Road discontinuous state early warning method
By setting up pavement data acquisition devices and data processing equipment on the highway, processing pavement status data in real time and performing layered early warnings, the problems of response delay and low coverage in the prior art are solved, and the rapid identification and early warning of road abnormal states are realized.
Patent Information
- Application Number
- CN202510596594.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-09
- Publication Date
- 2025-08-26
AI Technical Summary
The prior art has delayed response and low road network coverage in the face of road accidents caused by extreme weather, resulting in traffic disruptions and security threats.
Multiple pavement data acquisition devices are set up at intervals on the highway. The pavement status data is processed in real time through the data processing equipment, the risk level is determined, and the response is instantly when there are high risks. The decision-making equipment is used for layered early warnings, and the adapted execution equipment is selected for early warning operations.
It realizes rapid identification and early warning of highway abnormal states, improves the accuracy of risk assessment and targeted warning, optimizes resource allocation, and reduces response delays.
Smart Images
Figure CN120544401A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure generally relates to the field of intelligent transportation technology, and in particular to a method for early warning of highway discontinuous conditions. Background Art
[0002] At present, disasters and accidents caused by extreme weather are becoming increasingly frequent, bringing significant impacts to numerous industries, especially land transportation within the transportation sector. As important components of the transportation industry, road and rail transportation are characterized by long distances, complex and variable geological conditions, and numerous structures. These factors make them highly susceptible to accidents such as roadbed collapses and bridge washouts caused by extreme weather, leading to the interruption of transportation routes and threatening the safety of drivers and passengers.
[0003] Traditional methods of warning these accidents usually rely on fixed cameras and manual inspections, but they have problems such as response delays and low road network coverage when facing emergencies. Therefore, there is an urgent need to propose a new method for monitoring land transportation to solve the above problems. Summary of the Invention
[0004] The present application provides a method for early warning of highway discontinuous states, which can achieve rapid identification and early warning of abnormal road states.
[0005] The present application discloses a method for warning a highway discontinuous state, which is applied to a warning system. The warning system includes a data acquisition device, a data processing device, a decision device, and an execution device. The data acquisition device includes multiple road surface data acquisition devices, and the multiple road surface data acquisition devices are adaptively spaced based on the curvature of the highway. The method includes: The data processing device receives the road surface state data collected by the road surface data collection device, and processes the road surface state data to obtain a first processing result; The data processing device determines a first risk level based on the first processing result, and sends the first risk level to the decision device if the first risk level is greater than a risk threshold; The decision-making device divides the warning level according to the first risk level, and determines the first target execution device according to the warning level; The decision-making device sends an early warning instruction to the first target execution device, where the early warning instruction is used to instruct the target execution device to perform an early warning operation.
[0006] In an optional embodiment, the road surface state data carries a first identifier for marking the road surface data acquisition device, characterized in that the processing of the road surface state data to obtain a first processing result includes: the data processing device matches the first identifier of the road surface data acquisition device with a first correspondence table to obtain the physical coordinates of the road surface data acquisition device, the second correspondence table including the second identifiers of multiple road surface data acquisition devices and the physical coordinates corresponding to the second identifier of each road surface data acquisition device; The data processing device retrieves historical status data based on the physical coordinates of the road surface data acquisition device, and determines the first processing result based on the road surface status data and the historical status data.
[0007] In an optional embodiment, the road surface state data includes vertical displacement data, the historical state data is historical vertical displacement data at a previous moment before the vertical displacement data is collected, and determining the first processing result based on the road surface state data and the historical image data includes: subtracting the vertical displacement data from the historical vertical displacement data to obtain a vertical displacement difference, and comparing the vertical displacement difference with a difference between adjacent time windows to obtain a vertical displacement change rate; The vertical displacement change rate is compared with a change rate threshold to obtain a comparison result, and the comparison result is used as the first processing result.
[0008] In an optional embodiment, the road surface state data includes horizontal displacement data, the historical state data is historical horizontal displacement data at a moment before the horizontal displacement data is collected, and determining the first processing result based on the road surface state data and the historical image data includes: Determining a displacement offset based on the horizontal displacement data and the historical horizontal displacement data by a baseline solution method; The displacement offset is compared with the standard deviation to obtain a comparison result, and the comparison result is used as the first processing result.
[0009] In an optional embodiment, the road surface state data includes displacement data within a monitoring period, the historical state data is historical displacement data, and determining the first processing result based on the road surface state data and the historical image data includes: Subtracting each of the displacement data from the corresponding historical displacement data to obtain a plurality of displacement differences, and summing the plurality of displacement differences to obtain a displacement accumulation; determining a displacement rate according to each displacement data and a time when each displacement data is collected; The displacement accumulation amount and the displacement rate are used as the first processing result.
[0010] In an optional embodiment, the road surface condition data further includes geological parameters and traffic load, and the data processing device determines the risk level based on the first processing result, including: The data processing device determines the risk level based on the sum of the product of the displacement rate and a first parameter, the product of the displacement accumulation and a second parameter, the product of the geological parameter and a third parameter, and the product of the traffic load and a fourth parameter.
[0011] In an optional embodiment, the data acquisition device further includes an image acquisition device provided at a target section of the highway, the target section being a section where the driving risk is greater than a preset risk, and the method further includes: For the target road section, if the data processing device determines that the first risk level is less than the risk threshold, acquiring image data captured by the image acquisition device, and performing a consistency comparison between the image data and historical image data to obtain a second processing result; If the second processing result is that there are targets in the image data that are inconsistent with the historical image data, and the number of the targets is greater than the number threshold, then generating a warning message, the warning message carrying a second risk level; The second risk level is sent to the decision-making device.
[0012] In an optional embodiment, the image data carries a second identifier for marking the image acquisition device, and the method further includes: The data processing device matches the second identifier of the target image collection device with a second correspondence table to obtain the physical coordinates of the target image collection device, wherein the first correspondence table includes the second identifiers of multiple image acquisition devices and the physical coordinates corresponding to the second identifier of each image acquisition device; The data processing device retrieves the historical image data based on the physical coordinates of the target image set device.
[0013] In an optional embodiment, the method further includes: If the processing device does not receive data sent by the data acquisition device during the monitoring period, the processing device determines a target data acquisition device adjacent to the data acquisition device according to the unique identifier of the data acquisition device, and sends a test instruction to the data acquisition device; After receiving the test instruction, the target data acquisition device sends a first test message to the data acquisition device, and if the target data acquisition device does not receive a response message based on the first test message within a preset time period, sends an exception message to the data processing device; The data processing device generates a risk message according to the abnormal message, and reports the risk message to the traffic control center.
[0014] In an optional embodiment, the early warning system further includes a power supply device, which is used to supply power to the data acquisition device, the data processing device and the execution device.
[0015] This application provides a method for warning of non-continuous highway conditions. Considering that current methods for providing traffic warnings often rely on fixed cameras and manual inspections, resulting in delayed responses and low road network coverage when faced with emergencies, this method employs multiple road surface data acquisition devices spaced apart along a highway. Each road surface data acquisition device collects road surface condition data for its corresponding road surface and transmits it to a data processing device in communication with it for processing to obtain a first processing result. Because the data processing device is located at the front end, it can process the road surface condition data collected by the road surface data acquisition device in real time, enabling rapid response to abnormal data. The data processing device also determines a first risk level based on the first processing result and, if the first risk level is greater than a risk threshold, transmits the first risk level to a decision-making device, enabling immediate response to abnormal conditions. Furthermore, the decision-making device can classify warning levels based on the first risk level and, based on the different warning levels, select target execution devices from multiple execution devices that are appropriate for the current risk scenario for tiered warnings. This enables precise scheduling and optimized allocation of risk response resources, thereby enabling rapid identification and warning of abnormal road conditions. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings: Figure 1 This is an application scenario diagram of a highway discontinuous state early warning method provided in this application; Figure 2 A flowchart of the steps of a highway discontinuous state warning method provided in this application; Figure 3 A flowchart of another method for warning of highway discontinuous status provided by this application; Figure 4 A flowchart of the steps of another highway discontinuous state warning method provided by this application; Figure 5 A flowchart of another method for warning of highway discontinuous status provided by this application; Figure 6 A flowchart of another method for warning of highway discontinuous status provided by this application; Figure 7A flowchart of the steps of another highway discontinuous state warning method provided by this application; Figure 8 This is a flowchart of the steps of another highway discontinuous state warning method provided by this application. DETAILED DESCRIPTION
[0017] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the invention are shown in the accompanying drawings.
[0018] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.
[0019] Please refer to Figure 1 , Figure 1 An application scenario diagram of the highway discontinuous state warning method provided in an embodiment of the present application. In this application scenario, an early warning system is included, which includes a data acquisition device 101, a data processing device 102, an execution device 103, and a decision-making device 104 placed in the background, which are arranged at intervals on the highway. The data acquisition device 101 is communicatively connected to the data processing device 102, and the data processing device 102 and the execution device 103 are both communicatively connected to the decision-making device 104. The data acquisition device 101 includes, for example, a road surface data acquisition device and an image acquisition device. The road surface data acquisition device is, for example, a vibration sensor, a displacement sensor, an inclination detection sensor, a meteorological sensor, a radar, etc. The image acquisition device is, for example, a camera, etc. The data processing device 102 is, for example, a single-chip microcomputer, a processing chip, a Beidou all-in-one machine, etc. The execution device 103 is, for example, an audible and visual alarm, an electronic sign, etc. The decision-making device 104 is, for example, a cloud platform, a server, a server cluster, etc. It is understandable that the above-mentioned early warning system further includes a power supply device (not shown in the figure), which is used to supply power to the data acquisition device 101 , the data processing device 102 and the execution device 103 .
[0020] The road surface data collection device transmits the collected road surface condition data (vertical displacement, horizontal displacement, soil moisture, etc.) to data processing device 102 via a network. Data processing device 102 processes the road surface condition data (filtering, normalizing, cleaning, subtraction, comparison, etc.) to obtain a first processing result. Based on the first processing result, data processing device 102 determines a first risk level (level 1, 2, 3, etc.). If data processing device 102 determines that the first risk level is greater than a risk threshold, it generates an exception message and sends the exception message containing the first risk level to decision device 104. After receiving the exception message, decision device 104 parses the exception message, assigns a warning level based on the first risk level obtained from the analysis, and determines a target execution device from multiple execution devices 103 based on the warning level. It then sends a warning instruction to the target execution device, instructing it to perform the corresponding warning operation, thereby achieving rapid identification and warning of abnormal road conditions.
[0021] Below, the highway discontinuous state early warning method provided in this application is described by taking the application of the highway discontinuous state early warning method provided in this application to the above-mentioned early warning system as an example.
[0022] like Figure 2 As shown, Figure 2 This application provides a flowchart of a method for early warning of a highway discontinuous state, which includes: Step S20: The data processing device receives the road surface state data collected by the road surface data collection device and processes the road surface state data to obtain a first processing result; The road surface data collection device can be any device installed along the highway, and the data processing equipment can be a device in communication with the road surface data collection device. Of course, multiple road surface data collection devices can share a single data processing device. That is, the data processing device can receive and process data transmitted by multiple road surface data collection devices. This eliminates the need for a dedicated data processing device for each road surface data collection device, saving resources and reducing costs.
[0023] If multiple road surface data acquisition devices share a data processing device, the road surface data acquisition device can encapsulate a unique tag that can mark itself together with the road surface status data and send it to the data processing device, so that the data processing device can perform sub-device processing when receiving the road surface status data carrying the unique tag, thereby avoiding data confusion.
[0024] As mentioned above, when the road surface data acquisition device is a vertical displacement sensor, the collected road surface state data is the vertical displacement; when the road surface data acquisition device is a horizontal displacement sensor, the collected road surface state data is the horizontal displacement; when the road surface data acquisition device is a meteorological sensor, the collected road surface state data is the soil moisture, etc. It is understandable that the types of road surface data acquisition devices set at intervals on the highway can be one or more. For example, point A on the highway is equipped with a vertical displacement sensor, a horizontal displacement sensor, and a meteorological sensor. Then, when multiple road surface data acquisition devices are set, the data processing device can receive multiple different road surface state data for the same location, which will not be elaborated here. Traditional early warning systems mostly rely on a single data to directly trigger an alarm (for example, an alarm is triggered when the vertical displacement exceeds a threshold), while the data processing device in this application can issue an early warning based on multi-source data, thereby improving the accuracy of risk assessment.
[0025] After the data processing device receives the road surface status data sent by the road surface data acquisition device, the data processing device enters the data processing stage. In this stage, the data processing device can perform the following operations on the road surface status data: 1. Data cleaning and standardization: Cleaning road condition data can eliminate noise and filter out abnormal data, providing accurate data for subsequent warnings. Standardizing road condition data can unify the format of data from different types of devices, facilitating subsequent processing by data processing equipment and improving processing efficiency.
[0026] 2. Perform feature extraction on road surface status data. For example, key features can be extracted by inputting road surface status data into machine vision models or time series analysis models to reduce the amount of data processing and further improve data processing efficiency.
[0027] 3. Generate a first processing result based on preset rules or lightweight models (such as threshold judgment and cluster analysis). For example, compare the vertical displacement data with the historical vertical displacement data to obtain a comparison result between the two.
[0028] The above is only an exemplary description of the method for processing road surface status data. The data processing device may also have other adaptive methods for processing road surface status data, which is not limited in this application.
[0029] It is understood that since road surface condition data includes multiple different types of data, the first processing result can be a comprehensive processing result for the different types of data, or a classification result for each type of data, and this application does not limit this. When the first processing result is a comprehensive processing result for the different types of data, the first processing result can be a risk value, a probability value, etc.
[0030] The road surface data collection device collects road surface condition data, and the data processing equipment processes the road surface condition data to obtain a first processing result. This first processing result is used to subsequently determine whether the road section has been affected by a natural disaster and to promptly implement appropriate remedial measures. The following describes how to determine whether the road section has been affected by a natural disaster, as shown in steps S30-S50.
[0031] Step S30: The data processing device determines a first risk level based on the first processing result, generates an exception message if the first risk level is greater than a risk threshold, and sends the exception message to the decision-making device, where the exception message carries the first risk level. As described above, the data processing device can process the road surface condition data using a variety of different methods. Therefore, the first processing result can be data that has undergone data cleaning and standardization processing; feature data that has undergone feature extraction processing; a judgment result after threshold determination; or a predicted risk value. For example, the first processing result is that the vertical displacement data is greater than the historical vertical displacement data.
[0032] The data processing device may determine the first risk level based on the first processing result in the following ways: 1. The data processing device may store correspondence information for different types of data, including a first processing result and a corresponding risk level. After processing the road surface condition data and obtaining the first processing result, the data processing device may match the first processing result with the correspondence information to obtain a first risk level. This method of determining risk levels based on established industry standards is simple and can quickly determine the first risk level, thereby improving early warning efficiency.
[0033] 2. Convert the first processing result into a risk value based on the risk value calculation method, and then match it with the risk value and risk level correspondence information table to obtain the corresponding risk level. For example, the data processing device may multiply the vertical displacement data by the first weight, multiply the horizontal displacement data by the second weight, and multiply the meteorological data by the third weight, and then add these three products to obtain the corresponding risk value.
[0034] The correspondence information table is, for example: Value at Risk Risk Level 0-30 blue 31-60 yellow 61-80 orange 81-100 red It can be understood that if the first risk level is blue, it means low risk; if it is yellow, it means there is a certain risk, but the risk is not large; if it is orange, it means there is a higher risk; if it is red, it means the risk has reached the highest level.
[0035] This application distinguishes different risks by determining risk levels, making subsequent warnings more targeted and the warning operations performed more in line with the current environment.
[0036] It is understandable that, in order to conserve resources, not all first risk levels obtained by the data processing device are reported to the decision-making device, but rather are reported selectively. This application sets the result obtained by comparing the first risk level with a preset risk threshold as a criterion for selective reporting, and only reports when the comparison result shows that the first risk level is greater than the risk threshold.
[0037] It should be noted that the risk threshold can be a threshold set based on historical data, human experience, etc., which is used to define the conditions for reporting to the decision-making device and the trigger conditions for generating an exception message. For example, the risk threshold is yellow, meaning that when the first risk level is yellow or higher, the situation will be reported to the decision-making device.
[0038] Exception messages are used to indicate the risks in the current road section. When generating an exception message, the data processing device will encapsulate the determined risk level in the exception message so that the decision-making device can make targeted decisions to make the warning more adapted to the current risk, avoiding secondary unsafe accidents caused by too little force to arouse pedestrians' vigilance or too much force to cause pedestrians to panic.
[0039] Step S40: The decision-making device classifies warning levels according to the first risk level in the abnormal message, and determines a target execution device from the plurality of execution devices according to the warning levels; After receiving the abnormal message sent by the data processing device, the decision-making device will parse the abnormal message to obtain a first risk level, and then determine the warning level according to the first risk level.
[0040] The decision-making device may, for example, compare the first risk level with a classification threshold and determine the warning level according to the comparison result.
[0041] For example, when the decision-making device determines that the first risk level is orange, the classification threshold is yellow, and the comparison result is that the first risk level exceeds the classification threshold by 30%, the warning level is determined to be level two.
[0042] The decision-making device may also directly determine the warning level based on pre-stored correspondence information, such as: First risk level warning level Blue Level 4 Yellow Level 3 Orange Level 2 Red Level 1 It can be understood that after receiving the abnormal message sent by the data processing device, the decision-making device directly matches the parsed first risk level with the above-mentioned correspondence information to obtain the warning level. This division method does not require a complex calculation process and can quickly divide the warning levels, further improving the warning efficiency.
[0043] Examples of enforcement devices include laser warning posts, variable information boards, directional acoustic wave devices, and intelligent road markings. Enforcement devices are assigned to road surface data collection devices in a one-to-one relationship. Therefore, enforcement devices are installed at every location on the highway where road surface data collection devices are installed. There are at least two types of enforcement devices. This allows the decision-making device to select the appropriate enforcement device to execute the corresponding warning action based on the warning level.
[0044] The decision-making device may also store a policy for selecting target execution devices based on the warning level. For example, the decisions in the decision-making device include the following: When the warning level reaches level one, the three patterns of the laser warning column are displayed in a cycle, a prompt message is displayed on the variable information board, the directional sound wave device is activated to broadcast the danger voice, dynamic guidance is provided through smart markings, on-board OBU broadcasts, and navigation APP pushes; When the warning level reaches level 2, two patterns of laser warning columns are displayed in a cycle, the directional sound wave device is activated to broadcast danger voice, dynamic guidance is provided through intelligent markings, on-board OBU broadcasts, and navigation APP push notifications are pushed; When the warning level reaches level three, the directional sound wave device is activated to broadcast danger voice, dynamic guidance is provided through intelligent markings, on-board OBU broadcasts, and navigation APP pushes; When the warning level is level four, dynamic guidance is provided through intelligent markings, on-board OBU broadcasts, and navigation APP pushes.
[0045] This application sets different execution devices for warnings according to different warning levels, making the warnings more targeted.
[0046] Step S50: The decision-making device sends an early warning instruction to the target execution device, where the early warning instruction is used to instruct the target execution device to perform an early warning operation.
[0047] The number of target execution devices can be one or more, selected based on the warning level. When there are multiple target execution devices, the decision-making device can simultaneously send warning instructions to the multiple target execution devices to instruct the target execution devices to execute the warning operation through the warning instructions.
[0048] For example, if the target execution device is a laser warning column, then the warning operation is to display a pattern; if the target execution device is a variable information board, then the warning operation is to display a prompt message; if the target execution device is a directional sound wave device, then the warning operation is to voice broadcast the danger, etc. The warning operations are different for different execution devices and will not be elaborated here.
[0049] In an optional embodiment, if Figure 3 As shown, Figure 3 An optional method embodiment of processing road surface status data by the data processing device provided in this application includes the following steps: In step S301, a data processing device matches a first identifier of a road surface data acquisition device with a first correspondence table to obtain the physical coordinates of the road surface data acquisition device. A second correspondence table includes the first identifiers of multiple road surface data acquisition devices and the physical coordinates corresponding to the first identifier of each road surface data acquisition device. As mentioned above, the road surface data acquisition device includes multiple road surface data acquisition devices, and the multiple road surface data acquisition devices are adaptively spaced based on the curvature of the highway. Because the data processing device can receive data sent by multiple road surface data acquisition devices, each road surface data acquisition device can encapsulate its own unique identifier (first identifier) in the road surface status data when sending road surface status data. On the one hand, it is convenient for the data processing device to distinguish, and on the other hand, it is convenient for the data processing device to identify the physical coordinates of the road surface data acquisition device that sent the road surface status data.
[0050] The first correspondence table is pre-set in the storage space of the data processing device, and includes the first identifications of multiple road surface acquisition devices and the physical coordinates corresponding to the first identification of each road surface acquisition device; then, when the data processing device receives road surface status data and parses the road surface status data to obtain the first identification of the road surface data acquisition device, it can quickly obtain the physical coordinates of the road surface data acquisition device by matching it with the first correspondence table.
[0051] The purpose of determining the physical coordinates is to retrieve the historical status data corresponding to the physical coordinates of the road surface data acquisition device through the physical coordinates, because the data processing device can store historical status data according to the coordinates of each road section.
[0052] Optionally, the decision-making device matches the first identification of each of the road surface data collection devices with the physical coordinates of each of the road surface data collection devices through Kalman filtering to generate the first correspondence table.
[0053] This application uses Kalman filtering to estimate the position of road data collection equipment, eliminating GPS positioning drift through a prediction-correction mechanism. The dynamic correction of Kalman filtering can effectively eliminate multipath interference in highway scenarios, ensuring that the position of the road data collection device accurately corresponds to the road section topology.
[0054] S302: The data processing device retrieves historical status data based on the physical coordinates of the road surface data acquisition device, and determines a first processing result based on the road surface status data and the historical status data.
[0055] After the data processing device retrieves the historical status data based on the physical coordinates of the road surface data acquisition device, the data processing device may determine a first processing result based on the road surface status data and the historical status data. The determination method may include, for example: Comparing the pavement state data with the historical state data for consistency, obtaining results such as consistency, consistency rate of 10%, consistency rate of 30%, and consistency rate of 50%, and using the results as the first processing result; Alternatively, the road surface condition data is compared with the average value of the historical condition data to obtain the deviation of the road surface condition data, such as a deviation of 40%, a deviation of 25%, a deviation of 10%, a deviation of 15%, etc., and the deviation result is used as the first processing result.
[0056] The data processing device may also have other methods for determining the first processing result, which will not be described here.
[0057] This application determines the first processing result through road surface status data and historical status data. Since historical status data is easy to obtain and has high authenticity, it can provide a reliable basis for analyzing road surface status data and improve the accuracy of early warning.
[0058] In an optional method embodiment, if the road surface state data includes vertical displacement data, then the historical state data is the historical vertical displacement data at the moment before the vertical displacement data is collected. Based on this data, the present application provides an optional method embodiment for determining a first processing result based on the road surface state data and the historical state data, such as Figure 4 As shown, the method embodiment includes the following steps: Step S401: Subtract the vertical displacement data from the historical vertical displacement data to obtain a vertical displacement difference, and compare the vertical displacement difference with the difference between adjacent time windows to obtain a vertical displacement change rate; Vertical displacement data refers to the position along the gravity direction, a change in the road surface position, including ground settlement, building uplift, or vertical deformation of the structure. Vertical displacement data can be obtained by using displacement sensors to capture vertical distance changes in real time.
[0059] The historical vertical displacement data of the previous moment is obtained from the memory address of the data processing device. Subtracting the vertical displacement data from the historical vertical displacement data can reduce the amount of calculation and improve the early warning efficiency.
[0060] By calculating the vertical displacement change rate in real time, the degree of damage to the highway structure can be accurately quantified, and the reinforcement effect can be quantitatively evaluated during remediation. It can also achieve a technological leap from static monitoring to dynamic early warning.
[0061] Step S402 : Compare the vertical displacement change rate with a change rate threshold to obtain a comparison result, and use the comparison result as the first processing result.
[0062] The change rate threshold may be a value set based on historical data, experiments, human experience, etc., and is used to provide a limit for measuring the degree of vertical change rate.
[0063] By comparing the vertical displacement change rate with the change rate threshold, abnormal settlement or uplift exceeding the change rate threshold can be quickly identified, thereby obtaining a processing result of whether it is abnormal.
[0064] Then the first processing result includes: the vertical displacement change rate is greater than or equal to the change rate threshold or the vertical displacement change rate is less than the change rate threshold.
[0065] Furthermore, the data processing device may determine that the first risk level is orange or red when the first processing result is that the vertical displacement change rate is greater than or equal to the change rate threshold; and determine that the first risk level is blue when the first processing result is that the vertical displacement change rate is less than the change rate threshold.
[0066] In an optional method embodiment, if the road surface state data includes horizontal displacement data, then the historical state data is the historical horizontal displacement data at the moment before the horizontal displacement data is collected. Based on this data, the present application provides an optional method embodiment for determining a first processing result based on the road surface state data and the historical state data, such as Figure 5 As shown, the method embodiment includes the following steps: Step S501, determining the displacement offset by using a baseline solution method based on the horizontal displacement data and historical horizontal displacement data; Horizontal displacement data differs from vertical displacement data in that the direction of horizontal displacement is orthogonal to the direction of gravity and assesses lateral slippage of the road and transverse deformation of the structure. Horizontal displacement data can be collected using equipment such as GNSS receivers and total stations.
[0067] The baseline solution method establishes a baseline based on the control network coordinate system and calculates the lateral offset of the current point relative to the baseline using the coordinate method. It uses the least squares method to eliminate observation errors.
[0068] Step S502: Compare the displacement offset with the standard deviation to obtain a comparison result, and use the comparison result as the first processing result.
[0069] The standard deviation can be obtained by calculating historical data, reflecting the statistical fluctuation range of normal deformation of the structure.
[0070] The first processing result includes a displacement offset exceeding a standard deviation or a displacement offset not exceeding a standard deviation. The data processing device may determine the first risk level as orange when the first processing result indicates that the displacement offset exceeds a standard deviation, and may determine the first risk level as blue when the first processing result indicates that the displacement offset does not exceed a standard deviation.
[0071] The present application determines the first processing result through a dynamic standard deviation threshold mechanism, which can adapt to different geological conditions.
[0072] In another optional embodiment, the road surface state data may further include displacement data within a detection period. The displacement data may be vertical displacement data, horizontal displacement data, or both vertical and horizontal displacement data, which is not limited in this application. Accordingly, the historical state data may be historical vertical displacement data, historical horizontal displacement data, or both vertical and horizontal displacement data.
[0073] Then, based on Figure 6 The data processing device determines the first processing result based on the road surface state data and the historical image data, including: Step S601: Subtract each displacement data from the corresponding historical displacement data to obtain a plurality of displacement differences, and sum the plurality of displacement differences to obtain a displacement accumulation; The historical displacement data may be corresponding historical benchmark values, such as the average displacement value during a stable period of highway structure.
[0074] The cumulative displacement can be the comprehensive displacement of the same monitored position within a monitoring period, which can be one month, one week, three months, etc.
[0075] Step S602, determining the displacement rate according to each displacement data and the time of collecting each displacement data; The instantaneous displacement rate can be calculated by the time difference Δt between two adjacent monitoring data and the displacement change ΔD: v = ΔD / Δt, where v is the instantaneous displacement rate. Thus, multiple instantaneous displacement rates within a monitoring cycle can be obtained.
[0076] The displacement rate within a monitoring period can be obtained by comparing the sum of all displacement data within a monitoring period with the monitoring time difference, which is not limited in this application.
[0077] Step S603: taking the displacement accumulation amount and the displacement rate as the first processing result.
[0078] Among them, this application reflects the long-term deformation trend through the cumulative displacement, identifies the risk of sudden deformation through the displacement rate, and uses dual data as a discrimination mechanism to improve the accuracy of early warning.
[0079] It is understandable that the data processing device can determine the first risk level based on the specific values of the displacement accumulation amount and the displacement rate. For example, the first risk level can be determined based on the stored corresponding relationship information, which will not be elaborated here.
[0080] Optionally, the data processing device may also determine the first risk level based on the sum of the product of the displacement rate and the first parameter, the product of the displacement accumulation and the second parameter, the product of the geological parameter and the third parameter, and the product of the traffic load and the fourth parameter.
[0081] It can be expressed by the formula: R=α*V+β*D+γ*G+δ*T Where V is the displacement rate, D is the cumulative displacement, G is the geological parameter, and T is the traffic load. α is the first parameter, β is the second parameter, γ is the third parameter, and δ is the fourth parameter. α, β, γ, and δ are all adjustable weights that can be dynamically adjusted based on historical accident data and real-time monitoring data. This application uses multi-parameter coupled analysis to distinguish short-term deformation caused by traffic load from long-term deformation caused by geological issues, improving early warning accuracy.
[0082] In addition to multiple road surface data acquisition devices set at adaptive intervals based on the curvature of the highway, the data acquisition equipment of the present application is also equipped with image acquisition devices (such as cameras) on sections of road where the driving risk is greater than the preset risk, so as to strengthen risk monitoring of sections of road where the driving risk is greater than the preset risk.
[0083] The following is based on Figure 7 , the early warning method of adding an image acquisition device is described, including the following steps: Step S701: For the target road section, if the data processing device determines that the first risk level is less than the risk threshold, image data collected by the image acquisition device is obtained, and the image data is compared with historical image data for consistency to obtain a second processing result; The target road section is a road section where the driving risk is greater than a preset risk. The preset risk can be set based on historical accidents, historical data, etc. Therefore, the number of image acquisition devices is less than that of road surface data acquisition devices, because road surface data acquisition devices are installed along the entire road, while image acquisition devices are only installed on the target road section.
[0084] This application will start the image acquisition device when the data processing device receives the road surface condition data collected by the road surface data acquisition device set on the target road section, and obtain the image data collected by the image acquisition device within the acquisition range when it is determined that the first risk level is less than the risk threshold based on the road surface condition data.
[0085] The historical image data may be an image of the target road section stored in a data processing device. The present application may use a feature point matching algorithm to identify objects that are newly added or reduced in the image data compared to the historical image data.
[0086] Then the second processing result is, for example, the similarity between the image data and the historical image data, the number of objects that are inconsistent between the image data and the historical image data, etc.
[0087] Step S702: If the second processing result is that there are objects in the image data that are inconsistent with the historical image data, and the number of objects is greater than the number threshold, then a warning message is generated, and the warning message carries a second risk level; Among them, the present application obtains a second processing result by comparing the image data with inconsistent targets in the historical image data, and determines a second risk level according to the second processing result.
[0088] The quantity threshold may be determined based on historical data, historical accident rates, etc., and is not limited here. For example, it may be 5.
[0089] For example, the data processing device may have pre-stored corresponding relationship information for determining the second risk level. The corresponding relationship information is as follows: Second risk level for target quantity Level 3-5 Level 6-8 8-10 Level 2 Level 10 or above In addition, the present application can further determine the second risk level according to the size of the target object, thereby enhancing the accuracy of the early warning. This technology can be implemented through the corresponding relationship information and will not be elaborated here.
[0090] Step S703: Send the warning information to the decision-making device, so that the decision-making device divides the warning level according to the second risk level.
[0091] The data processing device further determines whether the target road section presents a risk based on the images captured by the image acquisition device. If the number of target objects exceeds a threshold, the data processing device may trigger the generation of a warning message. This warning message is then sent to the decision-making device, which then reclassifies the warning level based on the second risk level in the warning message. This dual verification mechanism strengthens risk monitoring of the target road section and improves the safety of the target road section.
[0092] It should be noted that the data processing device can retrieve the historical image data by the same method as that for retrieving the historical status data. For details, please refer to the following method, which will not be described in detail here: The data processing device matches the second identifier of the target image collection device with a second correspondence table to obtain the physical coordinates of the target image collection device, wherein the second correspondence table includes the second identifiers of the plurality of image acquisition devices and the physical coordinates corresponding to the second identifier of each image acquisition device; The data processing device retrieves the historical image data based on the physical coordinates of the target image set device.
[0093] Optionally, the decision-making device matches the first identification of each image acquisition device with the physical coordinates of each image acquisition device through Kalman filtering to generate a second correspondence table.
[0094] This application uses a Kalman filter to estimate the position of the image acquisition device, eliminating GPS positioning drift through a prediction-correction mechanism. The dynamic correction of the Kalman filter effectively eliminates multipath interference in road scenes, ensuring that the image acquisition device position accurately corresponds to the road section topology.
[0095] In another optional method embodiment, the data processing device can also perform risk warning according to the following method, such as Figure 8 As shown, the following steps are included: Step S801: If the data processing device does not receive data sent by the data acquisition device during the monitoring period, the data processing device determines a target data acquisition device adjacent to the data acquisition device according to the unique identifier of the data acquisition device, and sends a test instruction to the data acquisition device. Among them, the present application prevents the occurrence of reporting link interruption by means of auxiliary verification by adjacent devices, so as to avoid the data processing device mistakenly judging a risky location as a non-risky location.
[0096] The data acquisition device can be the road surface data acquisition device or an image acquisition device, which is not limited here. The monitoring period can be one week, two days, two weeks, etc.
[0097] The unique identifier of a data acquisition device is the first identifier of a road data acquisition device or the second identifier of an image acquisition device. Since each data acquisition device has an identifier that distinguishes it from other devices, and the coordinates of each data acquisition device are stored within the data acquisition device, the unique identifier of the data acquisition device can be used to identify target data acquisition devices adjacent to the data acquisition device.
[0098] Step S802: After receiving the test instruction, the target data acquisition device sends a first test message to the data acquisition device. If the target data acquisition device does not receive a response message based on the first test message within a preset time period, the target data acquisition device sends an exception message to the data processing device. Step S803: The data processing device generates a risk message based on the abnormal message and reports the risk message to the traffic control center.
[0099] The interaction sequence is as follows: Data processing device ->> target device: send test instructions (for example, TCP port 502) Target data collection device >> Data collection device: Send, for example, an ICMPv6 test packet Note right of data acquisition device: Waiting for response timeout (default 3s) Target data collection device >> data processing device: Report risk message (SNMP Trap).
[0100] Through collaborative verification of adjacent devices, the two scenarios of network congestion (temporary interruption) and device damage (permanent offline) can be effectively distinguished, so that emergency response can be carried out immediately and the interruption of risk warning monitoring can be avoided.
[0101] The present application provides a method for warning of non-continuous highway conditions, taking into account that traffic warnings are currently often carried out through some fixed cameras, manual inspections, etc., which leads to problems such as response delays and low road network coverage when facing emergencies. The present application sets up multiple road surface data acquisition devices at intervals on the highway. Each road surface data acquisition device can collect road surface status data of the corresponding road surface and send it to a data processing device with a communication relationship therewith for processing to obtain a first processing result. Since the data processing device is placed at the front end, it can process the road surface status data collected by the road surface data acquisition device in real time, and achieve a rapid response to abnormal data. The data processing device will also determine the first risk level based on the first processing result, and send the first risk level to the decision-making device when it is determined that the first risk level is greater than the risk threshold, so that the abnormal situation can be responded to immediately. In addition, the decision-making device can also divide the warning level according to the first risk level, and select the target execution device that is suitable for the current risk scenario from multiple execution devices according to different warning levels for layered warning, thereby realizing the precise scheduling and optimal configuration of risk response resources, and thus realizing the rapid identification and warning of abnormal road conditions. It should be noted that although the operations of the present invention are described in a particular order in the accompanying drawings, this does not require or imply that these operations must be performed in this particular order, or that all the operations shown must be performed to achieve the desired results. On the contrary, the steps depicted in the flowchart can be performed in a different order.
[0102] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention herein is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features having similar functions disclosed in this application.
Claims
1. A method for warning a highway discontinuous state, the method being applied to a warning system comprising a data acquisition device, multiple data processing devices, multiple decision-making devices, and multiple execution devices. The data acquisition device comprises multiple road surface data acquisition devices, the multiple road surface data acquisition devices being adaptively spaced based on the curvature of the highway. Each data processing device is configured to correspond to at least one road surface data acquisition device, and each execution device is configured to correspond to each road surface data acquisition device. The method comprises: The data processing device receives the road surface state data collected by the road surface data collection device, and processes the road surface state data to obtain a first processing result; The data processing device determines a first risk level based on the first processing result, generates an exception message if the first risk level is greater than a risk threshold, and sends the exception message to the decision-making device, wherein the exception message carries the first risk level; The decision-making device divides the warning level according to the first risk level in the abnormal message, and determines a target execution device from the plurality of execution devices according to the warning level; The decision-making device sends an early warning instruction to the target execution device, where the early warning instruction is used to instruct the target execution device to perform an early warning operation.
2. The method according to claim 1, wherein the road surface state data carries a first identifier for marking the road surface data acquisition device, characterized in that: The processing of the road surface state data to obtain a first processing result includes: The data processing device matches the first identifier of the road surface data acquisition device with a first correspondence table to obtain the physical coordinates of the road surface data acquisition device, wherein the first correspondence table includes the first identifiers of multiple road surface data acquisition devices and the physical coordinates corresponding to the first identifier of each road surface data acquisition device; The data processing device retrieves historical status data based on the physical coordinates of the road surface data acquisition device, and determines the first processing result based on the road surface status data and the historical status data.
3. The method according to claim 2, wherein the road surface state data includes vertical displacement data, and the historical state data is the historical vertical displacement data at the moment before the vertical displacement data is collected, characterized in that: The determining the first processing result based on the road surface state data and the historical state data includes: Subtracting the vertical displacement data from the historical vertical displacement data to obtain a vertical displacement difference, and comparing the vertical displacement difference with a difference between adjacent time windows to obtain a vertical displacement change rate; The vertical displacement change rate is compared with a change rate threshold to obtain a comparison result, and the comparison result is used as the first processing result.
4. The method according to claim 3, wherein the road surface state data includes horizontal displacement data, and the historical state data is the historical horizontal displacement data at the moment before the horizontal displacement data is collected, characterized in that: The determining the first processing result based on the road surface state data and the historical image data includes: Determining a displacement offset based on the horizontal displacement data and the historical horizontal displacement data by a baseline solution method; The displacement offset is compared with the standard deviation to obtain a comparison result, and the comparison result is used as the first processing result.
5. The method according to claim 3, wherein the road surface state data includes displacement data within a monitoring period, and the historical state data is historical displacement data, characterized in that: Determining the first processing result based on the road surface state data and the historical image data includes: Subtracting each of the displacement data from the corresponding historical displacement data to obtain a plurality of displacement differences, and summing the plurality of displacement differences to obtain a displacement accumulation; determining a displacement rate according to each displacement data and a time when each displacement data is collected; The displacement accumulation amount and the displacement rate are used as the first processing result.
6. The method according to claim 5, wherein the road surface condition data further includes geological parameters and traffic load, The data processing device determines a first risk level based on the first processing result, including: The data processing device determines the first risk level based on the sum of the product of the displacement rate and a first parameter, the product of the displacement accumulation and a second parameter, the product of the geological parameter and a third parameter, and the product of the traffic load and a fourth parameter.
7. The method according to claim 1, wherein the data acquisition device further comprises an image acquisition device provided on the target road section of the highway, characterized in that: The target road section is a road section where the driving risk is greater than a preset risk, and the method further includes: For the target road section, if the data processing device determines that the first risk level is less than the risk threshold, acquiring image data captured by the image acquisition device, and performing a consistency comparison between the image data and historical image data to obtain a second processing result; If the second processing result is that there are targets in the image data that are inconsistent with the historical image data, and the number of the targets is greater than the number threshold, then generating a warning message, the warning message carrying a second risk level; The warning information is sent to the decision-making device, so that the decision-making device divides the warning level according to the second risk level.
8. The method according to claim 7, wherein the image data carries a second identifier for marking the image acquisition device, The method further comprises: The data processing device matches the second identifier of the target image collection device with a second correspondence table to obtain the physical coordinates of the target image collection device, wherein the second correspondence table includes the second identifiers of multiple image acquisition devices and the physical coordinates corresponding to the second identifier of each image acquisition device; The data processing device retrieves the historical image data based on the physical coordinates of the target image set device.
9. The method according to claim 1, characterized in that The method further comprises: If the data processing device does not receive data sent by the data acquisition device during the monitoring period, determining a target data acquisition device adjacent to the data acquisition device according to the unique identifier of the data acquisition device, and sending a test instruction to the data acquisition device; After receiving the test instruction, the target data acquisition device sends a first test message to the data acquisition device, and if the target data acquisition device does not receive a response message based on the first test message within a preset time period, sends an exception message to the data processing device; The data processing device generates a risk message according to the abnormal message, and reports the risk message to the traffic control center.
10. The method according to claim 1, characterized in that The early warning system further includes a power supply device, which is used to supply power to the data acquisition device, the data processing device and the execution device.