; Define a set of multi-scale sliding window sizes: ,in Indicates the first Various scales of sliding window size, This represents the total number of scale layers; Calculate the instruction execution offset coefficient: ,in Indicates the instruction execution offset coefficient. Indicates the number of sliding windows. , ; It should be noted that the above formulas are all dimensionless calculations. Commonly used methods for removing dimensions include Min-Max normalization and Z-Score standardization, which will not be elaborated here. The soft fault node identification module is used to construct a potential soft fault node evaluation model based on the brightness control response delay coefficient and the command execution offset coefficient, output a potential soft fault node evaluation index, and identify potential soft fault triggering nodes of guardrail tube lights, as detailed below: A potential soft fault node evaluation model is constructed based on the brightness control response delay coefficient and the instruction execution offset coefficient, and the potential soft fault node evaluation index is output. The formula used in the potential soft fault node evaluation model is as follows: In the formula As an evaluation index for potential soft failure nodes, The brightness control response delay coefficient, The instruction execution offset coefficient. These represent the preset proportional coefficients for the brightness control response delay coefficient and the command execution offset coefficient, respectively. All are greater than 0; It should be noted that the above formulas are all dimensionless calculations. Commonly used methods for removing dimensions include Min-Max normalization and Z-Score standardization, which will not be elaborated here. The settings should be tailored to the specific circumstances. For example, an expert-empowered approach could be adopted, where experts in relevant fields are invited to determine the pre-defined proportions for each indicator through professional opinion surveys and comprehensive evaluations. It can be 0.5 or 0.5; As shown in the above calculation expressions, the larger the brightness control response delay coefficient and the larger the command execution offset coefficient, the larger the potential soft fault node evaluation index. This indicates that the guardrail lamp node exhibits a significant behavioral deviation in the execution of control commands and the feedback brightness response, potentially triggering or already in a state on the verge of soft fault evolution. Conversely, the smaller the brightness control response delay coefficient and the smaller the command execution offset coefficient, the smaller the potential soft fault node evaluation index. This indicates that the guardrail lamp node can respond to the brightness quickly after receiving the control command, and the deviation between the actual execution trajectory and the expected trajectory is small, demonstrating good timeliness and command consistency. In this case, the potential soft fault node evaluation index decreases significantly, indicating that the node is in a stable and healthy working state, and its response behavior has high determinism, predictability, and control compliance. The potential soft fault node evaluation index of each guardrail tube light node is compared with the preset potential soft fault node evaluation index threshold to identify the guardrail tube light node that triggers a potential soft fault, as follows: If the evaluation index of a potential soft fault node is greater than the threshold of the evaluation index of a potential soft fault node, it indicates that the current guardrail tube light node has an abnormal deviation in brightness control response and command execution behavior that exceeds the normal fluctuation range. This is manifested as a significant response delay or inaccurate execution effect. Although the system alarm mechanism has not yet been triggered, it has early signs of a soft fault and may have an interfering effect on adjacent nodes. Therefore, the current guardrail tube light node is marked as a guardrail tube light node that has a potential soft fault. If the potential soft fault node evaluation index is less than or equal to the potential soft fault node evaluation index threshold, it indicates that the current guardrail tube light node is within an acceptable normal range in terms of brightness response timeliness and execution consistency. Its brightness control response delay coefficient and command execution offset coefficient have not significantly deviated from the preset standards, indicating that the system is operating stably and reliably, without showing obvious functional degradation or potential abnormal trends. In this case, the guardrail tube light node can be considered a normal node and will not trigger the potential soft fault flag. The propagation chain area perception module is used to construct a propagation sensitivity weight matrix for guardrail light nodes based on the physical distance and state coordination between them, and to calculate the disturbance weight index for each guardrail light node to determine whether the guardrail light node is within the propagation chain influence zone, as detailed below: Construct a spatial topology diagram of the guardrail light nodes based on potential soft fault triggering. ,in This is a collection of guardrail tube light nodes. Given an edge set, the propagation sensitivity weight matrix is ​​obtained by calculating the propagation sensitivity weights of guardrail light nodes u and v based on the physical distance and state synergy between them. : ,in This represents the propagation sensitivity weights of guardrail light nodes u and v. Let u be the physical distance between guardrail tube light node and v. This is to prevent division by zero by a very small constant (generally taken as...). ), The state coordination degree between guardrail tube light node u and guardrail tube light node v; In an optional example, the state cooperability is obtained as follows: Define the state feature vector for each guardrail tube light node: ,in The brightness control response delay coefficient for the guardrail tube light node u. As the instruction execution offset coefficient for guardrail lamp node u, calculate the state coordination degree between guardrail lamp node u and guardrail lamp node v: ,in This represents the state feature vector of the guardrail lamp node u. This represents the state feature vector of the guardrail lamp node v. The dot product operation represents the state feature vectors. and Let represent the magnitudes of the state feature vectors of guardrail tube light nodes u and v, respectively. Calculate the disturbance weight index for each guardrail light node: ,in This is the perturbation weighting index, used to comprehensively reflect the propagation impact of the failure risk of its neighboring nodes on this node. This represents the potential soft-fault node evaluation index for the guardrail tube light node u. Let v be the propagation sensitivity weight for guardrail light nodes u and v, where v = {1, 2, ..., V} and V is a positive integer; It should be noted that the above formulas are all dimensionless calculations. Commonly used methods for removing dimensions include Min-Max normalization and Z-Score standardization, which will not be elaborated here. The disturbance weight index of each guardrail tube light node is compared with a preset disturbance weight index threshold to determine whether the guardrail tube light node is in the propagation chain influence zone, as follows: If the disturbance weight index is greater than the disturbance weight index threshold, it means that the current guardrail tube light node is in the propagation chain influence zone, that is, it is greatly affected by the potential soft fault nodes in the neighborhood and has the possibility of soft fault propagation reception. If the disturbance weight index is less than or equal to the disturbance weight index threshold, it means that the current guardrail tube light node is not in the propagation chain influence zone and its propagation influence is weak. The node degradation identification module is used to identify high-risk nodes for propagation from soft-fault triggered guardrail light nodes within the propagation chain's influence zone through fuzzy reasoning. The specific process is as follows: Step A1: Obtain the potential soft fault node evaluation index and the disturbance weight index of the soft fault-triggered guardrail light node within the propagation chain influence zone. Define the potential soft fault node evaluation index and the disturbance weight index of the soft fault-triggered guardrail light node as input variables and divide them into different fuzzy sets.

[0018] For example, "Low", "Medium", and "High" are evaluation indices for potential soft fault nodes, and "Low", "Medium", and "High" are disturbance weight indices.

[0019] Step A2: Define the node propagation risk probability as an output variable and divide it into fuzzy sets, such as "Low", "Medium", and "High" for the node propagation risk probability.

[0020] Step A3: Develop a set of fuzzy rules to describe the impact of different input variables on the output variable. The rules can be defined based on professional knowledge or obtained through data analysis and experimentation. For example: If the node propagation risk probability is denoted as JCB, then it can be defined as follows: Rule 1: IF ( is Low) AND ( (is Low) THEN (JCB is Low) Rule 2: IF ( is High) AND ( (is High) THEN (JCB is High) ... Step A4: Perform fuzzy reasoning based on fuzzy rules to identify nodes with high propagation risk.

[0021] It should be noted that only when the node propagation risk probability is "High" will the guardrail tube light node be marked as a high propagation risk node; It should be noted that the division of fuzzy sets can be adjusted according to the actual situation. For example, although this embodiment uses three fuzzy sets as an example, in reality, the potential soft fault node evaluation index, the disturbance weight index, and the node propagation risk probability can be divided into more than three sets to facilitate more accurate identification of high propagation risk nodes.

[0022] Furthermore, the judgment of the potential soft fault node assessment index and the disturbance weight index as high, medium and low can be made by setting thresholds according to the actual situation. For example, when the potential soft fault node assessment index and the disturbance weight index exceed 90% of the maximum value, they can be marked as "High", etc., which will not be elaborated here. This invention significantly improves the adaptive perception capability and fault defense level of the IoT-based remote monitoring and intelligent control system for guardrail lights by introducing multi-dimensional dynamic response characteristics and a networked propagation modeling mechanism. The node status acquisition module not only acquires two key performance indicators in real time—brightness control response delay coefficient and instruction execution offset coefficient—but also accurately separates early signs of soft faults from smoothed traffic through continuous time-series modeling and normalization processing. The soft fault node identification module, based on a comprehensive consideration of response delay and execution deviation, constructs a potential soft fault node evaluation index, achieving high-sensitivity capture of "hidden degradation" nodes and providing early warning before the hardware alarm mechanism fails. The propagation chain area perception module constructs a high-precision propagation-sensitive weight matrix based on two-dimensional information of physical distance and state coordination, and calculates the disturbance weight index based on the coupling relationship between potential fault sources and their neighborhoods, thereby achieving accurate positioning of the fault propagation path and the scope of influence. The node degradation identification module maps multi-source fuzzy inputs to node propagation risk probabilities through fuzzy inference rules, accurately identifying high-propagation-risk nodes while ensuring a low false alarm rate. This significantly improves the system's ability to detect and prevent soft fault ghost effects, and also has scalability and engineering feasibility, significantly enhancing the reliability of system operation, reducing operation and maintenance costs, and improving the safety and energy efficiency of smart city lighting systems.

[0023] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0024] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0025] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0026] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0027] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A remote monitoring and intelligent control system for guardrail lights based on the Internet of Things, characterized in that: It includes a node status acquisition module, a soft fault node identification module, a propagation chain area perception module, and a node degradation identification module; The node status acquisition module is used to mark each guardrail tube light as a guardrail tube light node and acquire the brightness control response delay information and instruction execution offset information of the guardrail tube light. The brightness control response delay information includes a brightness control response delay coefficient, and the instruction execution offset information includes an instruction execution offset coefficient. The soft fault node identification module is used to construct a potential soft fault node evaluation model based on the brightness control response delay coefficient and the instruction execution offset coefficient, output the potential soft fault node evaluation index, and identify the guardrail tube light node that triggers potential soft faults. The formula used in the potential soft failure node assessment model is as follows: In the formula As an evaluation index for potential soft failure nodes, The brightness control response delay coefficient, The instruction execution offset coefficient. These represent the preset proportional coefficients for the brightness control response delay coefficient and the command execution offset coefficient, respectively. All greater than 0 The propagation chain area perception module is used to construct the propagation sensitivity weight matrix of the guardrail light nodes based on the physical distance and state coordination between the guardrail light nodes, and to calculate the disturbance weight index of each guardrail light node to determine whether the guardrail light node is in the propagation chain influence zone. The node degradation identification module is used to identify high-risk nodes for propagation by using fuzzy reasoning for guardrail light nodes that are triggered by soft faults within the propagation chain's influence zone.

2. The remote monitoring and intelligent control system for guardrail lights based on the Internet of Things as described in claim 1, characterized in that: By acquiring the brightness control response delay information of the guardrail tube lights, the brightness control response delay of the guardrail tube lights is analyzed, and the brightness control response delay coefficient is calculated to measure the degree of brightness control response delay of the guardrail tube lights. The logic for obtaining the brightness control response delay coefficient is as follows: The periodic data collected for each guardrail light node includes: command issuance time. Actual brightness change start time Target brightness value Actual brightness change curve ,in This is the time offset after the instruction is issued; Calculate the time difference of delay: ,in For the time difference of delay; Calculate the average response latency: ,in For the guardrail tube light nodes in the control cycle Average response latency within, The time difference of delay calculated at time k; Calculate the periodic response delay fluctuation value: ,in This represents the periodic response delay fluctuation value. Calculate the actual luminance response deviation rate: ,in This represents the actual brightness response deviation rate. Control cycle The time difference data within the interval is divided into For each of the three non-overlapping equal-width sub-intervals, count the number of delay time differences exceeding a preset delay time difference threshold, and calculate the delay probability. ,in For the first The delay probability of each sub-interval, For the first The number of sub-intervals with a delay time difference greater than the delay time difference threshold. , The total number of sub-intervals; calculate the information entropy of the delayed response: ,in For delayed response information entropy; Calculate the brightness control response delay factor: ,in The brightness control response delay coefficient, These represent the preset proportional coefficients for the average response delay rate, periodic response delay fluctuation value, actual brightness response deviation rate, and delay response information entropy, respectively. All are greater than 0.

3. The remote monitoring and intelligent control system for guardrail lights based on the Internet of Things as described in claim 1, characterized in that: By analyzing the command execution offset information of the guardrail tube lights, the command execution offset situation of the guardrail tube lights is analyzed, and the command execution offset coefficient is calculated to measure the degree of command execution offset of the guardrail tube lights. The logic for obtaining the instruction execution offset coefficient is as follows: If the guardrail light node receives a brightness control command at time t, the control target will be adjusted according to the time interval. The changing function is expressed as: ,in Indicates the relative time after the control command is executed. To control the brightness value of the target; Using the empirical parameter library Establish the family of expected response functions: ,in Indicates the parameter index of the empirical response function. Indicates in the parameter The expected response function generated below, This represents the set of expected response functions; Construct the desired trajectory function: ,in Represents the desired brightness trajectory. This indicates the target brightness change range of the control command. , The optimal matching parameters are those that best match the actual behavior of the guardrail lamp nodes in the expected response function family. For optimal matching parameters The expected response function generated; Obtain the actual brightness trajectory and compare it with the expected trajectory to construct the offset residual function: ,in For offset residual function, For the actual brightness trajectory; generate an offset residual vector sequence: ,in For the d-th discrete sampling point, , This represents the total number of sampling points; Define a set of multi-scale sliding window sizes: ,in Indicates the first Various scales of sliding window size, This represents the total number of scale layers; Calculate the instruction execution offset coefficient: ,in Indicates the instruction execution offset coefficient. Indicates the number of sliding windows. , .

4. The remote monitoring and intelligent control system for guardrail lights based on the Internet of Things as described in claim 1, characterized in that: The potential soft fault node evaluation index of each guardrail tube light node is compared with the preset potential soft fault node evaluation index threshold to identify the guardrail tube light node that triggers a potential soft fault, as follows: If the evaluation index of a potential soft fault node is greater than the threshold of the evaluation index of a potential soft fault node, then the current guardrail tube light node is marked as a guardrail tube light node that is triggered by a potential soft fault. If the evaluation index of a potential soft fault node is less than or equal to the threshold of the evaluation index of a potential soft fault node, then the guardrail tube light node is regarded as a normal node and the potential soft fault flag is not triggered.

5. The remote monitoring and intelligent control system for guardrail lights based on the Internet of Things as described in claim 1, characterized in that: Construct a spatial topology diagram of the guardrail light nodes based on potential soft fault triggering. ,in This is a collection of guardrail tube light nodes. Given an edge set, the propagation sensitivity weight matrix is ​​obtained by calculating the propagation sensitivity weights of guardrail light nodes u and v based on the physical distance and state synergy between them. : ,in This represents the propagation sensitivity weights of guardrail light nodes u and v. Let u be the physical distance between guardrail tube light node and v. This is to prevent division by zero by a very small constant. The state coordination degree between guardrail tube light node u and guardrail tube light node v; Calculate the disturbance weight index for each guardrail light node: ,in This is the perturbation weighting index, used to comprehensively reflect the propagation impact of the failure risk of its neighboring nodes on this node. This represents the potential soft-fault node evaluation index for the guardrail tube light node u. Let v be the propagation sensitivity weight for guardrail light nodes u and v, where v = {1, 2, ..., V} and V is a positive integer.

6. The remote monitoring and intelligent control system for guardrail lights based on the Internet of Things as described in claim 5, characterized in that: The disturbance weight index of each guardrail tube light node is compared with a preset disturbance weight index threshold to determine whether the guardrail tube light node is in the propagation chain influence zone, as follows: If the disturbance weight index is greater than the disturbance weight index threshold, it means that the current guardrail tube light node is in the propagation chain influence zone. If the disturbance weight index is less than or equal to the disturbance weight index threshold, it means that the current guardrail tube light node is not in the influence zone of the propagation chain.

7. The remote monitoring and intelligent control system for guardrail lights based on the Internet of Things as described in claim 1, characterized in that: For soft fault-triggered guardrail light nodes located within the propagation chain's influence zone, high-risk propagation nodes are identified through fuzzy inference. The specific process is as follows: Step A1: Obtain the potential soft fault node evaluation index and the disturbed weight index of the soft fault-triggered guardrail tube light node within the influence zone of the propagation chain. Define the potential soft fault node evaluation index and the disturbed weight index of the soft fault-triggered guardrail tube light node as input variables and divide them into different fuzzy sets respectively. Step A2: Define the node propagation risk probability as an output variable and partition it into a fuzzy set. Step A3: Develop a set of fuzzy rules to describe the impact of different input variables on the output variable; Step A4: Perform fuzzy reasoning based on fuzzy rules to identify nodes with high propagation risk.

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