Intelligent traffic internet-of-things fault monitoring and control system

Abstract

The invention discloses an intelligent traffic Internet of Things fault monitoring and control system, and belongs to the technical field of fault detection. Comprising a data acquisition module configured to acquire operation state data of traffic Internet of Things terminal equipment in real time through a heterogeneous communication interface, and the terminal equipment at least comprises a traffic signal lamp, a vehicle-mounted sensor and road monitoring equipment; according to the invention, equipment attributes, data timeliness and environment state parameters are effectively fused through the multi-dimensional dynamic priority scheduling module, transmission priority weights are dynamically generated, and network load self-adaptive polling and competition time slot ratio dynamic adjustment are realized in combination with the hybrid access control module. And the fault positioning precision, the resource utilization efficiency and the emergency response real-time performance of the system in a high-density equipment scene are improved.

Description

Technical Field

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

[0002] With the rapid development of intelligent transportation systems, the sharp increase in the scale of Internet of Things terminal devices has led to a significant increase in the complexity of network topologies, and traditional fault monitoring and control solutions face multiple technical bottlenecks. In the prior art, heterogeneous device data collection mostly adopts a fixed polling mechanism, which is difficult to adapt to sudden traffic scenarios and easily causes network congestion and critical data delays; the priority scheduling strategy relies on static weight allocation and lacks the dynamic response ability to data timeliness, network load status, and environmental risks, resulting in the inability to transmit high-value data in a timely manner.

[0003] Existing methods mostly focus on the state detection of a single device and do not fully consider the spatio-temporal correlation characteristics between devices, making it impossible to accurately identify the fault propagation path and influence range, and often resulting in misjudgment or missed judgment problems. Especially in areas with high-density device deployment, the resource allocation efficiency, fault location accuracy, and emergency control real-time performance of existing systems are no longer able to meet the reliability requirements of intelligent transportation systems. Summary of the Invention

[0004] In view of the above technical problems, a fault monitoring and control system for the Internet of Things in intelligent transportation is characterized by comprising: A data collection module configured to obtain the operation status data of traffic Internet of Things terminal devices in real time through heterogeneous communication interfaces, where the terminal devices at least include traffic lights, vehicle-mounted sensors, and road monitoring devices.

[0005] A dynamic scheduling module connected to the data collection module, including a priority evaluation unit and a hybrid access control unit. The priority evaluation unit is used to perform multi-dimensional priority evaluation matrix calculations, including a device attribute evaluation subunit for calculating the reference weight associated with the device type. The priority evaluation unit also includes a data feature analysis subunit for evaluating dynamic parameters including a timeliness-sensitive function and a data scale impact function; the priority evaluation unit also includes an environmental state perception subunit for monitoring the network load rate and calculating the environmental risk coefficient; the priority evaluation unit also includes a weight comprehensive calculation subunit for synthesizing the transmission priority weight value according to a preset formula.

[0006] 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 pre-define the dynamic time slot ratio of the polling stage and the competition stage for each interval; the resource allocation component is used to perform fixed time slot allocation in the polling stage and implement a collision avoidance mechanism in the competition stage.

[0007] The analysis and decision-making module, connected to the dynamic scheduling module, includes a spatio-temporal correlation analysis unit, a multi-level instruction generation unit, and an adaptive routing unit. The spatio-temporal correlation analysis unit includes a topology map construction component for establishing a correlation map based on the physical location and functional dependency of devices; the spatio-temporal correlation analysis unit also includes a timing feature extraction component for processing device data streams using a neural network model; the spatio-temporal correlation analysis unit also includes a fault propagation analysis component for calculating the fault diffusion path and generating a heat map of the influence range using a graph structure analysis mechanism.

[0008] The control and execution module, connected to the analysis and decision-making module, contains a multi-level instruction generation unit. The multi-level instruction generation unit is used to pre-define a dynamic distance threshold interval based on device deployment density; trigger device-level, association-level, or regional-level control strategies according to the interval where the fault influence range is located; the control and execution module also includes an adaptive routing unit configured to select the optimal path to issue a hierarchical control instruction set.

[0009] Further, in the weight comprehensive calculation sub-unit, the time-sensitive function adopts a non-linear model with a decreasing delay over time; the data scale influence function adopts a conversion model with a non-linear growth with the data volume.

[0010] The time slot window division component is configured to: set a time slot ratio dominated by the polling stage in the low load interval; set a balanced time slot ratio of polling and competition stages in the medium load interval; set a time slot ratio dominated by the competition stage in the high load interval.

[0011] Further, the execution strategies of the multi-level instruction generation unit include: the first-level strategy triggers the reset or status recovery operation of a single device parameter; the second-level strategy triggers the cooperative control strategy of associated devices; the third-level strategy triggers the regional-level emergency response control strategy.

[0012] Further, the data acquisition module also includes an abnormal filtering sub-module configured to: when the device data volume exceeds a preset threshold, start multi-source data cross-verification and legitimacy verification; store the data with failed verification in a buffer queue and mark the abnormal status.

[0013] Further, the topology map construction component defines the association strength between nodes as: a weighted combination of an attenuation function based on device distance and a functional dependency coefficient.

[0014] Further, when the system processes fault monitoring and control, the following steps are executed, Step S1, real-time collect the operation status data of terminal devices through a heterogeneous communication interface, and trigger a multi-source cross-verification mechanism when the data volume exceeds the threshold; calculate the dynamic priority based on the device type weight, data timeliness, and network load status, and generate a data stream with a priority identifier.

[0015] Step S2: Construct a device topology map, extract timing features, and determine the fault impact boundary by combining graph structure propagation analysis; select device-level, association-level, or regional-level control instruction sets according to the dynamic distance threshold interval where the fault impact range is located.

[0016] Step S3: Feedback the control instruction to the target device through an adaptive routing strategy and monitor the instruction execution status.

[0017] In Step S1, the multi-source cross-validation mechanism includes the logical consistency check of device data within the same geographical area; the continuity and rationality verification of the data packet timestamp; and the compliance check that the data format conforms to the preset protocol specifications.

[0018] 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, where the higher the density, the smaller the threshold interval; binding different control strategies to each interval: the first interval triggers device-level local recovery operations; the second interval triggers the coordinated adjustment of signal light phases and the linkage of information release; the third interval triggers the global optimization of the navigation paths at the regional road network level.

[0019] The beneficial effects of the present invention compared with the prior art are as follows: (1) The present invention effectively integrates device attributes, data timeliness, and environmental state parameters through a multi-dimensional dynamic priority scheduling module, dynamically generates transmission priority weights, and combines a hybrid access control module to dynamically adjust the polling and contention time slot ratio for network load adaptation; (2) The present invention accurately identifies the fault propagation path based on the construction of a spatio-temporal correlation topology map and a neural network timing analysis module, and combines an adaptive dynamic threshold hierarchical control strategy based on device deployment density to achieve accurate matching of the fault impact range and the emergency response intensity, improving the fault location accuracy, resource utilization efficiency, and emergency response real-time performance of the system in high-density device scenarios, and overall enhancing the reliability and robustness of the intelligent transportation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the overall structure composition of the system of the present invention.

[0021] Figure 2 It is a block diagram of the composition of the priority evaluation unit of the present invention.

[0022] Figure 3 It is a block diagram of the composition of the spatio-temporal correlation analysis unit of the present invention.

[0023] Figure 4 It is an exemplary step flowchart of the monitoring and control process of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0024] 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 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 only used to explain the present invention and are not used to limit the present invention.

[0025] Embodiment: As Figure 1 shown, a fault monitoring and control system for an intelligent transportation Internet of Things includes a data acquisition module configured to obtain the operation status data of traffic Internet of Things terminal devices in real time through a heterogeneous communication interface, and the terminal devices at least include traffic lights, vehicle-mounted sensors and road monitoring devices.

[0026] a dynamic scheduling module connected to the data acquisition module, which includes a priority evaluation unit and a hybrid access control unit.

[0027] As Figure 2 shown, the priority evaluation unit is used to perform multi-dimensional priority evaluation matrix calculation, including a device attribute evaluation subunit for calculating a reference weight associated with the device type. The priority evaluation unit further includes a data feature analysis subunit for evaluating dynamic parameters including a time-sensitive function and a data scale impact function; the priority evaluation unit further includes an environmental state perception subunit for monitoring the network load rate and calculating the environmental risk coefficient; the priority evaluation unit further includes a weight comprehensive calculation subunit for synthesizing a transmission priority weight value according to a preset formula.

[0028] 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 pre-define the dynamic time slot ratio of the polling stage and the contention stage for each interval; the resource allocation component is used to perform fixed time slot allocation in the polling stage and implement a collision avoidance mechanism in the contention stage.

[0029] An analysis and decision-making module connected to the dynamic scheduling module includes a spatio-temporal association analysis unit, a multi-level instruction generation unit and an adaptive routing unit.

[0030] As Figure 3 shown, the spatio-temporal association analysis unit includes a topology graph construction component for establishing an association graph based on the physical location and functional dependency of the devices; the spatio-temporal association analysis unit further includes a time series feature extraction component for processing the device data stream by using a neural network model; the spatio-temporal association analysis unit further includes a fault propagation analysis component for calculating the fault diffusion path by applying a graph structure analysis mechanism and generating a heat map of the influence range.

[0031] The control execution module, connected to the analysis and decision-making module, includes a multi-level instruction generation unit. The multi-level instruction generation unit is used to pre-define a dynamic distance threshold interval based on device deployment density; trigger device-level, association-level, or area-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.

[0032] In the weight comprehensive calculation sub-unit, the time-sensitive function adopts a non-linear model with a decreasing delay over time; the data scale impact function adopts a conversion model with non-linear growth with the data volume.

[0033] The time slot window division component is configured to: set a time slot ratio with polling phase dominant in the low load interval; set a time slot ratio with balanced polling and competition phases in the medium load interval; set a time slot ratio with competition phase dominant in the high load interval.

[0034] The execution strategies of the multi-level instruction generation unit include: the first-level strategy triggers the reset or status recovery operation of single device parameters; the second-level strategy triggers the cooperative control strategy of associated devices; the third-level strategy triggers the area-level emergency response control strategy.

[0035] The data acquisition module also includes an exception filtering sub-module, configured to: when the device data volume exceeds the preset threshold, start multi-source data cross-verification and legitimacy verification; store the data with failed verification in the buffer queue and mark the exception status.

[0036] The topology graph construction component defines the association strength between nodes as: a weighted combination of an attenuation function based on device distance and a functional dependence coefficient.

[0037] When the system processes for fault monitoring and control, the following steps are executed, Step S1, real-time collect the operation status data of terminal devices through the heterogeneous communication interface, and trigger the multi-source cross-verification mechanism when the data volume exceeds the threshold; calculate the dynamic priority based on device type weight, data timeliness, and network load status, and generate a data stream with a priority identifier.

[0038] Step S2, construct a device topology graph and extract time series features, combine graph structure propagation analysis to determine the fault impact boundary; according to the dynamic distance threshold interval where the fault impact range is located, select a device-level, association-level, or area-level control instruction set.

[0039] Step S3, feedback the control instruction to the target device through the adaptive routing strategy, and monitor the instruction execution status.

[0040] In step S1, the multi-source cross-verification mechanism includes the logical consistency verification of device data within the same geographical area; the continuity and rationality verification of the data packet timestamp; the compliance check that the data format conforms to the preset protocol specification.

[0041] 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; binding differentiated control strategies 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.

[0042] 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), 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 duration of the red light of the traffic light is abnormal (conflicting with the data of adjacent devices); timestamp verification: it is detected 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: identify the CRC check field required by the protocol when 20% of the data packets are missing; 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 the traffic light is set to 0.6 (higher than 0.4 for the camera). Data timeliness: Using the attenuation function, the weight of the abnormal data decreases by 15% for every 1 second delay after it is generated. Network load: The current load rate is 65% (triggering high load mode). The priority queue is generated comprehensively, and the abnormal traffic light data obtains the highest transmission level. Fault propagation and hierarchical control (corresponding to steps S3-S4). Heat map analysis, identifying the faulty traffic light as the source of influence, and calculating the propagation path weight: the influence value of the directly connected camera node is 0.85; the influence value of the traffic light node at the adjacent intersection is 0.62; the heat map is generated to show that the core influence radius is 200 meters (equipment density: 85 units / km²). Dynamic threshold matching: Set the threshold interval under the current density: The first interval (<100 meters): trigger the traffic light self-check and status reset; the second interval (100-300 meters): link the phases of the three adjacent traffic lights (shorten the cycle by 20%); the third interval (>300 meters): push the detour suggestion to the navigation platform and open the emergency lane.

[0043] 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 specific embodiments described or replace them in a similar manner. As long as they do not deviate from the scope defined by the invention, they shall all fall within the protection scope 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 the 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 signal light, a vehicle-mounted sensor, and a road monitoring device; A 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 a reference weight associated with a device type. The priority evaluation unit also includes 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 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; An analysis and decision module, connected to the dynamic scheduling module, includes a spatiotemporal correlation analysis unit, a multi-level instruction generation unit, and an adaptive routing unit, wherein the spatiotemporal correlation analysis unit includes a topology map construction component for establishing a correlation map based on the physical location of the device and the functional dependency; A control execution module is connected to the analysis and decision-making module and includes a multi-level instruction generation unit, which 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 where the fault impact range is located; the control execution module also includes an adaptive routing unit, which is configured to select the optimal path to issue a hierarchical control instruction set.

2. According to claim 1, the intelligent transportation Internet of Things fault monitoring and control system is characterized by: 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 that uses a graph structure analysis mechanism to calculate the fault diffusion path and generate an impact range heat map.

3. According to claim 1, the intelligent transportation Internet of Things fault monitoring and control system is characterized in that: 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 comprehensive 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 is characterized in that: In the weight 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; 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; 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 legality verification; store the verification failed data into a buffer queue and mark the abnormal state.

7. The intelligent transportation Internet of Things fault monitoring and control system according to claim 1 is characterized in that: The topology map construction component defines the strength of association 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 is characterized by: The system performs the following steps when performing fault monitoring and control: Step S1, real-time collection of terminal equipment operation status data through heterogeneous communication interfaces, triggering a multi-source cross-validation mechanism when the data volume exceeds a threshold; calculating a dynamic priority based on equipment type weight, data timeliness, and network load status, and generating a data stream with a priority identifier; Step S2, constructing a device topology map and extracting timing features, combining the graph structure propagation analysis to determine the fault impact boundary; selecting a device-level, association-level or regional-level control instruction set according to the dynamic distance threshold interval of the fault impact range; Step S3, feeding back the control instruction to the target device through the adaptive routing strategy, and monitoring the instruction execution status.

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

10. The intelligent transportation Internet of Things fault monitoring and control system according to claim 8, 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; binding differentiated control strategies 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.

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