Intelligent modular guardrail and control method thereof

Abstract

The invention relates to the technical field of intelligent guardrails, solves the problem that a modular guardrail in the prior art lacks comprehensive monitoring and unified power supply, and provides an intelligent modular guardrail. The guardrail comprises at least two guardrail stand columns and a connecting rod structure arranged between the stand columns, each guardrail stand column comprises a stand column base and a stand column body, and a mounting groove is formed in the side face of each stand column body; the connecting rod structure is detachably connected with the stand column body through a connecting structural part. The top of the stand column body is provided with a power module, the power module comprises a photovoltaic panel, a first energy storage unit and a wireless displacement vibration unit, the photovoltaic panel is electrically connected with the first energy storage unit, and the first energy storage unit is electrically connected with the wireless displacement vibration unit; the stand column body further comprises an imaging module, and the imaging module comprises a second energy storage unit and an imaging unit. The modularized guardrail has the advantages of being convenient to install and maintain, stable in energy supply and capable of achieving real-time monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent guardrails, and particularly to an intelligent modular guardrail and its control method. Background Art

[0002] With the development of urban infrastructure and intelligent transportation, as one of the most common safety protection facilities on roads and public places, the functions of guardrails are no longer limited to physical isolation and protection. In recent years, in order to improve the level of road safety management, the relevant fields have proposed integrating intelligent devices such as sensors and cameras into guardrails to achieve functions of real-time monitoring, accident warning, and information interaction.

[0003] In the prior art, there are already solutions for modular guardrails. The columns and crossbars are quickly installed and replaced through detachable connection structures, and installation slots are reserved on the guardrail columns for installing functional modules such as lighting fixtures, reflective sheets, or camera devices. Such modular guardrails have improved the expandability and maintenance convenience of guardrails to a certain extent. Some solutions also attempt to introduce photovoltaic power supply units to provide energy for low-power sensors and indicator modules to achieve a certain degree of independent operation.

[0004] For example, Chinese Utility Model Patent CN213014061U discloses a modular assembled guardrail. The guardrail columns and crossbars are assembled by plug-in connection, and installation slots are provided on the column body for installing functional components such as lighting lamps and reflective parts. This solution improves the expandability and maintainability of the guardrail and can flexibly replace different functional modules according to the use environment.

[0005] However, although the above technical solutions have the function of modular installation, their main problem is that the connection method between the installation of functional modules and the guardrail columns is relatively single, and there is a lack of a collaborative working mechanism with the overall guardrail monitoring system. For example, the imaging unit or sensing unit only exists as an independent additional device, lacking linkage with the data of the guardrail state (such as displacement, vibration, etc.). It is impossible to achieve instant data collection and linkage warning when the guardrail is impacted or displaced. This independent design limits the intelligent level of the guardrail and is difficult to meet the requirements of real-time and comprehensive monitoring and alarm for traffic accidents, abnormal intrusions, and other events.

[0006] Therefore, a problem to be solved by the present invention is: how to achieve an effective linkage between functional modules and the operation state of the guardrail in the modular guardrail structure, so as to improve the monitoring accuracy and real-time alarm ability of the guardrail when abnormal events occur. Summary of the Invention

[0007] In view of this, embodiments of the present invention provide intelligent modular guardrails and their control methods to solve the problem in the prior art of how to improve the monitoring accuracy and real-time alarm capability of guardrails when abnormal events occur.

[0008] In a first aspect, embodiments of the present invention provide an intelligent modular guardrail, the guardrail comprising: at least two guardrail posts and a connecting rod structure disposed between the two guardrail posts, wherein the guardrail post comprises: a post base and a post body disposed on the post base; The side of each column body is provided with a mounting groove, and the mounting groove is provided with several through holes. The mounting groove is used to install a functional module. The functional module is provided with several connecting structural members that are adapted to the mounting groove. The functional module is detachably connected to the column body through the connecting structural members. The connecting rod structure includes the connecting structural member, and the connecting rod structure is detachably connected to the column body through the connecting structural member; The top of the column body is provided with a power module, which includes a photovoltaic panel, a first energy storage unit and a wireless displacement vibration unit. The photovoltaic panel is disposed on the surface of the power module, and the energy storage unit is electrically connected to the wireless displacement vibration unit and the photovoltaic panel respectively. The column body also includes an imaging module and a wireless communication module. The imaging module includes a second energy storage unit and an imaging unit. The first energy storage unit and the second energy storage unit are electrically connected, and the second energy storage unit and the imaging unit are electrically connected. The wireless communication module is electrically connected to the wireless displacement vibration unit and the imaging module, and is communicatively connected to the monitoring platform. It is used to receive displacement data and vibration data collected by the wireless displacement vibration unit, and transmit the displacement data, vibration data and video image data to the monitoring platform in real time through a preset wireless communication protocol.

[0009] Preferably, the functional module includes one or more of a lighting unit, a crash warning post, and a luminous road sign. The lighting unit includes a light-emitting element and reflective strips disposed on both sides of the light-emitting element. The lighting unit and the road sign are electrically connected to the power module through the through hole.

[0010] Preferably, the imaging module further includes a photosensitive unit, which is electrically connected to the second energy storage unit. The photosensitive unit is used to detect ambient light intensity and control the lighting unit and / or the road sign to emit light according to the ambient light intensity.

[0011] Preferably, the imaging unit includes a first camera and a second camera, which are electrically connected to the second energy storage unit. The first camera and the second camera are disposed on opposite sides of the column body to simultaneously collect environmental video data from both sides of the guardrail and transmit the environmental video images to the monitoring platform.

[0012] Preferably, the wireless vibration unit is used to detect the displacement and vibration status of the intelligent modular guardrail, and to determine whether the displacement and vibration status is abnormal. If so, the displacement and vibration status is transmitted to the monitoring platform through a preset wireless communication protocol, and an alarm is triggered through the monitoring platform. The preset wireless communication protocol includes at least one of MQTT, HTTP, CoAP, or LWM2M.

[0013] Preferably, the column body has a waterproof wiring channel inside, which is used to isolate and protect the electrical connections of the power module, imaging module and functional modules.

[0014] Secondly, embodiments of the present invention also provide a control method for an intelligent modular guardrail, used for the intelligent modular guardrail described in the first aspect, the method comprising: The displacement and vibration data of the intelligent modular guardrail are obtained through the wireless displacement and vibration unit. The displacement data and vibration data are compared with preset displacement thresholds and preset vibration thresholds, respectively, to determine whether there are any abnormal events; If an abnormal time point exists, then obtain the abnormal time point and, based on the abnormal time point, obtain the displacement data and vibration data within the first preset time range to obtain the abnormal displacement data sequence and the abnormal vibration data sequence. Based on the abnormal time point and the second preset time range, the monitoring video collected by the imaging unit is cropped to obtain the abnormal event video; The abnormal displacement data sequence, abnormal vibration data sequence, and abnormal event video are transmitted to the monitoring platform. An alarm message is generated based on the abnormal displacement data sequence, the abnormal vibration data sequence, and the abnormal event video, and the alarm message is transmitted to the terminal devices of relevant personnel.

[0015] Preferably, the step of comparing the displacement data and vibration data with preset displacement thresholds and preset vibration thresholds respectively to determine whether there is an abnormal event includes: The displacement data is compared with a preset displacement threshold to obtain an initial displacement judgment result; The vibration data is compared with a preset vibration threshold to obtain an initial vibration judgment result; Based on the initial displacement judgment result and the initial vibration judgment result, the number of consecutive exceedances of displacement data and vibration data are counted within a preset time window. Based on the number of consecutive threshold exceedances, a preliminary determination is made as to whether any abnormal events exist.

[0016] Preferably, the step of generating alarm information based on the abnormal displacement data sequence, the abnormal vibration data sequence, and the abnormal event video, and transmitting the alarm information to the terminal device of relevant personnel, includes: The abnormal displacement data sequence and the abnormal vibration data sequence are preprocessed to obtain the target displacement sequence and the target vibration sequence. Feature extraction is performed on the target displacement sequence and the target vibration sequence respectively to obtain a comprehensive feature set, wherein the comprehensive feature set includes displacement features and vibration features. The displacement features include displacement amplitude, displacement change rate and displacement duration, and the vibration features include vibration peak value, vibration root mean square value and vibration dominant frequency. Based on the abnormal time points and the preset time window, the comprehensive feature set is continuously sampled to obtain the feature sliding sequence of each displacement feature and vibration feature; The feature sliding sequence is compared with the corresponding feature threshold by sampling to obtain the number of consecutive exceedances of each displacement feature and vibration feature within the preset time window; Based on the number of consecutive exceedances of each displacement feature and vibration feature and the preset anomaly judgment conditions, the stable anomaly type of the abnormal event is determined. Based on the stable anomaly type, a third preset time range is obtained, wherein the third preset time range is less than or equal to the second preset time range; The abnormal event video is cropped according to the third preset time range to obtain an abnormal event video segment; Based on the abnormal time video segment and the stable anomaly type, alarm information is obtained, wherein the alarm information includes: abnormal event information and abnormal video information; Based on the level of abnormality, the alarm information is transmitted to the terminal devices of relevant personnel via a preset wireless communication protocol.

[0017] Preferably, after generating alarm information based on the abnormal displacement data sequence, the abnormal vibration data sequence, and the abnormal event video, and transmitting the alarm information to the terminal devices of relevant personnel, the method further includes: Based on the data storage of the monitoring platform, historical alarm information of the intelligent modular guardrail within a preset time range is obtained to obtain a historical alarm dataset; Each record is screened based on the historical alarm dataset to obtain a set of false alarm events and a set of valid alarm events; Based on the set of false alarm events and the set of valid alarm events, the corresponding comprehensive feature set is extracted to obtain the set of valid event features and the set of false alarm features. Based on the set of valid event features and the set of false alarm features, the distribution of valid event features and the distribution of false alarm event features are obtained respectively. Based on the distribution of valid event characteristics and the distribution of false alarm event characteristics, the preset displacement threshold and the preset vibration threshold are corrected to obtain an updated adaptive threshold set; The threshold of the intelligent modular guardrail is updated based on the adaptive threshold set.

[0018] Preferably, the step of correcting the preset displacement threshold and preset vibration threshold based on the valid event feature distribution and the false alarm event feature distribution to obtain an updated adaptive threshold set includes: Based on the geographical location of each intelligent modular guardrail that is connected to the monitoring platform, environmental information of each guardrail is obtained, wherein the environmental information includes: road type, road speed limit information and traffic flow. Feature extraction is performed on the environmental information to obtain the environmental feature matrix for each guardrail; Unsupervised clustering analysis is performed based on the environmental feature matrix to obtain several guardrail clustering results, wherein each clustering result corresponds to a set of guardrails under a certain environmental condition. Based on the environmental information, valid event feature distribution, and false alarm event feature distribution of each guardrail set, a time-segmented operation dataset is obtained. The time-segmented operation dataset includes at least: traffic flow, historical false alarm rate, and valid alarm rate for different time periods. Based on the time-segmented operation dataset, time period boundaries are determined to obtain a time period label set, which includes one or more of the following: morning peak, evening peak, nighttime, and regular time periods. Based on the traffic flow, historical false alarm rate, and effective alarm rate of different time periods under the aforementioned time period label, obtain the time period adjustment factor; Based on the time-segmented adjustment factor, the adaptive threshold set of each guardrail set is modified according to time segment to obtain the time-segmented adaptive threshold table. Based on the time-segmented adaptive threshold table and the current time, the preset displacement threshold and preset vibration threshold are corrected to obtain an updated adaptive threshold set.

[0019] In summary, the beneficial effects of the present invention are as follows: In summary, the intelligent modular guardrail of this invention, by setting mounting slots and through holes on the post body, allows for the quick and easy installation and replacement of functional modules in a detachable manner, thereby achieving flexible expansion of guardrail functions to meet the needs of different application scenarios. The detachable connection between the connecting rod structure and the post body improves the overall assembly and maintenance efficiency of the guardrail, reducing construction and replacement costs. By integrating photovoltaic panels, energy storage units, and wireless displacement and vibration units at the top of the post, it not only achieves self-powering and energy management but also enables real-time monitoring of the guardrail's displacement and vibration status, enhancing accident early warning capabilities. By setting an imaging module in the post body and electrically connecting it to the energy storage unit, it can acquire images of the surrounding environment, achieving real-time monitoring of roads or sites. Therefore, this invention effectively solves the problems of insufficient expandability, reliance on external power supply, and limited monitoring functions of existing guardrails, possessing comprehensive advantages such as convenient installation and maintenance, stable power supply, comprehensive monitoring, and timely early warning. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments of the present invention will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, and these are all within the protection scope of the present invention.

[0021] Figure 1 This is a schematic diagram of the overall structure of the intelligent modular guardrail according to an embodiment of the present invention.

[0022] Figure 2 This is a structural schematic diagram of the guardrail body according to an embodiment of the present invention.

[0023] Figure 3 This is a schematic diagram of the power module structure according to an embodiment of the present invention.

[0024] Figure 4 This is a schematic diagram of the imaging module in an embodiment of the present invention.

[0025] Figure 5 This is a structural schematic diagram of the connecting structure in an embodiment of the present invention.

[0026] Figure 6 This is a flowchart illustrating the control method for the intelligent modular guardrail according to an embodiment of the present invention.

[0027] Figure label: 1. Guardrail post; 2. Connecting rod structure; 3. Power module; 4. Imaging module; 5. Mounting slot; 6. Connecting structural component; 11. Post body; 12. Post base; 31. Photovoltaic panel; 32. First energy storage unit; 33. Wireless displacement vibration unit; 41. Second energy storage unit; 42. First camera; 43. Second camera; 44. Photosensitive unit. Detailed Implementation

[0028] The features and exemplary embodiments of various aspects of the present invention will now be described in detail. To make the objectives, technical solutions, and advantages of the present invention clearer, 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 configured to explain the present invention and are not configured to limit the present invention. For those skilled in the art, the present invention can be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the invention.

[0029] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0030] It should be noted that all actions involving the acquisition of signals, information, or data in this invention are carried out in compliance with the relevant data protection laws and regulations of the locality and with authorization from the owner of the relevant device.

[0031] Example 1 Please see Figures 1-5 This invention provides an intelligent modular guardrail, which includes at least two guardrail posts 1 and a connecting rod structure 2 disposed between the two guardrail posts 1. The guardrail post 1 includes a post base 12 and a post body 11 disposed on the post base 12. The side of the column body 11 is provided with a mounting groove 5, and the mounting groove is provided with several through holes. The mounting groove is used to install a functional module. The functional module is provided with several connecting structural members 6 that are adapted to the mounting groove. The functional module is detachably connected to the column body 11 through the connecting structural members 6. The connecting rod structure 2 includes the connecting structural member, and the connecting rod structure 2 is detachably connected to the column body 11 through the connecting structural member; The top of the column body 11 is provided with a power module 3. The power module 3 includes a photovoltaic panel 31, a first energy storage unit 32 and a wireless displacement vibration unit 33. The photovoltaic panel 31 is disposed on the surface of the power module, and the energy storage unit is electrically connected to the wireless displacement vibration unit and the photovoltaic panel respectively. The column body 11 also includes an imaging module 4 and a wireless communication module. The imaging module 4 includes a second energy storage unit 41 and an imaging unit. The first energy storage unit and the second energy storage unit are electrically connected, and the second energy storage unit and the imaging unit are electrically connected. The wireless communication module is electrically connected to the wireless displacement vibration unit and the imaging module, and is communicatively connected to the monitoring platform. It is used to receive displacement data and vibration data collected by the wireless displacement vibration unit, and transmit the displacement data, vibration data and video image data to the monitoring platform in real time through a preset wireless communication protocol.

[0032] Specifically, this embodiment of the invention provides an intelligent modular guardrail. The guardrail includes at least two guardrail posts 1 and a connecting rod structure 2 disposed between the two guardrail posts 1. Each guardrail post includes a post base 12 and a post body 11. The post base 12 is used to fix it to the ground to ensure the stability of the overall structure, and the post body is vertically mounted on the post base.

[0033] The connecting rod structure is also detachably connected to the column body via connecting structural components. This design allows for quick assembly and easy disassembly of the guardrail, making it suitable for temporary protection in road construction areas or movable isolation in public places.

[0034] Regarding energy supply, a power module is installed at the top of the column body. The power module includes a photovoltaic panel, a first energy storage unit, and a wireless displacement vibration unit. The photovoltaic panel is arranged on the surface of the power module to collect solar energy during the day; the first energy storage unit stores energy and powers the monitoring and lighting modules of the guardrail; the wireless displacement vibration unit can sense the displacement or impact vibration of the guardrail and feed the corresponding information back to the monitoring platform. For example, when a vehicle collides with the guardrail, the unit can collect the vibration signal in real time and trigger an alarm.

[0035] Furthermore, the pillar body integrates an imaging module and a wireless communication module. The imaging module includes a second energy storage unit and an imaging unit, which are electrically connected to the first energy storage unit to ensure stable power supply even when the top energy storage is insufficient. The imaging unit can use a high-definition camera or a night vision camera to collect real-time environmental images or video information around the guardrail, providing data support for road monitoring, traffic incident identification, and security evidence collection. The wireless communication module is electrically connected to both the wireless displacement and vibration unit and the imaging module, and establishes a communication link with a remote monitoring platform. The wireless communication module uses protocols such as MQTT, HTTP, or LWM2M to achieve low-latency, low-power data transmission, uploading the collected displacement data, vibration data, and video images to the monitoring platform in real time. The monitoring platform performs fusion analysis on the received data, and when abnormal vibration or displacement characteristics are detected, it can immediately trigger an alarm or video recording, assisting road management personnel in achieving remote monitoring and rapid response.

[0036] Preferably, the functional module includes one or more of a lighting unit, a crash warning post, and a luminous road sign. The lighting unit includes a light-emitting element and reflective strips disposed on both sides of the light-emitting element. The lighting unit and the road sign are electrically connected to the power module through the through hole.

[0037] Specifically, the functional module is part of road traffic safety facilities and may include one or more of the following: lighting unit, crash warning post, and luminous road sign. The lighting unit is a typical road lighting device and can use LED light sources, fluorescent tubes, or low-voltage energy-saving lamps, among which LED light sources are preferred due to their low power consumption, long lifespan, and high luminous efficiency. The lighting unit also includes reflective strips on both sides of the light source, which can passively reflect external vehicle headlights to create a warning when there is no power, thereby ensuring nighttime visibility.

[0038] Crash warning posts are traffic safety buffer components, made of flexible materials such as rubber and polyurethane. Reflective stickers can be attached to their outer surface to cushion and warn vehicles when they collide with the guardrail. Illuminated road signs are a type of road information interaction device, using LED display panels or backlit transparent signs to actively display traffic information, driving tips, or construction reminders.

[0039] The lighting unit and the road sign are electrically connected to the power module through through holes in the column body, thereby directly utilizing the electrical energy provided by the photovoltaic panel and energy storage unit for illumination. For example, on highway sections, the lighting unit uses LED light sources to provide continuous low-energy lighting, and the sign displays speed limit information in real time. At tunnel entrances or around schools, LED lighting is used in combination with reflective strips to simultaneously meet the needs of both active and passive warnings.

[0040] Preferably, the imaging module further includes a photosensitive unit 44, which is electrically connected to the second energy storage unit. The photosensitive unit is used to detect the ambient light intensity and control the lighting unit and / or the road sign to emit light according to the ambient light intensity.

[0041] Specifically, in this embodiment, the imaging module further includes a photosensitive unit electrically connected to the second energy storage unit, used to automatically detect ambient light intensity without relying on external control. When the ambient light intensity is lower than a preset threshold, the photosensitive unit can output a control signal to drive the lighting unit and / or road signs to emit light; when the ambient light intensity is higher than the preset threshold, the photosensitive unit stops outputting the control signal to achieve energy-saving control.

[0042] In practical applications, such as at night or in tunnel environments, when the photosensitive unit detects insufficient light, it can automatically illuminate the LED lighting unit and simultaneously light up road signs to display warning information or traffic prompts, thereby improving road visibility and safety. During the day when there is sufficient sunlight, if the photosensitive unit detects that the ambient brightness has reached a set range, it will automatically turn off the lighting and indicator modules, reducing energy consumption and extending the lifespan of the energy storage unit.

[0043] Preferably, the imaging unit includes a first camera 42 and a second camera 43. The first camera and the second camera are electrically connected to the second energy storage unit, respectively. The first camera and the second camera are disposed on opposite sides of the column body to simultaneously collect environmental video data on both sides of the guardrail and transmit the environmental video images to the monitoring platform.

[0044] Specifically, the imaging unit includes a first camera and a second camera, both electrically connected to the second energy storage unit to ensure continuous operation even in the event of an external power outage. The first and second cameras are symmetrically positioned on opposite sides of the column body to simultaneously collect environmental video data from both sides of the guardrail. The collected video data is uploaded to the monitoring platform via wireless communication or wired transmission for real-time monitoring, event playback, and data archiving by management personnel.

[0045] In practical applications, such as in the central median barrier of a highway, the first camera can capture traffic images from the side closest to the fast lane, while the second camera can capture traffic images from the side closest to the slow lane or auxiliary road. The management platform can simultaneously acquire traffic conditions from both directions. When a vehicle collides with the barrier, the cameras on both sides can simultaneously record the accident process from different angles, facilitating subsequent liability determination and accident analysis.

[0046] In one specific embodiment, in an urban road guardrail application scenario, the first camera monitors traffic flow on the main road, while the second camera monitors sidewalks and non-motorized vehicle lanes. When the monitoring platform detects an abnormal signal triggered by the guardrail vibration unit, the system can automatically retrieve the corresponding video clips from both cameras to quickly determine whether it is a vehicle collision, a person climbing over, or other abnormal behavior, thereby achieving accurate event localization.

[0047] Preferably, the wireless vibration unit is used to detect the displacement and vibration status of the intelligent modular guardrail, and to determine whether the displacement and vibration status is abnormal. If so, the displacement and vibration status is transmitted to the monitoring platform through a preset wireless communication protocol, and an alarm is triggered through the monitoring platform. The preset wireless communication protocol includes at least one of MQTT, HTTP, CoAP, or LWM2M.

[0048] Specifically, preferably, the wireless vibration unit is used to detect the displacement and vibration status of the intelligent modular guardrail and to perform preliminary analysis on the collected data to determine whether any abnormal events exist. When the detection results show that the displacement exceeds the set range, or the vibration intensity or frequency is abnormal, the wireless vibration unit will determine it as an abnormal state and transmit the displacement and vibration status to the monitoring platform through a preset wireless communication protocol. After receiving the data, the monitoring platform can further process it according to the set logical rules, such as generating alarm prompts, pushing them to the management terminal, or triggering linkage mechanisms. In specific applications, for example, when a vehicle collision occurs in the central guardrail of a highway, the wireless vibration unit can detect strong vibrations in real time and report them immediately; or, in a construction area, when the guardrail is moved, the wireless vibration unit will detect abnormal displacement and trigger an alarm. Through the support of different communication protocols, the system can adapt to various application needs in cellular network, local area network, or Internet of Things environments.

[0049] The preset wireless communication protocol may include at least one of MQTT, HTTP, CoAP, or LWM2M. MQTT is a lightweight publish / subscribe messaging protocol with strong real-time performance and low transmission overhead, suitable for quickly pushing vibration data to a monitoring platform when a highway guardrail collides, thus achieving immediate accident alarm. HTTP is a widely used hypertext transfer protocol with good compatibility and high stability, suitable for urban roads or municipal management platforms for routine data reporting and remote management of guardrail daily operation. CoAP is a restricted application protocol designed for the Internet of Things (IoT), capable of efficient operation in low-power, low-bandwidth environments, suitable for locations with limited network conditions such as construction areas and temporary road guardrails, achieving low-energy transmission of displacement and vibration status. LWM2M is a lightweight protocol for device management, supporting remote configuration and status monitoring, suitable for scenarios involving unified access and centralized management of large-scale guardrail devices. By supporting multiple communication protocols, the intelligent modular guardrail can flexibly select the most suitable transmission method according to the actual application environment, thereby ensuring the real-time performance and reliability of alarm data.

[0050] Preferably, the column body has a waterproof wiring channel inside, which is used to isolate and protect the electrical connections of the power module, imaging module and functional modules.

[0051] In this embodiment, the column body is equipped with a waterproof wiring channel. This channel provides an independent space for electrical wiring inside the guardrail and isolates and protects the electrical connections between the power module, imaging module, and functional modules. The wiring channel is typically made of waterproof material or has a sealed structure, such as adding a waterproof coating, sealing rings, or sleeves to the outer wall of the channel, thereby preventing rainwater, dust, and other external factors from seeping in and ensuring the long-term stable operation of the internal wiring.

[0052] In practical applications, such as highway guardrails which are constantly exposed to wind and rain, exposed electrical wiring or the use of only conventional conduits can easily lead to water ingress, short circuits, or aging wiring. By installing waterproof wiring channels inside the column body, the connection lines between the power module and functional modules such as the imaging module, lighting unit, crash warning post, and road signs can be centrally laid out and effectively protected, ensuring both neat wiring and improved reliability of the electrical system.

[0053] Example 2 Based on the solution in Example 1, please refer to... Figure 6 This invention also provides a control method for an intelligent modular guardrail, the method comprising: S1. Obtain the displacement data and vibration data of the intelligent modular guardrail through the wireless displacement and vibration unit; The wireless displacement and vibration unit in this step is a monitoring device integrated with an acceleration sensor, a gyroscope, and a wireless communication module, which is installed inside or on the surface of the guardrail. Its typical examples include inertial measurement units (IMUs) with LoRa, NB-IoT, or ZigBee communication modules, which are used to collect the displacement changes and vibration responses of the guardrail without wiring. The purpose of this step is to monitor the stability state of the guardrail structure in real time and provide basic data support for subsequent abnormal judgment and linkage operations through continuous data acquisition. The IMU module collects three-axis acceleration and angular velocity signals at a fixed time interval (such as 10 times per second), and obtains displacement data through filtering and integration processing. All the collected data is sent to the edge processing node or local controller through a wireless protocol for real-time uploading and storage, realizing non-contact, low-power, and wide-coverage state acquisition, and providing a stable and reliable data basis for the guardrail system with high-density deployment and long-term operation.

[0054] S2. Compare the displacement data and vibration data with a preset displacement threshold and a preset vibration threshold respectively to determine whether there is an abnormal event; The preset threshold refers to the numerical range predefined for abnormal discrimination during the initialization or operation stage. The displacement threshold can be more than 5 cm for a single movement, and the vibration threshold can be an acceleration exceeding 0.3 g. The purpose of this step is to screen out potential abnormal events from a large number of normal operating states as the trigger points for subsequent video cropping and early warning, improving the accuracy and efficiency of the system response. In implementation, the system judges the data of each time segment. If the sampling values exceed the threshold range for multiple consecutive times, it is determined as an abnormal state. The sliding window mechanism can be combined to improve the detection accuracy, avoiding false triggers and resource waste, ensuring that subsequent video processing and data uploading are only executed during necessary periods, and improving the overall system energy efficiency and processing capacity.

[0055] S3. If so, obtain the abnormal time point and, based on the abnormal time point, obtain the displacement data and vibration data within the first preset time range to obtain an abnormal displacement data sequence and an abnormal vibration data sequence; Specifically, after detecting an abnormal event, in order to further accurately analyze the cause and development process of the abnormality, it is necessary to perform windowed extraction of historical data based on the key time point when the abnormality appears. The "abnormal time point" here usually refers to the moment when the displacement or vibration data is first or continuously detected to exceed the preset threshold. This time point serves as the anchor point for analysis and is the core reference for subsequent video editing, data alignment, and behavior recognition.

[0056] To achieve continuous data tracking, this step introduces the concept of a "first preset time range," such as 30 seconds before and after the anomaly, or a custom interval defined by the system based on the application scenario. By sliding sampling around the time window before and after this point, complete abnormal displacement and vibration data sequences can be constructed. This processing method ensures that not only the transient data at the moment of anomaly triggering is recorded, but also the antecedents and subsequent changes are preserved, providing full-process support for false alarm investigation, behavior analysis, and equipment response assessment.

[0057] For example, if the abnormal time point is 10:32:10, all raw data between 10:31:40 and 10:32:40 will be automatically extracted and a precise timestamp will be added. This process ensures complete coverage of the entire event and provides a solid foundation for subsequent video frame matching, displacement / vibration trend analysis, and impact range assessment, while also improving the intelligent analysis capabilities and response efficiency of the monitoring platform.

[0058] S4. Based on the abnormal time point and the second preset time range, the monitoring video collected by the imaging unit is cropped to obtain the abnormal event video; To quickly locate video evidence after anomaly detection, this step extracts the target segment from the video data based on the aforementioned anomaly time point and using a second preset time range. The second preset time range can be consistent with the data sequence extraction range, for example, 30 seconds before and after the anomaly time point, or it can be flexibly adjusted according to the specific scenario (such as the duration of the event).

[0059] Specifically, the system searches for time periods corresponding to abnormal times in the continuously acquired video stream by the imaging unit and then crops out video segments within that interval. For example, if the abnormal time is 10:32:10, the system will simultaneously extract the video segment from 10:31:40 to 10:32:40 to form a "normal event video." This video segment has high temporal correlation and is the core basis for reconstructing the scene, analyzing personnel behavior, and confirming the accuracy of the alarm.

[0060] This method effectively avoids browsing lengthy surveillance videos, improves video screening efficiency, and achieves accurate alignment with abnormal data, which is beneficial for subsequent multimodal information fusion analysis, such as behavior recognition, intrusion path tracing, and alarm credibility verification.

[0061] S5. Transmit the abnormal displacement data sequence, the abnormal vibration data sequence, and the abnormal event video to the monitoring platform; In this step, the previously extracted structured data and video data are sent together to the remote monitoring platform for centralized display and unified management. This monitoring platform is the control center for receiving, storing, and analyzing information collected from the front end. It is typically a cloud server or local server system that supports functions such as data parsing, event annotation, and remote alarm linkage.

[0062] Abnormal data and videos are packaged and uploaded using stable wireless communication protocols (such as MQTT and HTTP), ensuring data integrity and timestamp alignment. For example, metadata such as sensor number, location, time range, and alarm level is added during data upload, facilitating platform categorization and rapid review by device, region, or time. This step ensures end-to-end information closure, improves the efficiency of remote management and alarm response, and provides rich original evidence for subsequent event archiving, statistical analysis, and false alarm screening, making it one of the fundamental links in the system's intelligent security operation and maintenance.

[0063] S6. Generate alarm information based on the abnormal displacement data sequence, the abnormal vibration data sequence, and the abnormal event video, and transmit the alarm information to the terminal devices of relevant personnel.

[0064] This step involves intelligent push processing after the monitoring platform receives the event data. "Alarm information" refers to structured prompts generated based on abnormal data and video analysis results. It typically includes key information such as the event type (e.g., impact, tipping), equipment location, time of occurrence, risk level, and video link, used to remind relevant management personnel to respond promptly.

[0065] In practice, the platform can set multi-level alarm strategies based on the magnitude, duration, video content, or multiple triggering behaviors of abnormal data, and automatically generate alarm information. Subsequently, the alarm information is sent to the responsible personnel's terminals through preset channels (such as APP push notifications, SMS, email, WeChat, etc.). The alarm information can also include a location link, directly redirecting to the video playback interface.

[0066] This mechanism significantly shortens the link from incident occurrence to personnel response, improves the timeliness and accuracy of incident handling, and is suitable for scenarios with high security requirements, such as urban isolation barriers, highway boundaries, and key engineering areas, to achieve closed-loop control of the entire process of automatic detection, intelligent early warning and remote linkage.

[0067] Preferably, the step of comparing the displacement data and vibration data with preset displacement thresholds and preset vibration thresholds respectively to determine whether there is an abnormal event includes: S21. Based on the displacement data, compare it with the preset displacement threshold to obtain the initial displacement judgment result; The so-called "preliminary displacement judgment result" refers to the result label obtained by comparing the real-time collected displacement data with the preset displacement threshold set by the system frame by frame, and judging whether there is a possible displacement anomaly in the current frame data. This threshold can be set according to the equipment installation type (such as fixed column, movable fence) and the usage environment (such as wind load area, road section prone to impact).

[0068] In practice, after each new displacement data is acquired, it is immediately compared with a threshold. If the value exceeds the threshold, it is recorded as a suspected anomaly. The core purpose of this step is to build a highly real-time initial judgment mechanism to quickly filter out data frames with large offsets, providing basic data support for subsequent trend judgment. Through this initial displacement judgment mechanism, large displacement changes can be marked instantly, effectively shortening the response time and preventing major structural offsets or external impact events from being missed.

[0069] S22. Based on the vibration data, compare it with the preset vibration threshold to obtain the initial vibration judgment result; In this step, the initial vibration assessment result refers to comparing the real-time output data of the vibration sensor with a preset vibration threshold to determine whether abnormal vibration behavior exists. The vibration threshold can be dynamically set based on the equipment's installation stability, usage scenario (such as whether it is near a driveway or construction area), and historical background noise data.

[0070] After obtaining the current vibration acceleration or frequency value, it is compared with a set threshold one by one. If the data exceeds the normal range, a suspected anomaly label is generated. Unlike the initial displacement judgment, vibration judgment is usually more sensitive to physical disturbances such as sudden impacts and continuous shaking. This step aims to build a second-dimensional anomaly identification system. Through cross-validation with the displacement dimension, it improves the overall detection system's ability to identify real risk events and reduces the probability of misjudgment or missed judgment caused by fluctuations in a single data point.

[0071] S23. Based on the initial displacement judgment result and the initial vibration judgment result, count the number of consecutive exceedances of displacement data and vibration data within a preset time window. Based on the initial assessment, this step further introduces a time window and a continuous statistical mechanism. The term "continuous threshold exceedance" refers to the number of times or time intervals within a sliding time window (e.g., 10 seconds, 30 seconds) that a data point continuously exceeds a threshold. This is achieved by maintaining a dynamically updated circular buffer queue, counting the number of data frames initially judged as abnormal within each time window, and independently counting displacement and vibration. If a certain type of data exceeds the threshold for multiple consecutive sampling periods, that type of signal is considered to have a trend of anomaly. This mechanism effectively suppresses false alarms caused by occasional interference, instantaneous errors, or random noise, thereby improving the system's accuracy in identifying real, continuous interference events (such as impacts, overturning, and severe disturbances).

[0072] S24. Based on the number of consecutive exceedances, determine whether there is an abnormal event.

[0073] Finally, based on the number of consecutive threshold exceedances obtained in S23, a risk event judgment is made. If the number of threshold exceedances for either displacement or vibration within a specified time window exceeds a preset judgment threshold, an abnormal event can be identified.

[0074] The judgment logic can be set as follows: an event is triggered when the number of times a single channel exceeds the limit reaches a threshold, or when both exceed the limit in combination (e.g., "2 displacements + 3 vibrations") together constitute the alarm trigger condition. This strategy can also be adapted to multi-level alarm mechanisms, such as minor anomalies and severe alarms. This judgment mechanism forms a multi-layer decision-making system based on a two-dimensional initial judgment combined with time continuity statistics, which can effectively reduce misjudgments caused by occasional interference, while enhancing the ability to perceive real threats, and providing reliable pre-triggered signals for subsequent video linkage, data feedback, and personnel notification.

[0075] Preferably, the step of generating alarm information based on the abnormal displacement data sequence, the abnormal vibration data sequence, and the abnormal event video, and transmitting the alarm information to the terminal device of relevant personnel, includes: S61. Preprocess the abnormal displacement data sequence and the abnormal vibration data sequence to obtain the target displacement sequence and the target vibration sequence. Specifically, in this step, the abnormal displacement data sequence and abnormal vibration data sequence refer to sensor data collected within a specific time period after a suspected abnormal event is detected. Preprocessing involves denoising, filtering, removing trend terms, and filling in missing values ​​on these raw data. The main purpose of this step is to improve the quality and stability of the raw data, making it more suitable for subsequent feature extraction and discriminant analysis. Methods such as median filtering, wavelet denoising, or sliding window averaging can be used to smooth the data sequence, eliminate abrupt changes and noise interference, and interpolate to fill in missing points. Through this preprocessing, a more continuous, stable, and accurate target displacement and vibration sequences are obtained, improving the overall accuracy of event identification.

[0076] S62. Extract features from the target displacement sequence and the target vibration sequence respectively to obtain a comprehensive feature set, wherein the comprehensive feature set includes displacement features and vibration features, the displacement features include displacement amplitude, displacement change rate and displacement duration, and the vibration features include vibration peak value, vibration root mean square value and vibration dominant frequency; Feature extraction refers to extracting key indicator parameters from target data. The "comprehensive feature set" contains representative features extracted from both displacement and vibration dimensions. Displacement features typically include maximum displacement amplitude, rate of change (i.e., velocity), and duration, while vibration features include peak value, root mean square (RMS) value, and frequency domain features such as dominant frequency. The purpose of this step is to quantitatively analyze displacement and vibration, forming a numerical foundation that can be used for behavior recognition and anomaly classification. In practice, various feature parameters can be calculated through time-domain statistical analysis and Fourier transform to construct a feature vector set with a unified structure. This step enables structured modeling of anomalous data, providing rich dimensional support for subsequent anomaly judgment, thereby enhancing the ability of the recognition model to distinguish between different types of anomalous events.

[0077] S63. Based on the abnormal time point and the preset time window, the comprehensive feature set is continuously sampled to obtain the feature sliding sequence of each displacement feature and vibration feature; An abnormal time point refers to the initial judgment of the event's start time, while a preset time window is a continuous time range defined around the abnormal time point; a feature sliding sequence refers to a sequence of multiple continuous feature values ​​obtained by sampling according to the sliding step size within this time range.

[0078] This step aims to capture the characteristic trends of an event's development to determine whether there is a persistent or incremental abnormal evolutionary behavior. Specifically, a fixed-width sliding window is defined centered on the abnormal time point and moves in a set step size to gradually extract the temporal evolution sequences of various features. This operation reflects whether the abnormal process possesses stability and sustainability, helping to distinguish it from sporadic noise events and improving the system's accuracy in identifying genuine anomalies.

[0079] S64. The feature sliding sequence is compared with the corresponding feature threshold by sampling to obtain the number of consecutive over-limits of each shift feature and vibration feature within the preset time window. Specifically, the feature threshold is the normal range limit for various indicators set based on experience or historical data. The number of consecutive exceedances refers to the number of samples continuously exceeding the threshold detected within the sliding window. The purpose of this step is to further screen time segments with abnormal trend characteristics, determining their stability and severity. This can be achieved by comparing each feature sliding sequence with the threshold point by point, counting the number and length of consecutive exceedances. This effectively identifies false anomalies and marginal interference, ensuring that truly persistently abnormal data is accurately captured, thereby improving the reliability of event classification and severity assessment.

[0080] S65. Based on the number of consecutive exceedances of each displacement feature and vibration feature and the preset abnormal judgment conditions, determine the stable abnormal type of the abnormal event. Stable anomaly types refer to the classification results of abnormal events that are confirmed by multiple consecutive exceedance features and possess persistence and reproducibility. Preset anomaly judgment conditions include thresholds for the number of exceedances and duration thresholds for various feature dimensions. The purpose of this step is to qualitatively classify and categorize abnormal events based on the aforementioned statistical results, clarifying the true severity and specific type of the event. During implementation, a weighted algorithm can be designed to comprehensively score the continuous exceedance of multiple features and compare it with a classification rule base to output anomaly type labels (such as "structural loosening," "severe impact," "periodic oscillation," etc.). This step significantly improves the interpretability and classification accuracy of alarm information, providing high-quality identification basis for subsequent video generation and alarm push notifications.

[0081] S66. Based on the stable anomaly type, obtain a third preset time range, wherein the third preset time range is less than or equal to the second preset time range; The third preset time range is a specific time period for the event, used for video cropping, and its length is determined by the type of anomaly. The second preset time range is the video buffer time interval mentioned earlier, used for reviewing complete records. The purpose of this step is to finely control the video length based on the severity and type of the anomaly, balancing information integrity and data transmission efficiency. In practice, different time windows can be defined according to the characteristics of the anomaly type; for example, a short vibration event can use a 1-2 second video segment, while a continuous structural deformation event can be extended to more than 5 seconds, always constrained within the second time range. This setting effectively reduces redundant video content, increases the effective information density of alarm videos, and reduces the load on remote transmission.

[0082] S67. The abnormal event video is cropped according to the third preset time range to obtain an abnormal event video segment; The abnormal event video is the original footage recorded by the system within a preset time period. Cropping refers to extracting video segments within a third time range to form the final clip. The purpose of this step is to extract the key processes of the abnormal event from the long video, forming concise video evidence that focuses on the sudden situation. In practice, the start and end frames can be cropped forward / backward based on the abnormal time point, and the video resolution and encoding format can be standardized to generate video clips that are easy to review and analyze. This step improves the practicality and accuracy of the alarm video, providing users with clear and visual evidence to quickly locate the occurrence of abnormal events.

[0083] S68. Based on the abnormal time video segment and the stable anomaly type, obtain alarm information, wherein the alarm information includes: abnormal event information and abnormal video information, the abnormal time information, the abnormal event category and the anomaly level; video segment index and video summary description; "Alarm information" is a structured collection of information used for push notifications, including text descriptions and video materials; abnormal event information clarifies the basic attributes of the abnormality, and abnormal video information annotates the video content and its search index. This step aims to merge the analysis results and visual evidence into a unified alarm record, achieving a panoramic presentation of the event. During implementation, the system integrates the abnormality type, event level, timestamp, and cropped video clip information, and automatically generates summary text for rapid identification. This step standardizes and clarifies alarm data, facilitating rapid decision-making and subsequent response.

[0084] S69. Based on the abnormality level, the alarm information is transmitted to the terminal device of the relevant personnel through a preset wireless communication protocol.

[0085] The anomaly level is a criterion for classifying the severity of an event. This step ensures that alarm information is delivered to relevant personnel in a timely manner according to priority, improving response speed and handling efficiency. The system is configured with different push mechanisms based on the anomaly level; for example, high-level anomalies use multi-channel push with strong alerts, while medium-level anomalies use regular message pushes, selecting the optimal communication channel based on network conditions. This step ensures that the monitoring system can still provide effective alarms even in environments with multiple concurrent events or limited communication, achieving a highly available and reliable security system.

[0086] Preferably, after generating alarm information based on the abnormal displacement data sequence, the abnormal vibration data sequence, and the abnormal event video, and transmitting the alarm information to the terminal devices of relevant personnel, the method further includes: S01. Based on the data storage of the monitoring platform, obtain the historical alarm information of the intelligent modular guardrail within a preset time range to obtain a historical alarm dataset; Specifically, to achieve dynamic adaptive optimization of alarm thresholds, the system first executes step S01, which involves acquiring historical alarm information of the intelligent modular guardrail within a preset time range based on the data storage function of the monitoring platform, forming a historical alarm dataset. This historical alarm information refers to all alarm events recorded during system operation, including alarm time, alarm type, displacement and vibration data before and after the alarm, and manual confirmation results. Setting a preset time range aims to limit the data window for analysis, ensuring the data is timely and consistent with current environmental characteristics. This step automatically extracts alarm records within the specified time period through interface calls or database retrieval, removing duplicates or incomplete entries to construct a high-quality dataset suitable for model learning. This provides a sufficient data foundation for subsequent event screening and feature extraction.

[0087] S02. Screen each record according to the historical alarm dataset to obtain a set of false alarm events and a set of valid alarm events; After acquiring the historical alarm dataset, each record was screened to identify false alarms and valid alarms, thus constructing two comparison datasets: one for false alarms and one for valid alarms. False alarms are triggered by non-human-caused disturbances such as environmental interference, construction noise, wind, rain, or vibration, while valid alarms are records of actual risks such as unauthorized intrusion or collisions. By integrating event handling records, manually labeled information, and event processing results, a rule engine was established to intelligently classify historical data, laying a solid foundation for subsequent feature analysis.

[0088] S03. Extract the corresponding comprehensive feature set based on the set of false alarm events and the set of valid alarm events to obtain the set of valid event features and the set of false alarm features. Next, based on the aforementioned two sets, corresponding comprehensive feature sets are extracted and further organized into a valid event feature set and a false alarm feature set. Comprehensive features refer to multi-dimensional data attributes associated with each alarm, such as displacement amplitude, vibration frequency, duration, and synchronization degree between adjacent modules when the alarm occurs. This step automatically summarizes the manifestation of each type of event in the feature space through feature extraction algorithms, forming feature templates for subsequent modeling and comparison. In this way, the typical characteristics of different types of events are systematically summarized, which helps to identify the distribution differences between the two types of events in the threshold space.

[0089] S04. Based on the set of valid event features and the set of false alarm features, obtain the distribution of valid event features and the distribution of false alarm event features, respectively; After obtaining these two feature sets, feature distribution models are established for the valid event feature set and the false alarm feature set, respectively. Here, feature distribution refers to the statistical probability distribution of various events along key feature dimensions, such as the frequency of displacements exceeding 0.5 cm and the proportion of vibration intensity exceeding a certain value. By constructing feature density maps or using kernel density estimation methods, the probability differences between valid alarms and false alarms along different feature dimensions can be clearly depicted, identifying which features are most discriminative for alarm judgment. The core of this step is to provide a reference standard for subsequent threshold evaluation, making the threshold setting more closely aligned with actual alarm performance.

[0090] S05. Based on the distribution of valid event characteristics and the distribution of false alarm event characteristics, the preset displacement threshold and the preset vibration threshold are corrected to obtain an updated adaptive threshold set. After completing the deviation analysis, the original preset displacement and vibration thresholds are adjusted based on the obtained effective time characteristic distribution and false alarm event characteristic distribution, resulting in an updated adaptive threshold set. The correction can be set based on logic such as distribution centroid, anomaly rate control, or misjudgment cost weighting to ensure that the adjusted thresholds can more accurately distinguish between abnormal and non-abnormal events. During the correction process, tolerance ranges or step parameters can also be set to prevent over-adjustment from causing system instability. The final adaptive threshold set is a responsive adjustment result based on real-world data feedback.

[0091] In one embodiment, after obtaining the distribution of valid event characteristics and the distribution of false alarm event characteristics, the system first compares and analyzes the currently preset displacement threshold and vibration threshold to determine their relative positional relationship within the overall characteristic distribution. Specifically, based on the distribution density curves of the two types of events, the relative offset of the current threshold point between these two distributions is calculated to obtain threshold deviation information. This threshold deviation information refers to the difference in the relative position of the current threshold with respect to the valid event characteristic area and the false alarm event characteristic area. For example, if the current displacement threshold falls in the high-frequency region of the false alarm event distribution but fails to cover the main peaks of the valid events, it indicates that the threshold is too low and prone to false alarms; if the threshold is located at the edge or even outside the valid event distribution, it indicates that the threshold is too high and may lead to missed alarms. Through this relative position quantification method, the system can accurately reflect the matching degree between the threshold and the actual event characteristics.

[0092] Subsequently, the threshold is directionally corrected based on the threshold deviation information: when the threshold is detected to be biased towards the false alarm area, the system automatically raises the corresponding threshold to enhance its anti-disturbance capability; when the threshold is detected to be biased towards the valid event area, the system appropriately lowers the threshold to improve sensitivity and detection coverage. During the adjustment process, the system automatically limits the adjustment step based on the overall trend of historical alarms to prevent over-correction due to instantaneous abnormal data. Through this correction method based on distribution comparison and offset analysis, adaptive optimization of displacement and vibration thresholds can be achieved without manual intervention, enabling the system to maintain reasonable sensitivity and stability under different environmental and time conditions.

[0093] By quantifying the offset relationship between threshold values ​​and event feature distribution, the system transitions from static setting to dynamic correction, providing clear physical basis and statistical support for threshold adjustments. The system can continuously learn and optimize over long-term operation, gradually converging to the optimal threshold range, thereby significantly reducing false alarm and false negative rates and improving the overall recognition accuracy and operational reliability of the intelligent modular guardrail.

[0094] In one embodiment, the step of correcting the preset displacement threshold and preset vibration threshold based on the valid event feature distribution and the false alarm event feature distribution to obtain an updated adaptive threshold set includes: S051. Based on the geographical location of each intelligent modular guardrail that is connected to the monitoring platform, obtain the environmental information of each guardrail, wherein the environmental information includes: road type, road speed limit information and traffic flow. Specifically, firstly, through the communication connection between the monitoring platform and each guardrail node, the basic environmental data of the node's location is automatically retrieved from the road information database or traffic management system using the geographic coordinate information registered during installation. This environmental information includes road type (e.g., urban arterial road, rural road, highway, etc.), road speed limits, and real-time or historical traffic flow data. This step provides environmental background input for subsequent environmental clustering and threshold correction, allowing threshold adjustments to consider road usage characteristics and traffic disturbance levels. By acquiring this type of environmental information, the problem of poor regional adaptability caused by using a single threshold model can be avoided, thus enabling guardrail systems on different types of roads to have matched sensitivity and anti-interference capabilities.

[0095] S052. Perform feature extraction on the environmental information to obtain the environmental feature matrix of each guardrail; After acquiring basic environmental information, the system standardizes and extracts features from various parameters, transforming the raw environmental information into a feature matrix suitable for computation and clustering. For example, road type can be quantified through category coding, traffic flow can be represented by daily average vehicle trips or traffic density indicators, and speed limit information can be used as a continuous numerical feature. After normalization, an environmental feature matrix containing multi-dimensional environmental variables is generated. The purpose of this step is to structure the environmental data, enabling different guardrail nodes to be compared for similarity in the same feature space during subsequent cluster analysis. This matrix transformation effectively improves the computability and versatility of environmental parameters.

[0096] S053. Perform unsupervised clustering analysis based on the environmental feature matrix to obtain several guardrail clustering results, wherein each clustering result corresponds to a set of guardrails under a certain environmental condition. Unsupervised learning algorithms (such as K-means) are used to cluster the environmental feature matrix to identify guardrail groups with similar road conditions and traffic characteristics. For example, high-density urban roads can form one cluster, while highways can form another. The purpose of this step is to group guardrail nodes with similar environments into the same category so that they can share adjustment models in subsequent threshold correction and policy generation, reducing computational redundancy and fluctuations caused by independent learning of single nodes. Through environmental clustering, regional collaborative correction can be achieved, that is, guardrail nodes in the same area or under similar conditions adopt a unified or approximate correction logic, ensuring the overall consistency and robustness of the guardrails.

[0097] S054. Based on the environmental information, effective event characteristic distribution and false alarm event characteristic distribution of each guardrail set, obtain a time-segmented operation dataset. The time-segmented operation dataset includes at least: traffic flow, historical false alarm rate and effective alarm rate for different time periods. For each clustered guardrail set, its environmental information and historical event data are integrated, and statistical periods are divided according to time dimensions (e.g., by hour, day / night, weekday / holiday, etc.) to generate a time-segmented operational dataset. Key metrics for this dataset include: traffic flow for each time period (reflecting traffic disturbance intensity), historical false alarm rate for each time period (to evaluate the system's anti-interference performance), and effective alarm rate for each time period (to measure identification sensitivity and accuracy). Through this step, the system can understand the differences in operational characteristics of the same guardrail set across different time periods, thus providing a quantitative basis for subsequent time-segmented threshold adjustments.

[0098] S055. Determine the time period boundaries based on the time-segmented operation dataset to obtain a time period label set, wherein the time period label set includes one or more of the following: morning peak, evening peak, nighttime, and regular time periods; Trend analysis is performed on traffic flow and alarm indicators in the time-segmented dataset to identify time periods with significant changes in traffic characteristics, and corresponding time period labels are automatically generated. Typically, periods with dense and fluctuating traffic are labeled as morning and evening peak hours, periods with insufficient lighting or sparse traffic are labeled as nighttime, and other periods are labeled as regular periods. The purpose of this step is to establish time partitions corresponding to actual road operations, making threshold adjustments time-sensitive. By dynamically determining time period boundaries, it can automatically adapt to seasonal or regional changes in traffic patterns, such as the delay of peak traffic during holidays or temporary increases in traffic flow due to nighttime construction.

[0099] S056. Based on the traffic flow, historical false alarm rate and effective alarm rate of different time periods under the time period label, obtain the time period adjustment factor; Specifically, the combination characteristics of the three types of indicators under different time periods are analyzed to generate corresponding time-based adjustment factors. For example, when traffic volume is high during the morning rush hour and the false alarm rate increases while the effective alarm rate decreases, an anti-interference enhancement adjustment factor is generated, which moderately increases the vibration threshold and raises the threshold for consecutive exceedances. If traffic volume is low at night but the effective alarm rate is high and the false alarm rate is low, a sensitivity enhancement adjustment factor is generated, which lowers the displacement threshold and extends the video buffering time. The time-based adjustment factor can contain weighted information from multiple dimensions to control the correction direction and magnitude of different parameters, thereby suppressing false alarms while ensuring detection accuracy.

[0100] In one specific embodiment, the adjustment factor is obtained as follows: When traffic volume is high, the guardrail experiences increased airflow impact, vehicle vibration coupling, and low-frequency structural resonance, potentially increasing the probability of false triggering. Therefore, a corresponding traffic disturbance coefficient is generated based on the traffic volume range. When traffic volume is higher than the historical average or the upper threshold, a larger traffic disturbance coefficient is used, indicating that the anti-interference capability should be enhanced; conversely, when traffic volume is low, a smaller traffic disturbance coefficient is used, indicating that the system's sensitivity can be appropriately increased.

[0101] If the historical false alarm rate for a certain period is significantly higher than the system's long-term average, it indicates that the current detection conditions are too sensitive. The vibration threshold or the threshold for consecutive exceedances should be appropriately increased to reduce false alarms. If the false alarm rate is low, it indicates that the system is operating stably during that period, and the existing threshold can be maintained or fine-tuned based on other indicators. When generating adjustment factors, the system uses the trend of false alarm rate changes as a "suppression weight" and performs a multiplicative correction based on the traffic disturbance coefficient to achieve robust false alarm control.

[0102] If the effective alarm rate is low during a certain period, it indicates that the threshold is too high or the judgment conditions are too strict. The system will generate a "sensitivity enhancement" weight factor to lower the displacement threshold and vibration threshold. If the effective alarm rate is high and the false alarm rate has not increased significantly, it indicates that the current threshold setting is reasonable and can be maintained or slightly adjusted. When generating the adjustment factor, the system uses the time-period trend of the effective alarm rate as the sensitivity weight, and calculates it in combination with the weights of the aforementioned two indicators to achieve a balance correction.

[0103] Finally, based on the weighted summation of traffic disturbance coefficient, false alarm suppression weight, and sensitivity weight, a time-segmented adjustment factor is generated for the corresponding time period. This adjustment factor can be further subdivided into multiple parameter dimensions, including: displacement threshold correction factor, vibration threshold correction factor, consecutive over-limit number correction factor, and sampling time window correction factor. When traffic volume is high and the false alarm rate is high while the effective alarm rate is low, the vibration threshold and consecutive over-limit number threshold are increased; when traffic volume is low and the false alarm rate is low while the effective alarm rate is high, the displacement threshold is decreased and the video sampling time window is appropriately extended; if both traffic volume and false alarm rate are at a moderate level, while the effective alarm rate decreases slightly, the vibration threshold and decision window length are slightly adjusted to ensure a balance between recognition accuracy and stability.

[0104] Through the above steps, the time-segmented adjustment factor achieves an "adaptive balance based on traffic flow disturbance, false alarm trend, and recognition effectiveness," enabling the system to automatically adjust detection sensitivity and anti-interference capability at different time periods. This avoids false alarms during high-traffic, high-disturbance periods and improves the event capture rate during low-traffic, nighttime periods, ensuring the time-segment stability and dynamic optimization of overall alarm performance.

[0105] S057. Based on the time-segmented adjustment factor, the adaptive threshold set of each guardrail set is modified according to time segment to obtain the time-segmented adaptive threshold table. After obtaining the time-segmented adjustment factors, the threshold parameters for each guardrail set are updated. The preset displacement and vibration thresholds are weighted and corrected based on the adjustment factors for different time periods, generating a time-segmented adaptive threshold table. This table records the optimal threshold configuration for each time period and is automatically invoked during system operation. For example, a higher vibration threshold and more lenient triggering conditions are used during morning rush hour, while a lower threshold and a more sensitive detection mode are used at night. Through this time-segmented adaptive mechanism, the system achieves proactive matching to the dynamic traffic environment, ensuring that the guardrails possess reasonable alarm response characteristics across different time periods.

[0106] S058. Based on the time-segmented adaptive threshold table and the current time, the preset displacement threshold and the preset vibration threshold are corrected to obtain an updated adaptive threshold set.

[0107] This step is the execution phase. Based on the current system time or the real-time acquired time period label, the threshold parameters for the corresponding time period are extracted from the time-segmented adaptive threshold table. The preset displacement and vibration thresholds of the currently running guardrail nodes are then corrected in real time to generate the final adaptive threshold set. The corrected thresholds are immediately applied to the detection logic of the wireless displacement and vibration unit and synchronized to the monitoring platform via the wireless communication module. Through real-time time period matching and threshold updates, the guardrail system can maintain optimal sensitivity and disturbance resistance balance during 24 / 7 operation, achieving continuous self-learning and refined management in dynamic environments.

[0108] In an optional embodiment, the system can also adjust the traffic flow disturbance weights by incorporating meteorological information (such as wind speed, rainfall, temperature changes, and humidity levels). Specifically, when high wind speeds or heavy rainfall are detected, it is recognized that such external disturbances may cause additional vibrations or displacement signals in the guardrail structure, thereby increasing the risk of false alarms. To address this, when calculating the time-segmented adjustment factor, a meteorological compensation coefficient is introduced to dynamically amplify or reduce the traffic disturbance weights: under high wind speeds or heavy rainfall conditions, the anti-disturbance weight is appropriately increased to enhance threshold stability; under stable or dry meteorological conditions, the compensation weight is reduced to restore the system's sensitivity to real events. By introducing dynamic compensation of meteorological parameters, the system can achieve more accurate signal recognition and judgment in complex environments, further reducing the probability of false alarms caused by environmental noise, and ensuring that the intelligent modular guardrail has stable detection performance and reliable alarm accuracy under all-weather and multi-climate conditions. S06. Update the thresholds of the intelligent modular guardrail according to the adaptive threshold set.

[0109] Finally, the updated adaptive threshold set is applied to the currently operating intelligent modular guardrail to complete the online update of the threshold parameters. This step typically involves remotely issuing configuration commands, with the control platform synchronizing the latest threshold settings to each monitoring module to ensure that all front-end devices use consistent judgment criteria. Through dynamic adjustment and automatic update mechanisms, the system can continuously adapt to vibration characteristics and displacement behavior under different environments, effectively reducing the false alarm rate while improving the sensitivity to real risk events, significantly enhancing the overall intelligence level and scene adaptability of the alarm system.

[0110] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0111] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0113] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0115] It should also be noted that the exemplary embodiments mentioned in this invention describe methods or systems based on a series of steps or apparatus. However, this invention is not limited to the order of the steps described above; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0116] The above description is merely a specific embodiment of the present invention. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the protection scope of the present invention.

Claims

1. An intelligent modular guardrail, characterized in that, The guardrail includes: at least two guardrail posts and a connecting rod structure disposed between the two guardrail posts, wherein the guardrail post includes: a post base and a post body disposed on the post base; The side of each column body is provided with a mounting groove, and the mounting groove is provided with several through holes. The mounting groove is used to install a functional module. The functional module is provided with several connecting structural members that are adapted to the mounting groove. The functional module is detachably connected to the column body through the connecting structural members. The connecting rod structure includes the connecting structural member, and the connecting rod structure is detachably connected to the column body through the connecting structural member; The top of the column body is provided with a power module, which includes a photovoltaic panel, a first energy storage unit and a wireless displacement vibration unit. The photovoltaic panel is disposed on the surface of the power module, and the energy storage unit is electrically connected to the wireless displacement vibration unit and the photovoltaic panel respectively. The column body also includes an imaging module and a wireless communication module. The imaging module includes a second energy storage unit and an imaging unit. The first energy storage unit and the second energy storage unit are electrically connected, and the second energy storage unit and the imaging unit are electrically connected. The wireless communication module is electrically connected to the wireless displacement vibration unit and the imaging module, and is communicatively connected to the monitoring platform. It is used to receive displacement data and vibration data collected by the wireless displacement vibration unit, and transmit the displacement data, vibration data and video image data to the monitoring platform in real time through a preset wireless communication protocol.

2. The intelligent modular guardrail according to claim 1, characterized in that, The column body has a waterproof wiring channel inside, which is used to isolate the electrical connection of the power module, imaging module and functional module. The functional module includes one or more of the following: lighting unit, anti-collision warning post, and luminous road sign. The lighting unit includes a light-emitting element and reflective strips on both sides of the light-emitting element. The lighting unit and the road sign are electrically connected to the power module through the through hole.

3. The intelligent modular guardrail according to claim 2, characterized in that, The imaging module further includes a photosensitive unit, which is electrically connected to the second energy storage unit. The photosensitive unit is used to detect ambient light intensity and control the lighting unit and / or the road sign to emit light according to the ambient light intensity.

4. The intelligent modular guardrail according to claim 1, characterized in that, The imaging unit includes a first camera and a second camera. The first camera and the second camera are electrically connected to the second energy storage unit, respectively. The first camera and the second camera are arranged on opposite sides of the column body to simultaneously collect environmental video data on both sides of the guardrail and transmit the environmental video images to the monitoring platform.

5. The intelligent modular guardrail according to claim 4, characterized in that, The wireless vibration unit is used to detect the displacement and vibration status of the intelligent modular guardrail and determine whether the displacement and vibration status is abnormal. If so, the displacement and vibration status is transmitted to the monitoring platform through a preset wireless communication protocol, and an alarm is triggered through the monitoring platform. The preset wireless communication protocol includes at least one of MQTT, HTTP, CoAP, or LWM2M.

6. A control method for an intelligent modular guardrail, characterized in that, The method for the intelligent modular guardrail according to any one of claims 1-5 includes: The displacement and vibration data of the intelligent modular guardrail are obtained through the wireless displacement and vibration unit. The displacement data and vibration data are compared with preset displacement thresholds and preset vibration thresholds, respectively, to determine whether there are any abnormal events; If an abnormal time point exists, then obtain the abnormal time point and, based on the abnormal time point, obtain the displacement data and vibration data within the first preset time range to obtain the abnormal displacement data sequence and the abnormal vibration data sequence. Based on the abnormal time point and the second preset time range, the monitoring video collected by the imaging unit is cropped to obtain the abnormal event video; The abnormal displacement data sequence, abnormal vibration data sequence, and abnormal event video are transmitted to the monitoring platform. An alarm message is generated based on the abnormal displacement data sequence, the abnormal vibration data sequence, and the abnormal event video, and the alarm message is transmitted to the terminal devices of relevant personnel.

7. The control method for the intelligent modular guardrail according to claim 6, characterized in that, The step of comparing the displacement data and vibration data with preset displacement thresholds and preset vibration thresholds respectively to determine whether there is an abnormal event includes: The displacement data is compared with a preset displacement threshold to obtain an initial displacement judgment result; The vibration data is compared with a preset vibration threshold to obtain an initial vibration judgment result; Based on the initial displacement judgment result and the initial vibration judgment result, the number of consecutive exceedances of displacement data and vibration data are counted within a preset time window. Based on the number of consecutive threshold exceedances, a preliminary determination is made as to whether any abnormal events exist.

8. The control method for the intelligent modular guardrail according to claim 7, characterized in that, The step of generating alarm information based on the abnormal displacement data sequence, the abnormal vibration data sequence, and the abnormal event video, and transmitting the alarm information to the terminal devices of relevant personnel, includes: The abnormal displacement data sequence and the abnormal vibration data sequence are preprocessed to obtain the target displacement sequence and the target vibration sequence. Feature extraction is performed on the target displacement sequence and the target vibration sequence respectively to obtain a comprehensive feature set, wherein the comprehensive feature set includes displacement features and vibration features. The displacement features include displacement amplitude, displacement change rate and displacement duration, and the vibration features include vibration peak value, vibration root mean square value and vibration dominant frequency. Based on the abnormal time points and preset time windows, the comprehensive feature set is continuously sampled to obtain the feature sliding sequence of each displacement feature and vibration feature; The feature sliding sequence is compared with the corresponding feature threshold by sampling to obtain the number of consecutive exceedances of each displacement feature and vibration feature within the preset time window; Based on the number of consecutive exceedances of each displacement feature and vibration feature and the preset anomaly judgment conditions, the stable anomaly type of the abnormal event is determined. Based on the stable anomaly type, a third preset time range is obtained, wherein the third preset time range is less than or equal to the second preset time range; The abnormal event video is cropped according to the third preset time range to obtain an abnormal event video segment; Based on the abnormal time video segment and the stable anomaly type, alarm information is obtained, wherein the alarm information includes: abnormal event information and abnormal video information; Based on the level of abnormality, the alarm information is transmitted to the terminal devices of relevant personnel via a preset wireless communication protocol.

9. The control method for the intelligent modular guardrail according to claim 8, characterized in that, After generating alarm information based on the abnormal displacement data sequence, the abnormal vibration data sequence, and the abnormal event video, and transmitting the alarm information to the terminal devices of relevant personnel, the method further includes: Based on the data storage of the monitoring platform, historical alarm information of the intelligent modular guardrail within a preset time range is obtained to obtain a historical alarm dataset; Each record is screened based on the historical alarm dataset to obtain a set of false alarm events and a set of valid alarm events; Based on the set of false alarm events and the set of valid alarm events, the corresponding comprehensive feature set is extracted to obtain the set of valid event features and the set of false alarm features. Based on the set of valid event features and the set of false alarm features, the distribution of valid event features and the distribution of false alarm event features are obtained respectively. Based on the distribution of valid event characteristics and the distribution of false alarm event characteristics, the preset displacement threshold and the preset vibration threshold are corrected to obtain an updated adaptive threshold set; The threshold of the intelligent modular guardrail is updated based on the adaptive threshold set.

10. The control method for the intelligent modular guardrail according to claim 9, characterized in that, The step involves correcting the preset displacement threshold and preset vibration threshold based on the distribution of valid event characteristics and the distribution of false alarm event characteristics to obtain an updated adaptive threshold set, including: Based on the geographical location of each intelligent modular guardrail that is connected to the monitoring platform, environmental information of each guardrail is obtained, wherein the environmental information includes: road type, road speed limit information and traffic flow. Feature extraction is performed on the environmental information to obtain the environmental feature matrix for each guardrail; Unsupervised clustering analysis is performed based on the environmental feature matrix to obtain several guardrail clustering results, wherein each clustering result corresponds to a set of guardrails under a certain environmental condition. Based on the environmental information, valid event feature distribution, and false alarm event feature distribution of each guardrail set, a time-segmented operation dataset is obtained. The time-segmented operation dataset includes at least: traffic flow, historical false alarm rate, and valid alarm rate for different time periods. Based on the time-segmented operation dataset, time period boundaries are determined to obtain a time period label set, which includes one or more of the following: morning peak, evening peak, nighttime, and regular time periods. Based on the traffic flow, historical false alarm rate, and effective alarm rate of different time periods under the aforementioned time period label, obtain the time period adjustment factor for each guardrail set; Based on the time-segment adjustment factor, the adaptive threshold set of the corresponding guardrail set is modified according to time segment to obtain the time-segment adaptive threshold table. Based on the time-segmented adaptive threshold table and the current time, the preset displacement threshold and preset vibration threshold are corrected to obtain an updated adaptive threshold set.

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