Intelligent cooperative guiding system for spikes

Abstract

The invention relates to the technical field of intelligent traffic control, in particular to a spike intelligent cooperative guidance system, which comprises a data acquisition module for acquiring spike surrounding environment data; the cooperative control module is used for determining whether the surrounding environment of the spike is abnormal or not based on the abnormal characteristic characterization parameters and generating an abnormal signal, so as to determine whether the abnormal signal is broadcasted to the surrounding spike or not according to the risk assessment index; the mode determining module is used for determining a luminous mode of the spike based on the adjacent distance characterization value and forming a dynamic path; the abnormal development prediction module is used for determining whether the abnormal signal development trend is stable or not based on the risk diffusion characteristic value so as to determine to increase the flicker frequency of the abnormal spike according to the ratio; and the abnormal area detection module is used for determining an inspection path based on the coverage area proportion so as to determine whether to adjust the preset adjacent distance characterization value or not according to the path deviation angle, and determining to reduce the preset adjacent distance characterization value according to the difference value.

Description

Technical Field

[0001] This invention relates to the field of intelligent traffic control technology, and in particular to a road stud intelligent collaborative guidance system. Background Technology

[0002] Road studs are mainly used for road marking, with a relatively simple function that cannot meet the higher requirements of modern transportation for road safety and traffic efficiency. With the increase in traffic flow and the increasing complexity of the road environment, the demand for real-time monitoring and intelligent guidance of the road environment is becoming more and more urgent. There are often information silos, and there is a lack of coordination and linkage between various monitoring devices, making it difficult to form an effective overall guidance plan. In addition, some intelligent warning systems can only provide simple warnings when danger occurs, lacking the ability to predict the dynamic development of abnormal situations and adjust in real time, and thus failing to provide accurate guidance paths for vehicles and pedestrians.

[0003] Chinese Patent Application Publication No. CN119229668A discloses a dynamic guidance system and method based on a novel road stud matrix, including a roadside unit module, a data processing module, and a road stud module; the roadside unit module includes a radar unit, a video unit, and a communication unit; the road stud module includes a roadside stud module and a road center stud matrix module, and both the roadside stud module and the road center stud matrix module include a lighting module and a communication module.

[0004] However, the existing technology has the following problems: lack of analysis of the road stud environment, which makes it impossible to effectively form path guidance, resulting in low inspection efficiency and low accuracy of road stud anomaly detection. Summary of the Invention

[0005] To address this issue, the present invention provides an intelligent collaborative guidance system for road studs, which overcomes the problem in the prior art that lacks analysis of the road stud environment, thus failing to effectively form path guidance, resulting in low inspection efficiency and low accuracy in road stud anomaly detection.

[0006] To achieve the above objectives, the present invention provides a road stud intelligent collaborative guidance system, comprising:

[0007] The data acquisition module is used to collect environmental data around the road spikes;

[0008] The collaborative control module, which is connected to the data acquisition module, is used to determine whether there is an anomaly in the environment around the road stud based on the anomaly characteristic characterization parameters of the environmental data and to generate an anomaly signal, so as to determine whether to broadcast the anomaly signal to the surrounding road studs according to the risk assessment index of the anomaly signal.

[0009] A mode determination module, connected to the collaborative control module, is used to determine the light emission mode of the road spikes based on the adjacent distance characterization value of the surrounding road spikes, and form a dynamic path, under the condition that the abnormal signal is broadcast to the surrounding road spikes.

[0010] An abnormal development prediction module, which is connected to the collaborative control module and the mode determination module respectively, is used to determine whether the development trend of the abnormal signal is stable based on the risk diffusion characteristic value of the abnormal signal, and to determine the flashing frequency of the abnormal road studs according to the ratio of the preset risk diffusion characteristic value to the risk diffusion characteristic value.

[0011] An abnormal area detection module, which is connected to the pattern determination module and the abnormal development prediction module respectively, is used to determine the inspection path based on the coverage area ratio of the abnormal signal, to determine whether to adjust the preset adjacent distance characterization value according to the path deviation angle between the inspection path and the dynamic path, and to determine to reduce the preset adjacent distance characterization value according to the difference between the path deviation angle and the preset path deviation angle.

[0012] Furthermore, the collaborative control module determines that there is an anomaly in the environment around the road stud based on the comparison result of the abnormal feature characterization parameter of the environmental data being greater than the preset abnormal feature characterization parameter, and determines to broadcast the abnormal signal to the surrounding road studs based on the comparison result of the risk assessment index being greater than the risk assessment index threshold.

[0013] Furthermore, when the mode determination module determines that the abnormal signal will be broadcast to the surrounding road spikes, it determines that the light emission mode of the road spike is a gradient breathing mode based on the comparison result that the adjacent distance characterization value of the surrounding road spikes is less than or equal to the preset adjacent distance characterization value.

[0014] Furthermore, the mode determination module, upon determining that the abnormal signal will be broadcast to the surrounding road spikes, determines that the light emission mode of the road spike is a high-frequency pulse mode based on the comparison result that the adjacent distance characterization value is greater than the preset adjacent distance characterization value.

[0015] Furthermore, under the condition that the abnormal signal is obtained, the abnormal development prediction module determines that the development trend of the abnormal signal is unstable based on the comparison result that the risk diffusion characteristic value of the abnormal signal is greater than the preset risk diffusion characteristic value.

[0016] Furthermore, under the condition that the development trend of the abnormal signal is determined to be unstable, the process of adjusting the flicker frequency includes:

[0017] Divide the preset risk diffusion characteristic value by the risk diffusion characteristic value;

[0018] Set several adjustment coefficients corresponding to the respective ratios;

[0019] The flashing frequency of the abnormal road spikes is increased based on several of the aforementioned adjustment coefficients;

[0020] Set the corresponding ratio to the relationship between the flashing frequency of the abnormal road stud and adjust the flashing frequency.

[0021] Furthermore, the abnormal area detection module determines the inspection path as a key inspection path based on the comparison result that the coverage area ratio of the abnormal signal is less than or equal to the preset coverage area ratio, and determines to adjust the preset adjacent distance characterization value based on the comparison result that the path deviation angle between the inspection path and the dynamic path is greater than the preset path deviation angle.

[0022] Furthermore, the abnormal area detection module determines that the inspection path is a comprehensive inspection path based on the comparison result that the coverage area ratio of the abnormal signal is greater than the preset coverage area ratio, and determines to adjust the preset adjacent distance characterization value based on the comparison result that the path deviation angle between the inspection path and the dynamic path is greater than the preset path deviation angle.

[0023] Furthermore, given the condition of adjusting the preset adjacent distance representation value, the process of adjusting the preset adjacent distance representation value includes:

[0024] The difference between the path deviation angle and the preset path deviation angle is calculated.

[0025] Set several adjustment coefficients corresponding to the respective differences;

[0026] The preset adjacent distance representation value is reduced based on several adjustment coefficients;

[0027] Set the corresponding relationship between the difference and the reduction of the preset adjacent distance characterization value to adjust the preset adjacent distance characterization value.

[0028] Furthermore, dynamic path guidance is implemented through software, linking with surrounding road studs to form a guide.

[0029] Compared with existing technologies, the advantages of this invention are as follows: The invention acquires real-time environmental data around the road spikes via a data acquisition module; the collaborative control module determines anomalies and decides whether to broadcast signals to surrounding road spikes based on anomaly characteristic parameters and risk assessment indices; the pattern determination module determines the illumination pattern and generates a dynamic path based on the relative distance between road spikes; and the anomaly development prediction module dynamically adjusts the flashing frequency based on risk diffusion characteristic values. The anomaly area detection module plans inspection paths based on the coverage area ratio and adaptively adjusts preset adjacent distance characterization values ​​through path deviation angles, achieving intelligent linkage of the road spike group. This ensures warning effects under different risk levels, reduces system energy consumption, dynamically optimizes inspection and path planning, making the UAV inspection route more aligned with anomaly development trends, significantly improving operation and maintenance efficiency. The dynamic change of flashing frequency based on risk diffusion characteristic values ​​and the intelligent correction of adjacent distance characterization values ​​through path deviation enhance the system's robustness in complex outdoor environments and significantly improve the comprehensiveness and accuracy of anomaly detection.

[0030] Furthermore, this invention determines the existence of anomalies by comparing the anomaly characteristic parameters with preset anomaly characteristic parameters, and generates anomaly signals when anomalies are present. Then, it determines whether to broadcast the anomaly signals to surrounding road studs through a risk assessment index, comprehensively capturing complex environmental changes around the road studs, avoiding misjudgments caused by environmental fluctuations. The risk assessment index also filters high-risk anomaly signals that need to be broadcast, reducing invalid communication, improving system energy efficiency, optimizing the communication mechanism and deployment adaptability, enhancing system adaptability, and enabling the monitoring standards to adapt to environmental characteristics under different regional and climatic conditions, significantly improving the accuracy and reliability of anomaly detection.

[0031] Furthermore, this invention determines the light emission mode of road studs by using adjacent distance characterization values ​​and forms a simple and efficient dynamic path, accurately conveying the distance of abnormal risks, improving the efficiency of rapid identification of risk levels, avoiding unnecessary energy consumption, extending the battery life of road stud equipment, ensuring that the inspection path conforms to the actual distribution of abnormalities, reducing the invalid flight distance of inspection equipment such as drones, significantly improving operation and maintenance efficiency, adapting to different road stud deployment densities, and enabling the warning mode and path planning to be flexibly adapted according to the actual scenario.

[0032] Furthermore, this invention determines whether the abnormal development trend is stable by using risk diffusion characteristic values, and then dynamically increases the flashing frequency of abnormal road studs to achieve adaptive adjustment of warning intensity according to the degree of risk diffusion, accurately capture abnormal diffusion trends, improve the identification efficiency of abnormal areas, enable warning strategies to dynamically match actual risk evolution patterns, and effectively reduce the missed detection rate and handling delay of abnormal events.

[0033] Furthermore, this invention determines the inspection path by the coverage area ratio, then adjusts the preset adjacent distance characterization value according to the path deviation angle, and finally dynamically reduces the preset adjacent distance characterization value according to the difference between the path deviation angle and the preset path deviation angle. This realizes the linkage adjustment between the inspection strategy and the road stud collaborative mode, avoids the resource waste or missed inspection risk of the fixed inspection mode, improves the accuracy and adaptability of path planning, and thus improves the accuracy of anomaly detection. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of the module connection for implementing the intelligent collaborative guidance system for road studs according to the present invention;

[0035] Figure 2 This is a flowchart illustrating how to determine whether there are any anomalies in the environment surrounding the rail spikes in this invention.

[0036] Figure 3 This is a flowchart illustrating the process of determining the light-emitting mode of a road stud in accordance with the present invention;

[0037] Figure 4 This is a flowchart for determining whether the development trend of an abnormal signal is stable in order to implement the present invention. Detailed Implementation

[0038] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0039] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0040] It should be noted that the data in this embodiment are all derived from a comprehensive analysis and evaluation of historical test data and corresponding historical test results from the three months prior to this test. Those skilled in the art will understand that the determination of the above-mentioned parameters for any single item in this invention can be achieved by selecting the value with the highest percentage based on the data distribution as the preset standard parameter, using weighted summation to obtain the value as the preset standard parameter, substituting each historical data point into a specific formula and using the value obtained from that formula as the preset standard parameter, or other selection methods, as long as the invention can clearly define different specific situations in the single-item judgment process through the obtained values.

[0041] See also Figure 1 As shown, it is a schematic diagram of the module connection of the intelligent collaborative guidance system for road spikes in this invention.

[0042] The data acquisition module is used to collect environmental data around the road spikes;

[0043] The collaborative control module, which is connected to the data acquisition module, is used to determine whether there is an anomaly in the environment around the road stud based on the anomaly characteristic characterization parameters of the environmental data and to generate an anomaly signal, so as to determine whether to broadcast the anomaly signal to the surrounding road studs according to the risk assessment index of the anomaly signal.

[0044] A mode determination module, connected to the cooperative control module, is used to determine the light emission mode of the road spikes based on the adjacent distance characterization value of the surrounding road spikes, and form a dynamic path, under the condition that the abnormal signal is broadcast to the surrounding road spikes.

[0045] An abnormal development prediction module, which is connected to the collaborative control module and the mode determination module respectively, is used to determine whether the development trend of the abnormal signal is stable based on the risk diffusion characteristic value of the abnormal signal, and to determine the flashing frequency of the abnormal road studs according to the ratio of the preset risk diffusion characteristic value to the risk diffusion characteristic value.

[0046] An abnormal area detection module, which is connected to the pattern determination module and the abnormal development prediction module respectively, is used to determine the inspection path based on the coverage area ratio of the abnormal signal, to determine whether to adjust the preset adjacent distance characterization value according to the path deviation angle between the inspection path and the dynamic path, and to determine to reduce the preset adjacent distance characterization value according to the difference between the path deviation angle and the preset path deviation angle.

[0047] In this invention, the cable displacement data is collected by a displacement sensor installed on the cable, which records the cable's positional changes; the road damage data uses a camera on the road spike to collect road images, and the road damage is analyzed using image recognition technology; the geological change data around the bottom of the road spike is measured by a geological sensor; and the gas leak data uses a gas sensor to detect the gas concentration around the road spike.

[0048] Specifically, this invention acquires real-time environmental data around road spikes through a data acquisition module. A collaborative control module, based on anomaly characteristic parameters and risk assessment indices, identifies anomalies and decides whether to broadcast signals to surrounding road spikes. A pattern determination module determines the illumination pattern and generates a dynamic path based on the relative distance between road spikes. An anomaly development prediction module dynamically adjusts the flashing frequency based on risk diffusion characteristic values. An anomaly area detection module plans inspection paths based on coverage area ratios and adaptively adjusts preset adjacent distance characterization values ​​through path deviation angles, achieving intelligent linkage of road spike groups. This ensures warning effects under different risk levels, reduces system energy consumption, dynamically optimizes inspection and path planning, making drone inspection routes more aligned with anomaly development trends, significantly improving operational efficiency. By dynamically changing the flashing frequency based on risk diffusion characteristic values ​​and intelligently correcting adjacent distance characterization values ​​through path deviations, the system's robustness in complex outdoor environments is enhanced, significantly improving the comprehensiveness and accuracy of anomaly detection.

[0049] See also Figure 2 As shown, it is a flowchart for determining whether there is an anomaly in the environment around the road spike in an embodiment of the present invention.

[0050] Specifically, the collaborative control module, upon determining the environmental data, determines whether there is an anomaly in the environment surrounding the road spikes based on the comparison result between the anomaly characteristic representation parameters of the environmental data and the preset anomaly characteristic representation parameters.

[0051] If the abnormal feature characterization parameter is less than or equal to the preset abnormal feature characterization parameter, then it is determined that there is no abnormality in the environment around the road spike;

[0052] If the abnormal feature characterization parameter is greater than the preset abnormal feature characterization parameter, then it is determined that there is an abnormality in the environment around the road spike.

[0053] In this embodiment of the invention, the preset abnormal feature characterization parameter is set to 0.25. The preset abnormal feature characterization parameter is obtained when the abnormal feature characterization parameter is set to the maximum value when there are no abnormalities in the environment around several historical road spikes. However, the above value is not limited to this. Those skilled in the art can also adjust the value according to actual needs.

[0054] During implementation, the abnormal characteristic characterization parameter is the product of cable displacement and cable displacement weight 0.3, plus the product of road damage area ratio and road loss area ratio weight 0.2, plus the product of geological change amplitude and geodetic change amplitude weight 0.3, plus the product of gas leakage concentration and gas leakage concentration weight 0.2.

[0055] In this embodiment of the invention, the cable displacement is the ratio of the difference between the current cable position and the initial position to the maximum allowable displacement; the road damage area ratio is the ratio of the pixel area of ​​the damaged road surface to the total pixel area of ​​the image; the geological change amplitude is the ratio of the geological change amplitude to the maximum allowable change amplitude; and the gas leakage concentration is the ratio of the real-time gas concentration value to the maximum allowable gas concentration value.

[0056] During implementation, the surrounding environment refers to the location of the road spike and a circular space with a radius of 5 meters centered on the road spike. This environment encompasses various elements related to the operation of the road spike and the safety of the surrounding area, mainly including the cable, road surface, geology around the bottom, and gas distribution. It is understandable that changes in the state of these elements will affect the normal use of the road spike and the safety of the surrounding area.

[0057] Specifically, when the collaborative control module determines that there is an anomaly in the environment around the road spike, it generates an anomaly signal, which is represented in text format, binary format, or XML format.

[0058] Specifically, when the collaborative control module determines that the abnormal signal has been obtained, it determines whether to broadcast the abnormal signal to the surrounding road spikes based on the comparison result between the risk assessment index of the abnormal signal and the risk assessment index threshold.

[0059] If the risk assessment index is less than or equal to the preset risk assessment index, then it is determined that the abnormal signal will not be broadcast to the surrounding road studs.

[0060] If the risk assessment index is greater than the preset risk assessment index, then it is determined to broadcast the abnormal signal to the surrounding road studs.

[0061] In this invention, the preset risk assessment index is set to 0.25. The preset risk assessment index is obtained by taking the maximum value of several historical risk assessment indices that do not broadcast abnormal signals to surrounding road spikes. However, the above value is not limited to this, and those skilled in the art can adjust the value according to actual needs.

[0062] During implementation, the risk assessment index is the ratio of the abnormal feature characterization parameter minus the preset abnormal feature characterization parameter to the abnormal feature characterization parameter.

[0063] During implementation, the surrounding road spikes are those located 10 meters away from the abnormal road spikes.

[0064] In this embodiment of the invention, the LoRa wireless protocol is used, the antenna is embedded in the sealing groove on the side of the road spike and covered with a wear-resistant silicone layer for waterproofing and dustproofing, and based on the swarm intelligence algorithm, abnormal signals are broadcast to the surrounding road spikes.

[0065] Specifically, this invention determines the existence of anomalies by comparing anomaly characteristic parameters with preset anomaly characteristic parameters, and generates anomaly signals when anomalies are found. Then, it determines whether to broadcast the anomaly signals to surrounding road studs through a risk assessment index, comprehensively capturing complex environmental changes around road studs, avoiding misjudgments caused by environmental fluctuations. Furthermore, the risk assessment index filters high-risk anomaly signals that need to be broadcast, reducing invalid communication, improving system energy efficiency, optimizing the communication mechanism and deployment adaptability, enhancing system adaptability, and enabling monitoring standards to adapt to environmental characteristics under different regional and climatic conditions, significantly improving the accuracy and reliability of anomaly detection.

[0066] See also Figure 3 As shown, it is a flowchart for determining the light emission mode of road studs in an embodiment of the present invention.

[0067] Specifically, the mode determination module determines the light emission mode of the road studs based on the comparison result of the adjacent distance characterization value of the surrounding road studs and the preset adjacent distance characterization value when it determines that the abnormal signal will be broadcast to the surrounding road studs.

[0068] If the adjacent distance characterization value is less than or equal to the preset adjacent distance characterization value, then the light emission mode of the road stud is determined to be a gradient breathing mode.

[0069] If the adjacent distance characterization value is greater than the preset adjacent distance characterization value, then the light emission mode of the road stud is determined to be a high-frequency pulse mode.

[0070] In this embodiment of the invention, the preset adjacent distance representation value is 0.5, but the above value is not limited to this, and those skilled in the art can also adjust the value according to actual needs.

[0071] During implementation, the adjacent distance is characterized as a relative distance measure between abnormal road spikes and surrounding abnormal road spikes, that is, the ratio of the actual distance between the coordinates of the abnormal road spike and the coordinates of surrounding abnormal road spikes to the preset maximum distance.

[0072] In this embodiment of the invention, the high-frequency pulse mode is a red LED flashing at a flashing frequency of 5Hz, and the gradient breathing mode is a yellow LED flashing in a gradually changing breathing pattern at a flashing frequency of 1Hz.

[0073] Specifically, the mode determination module first determines the light-up mode of each road stud based on the adjacent distance characterization value. Then, it filters out all road studs that are in the light-up state. The starting point of the dynamic path is any road stud that has an anomaly, and the ending point is the inspection point closest to the same abnormal road stud. The filtered light-up road studs are connected according to preset rules to form a dynamic path.

[0074] In this embodiment of the invention, the specific rules are as follows: connect the road studs that are closest to each other and have the same light emission mode. When there are no road studs that are at a suitable distance and have the same light emission mode, consider connecting road studs that are slightly farther away but have different light emission modes. Optimize the initially formed dynamic path, remove unnecessary twists and turns, and make the path simpler and more efficient.

[0075] Specifically, this invention determines the light emission mode of road studs by using adjacent distance characterization values ​​and forms a simple and efficient dynamic path. It accurately conveys the distance of abnormal risks, improves the efficiency of rapid identification of risk levels, avoids unnecessary energy consumption, extends the battery life of road stud equipment, ensures that the inspection path conforms to the actual distribution of abnormalities, reduces the invalid flight distance of inspection equipment such as drones, significantly improves operation and maintenance efficiency, adapts to different road stud deployment densities, and enables the warning mode and path planning to be flexibly adapted according to the actual scenario.

[0076] See also Figure 4 As shown, it is a flowchart for determining whether the development trend of an abnormal signal is stable according to an embodiment of the present invention.

[0077] Specifically, the abnormal development prediction module, upon determining that the abnormal signal has been obtained, determines whether the development trend of the abnormal signal is stable based on the comparison result between the risk diffusion characteristic value of the abnormal signal and the preset risk diffusion characteristic value.

[0078] If the risk diffusion characteristic value is less than or equal to the preset risk diffusion characteristic value, then the development trend of the abnormal signal is determined to be stable.

[0079] If the risk diffusion characteristic value is greater than the preset risk diffusion characteristic value, then the development trend of the abnormal signal is determined to be unstable.

[0080] In this invention, the preset risk diffusion characteristic value is 0.85, but the above value is not limited to this, and those skilled in the art can adjust the value according to actual needs.

[0081] During implementation, the risk diffusion characteristic value is the result of multiplying the ratio of the actual propagation speed of the abnormal signal to the preset maximum possible propagation speed by the difference between the current affected area and the initial area, divided by the initial area.

[0082] Specifically, the abnormal development prediction module determines the flashing frequency of the abnormal road stud under the corresponding light-emitting mode based on the comparison result of the ratio of the preset risk diffusion characteristic value to the preset ratio when the abnormal signal development trend is determined to be unstable.

[0083] If the ratio is less than or equal to the preset ratio, then it is determined that the flashing frequency will be increased to the corresponding value by a first preset frequency adjustment coefficient of 1.03;

[0084] If the ratio is greater than the preset ratio, then it is determined that the flashing frequency will be increased to the corresponding value by the second preset frequency adjustment coefficient of 1.07;

[0085] The ratio is the ratio of the preset risk diffusion characteristic value to the risk diffusion characteristic value.

[0086] In this embodiment of the invention, the preset ratio is 0.29, but the above value is not limited to this, and those skilled in the art can adjust the value according to actual needs.

[0087] In this embodiment of the invention, the increased flashing frequency is the product of the flashing frequency and the preset frequency adjustment coefficient. The preset frequency adjustment coefficient includes a first preset frequency adjustment coefficient with a value of 1.03 and a second preset frequency adjustment coefficient with a value of 1.07.

[0088] Specifically, this invention determines whether the abnormal development trend is stable by using risk diffusion characteristic values, and then dynamically increases the flashing frequency of abnormal road studs to achieve adaptive adjustment of warning intensity according to the degree of risk diffusion. This accurately captures the abnormal diffusion trend, improves the identification efficiency of abnormal areas, and enables the warning strategy to dynamically match the actual risk evolution pattern, effectively reducing the missed detection rate and handling delay of abnormal events.

[0089] Specifically, the abnormal area detection module determines the inspection path based on the comparison result of the coverage area ratio of the abnormal signal and the preset coverage area ratio;

[0090] If the coverage area percentage is less than or equal to the preset coverage area percentage, then the inspection path is determined to be a key inspection path.

[0091] If the coverage area percentage is greater than the preset coverage area percentage, then the inspection path is determined to be a comprehensive inspection path.

[0092] In this embodiment of the invention, the preset coverage area ratio is 0.3, but the above value is not limited to this, and those skilled in the art can adjust the value according to actual needs.

[0093] During implementation, the coverage area ratio is obtained by marking the geographic coordinates of all road spike locations that received abnormal signals, calculating the area enclosed by these road spikes using Geographic Information System (GIS) software, and then dividing this area by the total area of ​​the entire road spike distribution area.

[0094] In this embodiment of the invention, the key inspection path prioritizes checking the core area with high abnormal signal intensity; the comprehensive inspection path checks each road stud in the entire abnormal area one by one.

[0095] In this embodiment of the invention, the inspection path is a planned route for a drone carrying various detection devices to move and inspect in an abnormal area.

[0096] Specifically, the abnormal area detection module, under the condition of determining the inspection path, determines whether to adjust the preset adjacent distance characterization value based on the comparison result of the path deviation angle between the inspection path and the dynamic path and the preset path deviation angle.

[0097] If the path deviation angle is less than or equal to the preset path deviation angle, then it is determined that the preset adjacent distance characterization value will not be adjusted.

[0098] If the path deviation angle is greater than the preset path deviation angle, then the preset adjacent distance characterization value is adjusted.

[0099] In this embodiment of the invention, the preset path deviation angle is 30°, but the above value is not limited to this, and those skilled in the art can adjust the value according to actual needs.

[0100] During implementation, the path deviation angle is obtained by the angle formed between the inspection path and the dynamic path at the starting point and the ending point.

[0101] Specifically, the abnormal region detection module, under the condition of determining to adjust the preset adjacent distance characterization value, determines to adjust the preset adjacent distance characterization value based on the comparison result of the difference between the path deviation angle and the preset path deviation angle and the preset difference.

[0102] If the difference is less than or equal to the preset difference, then the preset adjacent distance representation value is reduced to the corresponding value by adjusting the first preset representation value adjustment coefficient of 0.97.

[0103] If the difference is greater than the preset difference, then the preset adjacent distance representation value is reduced to the corresponding value by adjusting the second preset representation value adjustment coefficient of 0.95.

[0104] The difference is the difference between the path deviation angle and the preset path deviation angle.

[0105] In this embodiment of the invention, the preset difference value is 5°, but the above value is not limited to this, and those skilled in the art can adjust the value according to actual needs.

[0106] In this embodiment of the invention, the reduced preset adjacent distance representation value is the product of the preset adjacent distance representation value and the preset representation value adjustment coefficient. The preset representation value adjustment coefficient includes a first preset representation value adjustment coefficient with a value of 0.97 and a second preset representation value adjustment coefficient with a value of 0.95.

[0107] Specifically, the dynamic path guidance is implemented through software, which is different from a single trigger or dynamic alert.

[0108] Specifically, this invention determines the inspection path by the coverage area ratio, then adjusts the preset adjacent distance characterization value according to the path deviation angle, and finally dynamically reduces the preset adjacent distance characterization value according to the difference between the path deviation angle and the preset path deviation angle. This realizes the linkage adjustment between the inspection strategy and the road stud collaborative mode, avoids the resource waste or missed inspection risk of fixed inspection mode, improves the accuracy and adaptability of path planning, and thus improves the accuracy of anomaly detection.

[0109] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0110] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A road stud intelligent collaborative guidance system, characterized in that, include: The data acquisition module is used to collect environmental data around the road spikes; The collaborative control module, which is connected to the data acquisition module, is used to determine whether there is an anomaly in the environment around the road stud based on the anomaly characteristic characterization parameters of the environmental data and to generate an anomaly signal, so as to determine whether to broadcast the anomaly signal to the surrounding road studs according to the risk assessment index of the anomaly signal. A mode determination module, connected to the collaborative control module, is used to determine the light emission mode of the road spikes based on the adjacent distance characterization value of the surrounding road spikes, and form a dynamic path, under the condition that the abnormal signal is broadcast to the surrounding road spikes. An abnormal development prediction module, which is connected to the collaborative control module and the mode determination module respectively, is used to determine whether the development trend of the abnormal signal is stable based on the risk diffusion characteristic value of the abnormal signal, and to determine the flashing frequency of the abnormal road studs according to the ratio of the preset risk diffusion characteristic value to the risk diffusion characteristic value. An abnormal area detection module, which is connected to the pattern determination module and the abnormal development prediction module respectively, is used to determine the inspection path based on the coverage area ratio of the abnormal signal, to determine whether to adjust the preset adjacent distance characterization value according to the path deviation angle between the inspection path and the dynamic path, and to determine to reduce the preset adjacent distance characterization value according to the difference between the path deviation angle and the preset path deviation angle.

2. The intelligent collaborative guidance system for road spikes according to claim 1, characterized in that, The collaborative control module determines that there is an anomaly in the environment around the road spikes based on the comparison result of the abnormal feature characterization parameter of the environmental data being greater than the preset abnormal feature characterization parameter, and determines to broadcast the abnormal signal to the surrounding road spikes based on the comparison result of the risk assessment index being greater than the risk assessment index threshold.

3. The intelligent collaborative guidance system for road spikes according to claim 2, characterized in that, The mode determination module, upon determining that the abnormal signal will be broadcast to the surrounding road spikes, determines that the light emission mode of the road spike is a gradient breathing mode based on the comparison result of the adjacent distance characterization value of the surrounding road spikes being less than or equal to a preset adjacent distance characterization value.

4. The intelligent collaborative guidance system for road spikes according to claim 2, characterized in that, The mode determination module, under the condition that the abnormal signal will be broadcast to the surrounding road studs, determines that the light emission mode of the road stud is a high-frequency pulse mode based on the comparison result that the adjacent distance characterization value is greater than the preset adjacent distance characterization value.

5. The intelligent collaborative guidance system for road spikes according to claim 1, characterized in that, Under the condition that the abnormal signal is obtained, the abnormal development prediction module determines that the development trend of the abnormal signal is unstable based on the comparison result of the risk diffusion characteristic value of the abnormal signal being greater than the preset risk diffusion characteristic value.

6. The intelligent collaborative guidance system for road spikes according to claim 5, characterized in that, Under the condition that the development trend of the abnormal signal is determined to be unstable, the process of adjusting the flicker frequency includes: Divide the preset risk diffusion characteristic value by the risk diffusion characteristic value; Set several adjustment coefficients corresponding to the respective ratios; The flashing frequency of the abnormal road spikes is increased based on several of the aforementioned adjustment coefficients; Set the corresponding ratio to the relationship between the flashing frequency of the abnormal road stud and adjust the flashing frequency.

7. The intelligent collaborative guidance system for road spikes according to claim 1, characterized in that, The abnormal area detection module determines the inspection path as a key inspection path based on the comparison result that the coverage area ratio of the abnormal signal is less than or equal to the preset coverage area ratio, and determines to adjust the preset adjacent distance characterization value based on the comparison result that the path deviation angle between the inspection path and the dynamic path is greater than the preset path deviation angle.

8. The intelligent collaborative guidance system for road spikes according to claim 1, characterized in that, The abnormal area detection module determines that the inspection path is a comprehensive inspection path based on the comparison result that the coverage area ratio of the abnormal signal is greater than the preset coverage area ratio, and determines to adjust the preset adjacent distance characterization value based on the comparison result that the path deviation angle between the inspection path and the dynamic path is greater than the preset path deviation angle.

9. The intelligent collaborative guidance system for road studs according to claim 7 or 8, characterized in that, Under the condition that the preset adjacent distance representation value is to be adjusted, the process of adjusting the preset adjacent distance representation value includes: The difference between the path deviation angle and the preset path deviation angle is calculated. Set several adjustment coefficients corresponding to the respective differences; The preset adjacent distance representation value is reduced based on several adjustment coefficients; Set the corresponding relationship between the difference and the reduction of the preset adjacent distance characterization value to adjust the preset adjacent distance characterization value.

10. The intelligent collaborative guidance system for road spikes according to claim 1, characterized in that, Dynamic path guidance is implemented through software, linking with surrounding road studs to form a guide.

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