A smart transportation Internet of Things fault monitoring and control system

Abstract

The present invention discloses a smart transportation Internet of Things fault monitoring and control system, which belongs to the field of fault detection technology; it includes a data acquisition module, which is configured to obtain the operating status data of the transportation Internet of Things terminal equipment in real time through a heterogeneous communication interface, and the terminal equipment at least includes traffic lights, vehicle-mounted sensors and road monitoring equipment; the invention effectively integrates equipment attributes, data timeliness and environmental status parameters through a multi-dimensional dynamic priority scheduling module, dynamically generates transmission priority weights, and combines with a hybrid access control module to realize adaptive polling of network load and dynamic adjustment of contention time slot ratio, thereby improving the system's fault location accuracy, resource utilization efficiency and real-time emergency response in high-density equipment scenarios.

Description

Technical Field

[0001] The present invention relates to the field of fault detection, and in particular to a smart transportation Internet of Things fault monitoring and control system. Background Art

[0002] With the rapid development of intelligent transportation systems and the dramatic increase in the number of IoT devices, network topology complexity has increased significantly, and traditional fault monitoring and control solutions face multiple technical bottlenecks. Existing technologies often rely on fixed polling mechanisms for heterogeneous device data collection, which struggles to adapt to bursty traffic scenarios and can easily cause network congestion and critical data delays. Priority scheduling strategies rely on static weight allocation and lack the ability to dynamically respond to data timeliness, network load status, and environmental risks, hindering the timely transmission of high-value data.

[0003] Existing methods often rely on single-device status detection, failing to fully consider the temporal and spatial correlations between devices. This makes it difficult to accurately identify fault propagation paths and impact areas, often leading to misjudgments or missed detections. Especially in areas with high-density device deployments, existing systems struggle to meet the reliability requirements of intelligent transportation systems in terms of resource allocation efficiency, fault location accuracy, and real-time emergency control. Summary of the Invention

[0004] In response to the above technical problems, a smart transportation Internet of Things fault monitoring and control system is proposed, which is characterized by:

[0005] The data acquisition module is configured to obtain the operating status data of the transportation Internet of Things terminal equipment in real time through a heterogeneous communication interface. The terminal equipment includes at least traffic lights, vehicle-mounted sensors and road monitoring equipment.

[0006] The dynamic scheduling module is connected to the data acquisition module and includes a priority evaluation unit and a hybrid access control unit. The priority evaluation unit is used to perform multi-dimensional priority evaluation matrix calculation, including a device attribute evaluation subunit for calculating the benchmark weight associated with the device type. The priority evaluation unit also includes a data feature analysis subunit for evaluating dynamic parameters including time-sensitive functions and data scale impact functions; the priority evaluation unit also includes an environmental status perception subunit for monitoring the network load rate and calculating the environmental risk coefficient; the priority evaluation unit also includes a weight synthesis calculation subunit for synthesizing the transmission priority weight value according to a preset formula.

[0007] The hybrid access control unit includes a time slot window division component and a resource allocation component. The time slot window division component is used to divide the network load rate into multiple continuous intervals and predefine the dynamic time slot ratio of the polling phase and the contention phase for each interval; the resource allocation component is used to perform fixed time slot allocation in the polling phase and implement a conflict avoidance mechanism in the contention phase.

[0008] The analysis and decision-making module is connected to the dynamic scheduling module and includes a spatiotemporal correlation analysis unit. The spatiotemporal correlation analysis unit includes a topology map construction component for establishing a correlation map based on the physical location and functional dependency of the equipment; the spatiotemporal correlation analysis unit also includes a timing feature extraction component for processing the equipment data stream using a neural network model; the spatiotemporal correlation analysis unit also includes a fault propagation analysis component, which applies a graph structure analysis mechanism to calculate the fault diffusion path and generate an impact range heat map.

[0009] A control execution module is connected to the analysis and decision-making module and includes a multi-level instruction generation unit. The multi-level instruction generation unit is used to predefine a dynamic distance threshold interval based on the device deployment density; trigger a device-level, association-level or regional-level control strategy according to the interval in which the fault impact range is located; the control execution module also includes an adaptive routing unit configured to select the optimal path to issue a hierarchical control instruction set.

[0010] Furthermore, in the weight comprehensive calculation subunit, the time-sensitive function adopts a nonlinear model that decreases with time delay; and the data scale impact function adopts a conversion model that increases nonlinearly with the amount of data.

[0011] The time slot window division component is configured as follows: in the low load interval, a time slot ratio in which the polling phase dominates is set; in the medium load interval, a time slot ratio in which the polling phase and the contention phase are balanced is set; in the high load interval, a time slot ratio in which the contention phase dominates is set.

[0012] Furthermore, the multi-level instruction generation unit execution strategy includes: the first level strategy triggers a single device parameter reset or state recovery operation; the second level strategy triggers an associated device collaborative control strategy; and the third level strategy triggers a regional emergency response control strategy.

[0013] Furthermore, the data acquisition module also includes an abnormal filtering submodule, which is configured to: when the device data volume exceeds a preset threshold, start multi-source data cross-validation and legitimacy verification; store the verification failed data in a buffer queue and mark the abnormal status.

[0014] Furthermore, the topology map construction component defines the association strength between nodes as a weighted combination of a decay function based on device spacing and a functional dependency coefficient.

[0015] Furthermore, the system performs the following steps when performing fault monitoring and control:

[0016] Step S1: collect terminal device operating status data in real time through heterogeneous communication interfaces, and trigger a multi-source cross-validation mechanism when the data volume exceeds a threshold; calculate dynamic priority based on device type weight, data timeliness, and network load status, and generate a data stream with a priority identifier.

[0017] Step S2: Build a device topology map and extract timing features, combine graph structure propagation analysis to determine the fault impact boundary; select device-level, association-level, or regional-level control instruction sets based on the dynamic distance threshold interval of the fault impact range.

[0018] Step S3: Feedback the control instruction to the target device through the adaptive routing strategy, and monitor the instruction execution status.

[0019] In step S1, the multi-source cross-validation mechanism includes logical consistency verification of device data within the same geographical area; verification of the continuity and rationality of data packet timestamps; and compliance check of data format compliance with preset protocol specifications.

[0020] In step S2, the method for setting the dynamic distance threshold interval includes automatically shrinking or expanding the threshold range according to the device deployment density, and the higher the density, the smaller the threshold interval; binding a differentiated control strategy for each interval: the first interval triggers the device-level local recovery operation; the second interval triggers the coordinated adjustment of the traffic light phase and the linkage of information release; the third interval triggers the global optimization of the regional road network-level navigation path.

[0021] Compared with the prior art, the present invention has the following advantages: (1) the present invention effectively integrates device attributes, data timeliness and environmental status parameters through a multi-dimensional dynamic priority scheduling module, dynamically generates transmission priority weights, and realizes dynamic adjustment of network load adaptive polling and contention time slot ratios in combination with a hybrid access control module; (2) the present invention accurately identifies fault propagation paths based on the construction of a spatiotemporal correlation topology map and a neural network timing analysis module, and realizes accurate matching of the fault impact range and emergency response intensity in combination with a dynamic threshold grading control strategy that is adaptive to the device deployment density, thereby improving the system's fault location accuracy, resource utilization efficiency and emergency response real-time performance in high-density device scenarios, and overall enhancing the reliability and robustness of the smart transportation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the overall structure of the system of the present invention.

[0023] Figure 2 This is a block diagram of the priority evaluation unit of the present invention.

[0024] Figure 3 This is a block diagram of the spatiotemporal correlation analysis unit of the present invention.

[0025] Figure 4 The figure is a flowchart of an exemplary step of the monitoring and control process of the present invention. DETAILED DESCRIPTION

[0026] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0027] Example: Figure 1 The intelligent transportation Internet of Things fault monitoring and control system shown includes:

[0028] The data acquisition module is configured to obtain the operating status data of the transportation Internet of Things terminal equipment in real time through a heterogeneous communication interface. The terminal equipment includes at least traffic lights, vehicle-mounted sensors and road monitoring equipment.

[0029] The dynamic scheduling module is connected to the data acquisition module and includes a priority evaluation unit and a hybrid access control unit.

[0030] like Figure 2 The priority evaluation unit shown is used to perform multi-dimensional priority evaluation matrix calculations, including a device attribute evaluation sub-unit for calculating a baseline weight associated with a device type. The priority evaluation unit also includes a data feature analysis sub-unit for evaluating dynamic parameters including a time-sensitive function and a data scale impact function; the priority evaluation unit also includes an environmental status perception sub-unit for monitoring the network load rate and calculating the environmental risk coefficient; the priority evaluation unit also includes a weight synthesis calculation sub-unit for synthesizing the transmission priority weight value according to a preset formula.

[0031] The hybrid access control unit includes a time slot window division component and a resource allocation component. The time slot window division component is used to divide the network load rate into multiple continuous intervals and predefine the dynamic time slot ratio of the polling phase and the contention phase for each interval; the resource allocation component is used to perform fixed time slot allocation in the polling phase and implement a conflict avoidance mechanism in the contention phase.

[0032] The analysis and decision-making module is connected with the dynamic scheduling module and includes a spatiotemporal correlation analysis unit.

[0033] like Figure 3 The spatiotemporal correlation analysis unit shown includes a topology map construction component for establishing a correlation map based on the physical location and functional dependency of the equipment; the spatiotemporal correlation analysis unit also includes a timing feature extraction component for processing equipment data streams using a neural network model; the spatiotemporal correlation analysis unit also includes a fault propagation analysis component, which applies a graph structure analysis mechanism to calculate the fault diffusion path and generate an impact range heat map.

[0034] The control execution module is connected to the analysis and decision-making module and includes a multi-level instruction generation unit. The multi-level instruction generation unit is used to predefine dynamic distance threshold intervals based on device deployment density; trigger device-level, association-level or regional-level control strategies according to the interval where the fault impact range is located; the control execution module also includes an adaptive routing unit configured to select the optimal path to issue a hierarchical control instruction set.

[0035] In the weighted comprehensive calculation subunit, the time-sensitive function adopts a nonlinear model that decreases with time delay; the data scale impact function adopts a conversion model that grows nonlinearly with the amount of data.

[0036] The time slot window division component is configured as follows: in the low load interval, the polling phase is set to dominate the time slot ratio; in the medium load interval, the polling and contention phases are balanced to set the time slot ratio; in the high load interval, the contention phase is set to dominate the time slot ratio.

[0037] The multi-level instruction generation unit execution strategy includes: the first-level strategy triggers the single device parameter reset or status recovery operation; the second-level strategy triggers the associated device collaborative control strategy; the third-level strategy triggers the regional emergency response control strategy.

[0038] The data acquisition module also includes an abnormal filtering submodule, which is configured to: when the amount of device data exceeds the preset threshold, start multi-source data cross-validation and legitimacy verification; store the verification failed data in the buffer queue and mark the abnormal status.

[0039] The topology map construction component defines the association strength between nodes as a weighted combination of a decay function based on device spacing and a functional dependency coefficient.

[0040] The system performs the following steps when performing fault monitoring and control:

[0041] Step S1: collect terminal device operating status data in real time through heterogeneous communication interfaces, and trigger a multi-source cross-validation mechanism when the data volume exceeds a threshold; calculate dynamic priority based on device type weight, data timeliness, and network load status, and generate a data stream with a priority identifier.

[0042] Step S2: Build a device topology map and extract timing features, combine graph structure propagation analysis to determine the fault impact boundary; select device-level, association-level, or regional-level control instruction sets based on the dynamic distance threshold interval of the fault impact range.

[0043] Step S3: Feedback the control instruction to the target device through the adaptive routing strategy, and monitor the instruction execution status.

[0044] In step S1, the multi-source cross-validation mechanism includes logical consistency verification of device data within the same geographical area; verification of the continuity and rationality of data packet timestamps; and compliance check of data format compliance with preset protocol specifications.

[0045] In step S2, the method for setting the dynamic distance threshold interval includes automatically shrinking or expanding the threshold range according to the device deployment density, and the higher the density, the smaller the threshold interval; binding a differentiated control strategy for each interval: the first interval triggers the device-level local recovery operation; the second interval triggers the coordinated adjustment of the traffic light phase and the linkage of information release; the third interval triggers the global optimization of the regional road network-level navigation path.

[0046] In this embodiment, taking the smart transportation system at the intersection of a main road in a certain city as an example, the deployed terminal devices include: traffic light groups (4 groups, supporting 5G / V2X communication), vehicle-mounted sensors (50 buses equipped), and road monitoring cameras (8, supporting fiber / wireless dual-mode transmission); implementation process: data collection and verification: when a traffic light suddenly uploads 200 abnormal status data per second (exceeding the preset threshold of 150 / second), multi-source cross-verification is triggered, logical verification: comparing the vehicle traffic status captured by adjacent cameras, it is found that the red light duration of the traffic light is abnormal (conflicting with the data of adjacent devices); timestamp verification: detecting that the timestamp interval of 10 consecutive data packets suddenly changes from 50ms to 5ms (not in line with the device sampling cycle); format verification: identifying that 20% of the data packets are missing the CRC check field required by the protocol; abnormal data is stored in the buffer queue, and only 80 valid data that have passed the verification are retained for subsequent processing; dynamic scheduling and topology construction, priority calculation, device attributes Weight: The baseline weight of traffic lights is set at 0.6 (higher than the 0.4 for cameras). Data timeliness: A decay function is used, with the weight of abnormal data decreasing by 15% for every 1-second delay. Network load: The current load rate is 65% (triggering high-load mode). A comprehensive priority queue is generated, and abnormal traffic light data receives the highest transmission level. Fault propagation and hierarchical control (corresponding to steps S3-S4) are implemented. Heat map analysis identifies faulty traffic lights as the source of influence and calculates propagation path weights: the impact value of directly connected camera nodes is 0.85; the impact value of traffic light nodes at adjacent intersections is 0.62. A heat map is generated to show a core influence radius of 200 meters (device density: 85 devices / km²). Dynamic threshold matching: Threshold intervals are set under the current density: First interval (<100 meters): Trigger traffic light self-test and status reset; Second interval (100-300 meters): Linked phase adjustments of three adjacent traffic lights are implemented (shortening the cycle by 20%); Third interval (>300 meters): Detour suggestions are sent to the navigation platform, and the emergency lane is opened.

[0047] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in similar ways. As long as they do not deviate from the scope defined by the invention, they should all fall within the scope of protection of the present invention.

Claims

1. A smart transportation Internet of Things fault monitoring and control system, characterized by: include, A data acquisition module configured to obtain, in real time, operating status data of a transportation Internet of Things terminal device through a heterogeneous communication interface, wherein the terminal device includes at least a traffic light, an on-board sensor, and a road monitoring device; a dynamic scheduling module connected to the data acquisition module, comprising a priority evaluation unit and a hybrid access control unit, wherein the priority evaluation unit is configured to perform multi-dimensional priority evaluation matrix calculations and includes a device attribute evaluation subunit for calculating a benchmark weight associated with a device type, and further comprising a data feature analysis subunit for evaluating dynamic parameters including a time-sensitive function and a data scale impact function; The hybrid access control unit includes a time slot window division component and a resource allocation component. The time slot window division component is used to divide the network load rate into multiple continuous intervals and predefine a dynamic time slot ratio between the polling phase and the contention phase for each interval; the resource allocation component is used to perform fixed time slot allocation in the polling phase and implement a conflict avoidance mechanism in the contention phase. An analysis and decision module, connected to the dynamic scheduling module, includes a spatiotemporal correlation analysis unit, wherein the spatiotemporal correlation analysis unit includes a topology map construction component for establishing a device topology map based on the physical location and functional dependency of the devices; A control execution module, connected to the analysis and decision module, includes a multi-level instruction generation unit, which is used to predefine dynamic distance threshold intervals based on device deployment density and trigger device-level, association-level, or regional-level control strategies based on the interval of the fault impact range. The control execution module also includes an adaptive routing unit configured to select the optimal path to issue a hierarchical control instruction set; The system performs the following steps when performing fault monitoring and control: Step S1: Real-time collection of terminal device operating status data via heterogeneous communication interfaces. When the data volume exceeds a threshold, a multi-source cross-validation mechanism is triggered. Dynamic priorities are calculated based on device type weights, data timeliness, and network load status, generating data streams with priority identifiers. Step S2: Build a device topology map and extract timing features. Combined with graph structure propagation path analysis, determine the fault impact boundary. Based on the dynamic distance threshold interval of the fault impact range, select a device-level, association-level, or regional-level control instruction set. Step S3: Feedback the control instruction to the target device through the adaptive routing strategy, and monitor the instruction execution status.

2. The intelligent transportation Internet of Things fault monitoring and control system according to claim 1, characterized in that: The spatiotemporal correlation analysis unit also includes a time series feature extraction component for processing device data streams using a neural network model; the spatiotemporal correlation analysis unit also includes a fault propagation analysis component for applying a graph structure analysis mechanism to calculate the fault diffusion path and generate an impact range heat map.

3. The intelligent transportation Internet of Things fault monitoring and control system according to claim 1 is characterized by: The priority evaluation unit also includes an environmental status perception subunit for monitoring the network load rate and calculating the environmental risk coefficient; the priority evaluation unit also includes a weight synthesis calculation subunit for synthesizing the transmission priority weight value according to a preset formula.

4. The intelligent transportation Internet of Things fault monitoring and control system according to claim 1, characterized in that: In the data feature analysis subunit, the time-sensitive function adopts a nonlinear model that decreases with time delay, and the data scale impact function adopts a conversion model that grows nonlinearly with the amount of data; the time slot window division component is configured as follows: in the low load interval, a time slot ratio dominated by the polling phase is set, in the medium load interval, a time slot ratio balanced between the polling and contention phases is set, and in the high load interval, a time slot ratio dominated by the contention phase is set.

5. The intelligent transportation Internet of Things fault monitoring and control system according to claim 1 is characterized in that The multi-level instruction generation unit execution strategy includes: the first level strategy triggers a single device parameter reset or state recovery operation, the second level strategy triggers an associated device collaborative control strategy, and the third level strategy triggers a regional emergency response control strategy.

6. The intelligent transportation Internet of Things fault monitoring and control system according to claim 1 is characterized in that The data acquisition module also includes an abnormal filtering submodule, which is configured to: when the device data volume exceeds a preset threshold, start multi-source data cross-validation and legitimacy verification; store the verification failed data in a buffer queue and mark the abnormal status.

7. The intelligent transportation Internet of Things fault monitoring and control system according to claim 1, characterized in that: The topology map construction component defines the association strength between nodes as a weighted combination of a decay function based on device spacing and a functional dependency coefficient.

8. The intelligent transportation Internet of Things fault monitoring and control system according to claim 1, characterized in that: In step S1, the multi-source cross-validation mechanism includes a logical consistency check of device data within the same geographical area, a continuity and rationality verification of data packet timestamps, and a compliance check of the data format to ensure compliance with preset protocol specifications.

9. The intelligent transportation Internet of Things fault monitoring and control system according to claim 1, characterized in that: In step S2, the method for setting the dynamic distance threshold interval includes automatically shrinking or expanding the threshold range according to the device deployment density. The higher the density, the smaller the threshold interval, and binding a differentiated control strategy for each interval: the first interval triggers the device-level local recovery operation, the second interval triggers the coordinated adjustment of the traffic light phase and the linkage of information release, and the third interval triggers the global optimization of the regional road network-level navigation path.

Images