Self-adaptive control method and device for tidal intelligent guardrail

Abstract

The invention discloses a self-adaptive control method and device for a tidal intelligent guardrail, and particularly relates to the technical field of traffic safety. Comprising the steps of S01, intelligent guardrail distribution node numbering, S02, traffic data acquisition, S03, data interaction communication, S04, road utilization rate data processing, S05, vehicle passing demand processing, S06, traffic flow early warning analysis, S07, intelligent guardrail control and S08, intelligent guardrail movement fault detection. The road use data and the vehicle passing data of the road interval and the distribution nodes at the two ends are collected, the road use efficiency monitoring index and the vehicle passing demand index are calculated, the traffic flow early warning coefficient is updated in combination with the time sequence model, intelligent guardrail self-adaptive movement is achieved, the vehicle passing efficiency is improved, and the fault detection step is utilized. Displacement abnormity is accurately and timely processed, and stable operation of the intelligent guardrail and road safety are guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of traffic safety technology, and more specifically, to an adaptive control method and device for a tidal intelligent guardrail. Background Art

[0002] The tidal smart guardrail is an innovative traffic safety facility that dynamically adjusts guardrail configuration by monitoring traffic flow and road conditions in real time. It addresses the problem of slowdowns or traffic delays caused by increased one-way traffic during specific time periods, such as morning and evening rush hours in cities and unexpected situations. By widening the congested lane and controlling vehicle traffic in a specific direction to increase capacity in that direction, it effectively alleviates road congestion. This helps optimize traffic flow, improve road congestion, enhance road efficiency, and reduce the risk of collisions. It is widely used in high-traffic areas such as highway toll gates, high-speed railway stations, school intersections, commercial districts, and office buildings.

[0003] However, in actual use, it still has some shortcomings. For example, smart guardrails need to process data from multiple sensors in a very short time to identify and predict traffic patterns. Existing control methods make it difficult to achieve data sharing and unified management, and cannot evaluate traffic flow in real time, resulting in insufficient real-time and accuracy of smart guardrail control.

[0004] The existing intelligent guardrail movement control lacks an effective fault detection and correction mechanism. It is impossible to monitor the movement status of the intelligent guardrail and promptly detect faults such as displacement anomalies, thus affecting vehicle traffic safety. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide an adaptive control method and device for a tidal intelligent guardrail, which are used to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an adaptive control method for a tidal intelligent guardrail, comprising the following steps:

[0007] Step S01: Numbering of smart guardrail distribution nodes: According to the road structure, smart guardrail distribution nodes are planned in the road section and at both ends, and the distribution nodes in the road section and at both ends are numbered in sequence.

[0008] Step S02: Traffic data collection: The road usage data of the road section and the distribution nodes at both ends are collected through the camera, and the vehicle traffic data of the road section and the distribution nodes at both ends are collected through the sensors installed at the distribution nodes of the smart guardrail.

[0009] Step S03: Data interactive communication: A traffic data center is established to centrally store and manage relevant data from each smart guardrail distribution node, and the data is uploaded to the traffic data center through communication technology.

[0010] Step S04: Road usage rate data processing: Based on the road usage data of the road section and the distribution nodes at both ends, the road usage efficiency monitoring index of each lane of the road section and the distribution nodes at both ends is calculated.

[0011] Step S05: Vehicle traffic demand processing: Based on the vehicle traffic data of the road section and the distribution nodes at both ends, the vehicle traffic demand index of each lane of the road section and the distribution nodes at both ends is calculated.

[0012] Step S06: Traffic flow warning analysis: used to obtain the road usage efficiency monitoring index and vehicle traffic demand index of each lane of the road section and the distribution nodes at both ends, input the data into the time series model, and analyze to obtain the traffic flow warning coefficient of each lane of the road section and the distribution nodes at both ends.

[0013] Step S07: Intelligent guardrail control: used to obtain the traffic flow warning coefficient of each lane of the road section and the distribution nodes at both ends, compare it with the preset traffic flow warning coefficient, and control the movement of the intelligent guardrail at the distribution nodes.

[0014] Step S08: Intelligent guardrail movement fault detection: including equipment positioning sub-step, displacement detection sub-step and displacement correction sub-step.

[0015] Preferably, the specific analysis method of step S02: traffic data collection is:

[0016] The road usage data is the lane occupancy rate and lane vehicle turning rate of the distribution nodes at both ends of the road section collected by the camera;

[0017] The vehicle traffic data includes lane vehicle flow, lane average speed, lane entry volume, lane exit volume, and queue length at distribution nodes of collected road sections.

[0018] Preferably, the specific analysis method of step S04: road usage rate data processing is:

[0019] Step S41: extracting the maximum lane occupancy at each distribution node of the road section;

[0020] Step S42: Calculating the turning smoothness at each smart guardrail distribution node by averaging the lane vehicle turning pass rate at each distribution node in the road section;

[0021] Step S43: combining the lane occupancy rate and lane vehicle turning pass rate of the node to obtain the road use efficiency monitoring index of each lane at each smart guardrail distribution node.

[0022] Preferably, the specific analysis method of step S05: vehicle traffic demand processing is:

[0023] Step S51: extracting the maximum lane vehicle flow and average driving speed at each distribution node of the road section, and calculating the road section traffic demand factor of each lane at each smart guardrail distribution node based on the lane vehicle flow and lane average driving speed of the node;

[0024] Step S52: Set the maximum lane vehicle entry volume, maximum lane vehicle exit volume, and maximum queue length at each distribution node at both ends of the road, and calculate the road end traffic demand factor for each lane at each smart guardrail distribution node based on the lane vehicle entry volume, lane vehicle exit volume, and queue length at the node;

[0025] Step S53: Integrate the road section traffic demand factor and the road end traffic demand factor to obtain the vehicle traffic demand index of each lane at each smart guardrail distribution node.

[0026] Preferably, the traffic flow warning coefficient is specifically:

[0027] Based on the frequency of data collection, the historical road utilization efficiency monitoring index and vehicle traffic demand index are input into the time series model to update the traffic flow warning coefficient of each lane in the road section and the distribution nodes at both ends.

[0028] Preferably, the specific analysis method of step S07: intelligent guardrail control is:

[0029] Obtain the traffic flow warning coefficient of each lane in the road section and the distribution nodes at both ends, and compare it with the preset traffic flow warning coefficient. If the traffic flow warning coefficient of a lane at the node is greater than the preset traffic flow warning coefficient, it indicates that the traffic flow in the lane is too large and the road traffic resources need to be optimized. At this time, the intelligent guardrail automatically moves in the direction of the smaller traffic flow warning coefficient to improve road traffic efficiency. Otherwise, it indicates that the lane meets the traffic flow requirements.

[0030] Preferably, the specific analysis method of step S08: intelligent guardrail movement fault detection is:

[0031] Equipment positioning sub-step: During the movement of the smart guardrail, the initial coordinates, target coordinates, and actual coordinates of each smart guardrail are collected;

[0032] Displacement detection sub-step: Using the initial coordinates and target coordinates, the theoretical movement distance of each smart guardrail is calculated using the distance formula; using the initial coordinates and actual coordinates, the actual movement distance of each smart guardrail is calculated; using the theoretical movement distance and actual movement distance of each smart guardrail, the displacement of the smart guardrail during movement is detected to see if there is any abnormality. If there is any abnormality, a displacement correction instruction is sent through the displacement correction sub-step;

[0033] Displacement correction sub-step: used to receive the displacement correction instruction and control the smart guardrail to execute the displacement correction instruction. After the displacement correction operation is executed, the displacement detection sub-step is repeated to verify the displacement correction effect. If the smart guardrail still cannot reach the target position, the position of the smart guardrail will be locked and the maintenance personnel will be notified to check.

[0034] Preferably, the detection of whether there is any abnormality in the displacement is: setting an allowable displacement difference. When the absolute value of the difference between the actual moving distance of the smart guardrail and the theoretical moving distance is greater than the allowable displacement difference, it indicates that there is a displacement abnormality in the movement of the smart guardrail, and the displacement correction sub-step is immediately triggered. Otherwise, it indicates that there is no abnormality in the movement of the smart guardrail.

[0035] Preferably, an adaptive control device for a tidal intelligent guardrail comprises a memory, a processor, and a machine executable program stored in the memory and running on the processor.

[0036] Technical effects and advantages of the present invention:

[0037] 1. The present invention provides an adaptive control method and device for tidal intelligent guardrails, which collects road usage data of the road section and the distribution nodes at both ends through cameras, calculates the road usage efficiency monitoring index of each lane of the road section and the distribution nodes at both ends, collects vehicle traffic data of the road section and the distribution nodes at both ends through sensors installed at the distribution nodes of the intelligent guardrails, calculates the vehicle traffic demand index of each lane of the road section and the distribution nodes at both ends, further inputs the historical road usage efficiency monitoring index and vehicle traffic demand index into the time series model, updates the traffic flow warning coefficient of each lane of the road section and the distribution nodes at both ends, and compares it with the preset traffic flow warning coefficient. The traffic flow warning coefficient is compared. If the traffic flow warning coefficient of a lane at a node is greater than the preset traffic flow warning coefficient, it indicates that the traffic flow in this lane is too large and road traffic resources need to be optimized. At this time, the intelligent guardrail automatically moves to the direction with a smaller traffic flow warning coefficient to improve road traffic efficiency. Combining the vehicle traffic data detected by the sensor with the road usage data identified by the camera can obtain more detailed traffic flow information and improve the accuracy and completeness of the data. The traffic flow warning coefficient is evaluated based on the time series, which can provide early warning of traffic flow trends, thereby realizing adaptive adjustment of the intelligent guardrail and improving the overall traffic capacity of the road.

[0038] 2. The present invention provides an adaptive control method and device for tidal smart guardrails. The method utilizes smart guardrail movement fault detection, collects the coordinate data of the smart guardrails in each lane at the distribution node through the equipment positioning sub-step, detects displacement anomalies during the movement of the smart guardrail through the displacement detection sub-step, and if there is an anomaly, controls the smart guardrail to execute the displacement correction instruction through the displacement correction sub-step. After the displacement correction operation is executed, repeats the displacement detection sub-step to verify the displacement correction effect. If the smart guardrail still cannot reach the target position, the position of the smart guardrail is locked and the maintenance personnel are notified to check. Coordinate collection and real-time displacement monitoring are conducive to accurate and timely fault detection, provide strong guarantees for the stable operation of the smart guardrail, and ensure the safety of road traffic. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 The figure is a flow chart of an adaptive control method for a tidal intelligent guardrail according to the present invention. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] See also Figure 1 As shown, the present invention provides an adaptive control method for a tidal intelligent guardrail, comprising the following steps:

[0042] The step S01: numbering of smart guardrail distribution nodes: planning smart guardrail distribution nodes in the road section and at both ends according to the road structure, and numbering the distribution nodes in the road section and at both ends in sequence as 1, 2, ...i, ...n.

[0043] Step S02: Traffic data collection: collecting road usage data of the road section and the distribution nodes at both ends through cameras, and collecting vehicle traffic data of the road section and the distribution nodes at both ends through sensors installed at the distribution nodes of the smart guardrails;

[0044] In a possible design, the specific analysis method of step S02: traffic data collection is:

[0045] The road usage data is collected by cameras, which are the lane occupancy rate and lane vehicle turning rate of the distribution nodes in the road section and at both ends, and are marked as dk i j 、dz ij , where i = 1, 2, ... n, i represents the number of the i-th distribution node, j = 1, 2, ... m, j represents the number of the j-th lane;

[0046] The vehicle traffic data is the lane vehicle flow and lane average speed of the distribution nodes of the road section collected by the geomagnetic sensor, which are marked as tl i j , tv i j The infrared sensor of the geomagnetic sensor collects the lane vehicle entry volume, lane vehicle exit volume, and queue length of the distribution nodes at both ends of the road, which are marked as tr i j 、tc i j 、tp i j .

[0047] The step S03: data interactive communication: establishing a traffic data center to centrally store and manage relevant data from each smart guardrail distribution node, and the data is uploaded to the traffic data center through communication technology.

[0048] The step S04: road usage rate data processing: based on the road usage data of the road section and the distribution nodes at both ends, the road usage efficiency monitoring index of each lane of the road section and the distribution nodes at both ends is calculated.

[0049] In a possible design, the specific analysis method of step S04: road usage rate data processing is:

[0050] Step S41: extracting the maximum lane occupancy at each distribution node of the road section;

[0051] Step S42: Calculating the turning smoothness at each smart guardrail distribution node by averaging the lane vehicle turning pass rate at each distribution node in the road section;

[0052] Step S43: combining the lane occupancy rate and lane vehicle turning pass rate of the node to obtain the road use efficiency monitoring index of each lane at each smart guardrail distribution node.

[0053] In this embodiment, it should be specifically explained that the calculation formula of the road use efficiency monitoring index is:

[0054]

[0055] in,

[0056] in, Expressed as the road utilization efficiency monitoring index of the jth lane of the i-th distribution node, dk i j Expressed as the occupancy rate of the jth lane of the i-th distribution node, dk i max Expressed as the maximum lane occupancy of the i-th distribution node, dz i j It is expressed as the vehicle turning rate of the jth lane at the i-th distribution node, S i It is expressed as the turning smoothness of the i-th distribution node, e is expressed as a natural constant, m is expressed as the number of lanes, λ1 and λ2 are the weight factors of occupancy rate and vehicle turning pass rate respectively, and λ1+λ2=1.

[0057] The step S05: vehicle traffic demand processing: based on the vehicle traffic data of the road section and the distribution nodes at both ends, the vehicle traffic demand index of each lane of the road section and the distribution nodes at both ends is calculated.

[0058] In a possible design, the specific analysis method of step S05: vehicle traffic demand processing is:

[0059] Step S51: extracting the maximum lane vehicle flow and average driving speed at each distribution node of the road section, and calculating the road section traffic demand factor of each lane at each smart guardrail distribution node based on the lane vehicle flow and lane average driving speed of the node;

[0060] Step S52: Set the maximum lane vehicle entry volume, maximum lane vehicle exit volume, and maximum queue length at each distribution node at both ends of the road, and calculate the road end traffic demand factor for each lane at each smart guardrail distribution node based on the lane vehicle entry volume, lane vehicle exit volume, and queue length at the node;

[0061] Step S53: Integrate the road section traffic demand factor and the road end traffic demand factor to obtain the vehicle traffic demand index of each lane at each smart guardrail distribution node.

[0062] In this embodiment, it should be specifically explained that the calculation formula of the road section traffic demand factor is:

[0063]

[0064] in, Expressed as the road interval traffic demand factor of the jth lane of the ith distribution node, tl i j It is expressed as the vehicle flow of the jth lane of the i-th distribution node, tl i maxExpressed as the maximum lane vehicle flow of the i-th distribution node, tv i j Expressed as the average speed of the jth lane at the i-th distribution node, Δtv i It is represented as the average driving speed of the i-th distribution node, ε1 and ε2 are the weight factors of vehicle flow and average driving speed respectively, and ε1+ε2=1;

[0065] The calculation formula for the traffic demand factor at both ends of the road is:

[0066]

[0067] in, Expressed as the traffic demand factor at both ends of the road at the jth lane of the i-th distribution node, D i 入 、D i 出 They are respectively represented as the traffic demand factors at both ends of the road at the i-th distribution node, tr i j Expressed as the number of vehicles entering the jth lane of the i-th distribution node, TR i max It is expressed as the maximum lane vehicle entry volume of the i-th distribution node, tp i j It is expressed as the queue length of the jth lane of the i-th distribution node, TP i max It is expressed as the maximum queue length of the i-th distribution node, tc i j It is expressed as the number of vehicles leaving the jth lane of the i-th distribution node, TC i max It is represented by the maximum lane vehicle exit volume of the i-th distribution node, ε3, ε4, and ε5 are the weight factors of vehicle entry volume, queue length, and vehicle exit volume, respectively, and ε3+ε4+ε5=1;

[0068] The calculation formula of the vehicle traffic demand index is:

[0069] in, It is expressed as the vehicle traffic demand index of the jth lane of the i-th distribution node.

[0070] Step S06: Traffic flow warning analysis: used to obtain the road usage efficiency monitoring index and vehicle traffic demand index of each lane of the road section and the distribution nodes at both ends, input the data into the time series model, and analyze to obtain the traffic flow warning coefficient of each lane of the road section and the distribution nodes at both ends.

[0071] In a possible design, the traffic flow warning coefficient is specifically:

[0072] Based on the frequency of data collection, the historical road utilization efficiency monitoring index and vehicle traffic demand index are input into the time series model to update the traffic flow warning coefficient of each lane in the road section and the distribution nodes at both ends.

[0073] In this embodiment, it should be specifically explained that the historical road usage efficiency monitoring index and vehicle traffic demand index are input into the time series model to update the traffic flow warning coefficient of each lane of the road section and the distribution nodes at both ends, specifically:

[0074] in, Expressed as the traffic flow warning coefficient of the jth lane of the i-th distribution node, It is expressed as the road utilization efficiency monitoring index of the jth lane of the i-th distribution node, It is expressed as the vehicle traffic demand index of the jth lane of the i-th distribution node.

[0075] The step S07: intelligent guardrail control: is used to obtain the traffic flow warning coefficient of each lane of the road section and the distribution nodes at both ends, compare it with the preset traffic flow warning coefficient, and control the movement of the intelligent guardrail at the distribution nodes.

[0076] In one possible design, the specific analysis method of step S07: intelligent guardrail control is:

[0077] Obtain the traffic flow warning coefficient of each lane in the road section and the distribution nodes at both ends, and compare it with the preset traffic flow warning coefficient. If the traffic flow warning coefficient of a lane at the node is greater than the preset traffic flow warning coefficient, it indicates that the traffic flow in the lane is too large and the road traffic resources need to be optimized. At this time, the intelligent guardrail automatically moves in the direction of the smaller traffic flow warning coefficient to improve road traffic efficiency. Otherwise, it indicates that the lane meets the traffic flow requirements.

[0078] The step S08: intelligent guardrail movement fault detection: includes a device positioning sub-step, a displacement detection sub-step and a displacement correction sub-step. The device positioning sub-step collects the coordinate data of the intelligent guardrail of each lane at the distribution node. The displacement detection sub-step is used to detect displacement anomalies during the movement of the intelligent guardrail. The displacement correction sub-step performs displacement correction.

[0079] In one possible design, the specific analysis method of step S08: intelligent guardrail movement fault detection is:

[0080] Equipment positioning sub-step: During the movement of the smart guardrail, collect the initial coordinates (Xq o , Y q o ), target coordinates (X q 目 , Y q 目 ), actual coordinates (X q 实 , Y q 实 );

[0081] Displacement detection sub-step: Using the initial coordinates and target coordinates, the theoretical movement distance of each smart guardrail is calculated using the distance formula; using the initial coordinates and actual coordinates, the actual movement distance of each smart guardrail is calculated; using the theoretical movement distance and actual movement distance of each smart guardrail, the displacement of the smart guardrail during movement is detected to see if there is any abnormality. If there is any abnormality, a displacement correction instruction is sent through the displacement correction sub-step;

[0082] The detection of whether the displacement is abnormal is as follows: a displacement allowable difference is set. When the absolute value of the difference between the actual moving distance of the smart guardrail and the theoretical moving distance is greater than the displacement allowable difference, it indicates that there is a displacement abnormality in the movement of the smart guardrail, and the displacement correction sub-step is immediately triggered. Otherwise, it indicates that there is no abnormality in the movement of the smart guardrail.

[0083] Displacement correction sub-step: used to receive the displacement correction instruction and control the smart guardrail to execute the displacement correction instruction. After the displacement correction operation is executed, the displacement detection sub-step is repeated to verify the displacement correction effect. If the smart guardrail still cannot reach the target position, the position of the smart guardrail will be locked and the maintenance personnel will be notified to check.

[0084] In this embodiment, it should be specifically explained that the theoretical moving distance of each intelligent guardrail is as follows:

[0085] Among them, L q It is expressed as the theoretical moving distance of the qth smart guardrail, (X q 目 , Y q 目 ) represents the target coordinate of the qth smart guardrail, (X q o , Y q o ) represents the initial coordinates of the qth smart guardrail;

[0086] The actual moving distance of each intelligent guardrail is calculated as follows:

[0087] Among them, S qIt is expressed as the actual moving distance of the qth smart guardrail, (X q 实 , Y q 实 ) represents the actual coordinates of the qth smart guardrail.

[0088] The above formulas are all dimensionless and numerically calculated, and the preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0089] In this embodiment, it should be specifically explained that the present invention provides an adaptive control device for a tidal intelligent guardrail, comprising a memory, a processor, and a machine executable program stored in the memory and running on the processor.

[0090] In this embodiment, it should be specifically explained that the present invention uses cameras to collect road usage data from the road section and the distribution nodes at both ends, calculates a road usage efficiency monitoring index for each lane in the road section and the distribution nodes at both ends, and uses sensors installed at the intelligent guardrail distribution nodes to collect vehicle traffic data from the road section and the distribution nodes at both ends, calculates a vehicle traffic demand index for each lane in the road section and the distribution nodes at both ends, further inputs the historical road usage efficiency monitoring index and vehicle traffic demand index into a time series model, updates the traffic flow warning coefficient for each lane in the road section and the distribution nodes at both ends, and compares the coefficient with a preset traffic flow warning coefficient. If the traffic flow warning coefficient for a lane at a node is greater than the preset traffic flow warning coefficient, it indicates that the traffic flow in that lane is excessive and road traffic resources need to be optimized. In this case, the intelligent guardrail automatically moves in the direction of the smaller traffic flow warning coefficient, thereby improving road traffic efficiency. Combining the vehicle traffic data detected by the sensors with the road usage data identified by the cameras can obtain more detailed traffic flow information, improving data accuracy and completeness. Evaluating the traffic flow warning coefficient based on the time series can provide early warning of traffic flow trends, thereby achieving adaptive adjustment of the intelligent guardrail and improving the overall traffic capacity of the road.

[0091] The present invention provides an adaptive control method and device for a tidal intelligent guardrail. The method utilizes intelligent guardrail movement fault detection, collects intelligent guardrail coordinate data of each lane at a distribution node through a device positioning sub-step, detects displacement anomalies during the movement of the intelligent guardrail through a displacement detection sub-step, and if an anomaly exists, controls the intelligent guardrail to execute a displacement correction instruction through a displacement correction sub-step. After the displacement correction operation is executed, the displacement detection sub-step is repeated to verify the displacement correction effect. If the intelligent guardrail still cannot reach the target position, the position of the intelligent guardrail is locked and maintenance personnel are notified for inspection. Coordinate collection and real-time displacement monitoring are conducive to accurate and timely fault detection, provide a strong guarantee for the stable operation of the intelligent guardrail, and ensure the safety of road traffic.

[0092] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An adaptive control method for tidal intelligent guardrail, characterized in that: The following steps are involved: Step S01: Smart guardrail distribution node numbering: plan smart guardrail distribution nodes in the road section and at both ends according to the road structure, and number the distribution nodes in the road section and at both ends in sequence; Step S02: Traffic data collection: collecting road usage data of the road section and the distribution nodes at both ends through cameras, and collecting vehicle traffic data of the road section and the distribution nodes at both ends through sensors installed at the distribution nodes of the smart guardrails; Step S03: Data interactive communication: Establish a traffic data center to centrally store and manage relevant data from each smart guardrail distribution node, and upload the data to the traffic data center through communication technology; Step S04: Road usage rate data processing: Based on the road usage data of the road section and the distribution nodes at both ends, the road usage efficiency monitoring index of each lane of the road section and the distribution nodes at both ends is calculated; Step S05: Vehicle traffic demand processing: Calculating the vehicle traffic demand index of each lane of the road section and the distribution nodes at both ends based on the vehicle traffic data of the road section and the distribution nodes at both ends; Step S06: Traffic flow early warning analysis: used to obtain the road use efficiency monitoring index and vehicle traffic demand index of each lane of the road section and the distribution nodes at both ends, input the data into the time series model, and analyze to obtain the traffic flow early warning coefficient of each lane of the road section and the distribution nodes at both ends; Step S07: Intelligent guardrail control: used to obtain the traffic flow warning coefficient of each lane of the road section and the distribution nodes at both ends, compare it with the preset traffic flow warning coefficient, and control the movement of the intelligent guardrail at the distribution nodes; Step S08: Intelligent guardrail movement fault detection: including equipment positioning sub-step, displacement detection sub-step and displacement correction sub-step.

2. The adaptive control method for tidal intelligent guardrail according to claim 1, characterized in that: The specific analysis method of step S02: traffic data collection is: The road usage data is the lane occupancy rate and lane vehicle turning rate of the distribution nodes at both ends of the road section collected by the camera; The vehicle traffic data includes lane vehicle flow, lane average speed, lane entry volume, lane exit volume, and queue length at distribution nodes of collected road sections.

3. The adaptive control method for tidal intelligent guardrail according to claim 1, characterized in that: The specific analysis method of step S04: road usage rate data processing is: Step S41: extracting the maximum lane occupancy at each distribution node of the road section; Step S42: Calculating the turning smoothness at each smart guardrail distribution node by averaging the lane vehicle turning pass rate at each distribution node in the road section; Step S43: combining the lane occupancy rate and lane vehicle turning pass rate of the node to obtain the road use efficiency monitoring index of each lane at each smart guardrail distribution node.

4. The adaptive control method for tidal intelligent guardrail according to claim 1, characterized in that: The specific analysis method of step S05: vehicle traffic demand processing is: Step S51: extracting the maximum lane vehicle flow and average driving speed at each distribution node of the road section, and calculating the road section traffic demand factor of each lane at each smart guardrail distribution node based on the lane vehicle flow and lane average driving speed of the node; Step S52: Set the maximum lane vehicle entry volume, maximum lane vehicle exit volume, and maximum queue length at each distribution node at both ends of the road, and calculate the road end traffic demand factor for each lane at each smart guardrail distribution node based on the lane vehicle entry volume, lane vehicle exit volume, and queue length at the node; Step S53: Integrate the road section traffic demand factor and the road end traffic demand factor to obtain the vehicle traffic demand index of each lane at each smart guardrail distribution node.

5. The adaptive control method for tidal intelligent guardrail according to claim 1, characterized in that: The traffic flow warning coefficient is specifically: Based on the frequency of data collection, the historical road utilization efficiency monitoring index and vehicle traffic demand index are input into the time series model to update the traffic flow warning coefficient of each lane in the road section and the distribution nodes at both ends.

6. The adaptive control method for tidal intelligent guardrail according to claim 1, characterized in that: The specific analysis method of step S07: intelligent guardrail control is: Obtain the traffic flow warning coefficient of each lane in the road section and the distribution nodes at both ends, and compare it with the preset traffic flow warning coefficient. If the traffic flow warning coefficient of a lane at the node is greater than the preset traffic flow warning coefficient, it indicates that the traffic flow in the lane is too large and the road traffic resources need to be optimized. At this time, the intelligent guardrail automatically moves in the direction of the smaller traffic flow warning coefficient to improve road traffic efficiency. Otherwise, it indicates that the lane meets the traffic flow requirements.

7. The adaptive control method for tidal intelligent guardrail according to claim 1, characterized in that: The specific analysis method of step S08: intelligent guardrail movement fault detection is: Equipment positioning sub-step: During the movement of the smart guardrail, the initial coordinates, target coordinates, and actual coordinates of each smart guardrail are collected; Displacement detection sub-step: Using the initial coordinates and target coordinates, the theoretical movement distance of each smart guardrail is calculated using the distance formula; using the initial coordinates and actual coordinates, the actual movement distance of each smart guardrail is calculated; using the theoretical movement distance and actual movement distance of each smart guardrail, the displacement of the smart guardrail during movement is detected to see if there is any abnormality. If there is any abnormality, a displacement correction instruction is sent through the displacement correction sub-step; Displacement correction sub-step: used to receive the displacement correction instruction and control the smart guardrail to execute the displacement correction instruction. After the displacement correction operation is executed, the displacement detection sub-step is repeated to verify the displacement correction effect. If the smart guardrail still cannot reach the target position, the position of the smart guardrail will be locked and the maintenance personnel will be notified to check.

8. The adaptive control method for tidal intelligent guardrail according to claim 7, characterized in that: The detection of whether the displacement is abnormal is as follows: setting the allowable displacement difference. When the absolute value of the difference between the actual moving distance of the smart guardrail and the theoretical moving distance is greater than the allowable displacement difference, it indicates that there is a displacement abnormality in the movement of the smart guardrail, and the displacement correction sub-step is immediately triggered. Otherwise, it indicates that there is no abnormality in the movement of the smart guardrail.

9. An adaptive control device for a tidal intelligent guardrail, characterized by: The invention comprises a memory, a processor and a machine executable program stored in the memory and running on the processor, and when the processor executes the machine executable program, an adaptive control method for a tidal smart guardrail according to any one of claims 1 to 8 is implemented.

Images