A road bridge disaster early warning information publishing system and device

Abstract

This invention discloses a road and bridge disaster early warning information dissemination system and device, relating to the field of traffic early warning. This system and device achieve rapid perception and accurate early warning of road or bridge disaster events through data linkage between distributed road and bridge disaster monitoring sensors and the Gaode Map navigation platform. The system integrates structural anomaly data collected by sensors with road disaster information pushed by Gaode Map to assess disaster risk and generate standardized early warning information. Early warning information is simultaneously disseminated in two ways: firstly, via cloud communication links, targeted SMS warnings are pushed to mobile communication terminals behind the disaster-stricken road section; secondly, visual display and voice broadcast reminders are provided through roadside warning devices, thereby achieving multi-channel, highly timely disaster early warning and effectively reducing the risk of secondary traffic accidents caused by road and bridge disasters.

Description

Technical Field

[0001] This invention relates to traffic early warning technology, specifically to a road and bridge disaster early warning information dissemination system and device. Background Technology

[0002] As vital infrastructure in modern transportation systems, roads and bridges are directly related to public safety and the safety of people's lives and property. During their long service life, roads and bridges are inevitably affected by various factors such as the natural environment, traffic loads, and structural aging. For example, under extreme natural disasters such as torrential rains, floods, earthquakes, and typhoons, bridge structures may fracture or collapse, and roadbeds may experience subsidence, landslides, and other disasters. Furthermore, under conditions of frequent heavy vehicle traffic or prolonged overload operation, critical bridge components may also fail, leading to sudden catastrophic events.

[0003] The aforementioned road and bridge disasters are typically characterized by their suddenness, rapid development, and high destructiveness. If clear and effective warnings are not issued to oncoming vehicles in a timely manner, they can easily lead to rear-end collisions, bridge falls, and other secondary traffic accidents, further exacerbating the disaster. Therefore, achieving rapid detection, timely assessment, and efficient early warning dissemination of road and bridge disasters is a crucial technical issue that urgently needs to be addressed in the field of road traffic safety.

[0004] In existing technologies, some road and bridge safety management systems rely primarily on manual inspections or periodic checks, which cannot meet the real-time monitoring needs of sudden disasters. While some solutions utilize sensors to monitor road and bridge conditions, these are mostly focused on structural health monitoring, with data primarily used for later analysis. They lack a mechanism to trigger immediate disaster events, making it difficult to promptly convert this data into public warnings. Furthermore, current methods of disseminating warnings largely depend on fixed road signs or manual direction, resulting in limited information dissemination channels and coverage, failing to accurately reach users of moving vehicles within a certain distance behind the damaged road section.

[0005] At the same time, with the widespread use of navigation map applications, platforms such as Gaode Maps have the ability to perceive road conditions and push disaster information. However, in the existing technology, there is a lack of effective linkage mechanism between disaster information of navigation platforms and road entity early warning devices, making it difficult to form a multi-source information collaborative early warning system.

[0006] Therefore, there is an urgent need for a device that can collect data instantly when roads or bridges are destroyed, integrate road monitoring sensor information with disaster information from navigation platforms, and provide rapid and accurate early warnings to vehicle users within a specific range through multiple dissemination channels. Summary of the Invention

[0007] To address the shortcomings of existing road and bridge disaster early warning technologies, such as delayed disaster data capture, limited information dissemination channels, limited coverage, and lack of multi-source information linkage and integration capabilities, this invention provides a road and bridge disaster early warning information dissemination system and device. This system enables accurate data collection at the moment of disaster, integration of multi-source early warning information, and rapid dissemination of early warning information through multiple channels and over a wide area, thereby ensuring the driving safety of vehicles passing through the surrounding area.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a road and bridge disaster early warning information release system, comprising a data acquisition module, a risk assessment module, a control module, an early warning information release module, and a feedback optimization module;

[0009] The data acquisition module serves as the device's input port, used to acquire disaster-related data from sensors at the moment a road or bridge is damaged. The data acquisition module includes multiple distributed road disaster monitoring sensors. These sensors are deployed near specific roads or bridges with a high probability of damage and include dedicated sensors capable of detecting the instantaneous state of a bridge in the event of damage and an edge-automated video module for real-time road monitoring. Simultaneously, the data acquisition module also establishes a data communication connection with the Gaode Maps app to collect road disaster warnings pushed by the app.

[0010] The road disaster monitoring sensors include vibration sensors, displacement sensors, pressure sensors, impact sensors, and image acquisition sensors. The vibration, displacement, and pressure sensors are used to monitor the structural vibration, displacement changes, and load-bearing pressure of roads and bridges in real time, capturing sudden data changes at the moment of disaster. The impact sensor is used to collect the impact force on the road and bridge structure. The image acquisition sensor is used to collect real-time image information of the road and bridge to help confirm the extent of the disaster. Wireless communication technology is used to transmit the sensor data to a protocol conversion module within the data acquisition module.

[0011] The protocol conversion module, located within the data acquisition module, is used to convert between wireless sensor data protocols and upper-layer business communication protocols. Specifically, the protocol conversion module parses the raw data uploaded by the wireless sensors via ZigBee, LoRa, NB-IoT, or BLE, extracting sensor identifiers, sampling timestamps, physical quantity types, and corresponding numerical information. After data parsing, the protocol conversion module encapsulates the data in a unified format, generating standardized data frames. Based on the interface specifications of the target business system, it converts these standardized data frames into uplink communication protocol data packets based on MQTT, HTTP, or HTTPS, enabling reliable interfacing with the risk assessment module, navigation platform interface, and early warning information dissemination system.

[0012] Preferably, the risk assessment module is connected to the data acquisition module and has a pre-stored early warning rule table. The early warning rule table uses the disaster risk level, the target vehicle type, and the distance between the target vehicle and the disaster point as matching dimensions to generate corresponding early warning methods and early warning intensity.

[0013] Preferably, the early warning method includes implementing graded and differentiated disaster early warning based on different risk scenarios to avoid insufficient early warning or excessive interference.

[0014] Preferably, the control module is connected to the risk assessment module and the early warning information release module respectively, and is used to integrate and process the instantaneous disaster data, specific vehicle information and disaster warning information pushed by the risk assessment module obtained by the data acquisition module to generate standardized early warning information.

[0015] Preferably, the vehicle information refers to the target vehicle information located within the disaster-affected area obtained by the system through calling the Gaode Map navigation cloud service platform. The target vehicle information includes at least the vehicle type, real-time location coordinates, driving speed, and driving direction. The control module filters out the target vehicles that need to be given early warning based on the relative positional relationship between the disaster location and the target vehicles, providing a data foundation for the accurate release of subsequent early warning information.

[0016] Preferably, the early warning information includes the location of the disaster, the type of disaster, the scope of impact (clearly covering the area within 1km behind the disaster point), and the content of the risk avoidance prompts, and the early warning information release module is controlled to perform the early warning information release operation according to the preset release strategy.

[0017] Preferably, the early warning information publishing module includes a first publishing unit and a second publishing unit, wherein:

[0018] The first publishing unit is used to send the early warning information to the cloud server through the cloud communication link. The cloud server forwards the early warning information to the mobile communication base station near the road and bridge. The base station pushes the early warning information to vehicle user terminals in the area within 1km behind the disaster site via SMS.

[0019] The second publishing unit is used to establish a communication connection with the warning devices along the road and send the warning information to the warning devices. The warning devices include road signs and loudspeakers. Through the visual display of the road signs and the voice broadcast of the loudspeakers, the information on the road conditions ahead is transmitted to vehicle users within 1km behind the disaster site.

[0020] Preferably, the feedback optimization module forms a closed-loop connection with the early warning information release module and the risk assessment module. The feedback optimization module is used to collect feedback information after the early warning is released.

[0021] Preferably, the feedback information includes: traffic operation status change data within the disaster-affected area, route adjustment statistics from the navigation platform, and trigger status and duration data of roadside warning devices. The feedback optimization module performs statistical analysis on the above feedback information to evaluate the overall response effect after the early warning information is issued, including whether there is insufficient improvement in traffic operation status, unreasonable early warning intensity settings, or excessively large early warning range settings. Based on the statistical analysis results, the feedback optimization module adaptively corrects the parameter weights in the risk assessment module and dynamically optimizes the risk assessment threshold and early warning issuance strategy, so that the system gradually improves the accuracy, rationality, and user acceptance of disaster early warnings during long-term operation.

[0022] A road and bridge disaster early warning information dissemination device, which applies the aforementioned road and bridge disaster early warning information dissemination system.

[0023] Compared with existing technologies, the road and bridge disaster early warning information dissemination system and device provided by this invention achieves the following beneficial effects:

[0024] Achieving accurate capture of disaster data in an instant: This invention uses distributed road disaster monitoring sensors, especially dedicated sensors for bridge disasters, to capture relevant data at the first moment of road and bridge damage, solving the problem of delayed disaster data capture in existing technologies and laying the foundation for the rapid release of subsequent early warning information;

[0025] Integrating multi-source early warning information: This device can not only acquire real-time data of the disaster site through sensors, but also collect disaster warning information in conjunction with the Gaode Map APP. It generates standardized early warning information through the information integration module, realizing the collaborative integration of multi-source information and improving the comprehensiveness and reliability of early warning information.

[0026] Multi-channel dissemination for precise coverage: This invention features a dual dissemination unit. On one hand, it pushes warning information to vehicle users within a 1km radius via a cloud-base station-SMS link. On the other hand, it uses road warning devices (road signs, loudspeakers) for visual and voice broadcasts. This multi-channel collaboration ensures that warning information can quickly and comprehensively reach target users. Simultaneously, it combines positioning devices as positioning units to achieve precise range filtering, avoid interference from irrelevant information, and improve warning efficiency.

[0027] High timeliness and rapid early warning response: The control module has a preset emergency release mode, which can immediately trigger the release operation for sudden disaster warnings, ensuring that the warning information is released in a short time, giving vehicle users sufficient time to react and avoid risks, effectively reducing the probability of chain traffic accidents and protecting the safety of people and property. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0029] Figure 1 A system architecture block diagram provided for embodiments of the present invention;

[0030] Figure 2 A system operation flowchart provided for embodiments of the present invention;

[0031] Figure 3 This is a schematic diagram of the multimodal release process of system early warning information provided in an embodiment of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0033] As attached Figure 1 To be continued Figure 3 As shown:

[0034] Example:

[0035] This invention provides a road and bridge disaster early warning information dissemination system, comprising:

[0036] The data acquisition module is used to collect disaster data at the moment when a road or bridge is destroyed, and to perform communication protocol parsing, data format conversion and unified encapsulation on data from different types of sensors and navigation platforms to generate standardized disaster data.

[0037] The risk assessment module is used to classify and assess the disaster risk based on the disaster data and target vehicle information, and generate corresponding early warning methods and early warning intensity.

[0038] The control module is used to integrate and process disaster damage data and disaster risk assessment results, generate standardized early warning information, and control the early warning information release module to execute early warning release according to the preset release strategy.

[0039] The early warning information release module is used to release early warning information to vehicle users within a preset range behind the disaster site through communication networks and roadside warning devices;

[0040] The feedback optimization module is used to collect response feedback information after the early warning information is released, and dynamically optimize the risk assessment parameters and early warning release strategy based on the response feedback information.

[0041] Each module is connected via wired or wireless communication links to ensure stable data transmission.

[0042] A road and bridge disaster early warning information dissemination device utilizes a road and bridge disaster early warning information dissemination system. The data acquisition module includes multiple road disaster monitoring sensors, which are distributed and deployed in areas of roads or bridges at risk of disaster. These sensors include vibration sensors, displacement sensors, pressure sensors, impact sensors, and image acquisition sensors, used to monitor the structural status of roads or bridges in real time and collect sudden change data at the moment of disaster. The roadside warning device includes road signs and loudspeakers, used to disseminate disaster early warning information through visual display and voice broadcasting, respectively.

[0043] Data acquisition module workflow:

[0044] After the device is powered on, the data acquisition module enters the working state. First, it initializes and configures the vibration sensors, displacement sensors, pressure sensors and impact sensors deployed on the key structural parts of the road and bridge. Then, it continuously monitors the structural state of the road and bridge according to the preset sampling frequency, and collects raw data such as structural vibration amplitude, displacement change, bearing pressure and impact intensity in real time.

[0045] Specifically, various sensors establish data connections with the data acquisition module via wireless communication methods, including but not limited to ZigBee, LoRa, NB-IoT, or Bluetooth Low Energy protocols. Upon receiving raw wireless data frames from different sensors, the data acquisition module first inputs the data frames to its internal protocol conversion module. When any sensor detects a sudden signal exceeding a preset threshold (including a sudden increase in vibration, a sudden change in displacement, an abnormal drop in pressure, or a high-energy impact signal), the data acquisition module immediately determines that a disaster event may have occurred and triggers a high-frequency sampling mode to synchronously acquire and buffer multi-dimensional sensor data at the moment of the disaster.

[0046] Specifically, the criteria for determining sudden disaster damage include, but are not limited to, any of the following situations: the criterion for sudden vibration damage is... The criterion for sudden displacement is The criterion for abnormal pressure is The impact trigger criterion is ,

[0047] in, The historical statistical standard deviation of the vibration signal. This is the vibration amplification factor. , , These are the disaster threshold parameters for displacement, pressure, and impact, respectively.

[0048] Furthermore, the protocol conversion module performs protocol parsing on the received disaster candidate data, extracts sensor identifiers, sampling timestamps, data types and corresponding physical quantity values, and maps data fields under different wireless communication protocols to a unified data structure.

[0049] Subsequently, the protocol conversion module standardizes and encapsulates the parsed data to generate a disaster data frame in a unified format. The standardized data frame includes at least the following fields: sensor type field, structural parameter field, sampling time field, location information field, and data reliability identifier field.

[0050] Furthermore, while completing local sensor data acquisition and protocol conversion, the data acquisition module establishes a data communication connection with the Gaode Map APP backend by calling the Gaode Map open API interface, and receives road disaster warning information pushed by the navigation platform in real time.

[0051] After completing the unification of multi-source data protocols, the data acquisition module timestamps the sensor-collected data and the disaster information from the navigation platform to form a fused disaster data set. The fused data is then transmitted to the risk assessment module via the internal communication bus for subsequent disaster risk analysis and early warning decision-making.

[0052] Risk assessment module workflow:

[0053] After receiving the disaster candidate data uploaded by the data acquisition module, the risk assessment module first verifies the integrity and validity of the sensor data and navigation platform data, and removes abnormal noise data and invalid data.

[0054] The valid data is then formatted in a unified way, transforming data from different sources into a unified data structure for comprehensive analysis.

[0055] The risk assessment module performs comprehensive calculations on disaster-related parameters based on a pre-built risk discrimination model. These parameters include peak vibration, displacement change rate, degree of pressure anomaly, impact intensity, disaster type, and risk level marked by the navigation platform.

[0056] The corresponding disaster risk assessment results are generated through weighted calculation, and the disaster risk is divided into extremely high risk, high risk, medium risk or low risk levels.

[0057] Specifically, its overall risk value R is determined as follows:

[0058]

[0059] in For the structure vibration normalization index structure, This represents the abrupt change in displacement. I represents the change in structural bearing pressure, T represents the impact strength index, and T represents the reliability factor of disaster information (derived from navigation platforms or multi-source verification). The weighting coefficients of the corresponding parameters, and

[0060]

[0061] After completing the risk level assessment, the risk assessment module combines the disaster type, risk level, and real-time traffic conditions to dynamically determine the scope of the disaster impact and generate a risk assessment result data package containing the disaster location, disaster type, risk level, impact radius, and recommended avoidance methods. The assessment result is then sent to the control module as an important basis for early warning decision-making.

[0062] Control module execution process:

[0063] After receiving the risk assessment results output by the risk assessment module, the control module first determines the risk level corresponding to the disaster event, and selects the corresponding early warning release mode and release strategy based on the risk level.

[0064] When an event is determined to be extremely high-risk or high-risk, the control module immediately enters emergency release mode and activates a high-priority early warning control mechanism. When an event is determined to be medium-risk, the control module enters normal release mode. When an event is determined to be low-risk, the control module enters delayed release mode or weak release mode to reduce interference with the traffic environment.

[0065] After determining the risk level, the control module matches the corresponding early warning release strategy based on the disaster risk level and the preset impact range parameters. The early warning release strategy includes at least the following:

[0066] The release unit selection strategy, warning information content template, warning intensity level, and information presentation method.

[0067] Specifically, the control module generates differentiated early warning issuance instructions according to preset rules for different risk levels:

[0068] When the risk level is extremely high or high, the control module generates a joint release command that simultaneously triggers the first and second release units, and configures it as a high-intensity early warning mode.

[0069] When the risk level is medium risk, the control module generates a regular release instruction that triggers the first release unit and / or the second release unit, and configures it as a medium-intensity warning mode;

[0070] When the risk level is low, the control module generates a low-intensity warning command that only triggers the second release unit or delays the triggering of the first release unit.

[0071] The control module further standardizes the content of the upcoming warning information based on the risk level, ensuring it matches the release format of the corresponding release unit. Specifically, for the first release unit, the warning information content corresponds to the SMS release level; for the second release unit, the warning information content corresponds to the display and broadcast modes of the roadside warning devices.

[0072] After completing the release strategy matching and warning content configuration, the control module generates a standardized warning release instruction and simultaneously sends the instruction along with the corresponding warning information data to the warning information release module, triggering the first release unit and the second release unit to execute a differentiated, multimodal warning information release process according to the risk level.

[0073] Execution process of the early warning information release module:

[0074] (1) Execution process of the first release unit

[0075] The first issuing unit is equipped with a 4G / 5G wireless communication module and has pre-established a long-term communication channel with the cloud server. After the control module triggers the emergency issuing command, the first issuing unit first performs data encapsulation processing on the warning information to be sent, uniformly encapsulating the disaster location coordinates, disaster type, risk level, impact range, and evacuation prompts into a data message that conforms to the cloud interface protocol.

[0076] Subsequently, the first publishing unit transmits the encapsulated warning information to the cloud server in real time via a 4G / 5G communication module, using the TCP / IP transmission protocol and HTTP or MQTT application layer protocol. After collecting the warning information, the cloud server parses and performs secondary verification based on a pre-set road and bridge disaster warning distribution mechanism, and automatically matches the nearest mobile communication base station to the disaster site according to the disaster location and communication network topology.

[0077] The cloud server further distributes the warning information to the corresponding base station nodes through an interface with the mobile communication operator's core network. The base station, based on the corresponding terminal identification information (including mobile phone number or vehicle communication terminal ID) in the mobile communication terminal list, sends the warning information via SMS to vehicle user terminals within the target area behind the disaster site. The SMS content includes at least the location of the disaster, the type of disaster, and clear prompts to slow down, stop, or detour, ensuring that drivers can still receive the disaster warning information in a timely manner even when navigation applications are not open or the screen is off.

[0078] Specifically, after completing the encapsulation of the early warning information data, the first issuing unit generates SMS messages in a hierarchical manner based on the disaster risk level, and its issuing rules are as follows:

[0079] When the risk level is extremely high, a mandatory warning message is generated. The message must include at least the precise location of the disaster, the type of disaster, clear emergency response instructions, and a no-passage warning, such as "Slow down and stop immediately" or "The road ahead is closed." It can also include detour route suggestions. At the same time, the control module can instruct the cloud server to send multiple repeated pushes to vehicle users in the target area to enhance the warning reach rate.

[0080] When the risk level is high, a warning message is generated. The message should include at least the location and type of the disaster, as well as clear instructions to slow down, drive with caution, or change lanes in advance. Drivers should also be reminded to pay attention to road warning devices.

[0081] When the risk level is medium, a warning message is generated. The message mainly reminds the driver that there are abnormal risks on the road ahead, and suggests reducing the speed and paying attention to changes in road conditions to avoid causing unnecessary traffic panic.

[0082] When the risk level is low, the first issuing unit may choose not to trigger SMS issuance, or only send alert messages to specific types of vehicles (such as large vehicles and hazardous chemical transport vehicles) to reduce interference with ordinary vehicle users.

[0083] Through the above methods, the first publishing unit realizes differentiated generation and publishing of SMS warning content based on risk level, ensuring that SMS warnings have sufficient intensity in emergency situations and maintain appropriate reminders in general risk situations.

[0084] In this embodiment, the total time delay from the control module triggering the release command to the target vehicle user terminal successfully collecting the SMS warning information is controlled within 10 seconds, thereby providing drivers with sufficient reaction time and effectively reducing the risk of secondary traffic accidents.

[0085] Specifically, the overall early warning release delay meets the requirements.

[0086]

[0087] Among them, T data T represents the data acquisition and transmission time. decision For risk assessment and control decision-making time, T publish The system design constraint for delaying SMS delivery is:

[0088]

[0089] (2) Execution process of the second release unit

[0090] The second publishing unit is mainly used to publish early warning information to physical warning devices along roads around disaster sites. It integrates a radio frequency (RF) communication module or a low-power wide-area communication module to establish a stable wireless communication connection with the warning devices along the road.

[0091] After the control module issues the roadside warning command, the second issuing unit automatically selects multiple roadside warning device nodes located within a preset range before and after the disaster site based on the disaster location coordinates, and sends corresponding warning control commands to them. The warning control commands include at least display content commands, broadcast content commands, and operational status control commands.

[0092] Specifically, when the warning information is sent to the road sign, the LED electronic display screen used by the road sign immediately switches from standby mode to warning mode after receiving the control command, and displays the warning content in the form of high brightness, scrolling text or graphic symbols, such as "The bridge has collapsed XX meters ahead, please slow down and detour immediately" to enhance visibility at night or in bad weather conditions.

[0093] When the warning information is sent to the loudspeaker or directional speaker, the broadcasting equipment, after receiving the voice broadcasting instruction, will broadcast the disaster warning content in a clear and continuous voice in a loop according to the preset volume and broadcasting frequency, so that the driver can obtain information about the road conditions ahead by hearing without having to look at the display device.

[0094] Specifically, when the second issuing unit issues early warning control instructions to the warning devices along the road, it also classifies and controls the displayed content, broadcast content, and working status according to the disaster risk level.

[0095] When the risk level is extremely high, road signs enter a strong warning state, displaying information such as "Road / bridge ahead has been damaged and passage is prohibited" through high brightness, flashing, or graphic symbol overlay; loudspeakers or directional speakers use a high-priority voice broadcast mode, broadcasting emergency evacuation tips in a loop at a high volume and high frequency.

[0096] When the risk level is high, road signs will display disaster warning information in the form of continuous scrolling text, and loudspeakers will broadcast warnings of slowing down and proceeding with caution at a medium volume and preset frequency.

[0097] When the risk level is medium, road signs will display risk warning information, such as "Road ahead is abnormal, please slow down". The loudspeaker can be selectively activated or only triggered when there is heavy traffic.

[0098] When the risk level is low, the second publishing unit can only enable the text prompts on road signs and not enable the voice broadcasting device, thereby reducing interference to the surrounding environment and drivers.

[0099] Through the aforementioned hierarchical control mechanism, the second issuing unit can dynamically adjust the working mode of the roadside warning device according to the degree of disaster risk, so as to achieve a reasonable match between the intensity of visual and auditory warnings.

[0100] By combining visual warnings from road signs with voice warnings from loudspeakers, vehicle users can simultaneously receive disaster warning information from different sensory dimensions, further improving the accessibility and effectiveness of warnings and avoiding the risk of omissions due to a single information presentation method.

[0101] Feedback optimization module workflow:

[0102] To achieve continuous optimization and adaptive adjustment of the road and bridge disaster early warning system, this embodiment further sets up a feedback optimization module. The feedback optimization module establishes data interaction connections with the early warning information release module and the risk assessment module respectively, forming a closed-loop optimization structure.

[0103] The feedback optimization module is used to collect system response feedback data after the warning information is released. The feedback data includes at least: traffic status change information within a preset time window after the warning is released, route adjustment statistics of the navigation platform, and the trigger status, trigger frequency and continuous working time of warning devices along the road.

[0104] The traffic status change information is a statistical description of the traffic operation status before and after the warning is issued, used to characterize the overall impact of the warning information on driving behavior, rather than analyzing a single vehicle.

[0105] The feedback optimization module further normalizes and statistically analyzes the collected multi-source feedback data to evaluate the effectiveness of the current early warning strategy and determine whether there are insufficient responses, delayed responses, or excessively high early warning intensity.

[0106] Specifically, the early warning effectiveness evaluation function is:

[0107]

[0108] in, This refers to the number of statistical units that generate an effective response after an early warning is issued. The total number of statistical units covered by the early warning release, whereby the statistical unit can be a time slice, road segment, or device node.

[0109] Furthermore, based on the effectiveness evaluation results of the early warning, the feedback optimization module dynamically corrects the system's preset early warning parameters, including the scope of impact of the early warning, the risk level threshold, and the release strategy parameters.

[0110] Specifically, the dynamic early warning radius range correction calculation is as follows:

[0111]

[0112] D0 is the basic warning distance (e.g., 1km). Risk level quantification values ​​(low=1, medium=2, high=3, very high=4). This is the expansion factor.

[0113] The parameters are updated adaptively as follows:

[0114]

[0115] in, For learning rate, This represents the expected response rate.

[0116] To avoid fluctuations or instability during parameter updates, the feedback optimization module further sets upper and lower limits for the updated parameters to ensure the system maintains stability and reliability during long-term operation.

[0117] In this embodiment, after collecting the standardized early warning information and the target vehicle user list issued by the control module, the early warning information release module performs multi-channel early warning information release operations through the first release unit and the second release unit respectively, according to the preset release strategy and synchronous control mechanism, so as to realize the rapid and reliable reminder to drivers in the target area behind the disaster point. Then, the early warning strategy is dynamically adjusted through the feedback optimization module to improve robustness.

[0118] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A road and bridge disaster early warning information dissemination system, characterized in that, include: The data acquisition module is used to collect disaster data at the moment when a road or bridge is destroyed, and to perform communication protocol parsing, data format conversion and unified encapsulation on data from different types of sensors and navigation platforms to generate standardized disaster data. The risk assessment module is used to classify and assess the disaster risk based on the disaster data and target vehicle information, and generate corresponding early warning methods and early warning intensity. The control module is used to integrate and process disaster damage data and disaster risk assessment results, generate standardized early warning information, and control the early warning information release module to execute early warning release according to the preset release strategy. The early warning information release module is used to release early warning information to vehicle users within a preset range behind the disaster site through communication networks and roadside warning devices; The feedback optimization module is used to collect response feedback information after the early warning information is released, and dynamically optimize the risk assessment parameters and early warning release strategy based on the response feedback information.

2. The road and bridge disaster early warning information release system according to claim 1, characterized in that, The data acquisition module establishes a data communication connection with the navigation map application by calling the open interface of the navigation platform in order to obtain road or bridge disaster warning information pushed by the navigation platform.

3. The road and bridge disaster early warning information release system according to claim 1, characterized in that, The risk assessment module has a pre-stored early warning rule table. The risk assessment module is used to classify and assess disaster risks based on disaster data and traffic status information in the disaster-affected area, and generate corresponding early warning methods and intensity.

4. The road and bridge disaster early warning information release system according to claim 3, characterized in that, The warning methods include one or more combinations of pop-up reminders, voice reminders, text reminders, roadside loudspeaker broadcasts, variable message sign displays, and flashing light warnings.

5. A road and bridge disaster early warning information dissemination system according to claim 1, characterized in that, The control module is equipped with timeliness judgment rules. When a sudden disaster event is detected, the emergency release mode is triggered so that the early warning information is released within a preset time.

6. The road and bridge disaster early warning information release system according to claim 1, characterized in that, The early warning information release module includes a first release unit and a second release unit; the first release unit is used to send SMS early warning information to mobile communication terminals in the disaster-affected area through a cloud communication link, and the second release unit is used to send early warning control instructions to warning devices along the road.

7. A road and bridge disaster early warning information dissemination system according to claim 1, characterized in that, The feedback optimization module is used to collect data on changes in vehicle traffic status within the disaster-affected area, including statistical characteristics of deceleration, stopping, or detour behavior, as well as route adjustment data from the navigation platform and trigger status data from roadside equipment. Based on the collected data, the feedback optimization module adjusts the parameter weights and warning thresholds in the risk assessment module to achieve adaptive optimization of the warning strategy.

8. A road and bridge disaster early warning information dissemination device, employing the road and bridge disaster early warning information dissemination system according to any one of claims 1-7, characterized in that, The data acquisition module includes multiple road disaster monitoring sensors, which are distributed and deployed in areas of roads or bridges at risk of disaster. The road disaster monitoring sensors include vibration sensors, displacement sensors, pressure sensors, impact sensors, and image acquisition sensors, which are used to monitor the structural status of roads or bridges in real time and collect sudden change data at the moment of disaster.

9. A road and bridge disaster early warning information dissemination device according to claim 8, characterized in that, The roadside warning devices include road signs and loudspeakers, which are used to release disaster warning information through visual display and voice broadcast, respectively.

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