Road fault prediction method and system

Abstract

The invention relates to a road fault prediction method and system, and belongs to the technical field of road safety, and the method comprises the steps: receiving alarm information reported by a vehicle terminal; determining a reporting scene according to the triggering condition, wherein the reporting scene comprises continuous reporting or interrupted reporting; under the condition that the reporting scene is continuous reporting, the position information of the vehicle terminal is determined based on the alarm information, and under the condition that the position information is determined to be not at the position where parking is allowed based on the position information and an address base, a road fault is determined according to multi-vehicle cooperative verification, vehicle density analysis and a road position state; and acquiring position information finally reported by the lost vehicles under the condition that the reporting scene is interrupted reporting, and determining a road fault according to a comparison result of the number of the lost vehicles and a preset threshold value and a road position state under the condition that the position information is determined not to be in a parking allowed position based on the position information and the address base. According to the method, efficient, accurate and real-time road anomaly detection and early warning are realized.

Description

A method and system for predicting road faults Technical Field

[0001] This invention belongs to the field of road safety technology, and in particular relates to a road fault prediction method and system. Background Technology

[0002] With the rapid development of intelligent transportation systems, real-time and accurate road condition monitoring and fault early warning have become crucial for ensuring smooth road network operation and improving driving safety. Traditional road fault detection mainly relies on fixed-location sensing devices (such as cameras and inductive loop detectors) or manual inspections. These methods suffer from limited coverage, high construction and maintenance costs, and long response delays, making them ill-suited to the increasingly complex and dynamically changing road traffic environment. Therefore, how to better predict road faults has become an urgent problem to be solved. Summary of the Invention

[0003] In view of the shortcomings of the prior art, the purpose of the invention is to provide a road fault prediction method and system.

[0004] In a first aspect, the present invention proposes a road fault prediction method, comprising: S1, receiving alarm information reported by a vehicle terminal; S2, determining a reporting scenario based on triggering conditions, wherein the reporting scenario includes continuous reporting or interrupted reporting; S3, in the case of continuous reporting, determining the location information of the vehicle terminal based on the alarm information, and determining, based on the location information and an address database, that the location information is not in a permitted parking location, and determining a road fault based on multi-vehicle collaborative verification, vehicle density analysis, and road position status; S4, in the case of interrupted reporting, obtaining the last reported location information of a missing vehicle, and determining, based on the location information and the address database, that the location information is not in a permitted parking location, and determining a road fault based on a comparison result of the number of missing vehicles with a preset threshold and the road position status.

[0005] Furthermore, the vehicle terminal reporting the alarm information includes: the vehicle terminal obtaining the current location based on the positioning module, and determining the location information previously reported by the vehicle terminal based on the timestamp of obtaining the current location; determining whether the vehicle locations overlap based on the current location and the location information previously reported by the vehicle terminal; if so, further determining that the number of times the vehicle locations overlap exceeds a preset number within a preset time range, so that the vehicle terminal reports the alarm information.

[0006] Furthermore, the reporting scenario is determined based on the triggering conditions, including: real-time monitoring of information sent by the vehicle terminal based on the communication network, and determining the reporting scenario as continuous reporting when a suspected abnormal parking alarm is received; real-time monitoring of the communication connection status of the vehicle terminal, and determining the reporting scenario as interrupted reporting when the vehicle terminal fails to report any data for a preset time.

[0007] Further, determining whether the location information is in a permitted parking location based on the location information and the address database includes: obtaining the location information from the address database; determining whether the location information of the vehicle terminal exists in the location information of the address database; wherein, if the location information of the vehicle terminal exists in the location information of the address database, then the location information is determined to be in a permitted parking location; if the location information of the vehicle terminal does not exist in the location information of the address database, then the location information is determined to be not in a permitted parking location.

[0008] Further, based on multi-vehicle collaborative verification, vehicle density analysis, and road location status, road faults are determined, including: based on the location information of the vehicle terminal, determining whether other vehicles reported the same alarm information at the same time as the vehicle terminal; if so, identifying the target vehicle; if multiple target vehicles are identified and they are clustered within a preset range, determining it as an abnormal accumulation situation; if the abnormal accumulation situation occurs on a high-risk road section, determining it as a road fault; combining the images captured by the vehicle terminal camera, determining whether the road fault is a major fault; wherein, in the case of determining it as a road fault and / or the road fault as a major fault, a road fault alarm is generated and other vehicles on the road are notified.

[0009] Further, determining the missing vehicle includes: obtaining a navigation vehicle list and determining the time when each vehicle in the navigation vehicle list last reported data; based on the time when the last reported data was reported, determining whether each vehicle reported data within a preset time period; if not, then the vehicle that did not report data within the preset time period is identified as the missing vehicle.

[0010] Furthermore, based on the comparison result of the number of missing vehicles with a preset threshold and the road location status, a road fault is determined, including: determining the number of missing vehicles based on the time window and spatial range of the target location; determining that if the number of missing vehicles exceeds the preset threshold, it is an abnormal situation; determining that the abnormal situation occurs in a high-risk road section at the target location, it is a road fault, and a road fault alarm is generated and other vehicles in the navigation vehicle list are notified.

[0011] A second aspect of the present invention provides a road fault prediction system, comprising: a receiving module for receiving alarm information reported by a vehicle terminal; a first determining module for determining a reporting scenario based on triggering conditions, wherein the reporting scenario includes continuous reporting or interrupted reporting; a second determining module for determining the location information of the vehicle terminal based on the alarm information when the reporting scenario is continuous reporting, and determining a road fault based on multi-vehicle collaborative verification, vehicle density analysis, and road position status when the location information and an address database determine that the location information is not in a permitted parking location; and a third determining module for obtaining the last reported location information of a missing vehicle when the reporting scenario is interrupted reporting, and determining a road fault based on a comparison of the number of missing vehicles with a preset threshold and the road position status when the location information and the address database determine that the location information is not in a permitted parking location.

[0012] A third aspect of the present invention provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method described in any one aspect of the present invention.

[0013] A fourth aspect of the present invention provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to perform the method described in any one of the first aspects of the present invention.

[0014] The beneficial effects of this invention are as follows: The road fault prediction method and system of this invention receive alarm information reported by vehicle terminals; determine the reporting scenario based on triggering conditions, including continuous reporting or interrupted reporting; in the case of continuous reporting, determine the location information of the vehicle terminal based on the alarm information; if the location information is not in a permitted parking location based on the location information and address database, determine the road fault based on multi-vehicle collaborative verification, vehicle density analysis, and road position status; in the case of interrupted reporting, obtain the last reported location information of the missing vehicle; if the location information is not in a permitted parking location based on the location information and address database, determine the road fault based on the comparison result of the number of missing vehicles with a preset threshold and the road position status. This method achieves efficient, accurate, and real-time road anomaly detection and early warning through dual-scenario intelligent analysis (continuous reporting and interrupted reporting), combined with multi-source data fusion and collaborative verification mechanisms. Furthermore, it covers comprehensive risks through dual-scenario coverage. Attached Figure Description

[0015] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. It is obvious that the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings.

[0016] Figure 1 is a flowchart of a road fault prediction method according to an embodiment of the present invention; Figure 2 is a flowchart of a method for determining road faults in a reporting scenario of continuous reporting according to an embodiment of the present invention; Figure 3 is a flowchart of a method for determining road faults in a reporting scenario of interrupted reporting according to an embodiment of the present invention; Figure 4 is a schematic diagram of a road fault prediction system according to an embodiment of the present invention; Figure 5 is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0017] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0018] Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts disclosed in this invention.

[0019] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The terms "installed," "connected," and "linked" should be interpreted broadly; for example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0020] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of methods and systems consistent with some aspects of the invention as detailed in the appended claims.

[0021] This invention proposes a road fault prediction method, system, and related equipment. Specifically, the road fault prediction method, system, and related equipment of this invention are described below with reference to the accompanying drawings.

[0022] Figure 1 is a flowchart of a road fault prediction method according to an embodiment of the present invention. It should be noted that the road fault prediction method of this embodiment can be applied to the road fault prediction system or Internet of Things platform of this embodiment. The road fault prediction system can be configured on an electronic device or in a server. This application does not limit the scope of the application.

[0023] As shown in Figure 1, the road fault prediction method includes: S110, receiving alarm information reported by the vehicle terminal.

[0024] In an embodiment of the present invention, the vehicle terminal obtains its current location based on the positioning module, and determines the location information previously reported by the vehicle terminal based on the timestamp of obtaining the current location; based on the current location and the location information previously reported by the vehicle terminal, it determines whether the vehicle locations overlap; if so, it further determines that if the number of times the vehicle locations overlap exceeds a preset number within a preset time range, and then causes the vehicle terminal to report an alarm message. The system then receives the alarm message reported by the vehicle terminal.

[0025] The positioning module includes, but is not limited to, GPS positioning and laser positioning.

[0026] The process involves comparing the current location with the location information previously reported by the vehicle terminal, determining the distance between the two locations, and then judging whether the vehicle locations overlap based on this distance. If the distance between the two locations is less than a distance threshold, the vehicle locations are considered to overlap. If the distance between the two locations is not less than the distance threshold, the vehicle locations are considered not to overlap.

[0027] If it is determined that there is no overlap in vehicle locations, the latest reported location information can be updated and reported to the IoT platform.

[0028] The alarm information includes, but is not limited to, vehicle identification information, location information, vehicle status information, and sensor data.

[0029] S120, determine the reporting scenario based on the triggering conditions. The reporting scenario includes continuous reporting or interrupted reporting.

[0030] In an embodiment of the present invention, the information sent by the vehicle terminal based on the communication network is monitored in real time. When a suspected abnormal parking alarm is received, the reporting scenario is determined to be continuous reporting. The communication connection status of the vehicle terminal is monitored in real time. When it is determined that the vehicle terminal has not reported any data for a preset time, the reporting scenario is determined to be interrupted reporting.

[0031] In other words, when it is detected that the vehicle terminal is continuously sending information but the content is abnormal (such as the location remaining unchanged for a long time), the reporting scenario is determined to be continuous reporting, and the analysis process for continuous reporting scenarios begins. When it is detected that the vehicle terminal suddenly stops sending any data, the reporting scenario is determined to be interrupted reporting, and the analysis process for interrupted reporting scenarios begins.

[0032] S130, in the case of continuous reporting, determines the location information of the vehicle terminal based on the alarm information. If the vehicle is not in a permitted parking location based on the location information and address database, a road fault is determined based on multi-vehicle collaborative verification, vehicle density analysis and road position status.

[0033] In an embodiment of the present invention, as shown in FIG2, the method for determining road faults in the case of continuous reporting includes: S210, determining the location information of the vehicle terminal based on the alarm information.

[0034] S220 determines whether the location is within a permitted parking area based on location information and an address database.

[0035] In an embodiment of the present invention, the location information of the address database is obtained; it is determined whether the location information of the vehicle terminal exists in the location information of the address database.

[0036] S230, if not, determine the road fault based on multi-vehicle collaborative verification, vehicle density analysis, and road location status.

[0037] In an embodiment of the present invention, if the location information of a vehicle terminal is not found in the address database, it is determined that the location information is not in a permitted parking location. Then, based on multi-vehicle collaborative verification, vehicle density analysis, and road location status, a road fault is determined.

[0038] In an embodiment of the present invention, if the location information is not in a permitted parking location, based on the location information of the vehicle terminal, it is determined whether other vehicles have reported the same alarm information at the same time on their vehicle terminals; if so, the target vehicle is identified; if multiple target vehicles are identified and they are clustered within a preset range, it is determined to be an abnormal accumulation situation; if the abnormal accumulation situation occurs on a high-risk road section, it is determined to be a road fault; and by combining the images captured by the vehicle terminal camera, it is determined whether the road fault is a major fault.

[0039] If no other vehicles reported the same alarm information on their vehicle terminals at the same time, it can be determined as a single-vehicle accident.

[0040] In cases where multiple target vehicles are identified, the position of each target vehicle is determined, and the distance between each vehicle and its reported position is calculated. If the distance between the reported positions of the vehicles is greater than a preset distance, the traffic jam is considered to be normal. For example, if there are 10 target vehicles, each 5 meters long, and the road has 3 lanes, the shortest distance between the reported positions of the vehicles should be approximately 10 / 3*5 ≈ 15 meters for a normal traffic jam.

[0041] High-risk road sections include, but are not limited to, bridges and steep slopes. This means that by combining information on high-risk road sections (bridges, steep slopes), priority will be given to areas prone to serious accidents, thereby improving the targeting and effectiveness of early warnings.

[0042] In cases where a road malfunction is identified, and the footage captured by the vehicle's terminal camera shows a bridge or road collapse, the road malfunction is classified as a major malfunction.

[0043] Specifically, in cases where a road malfunction is identified as a road fault and / or a major road fault, a road fault alarm will be generated, and other vehicles on the road will be notified. Vehicles will be guided to detour or slow down to avoid secondary accidents. Furthermore, in cases of a major road fault, a higher-level emergency response will be triggered, such as coordinating with traffic management and rescue departments.

[0044] S240, if so, end the process.

[0045] In an embodiment of the present invention, if the location information of a vehicle terminal is found in the location information of the address database, then the location information is determined to be at a location where parking is permitted.

[0046] In embodiments of the present invention, in the case of continuous reporting, a triple mechanism—multi-vehicle collaborative verification, vehicle density analysis, and road location status—effectively identifies vehicle stagnation caused by congestion, accidents, and road damage. In continuous reporting scenarios, combining camera footage directly identifies major faults such as bridge collapses and road surface damage, improving the accuracy of judgment.

[0047] S140: In the case of interrupted reporting, obtain the last reported location information of the missing vehicle. Based on the location information and address database, if the location information is not in a permitted parking location, determine the road fault based on the comparison result of the number of missing vehicles with a preset threshold and the road location status.

[0048] In an embodiment of the present invention, as shown in FIG3, the method for determining a road fault in the case of interrupted reporting includes: S310, obtaining the missing vehicle and obtaining the last reported location information of the missing vehicle.

[0049] In an embodiment of the present invention, a navigation vehicle list is obtained, and the time when each vehicle in the navigation vehicle list last reported data is determined; based on the time when the last data was reported, it is determined whether each vehicle reported data within a preset time period; if not, the vehicles that did not report data within the preset time period are designated as missing vehicles.

[0050] The time when a vehicle last reported data can be understood as the time when each vehicle in the navigation vehicle list last reported data, based on the current system time.

[0051] S320 determines whether the location is within a permitted parking area based on location information and an address database.

[0052] In an embodiment of the present invention, the location information of the address database is obtained; it is determined whether the location information of the vehicle terminal exists in the location information of the address database.

[0053] S330, if not, determine the road fault based on the comparison results of the number of missing vehicles with the preset threshold and the road location status.

[0054] In an embodiment of the present invention, if the location information of a vehicle terminal is not found in the address database, it is determined that the location information is not in a permitted parking location. Then, based on a comparison of the number of missing vehicles with a preset threshold and the road location status, a road fault is determined.

[0055] In an embodiment of the present invention, if the location information is not in a permitted parking location, the number of missing vehicles is determined based on the time window and spatial range of the target location; if the number of missing vehicles exceeds a preset threshold, it is determined to be an abnormal situation; if the abnormal situation occurs in a high-risk road section at the target location, it is determined to be a road fault, and a road fault alarm is generated and other vehicles in the navigation vehicle list are notified.

[0056] The method involves determining the number of missing vehicles based on the time window and spatial range of the target location. For example, with a time window of 3 minutes and a spatial range of 100 meters, the method identifies the number of vehicles that simultaneously go missing within both the time window and the spatial range. Dynamically counting the number of missing vehicles using time windows (e.g., 3 minutes) and spatial ranges (e.g., 100 meters) avoids interference from isolated events.

[0057] Among them, by setting a preset threshold for the number of missing vehicles, a road fault alarm is only triggered when multiple vehicles collectively lose contact in the same area, thereby improving reliability.

[0058] High-risk road sections include, but are not limited to, bridges and steep slopes.

[0059] S340, if so, end the process.

[0060] In an embodiment of the present invention, if the location information of a vehicle terminal is found in the location information of the address database, then the location information is determined to be at a location where parking is permitted.

[0061] In embodiments of the present invention, when reporting is interrupted, the system effectively captures collective vehicle disconnection caused by tunnel signal loss, bridge collapse, power outage, etc., by comparing the number of disconnected vehicles with a preset threshold and the road location status, thus covering the blind spots of traditional monitoring methods.

[0062] In embodiments of the present invention, leveraging the advantages of the IoT platform in aggregating vehicle location information, the analysis of multiple vehicle location information improves fault accuracy; based on the advantages of the IoT platform in aggregating and continuously storing vehicle location information, the location of suddenly lost-connection vehicles can be analyzed, enabling a method for road fault detection; based on the coordination between the IoT platform and edge capabilities, the analysis and detection of road faults can be achieved in both offline and online scenarios; based on the analysis of multiple vehicle information, real-time road fault detection is achieved; leveraging the advantages of the IoT platform in aggregating vehicle information, online vehicles can be promptly notified after a fault occurs; and under vehicle networking conditions, the analysis of the vehicle's location in conjunction with other IoT devices can be obtained to determine whether it is in the event of a major fault such as a broken bridge or landslide.

[0063] According to an embodiment of the present invention, a road fault prediction method receives alarm information reported by vehicle terminals; determines a reporting scenario based on triggering conditions, including continuous reporting or interrupted reporting; in the case of continuous reporting, determines the location information of the vehicle terminal based on the alarm information; if the location information is not in a permitted parking location based on the location information and an address database, determines a road fault based on multi-vehicle collaborative verification, vehicle density analysis, and road location status; in the case of interrupted reporting, obtains the last reported location information of the missing vehicle; if the location information is not in a permitted parking location based on the location information and an address database, determines a road fault based on the comparison result of the number of missing vehicles with a preset threshold and the road location status. This method achieves efficient, accurate, and real-time road anomaly detection and early warning through dual-scenario intelligent analysis (continuous reporting and interrupted reporting), combined with multi-source data fusion and collaborative verification mechanisms. Furthermore, it covers comprehensive risks through dual-scenario coverage.

[0064] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0065] According to one aspect of the present invention, a road fault prediction system is also proposed. Figure 4 is a schematic diagram of the road fault prediction system according to an embodiment of the present invention. As shown in Figure 4, the system includes: a receiving module 410, used to receive alarm information reported by a vehicle terminal; a first determining module 420, used to determine a reporting scenario based on triggering conditions, wherein the reporting scenario includes continuous reporting or interrupted reporting; a second determining module 430, used to determine the location information of the vehicle terminal based on the alarm information when the reporting scenario is continuous reporting, and to determine a road fault based on multi-vehicle collaborative verification, vehicle density analysis, and road position status when the location information and address database determine that the location information is not in a permitted parking location; and a third determining module 440, used to obtain the last reported location information of a missing vehicle when the reporting scenario is interrupted reporting, and to determine a road fault based on the comparison result of the number of missing vehicles with a preset threshold and the road position status when the location information and address database determine that the location information is not in a permitted parking location.

[0066] According to an embodiment of the present invention, a road fault prediction system receives alarm information reported by vehicle terminals; determines the reporting scenario based on triggering conditions, including continuous reporting or interrupted reporting; in the case of continuous reporting, determines the location information of the vehicle terminal based on the alarm information; if the location information is not in a permitted parking location based on the location information and an address database, determines a road fault based on multi-vehicle collaborative verification, vehicle density analysis, and road location status; in the case of interrupted reporting, obtains the last reported location information of the missing vehicle; if the location information is not in a permitted parking location based on the location information and an address database, determines a road fault based on the comparison result of the number of missing vehicles with a preset threshold and the road location status. Thus, through dual-scenario intelligent analysis (continuous reporting and interrupted reporting), combined with multi-source data fusion and collaborative verification mechanisms, efficient, accurate, and real-time road anomaly detection and early warning are achieved. Furthermore, the dual-scenario approach covers comprehensive risks.

[0067] Optionally, the vehicle terminal reporting the alarm information includes: the vehicle terminal obtaining the current location based on the positioning module, and determining the location information previously reported by the vehicle terminal based on the timestamp of obtaining the current location; determining whether the vehicle locations overlap based on the current location and the location information previously reported by the vehicle terminal; if so, further determining that the number of times the vehicle locations overlap exceeds a preset number within a preset time range, so that the vehicle terminal reports the alarm information.

[0068] Optionally, the first determining module 420 is specifically used to monitor the information sent by the vehicle terminal based on the communication network in real time, and determine the reporting scenario as continuous reporting when receiving suspected abnormal parking alarm information; and to monitor the communication connection status of the vehicle terminal in real time, and determine the reporting scenario as interrupted reporting when the vehicle terminal has not reported any data for a preset time.

[0069] Optionally, the second determining module 430 is specifically used to obtain the location information of the address database; determine whether the location information of the vehicle terminal exists in the location information of the address database; wherein, if the location information of the vehicle terminal exists in the location information of the address database, then the location information is determined to be in a permitted parking location; if the location information of the vehicle terminal does not exist in the location information of the address database, then the location information is determined to be not in a permitted parking location.

[0070] Optionally, the second determining module 430 is specifically used to determine, based on the location information of the vehicle terminal, whether there are other vehicles that reported the same alarm information at the same time as the vehicle terminal; if so, then determine the target vehicle; if it is determined that there are multiple target vehicles and the multiple target vehicles are gathered within a preset range, then it is determined to be an abnormal accumulation situation; if it is determined that the abnormal accumulation situation occurs in a high-risk road section, then it is determined to be the road fault; combining the image captured by the vehicle terminal camera, determine whether the road fault is a major fault; wherein, if it is determined to be the road fault and / or the road fault is a major fault, then a road fault alarm is generated and other vehicles on the road are notified.

[0071] Optionally, the third determining module 440 is used to obtain a list of navigation vehicles and determine the time when each vehicle in the list last reported data; based on the time when the last reported data was reported, determine whether each vehicle reported data within a preset time period; if not, then the vehicles that did not report data within the preset time period are designated as the missing vehicles.

[0072] Optionally, the third determining module 440 is used to determine the number of missing vehicles based on the time window and spatial range of the target location; if the number of missing vehicles exceeds the preset threshold, it is determined to be an abnormal situation; if the abnormal situation occurs in a high-risk road section at the target location, it is determined to be a road fault, and a road fault alarm is generated and other vehicles in the navigation vehicle list are notified.

[0073] According to one aspect of the present invention, an electronic device is provided.

[0074] Figure 5 is a schematic diagram of an electronic device according to an embodiment of the present invention. As shown in Figure 5, the electronic device may include one or more (only one is shown in Figure 5) processors 102 (processors 102 may include, but are not limited to, microprocessors (MPUs) or programmable logic devices (PLDs)) and a memory 104 for storing data. In an exemplary embodiment, the electronic device may also include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that the structure shown in Figure 5 is merely illustrative and does not limit the structure of the terminal device described above. For example, the terminal device may include more or fewer components than shown in Figure 5, or have different configurations with equivalent or more functions than those shown in Figure 5.

[0075] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the road fault prediction method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to terminal devices via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0076] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the switching device. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0077] This invention proposes a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute a road fault prediction method.

[0078] The applicant of this invention has provided a detailed description of the embodiments of the invention in conjunction with the accompanying drawings. However, those skilled in the art should understand that the above embodiments are merely preferred embodiments of the invention. The detailed description is only intended to help readers better understand the spirit of the invention and is not intended to limit the scope of protection of the invention. On the contrary, any improvements or modifications made based on the inventive spirit of the invention should fall within the scope of protection of the invention.

[0079] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0080] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. A road fault prediction method, characterized in that, include: S1, Receive alarm information reported by the vehicle terminal; S2, Determine the reporting scenario based on the triggering conditions, the reporting scenario including continuous reporting or interrupted reporting; S3, If the reporting scenario is continuous reporting, determine the location information of the vehicle terminal based on the alarm information, and if the location information is not in a permitted parking location based on the location information and the address database, determine the road fault based on multi-vehicle collaborative verification, vehicle density analysis, and road location status; S4, If the reporting scenario is interrupted reporting, Obtain the last reported location information of the missing vehicle, and if the location information is not in a permitted parking location based on the location information and the address database, determine the road fault based on the comparison result of the number of missing vehicles with a preset threshold and the road location status.

2. The road fault prediction method according to claim 1, characterized in that, The vehicle terminal reporting the alarm information includes: the vehicle terminal obtaining the current location based on the positioning module, and determining the location information previously reported by the vehicle terminal based on the timestamp of obtaining the current location; determining whether the vehicle locations overlap based on the current location and the location information previously reported by the vehicle terminal; if so, further determining that the number of times the vehicle locations overlap exceeds a preset number within a preset time range, so that the vehicle terminal reports the alarm information.

3. The road fault prediction method according to claim 1, characterized in that, The reporting scenario is determined based on the triggering conditions, including: real-time monitoring of information sent by the vehicle terminal based on the communication network, and determining the reporting scenario as continuous reporting when a suspected abnormal parking alarm is received; real-time monitoring of the communication connection status of the vehicle terminal, and determining the reporting scenario as interrupted reporting when the vehicle terminal has not reported any data for a preset time.

4. The road fault prediction method according to claim 1, characterized in that, Determining whether the location information is in a permitted parking location based on the location information and the address database includes: obtaining the location information from the address database; determining whether the location information of the vehicle terminal exists in the location information of the address database; wherein, if the location information of the vehicle terminal exists in the location information of the address database, then the location information is determined to be in a permitted parking location; if the location information of the vehicle terminal does not exist in the location information of the address database, then the location information is determined not to be in a permitted parking location.

5. The road fault prediction method according to claim 1, characterized in that, Based on multi-vehicle collaborative verification, vehicle density analysis, and road location status, a road fault is determined, including: based on the location information of the vehicle terminal, determining whether other vehicles reported the same alarm information at the same time as the vehicle terminal; if so, identifying the target vehicle; if multiple target vehicles are identified and they are clustered within a preset range, determining it as an abnormal accumulation situation; if the abnormal accumulation situation occurs in a high-risk road section, determining it as a road fault; combining the images captured by the vehicle terminal camera, determining whether the road fault is a major fault; wherein, in the case of determining it as a road fault and / or the road fault as a major fault, a road fault alarm is generated and other vehicles on the road are notified.

6. The road fault prediction method according to claim 1, characterized in that, Identifying the missing vehicle includes: obtaining a list of navigation vehicles and determining the time when each vehicle in the list last reported data; based on the time when the last reported data was reported, determining whether each vehicle reported data within a preset time period; if not, then the vehicle that did not report data within the preset time period is identified as the missing vehicle.

7. The road fault prediction method according to claim 6, characterized in that, Based on the comparison between the number of missing vehicles and a preset threshold and the road location status, a road fault is determined, including: determining the number of missing vehicles based on the time window and spatial range of the target location; determining an abnormal situation if the number of missing vehicles exceeds the preset threshold; determining a road fault if the abnormal situation occurs in a high-risk road section at the target location, generating a road fault alarm and notifying other vehicles in the navigation vehicle list.

8. A road fault prediction system, characterized in that, include: The receiving module is used to receive alarm information reported by the vehicle terminal; The first determining module is used to determine the reporting scenario based on the triggering conditions, wherein the reporting scenario includes continuous reporting or interrupted reporting. The second determining module is used to determine the location information of the vehicle terminal based on the alarm information when the reporting scenario is continuous reporting, and to determine the road fault based on multi-vehicle collaborative verification, vehicle density analysis, and road location status when the location information and address database determine that the location information is not in a permitted parking location. The third determining module is used to obtain the last reported location information of the missing vehicle when the reporting scenario is interrupted reporting, and to determine the road fault based on the comparison result of the number of missing vehicles with a preset threshold and the road location status when the location information and address database determine that the location information is not in a permitted parking location.

9. An electronic device, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1 to 7.

Images