A method, apparatus, electronic device, and storage medium for detecting abnormal road conditions.
By determining the location of tire failure and the distribution of vehicles within the failure area, the system automatically detects abnormal road conditions caused by human-caused spillage or items scattered during transportation, solving the problem of the lack of automatic detection in existing technologies and improving driving safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHEJIANG UNIVIEW TECH CO LTD
- Filing Date
- 2024-05-23
- Publication Date
- 2026-07-17
Smart Images
Figure CN121011074B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, electronic device, and storage medium for detecting abnormal road conditions. Background Technology
[0002] With the continuous development of vehicle manufacturing technology and autonomous driving technology, vehicles have become a common means of transportation, and driving safety has become an important issue.
[0003] Tires, as a crucial component of a vehicle, directly impact driving comfort, stability, and safety. During driving, if a tire is punctured by sharp objects such as nails or broken glass, it can cause varying degrees of damage, or even a tire blowout, leading to an accident. Therefore, situations such as intentionally throwing sharp objects onto the road or goods scattering during transport, resulting in sharp objects on the road, significantly increase the risk of accidents. However, currently, there is a lack of automated detection solutions for these abnormal road conditions. Summary of the Invention
[0004] This invention provides a method, device, electronic equipment, and storage medium for detecting abnormal road conditions, in order to automatically detect abnormal road conditions such as sharp objects being maliciously thrown onto the road or sharp objects appearing on the road due to goods being scattered during transportation, thereby reducing safety hazards.
[0005] In a first aspect, embodiments of the present invention provide a method for detecting abnormal road conditions, the method comprising:
[0006] Determine the location of the tire failure on the vehicle with the tire failure.
[0007] Determine the tire failure area based on the tire failure locations of at least two vehicles with tire failures;
[0008] Determine the distribution of the number of vehicles with tire failures in the tire failure area at different times, and determine whether the tire failure area is an area with abnormal road conditions based on the distribution of the number of vehicles with tire failures.
[0009] Secondly, embodiments of the present invention also provide a road condition anomaly detection device, the device comprising:
[0010] The tire failure location determination module is used to determine the location of the tire failure on a vehicle.
[0011] The tire failure area determination module is used to determine the tire failure area based on the tire failure locations of at least two vehicles with tire failures.
[0012] The road condition anomaly judgment module is used to determine the distribution of the number of vehicles with tire malfunctions in the tire malfunction area at different times, and to determine whether the tire malfunction area is a road condition anomaly area based on the distribution of the number of vehicles with tire malfunctions.
[0013] Thirdly, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the road condition anomaly detection method as described in any of the embodiments of the present invention.
[0014] Fourthly, embodiments of the present invention also provide a storage medium for storing computer-executable instructions, which, when executed by a computer processor, are used to perform the road condition anomaly detection method as described in any of the embodiments of the present invention.
[0015] The technical solution of this invention determines the location of a tire failure by identifying the location of the tire failure on a vehicle, and by combining the locations of multiple tire failures, identifies a tire failure area. Furthermore, it determines whether the tire failure area represents an abnormal road condition based on the distribution of the number of vehicles with tire failures within that area at different times. This invention can automatically detect abnormal road conditions such as malicious acts of throwing sharp objects onto the road or sharp objects appearing on the road due to goods scattering during transportation, reducing safety hazards and ensuring driving safety.
[0016] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a flowchart of a road condition anomaly detection method provided in Embodiment 1 of the present invention;
[0019] Figure 2 This is a schematic diagram of a tire pressure change curve provided in Embodiment 1 of the present invention;
[0020] Figure 3 This is a schematic diagram illustrating the distribution of the number of vehicles with tire failures according to Embodiment 1 of the present invention;
[0021] Figure 4This is a flowchart of a road condition anomaly detection method provided in Embodiment 2 of the present invention;
[0022] Figure 5 This is a schematic diagram of the structure of a road condition anomaly detection device provided in Embodiment 3 of the present invention;
[0023] Figure 6 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation
[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.
[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices. In the embodiments of this application, certain software, components, models, and other existing industry solutions may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solutions of this application, and do not imply that the applicant has already used or necessarily used such solutions.
[0026] The acquisition, transmission, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.
[0027] Example 1
[0028] Figure 1The flowchart of a road condition anomaly detection method is provided in Embodiment 1 of the present invention. This embodiment is applicable to the automatic detection of road condition anomalies such as sharp objects being maliciously thrown on the road or sharp objects appearing on the road due to the scattering of goods during transportation. The method can be executed by a road condition anomaly detection device, which can be implemented in hardware and / or software and can be configured in a server.
[0029] like Figure 1 As shown, the method includes:
[0030] S110. Determine the location of the tire failure on the vehicle with the tire failure.
[0031] In this context, a tire-fault vehicle refers to a vehicle with at least one tire experiencing abnormal tire pressure. In this embodiment, a tire pressure monitoring system (TPMS) can be used to determine if a vehicle's tires have abnormal pressure. Specifically, the TPMS continuously monitors the tire pressure of each tire. If the tire pressure of a particular tire is lower than a preset tire pressure threshold, the TPMS will issue a tire pressure abnormality alert and send the alert to the server. Alternatively, a tire pressure abnormality can be detected by monitoring a tire pressure abnormality report on the vehicle's central control system or the user interface of the client application.
[0032] The tire failure location refers to the point where the tire pressure of the faulty tire on a vehicle begins to become abnormal. Understandably, if a tire is punctured by a sharp object such as a nail or broken glass during driving, the tire pressure will gradually decrease. As the tire pressure decreases, the impact on vehicle operation becomes increasingly significant until the tire pressure monitoring system issues a warning or the driver notices the abnormal pressure. Therefore, the location where the tire is punctured by a sharp object is the tire failure location. This location is some distance from the point where the vehicle is confirmed to have a tire failure (i.e., the tire pressure is confirmed to be abnormal), and there is a time difference between the time the tire is punctured and the time the vehicle is confirmed to have a tire failure. In this embodiment, it is necessary to deduce the time when the tire pressure of the faulty tire began to become abnormal based on the time the vehicle was confirmed to have a tire failure, thereby deducing the tire failure location.
[0033] In an optional embodiment, an empirical value for the distance between the location of the vehicle with the confirmed tire pressure abnormality and the location of the tire failure can be set based on the distance from historical abnormal road sections to the location of the vehicle with the confirmed tire pressure abnormality. When a vehicle with a tire failure is identified, the location of the tire failure is determined based on the vehicle's current location, trajectory, and the empirical distance value. Furthermore, different empirical distance values can be set for different types of vehicles. Vehicle types can be categorized based on data such as tire material and vehicle weight, but this embodiment does not impose such limitations.
[0034] In another optional embodiment, a tire pressure anomaly simulation can be performed in advance, and the simulated distance from the simulated location where the tire is punctured by a sharp object to the tire-faulted vehicle in the tire pressure anomaly simulation can be determined, thus establishing an empirical distance value. Similarly, when a tire-faulted vehicle is identified, the location of the tire fault is determined based on the vehicle's current position, trajectory, and the empirical distance value. Furthermore, tire pressure anomaly simulations can be performed separately for different types of vehicles, and separate empirical distance values can be set for each type; this will not be elaborated further in this embodiment.
[0035] In another optional embodiment, a tire pressure anomaly simulation can be performed in advance to determine the tire pressure change over time from when a sharp object punctures the tire until the tire pressure anomaly is detected. When a vehicle with a tire malfunction is identified, the time when the tire was punctured is deduced from the vehicle's current tire pressure and the simulated tire pressure change over time. Based on this time and the vehicle's trajectory, the location of the vehicle at that time is determined as the location of the tire malfunction. Similarly, tire pressure anomaly simulations can be performed separately for different types of vehicles, and the tire pressure change over time can be determined separately for each type; this will not be elaborated further in this embodiment.
[0036] In this embodiment, by analyzing the location of the tire failure of the vehicle with tire failure, it is easier to determine the abnormal road conditions in the future. It is understood that if the tire failure locations of multiple vehicles with tire failure are concentrated in a certain area, there may be abnormal situations such as the throwing of sharp objects or the spilling of sharp objects in that area.
[0037] Furthermore, S110 may include:
[0038] A1. Determine the vehicle information, movement trajectory, and current tire pressure of the vehicle with the tire malfunction;
[0039] A2. Based on the vehicle information, determine the tire pressure change curve that matches the vehicle with the tire malfunction;
[0040] A3. Based on the tire pressure change curve and the current tire pressure, determine the tire failure time that matches the vehicle with the tire failure.
[0041] A4. Based on the tire failure time, the movement trajectory, and road network data, determine the location of the tire failure of the vehicle.
[0042] The vehicle information may include tire size, tire material, and vehicle weight, but this embodiment does not limit the specific content of the vehicle information. It is understood that after a tire is punctured by a sharp object, factors such as tire size, tire material, and vehicle weight will affect the rate of change in tire pressure.
[0043] The motion trajectory can be represented by multiple consecutive trajectory points. The position of each trajectory point of the vehicle can be determined by one or a combination of the following methods: uploading the position of the vehicle trajectory points to the server through a vehicle positioning system; uploading the physical address of the network card of the driver's or passenger's mobile terminal as the position of the vehicle trajectory points to the server; or using a camera installed on the road to monitor the area within its field of view in real time, and when the vehicle is detected, determining the position of the vehicle trajectory points based on the image recognition algorithm and the position of the camera, and uploading the position of the vehicle trajectory points to the server. However, this embodiment does not limit the method of confirming the trajectory points. The motion trajectory can be represented in tabular form, recording information such as vehicle identification, the position of the trajectory points (which can be represented by latitude and longitude), the collection time corresponding to the trajectory points, and the method of collection of the trajectory point positions. Table 1 provides an example of a motion trajectory table:
[0044] Table 1
[0045]
[0046] Current tire pressure refers to the tire pressure of the faulty tire when the vehicle with a tire malfunction is confirmed to have a tire abnormality. Current tire pressure can be detected by a tire pressure monitoring device and sent to the server.
[0047] A tire pressure variation curve is a curve showing how the tire pressure of a faulty tire changes over time after it has been punctured by a sharp object. Tire pressure variation curves can be determined by: performing a pre-simulated tire pressure anomaly to determine the tire pressure changes over time from the moment the tire is punctured until the anomaly is detected, and then plotting the tire pressure variation curves based on the tire pressure at each time point. Furthermore, tire pressure variation curves can be determined separately for vehicles with different tire sizes, tire materials, and weights. Figure 2 A schematic diagram of tire pressure variation curves is provided, wherein the yellow curve represents the tire pressure variation curve of a vehicle with material A1 and weight B1, the green curve represents the tire pressure variation curve of a vehicle with material A2 and weight B2, and the red curve represents the tire pressure variation curve of a vehicle with material A2 and weight B3.
[0048] Tire failure time is the time when a tire punctures a sharp object and the tire pressure of the faulty tire begins to become abnormal. Road network data refers to a collection of various information and data related to the road network, including basic geometric information such as road location, direction, length, and width, road connectivity, and traffic facility information. Similarly, road network data can be represented in tabular form. This embodiment does not limit the specific content and presentation format of the road network data.
[0049] In this embodiment, after determining the vehicle information of the vehicle with the tire malfunction, the tire pressure change curve matching the vehicle can be determined. Then, based on the current tire pressure and the tire pressure change curve of the vehicle with the tire malfunction, the time period that the faulty tire took to go from normal tire pressure to the current tire pressure can be determined. Based on the current time and the aforementioned time period, the tire malfunction time can be calculated.
[0050] In an optional embodiment, the location of the tire failure is determined based on the tire failure time, movement trajectory, and road network data. A movement trajectory curve of the vehicle with tire failure is plotted on a map based on the movement trajectory and road network data, and the location and data collection time of each trajectory point are marked on the trajectory curve. By using the tire failure time and the data collection time of each trajectory point, the distance between the two trajectory points where the tire failure occurred is determined. Then, the ratio between the difference between the tire failure time and the data collection time of the preceding trajectory point, and the ratio between the data collection time of the subsequent trajectory point and the tire failure time, is determined. Based on this ratio and the road distance between the two trajectory points, the specific location of the tire failure is determined.
[0051] For example, if the tire failure time is determined to be 15:04:00, the acquisition time of trajectory point A is 15:03:45, the acquisition time of trajectory point B is 15:04:45, and the road distance between trajectory point A and trajectory point B is 100m, then the tire failure location can be determined to be 25m after trajectory point A. The specific latitude and longitude or coordinates of the tire failure location can be determined based on the location information of trajectory point A.
[0052] In another optional embodiment, the trajectory curve of the vehicle with tire failure can be plotted on a map based on the movement trajectory and road network data. The speed of the vehicle with tire failure is determined. This speed can be determined based on changes in position information from the vehicle positioning system; it can also be measured by radar speedometers, laser speedometers, or other equipment installed on the road; alternatively, it can be obtained by photographing vehicles within the field of view using roadside cameras, and image processing of the captured images of the vehicle with tire failure using image recognition technology; or it can be obtained by real-time recording and transmission of vehicle speed through the vehicle control system. This embodiment does not limit the method of obtaining the speed of the vehicle with tire failure. This speed can be either the average speed of the vehicle with tire failure from the time of tire failure to the current time, or it can be the real-time speed. This embodiment does not limit this. Based on the speed of the vehicle with tire failure and the time period from the time of tire failure to the current time, the distance traveled by the vehicle with tire failure from the time of tire failure to the current time is calculated. Based on the trajectory curve of the vehicle with tire failure, the current position of the vehicle with tire failure, and the distance traveled by the vehicle with tire failure from the time of tire failure to the current time, the specific location of the tire failure is marked on the trajectory curve of the vehicle with tire failure.
[0053] In this embodiment, based on the tire pressure changes of the vehicle with tire failure, combined with the vehicle's movement trajectory and road network data, the location of the incident when the tire was punctured by a sharp object can be analyzed. This allows for the accurate location of the vehicle with tire failure at the time of the tire failure, enabling precise localization of the puncture point. This facilitates subsequent spatial location analysis of tire failure locations for multiple vehicles.
[0054] Furthermore, A4 can include:
[0055] A41. Based on the tire failure time and the movement trajectory, determine the road segment where the vehicle with the tire failure was located at the time of the tire failure;
[0056] A42. Determine the average speed and ineffective driving time of the vehicle with the tire malfunction on the road segment;
[0057] A43. Based on the road segment, the average driving speed, the invalid driving time, and the road network data, determine the location of the tire failure of the vehicle with the tire failure.
[0058] The location of a vehicle with a tire failure at the time of the failure can be determined based on the time of the tire failure and the data collection time of each trajectory point in the vehicle's trajectory. Specifically, the location of the vehicle at the time of the tire failure is determined by identifying the time interval between the data collection times of two trajectory points.
[0059] Average driving speed refers to the average speed of the vehicle with tire failure on that road segment. The method for obtaining driving speed has been explained in the previous embodiments and will not be repeated here. Ineffective driving time refers to the time the vehicle with tire failure is paused during driving, such as the time spent by the vehicle with tire failure parking on the side of the road, or the time spent by the vehicle with tire failure waiting at a traffic light at an intersection. Ineffective driving time can be determined based on the vehicle control system or road network data. This embodiment does not limit the specific content and determination method of ineffective driving time.
[0060] Based on road segment data, average driving speed, ineffective driving time, and road network data, the location of the tire failure of a vehicle is determined. Specifically, the ineffective driving time within that road segment prior to the tire failure time can be identified. The difference between the tire failure time and the acquisition time of the preceding trajectory point is calculated. This difference is subtracted from the ineffective driving time before the tire failure time, and multiplied by the average driving speed to obtain the distance already traveled by the vehicle within that road segment at the time of the tire failure. Based on this traveled distance and road network data, the exact location of the tire failure within that road segment is determined.
[0061] S120. Determine the tire failure area based on the tire failure locations of at least two vehicles with tire failures.
[0062] At least two vehicles with tire failures can be identified within a preset time period, such as within 10 days. Understandably, if the time interval between identifying two vehicles with tire failures is too long, even if the locations of the tire failures overlap or are close together, the probability of abnormal tire pressure due to road conditions at those two locations is low. Therefore, a time period needs to be set to limit the time interval between vehicles with tire failures identified at spatial locations.
[0063] Tire failure area refers to the area where tires are frequently punctured. Tire failure area can be represented by a road segment or a circular area. This embodiment does not limit the representation of tire failure area.
[0064] Understandably, once the location of a tire failure is determined, the abnormal tire pressure may not necessarily be due to abnormal road conditions (such as someone throwing sharp objects or objects scattering). Therefore, it is necessary to conduct spatial analysis based on the locations of multiple tire failures to determine if there are high-incidence areas of abnormal tire pressure, and then designate these high-incidence areas as the tire failure zones.
[0065] In an optional embodiment, the tire failure locations of multiple vehicles with tire failures can be clustered. Clustering can be based on the distance between the tire failure locations, but this embodiment does not limit the clustering method. Based on the obtained clustering results, a minimum circumcircle is determined for each tire failure location belonging to the same cluster. This minimum circumcircle area can be directly used as the tire failure area, or the road segment area covered by the minimum circumcircle can be used as the tire failure area based on the minimum circumcircle and road network data; this embodiment does not limit this approach.
[0066] In another optional embodiment, a circle can be drawn with the tire failure location as the center and a preset distance as the radius. This circular area serves as the extended area corresponding to the tire failure location. For extended areas of multiple tire failure vehicles, if the number of tire failure vehicles among the intersecting extended areas is large, the union of the intersecting extended areas can be taken, and this union can be directly used as the tire failure area, or the road segment area covered by the union can be used as the tire failure area. This setting can avoid missing tire failure vehicles when subsequently determining the number of tire failure vehicles in the tire failure area. Alternatively, the intersection of the intersecting extended areas can be taken, and this intersection can be directly used as the tire failure area, or the road segment area covered by the intersection can be used as the tire failure area. This setting can improve the accuracy of tire failure vehicle number determination and save computational resources.
[0067] In this embodiment, by spatially aggregating the tire failure locations of multiple vehicles with tire failures, and analyzing them from a point-to-area perspective, areas with high incidence of tire abnormalities are identified.
[0068] Furthermore, S120 may include:
[0069] B1. Determine the expanded area for the tire failure locations of at least two vehicles with tire failures;
[0070] B2. If the number of intersecting expansion areas is greater than or equal to a preset first number threshold, then the intersecting area of each intersecting expansion area is taken as the tire fault area.
[0071] B3. Otherwise, the expanded area will be designated as the tire failure area.
[0072] This embodiment uses the expanded area based on the location of the tire failure as an example to illustrate the specific method for determining the tire failure area.
[0073] Specifically, a circle is drawn with the tire failure location as the center and a preset distance as the radius. This circular area serves as the expanded area corresponding to the tire failure location. The preset distance can be a single value, such as 20m, or multiple values, such as 20m, 30m, and 50m. When multiple preset distance values exist, the smallest preset distance is used to define the expanded area. If expanded areas greater than or equal to a preset first threshold intersect, the intersecting area is directly designated as the tire failure area. The first threshold can be set to 3, but this embodiment does not limit the specific value of the first threshold.
[0074] Furthermore, if the number of intersecting expansion areas is less than a preset first threshold, or even if there are no intersecting expansion areas, then the next preset distance is selected to redetermine the expansion area, and it is determined whether the redetermined expansion area meets the conditions, until the tire fault area can be determined, or when the preset distance with the largest value is selected to determine the expansion area, the redetermined expansion area still does not meet the conditions.
[0075] Understandably, sharp objects on the road may be distributed in long strips, and in this case, there may be a certain distance between the various extended areas. Therefore, extended areas that do not intersect with other extended areas, or whose number of intersections does not meet the above conditions, are still considered tire failure areas for subsequent judgment of the distribution of tire failure vehicles. The advantage of this setting is that it can avoid missing tire failure areas and improve the accuracy of identifying abnormal road conditions.
[0076] S130. Determine the distribution of the number of vehicles with tire malfunctions in the tire malfunction area at different times, and determine whether the tire malfunction area is an area with abnormal road conditions based on the distribution of the number of vehicles with tire malfunctions.
[0077] The distribution of the number of vehicles with tire failures refers to the change in the number of vehicles with tire failures corresponding to the tire failure area over time. The tire failure area refers to the area where the expansion area intersects with the tire failure area. In this embodiment, the abnormal road condition area specifically refers to the road area where sharp objects appear on the road due to reasons such as malicious throwing of sharp objects or goods scattering during transportation, causing tire failures or abnormal tire pressure in passing vehicles.
[0078] In this embodiment, the distribution of the number of vehicles with tire failures can be in the form of a line graph, a histogram, or a table; this embodiment does not impose any restrictions on this. Figure 3 A schematic diagram illustrating the distribution of the number of vehicles with tire failures is provided, such as... Figure 3As shown, the horizontal axis represents time, and the vertical axis represents the number of vehicles with tire failures at different times. The distribution of the number of vehicles with tire failures is a line graph obtained by connecting the number of vehicles with tire failures at different times.
[0079] In an optional embodiment, the tire failure area is determined to be an abnormal road condition area based on the distribution of the number of vehicles with tire failures. This can be determined by checking whether the number of vehicles with tire failures within a preset time period is greater than or equal to a threshold number of vehicles with tire failures. If so, the tire failure area is considered to be an abnormal road condition area.
[0080] In another optional embodiment, determining whether a tire-fault area is an area of abnormal road conditions can also be based on the distribution of the number of vehicles with tire malfunctions over time. If it is determined that the distribution of the number of vehicles with tire malfunctions generally shows an increasing trend over time, or, although the distribution of the number of vehicles with tire malfunctions shows both increasing and decreasing trends over time, but the number of times the increasing trend occurs is greater than a threshold, then the tire-fault area is considered an area of abnormal road conditions.
[0081] In this embodiment, after determining the tire failure area, the distribution of the number of vehicles with tire failures in that area is analyzed. This setting can improve the accuracy of road condition anomaly judgment and avoid the influence of accidental factors on road condition anomaly judgment.
[0082] The technical solution of this invention determines the location of a tire failure by identifying the location of the tire failure on a vehicle, and by combining the locations of multiple tire failures, identifies a tire failure area. Furthermore, it determines whether the tire failure area represents an abnormal road condition based on the distribution of the number of vehicles with tire failures within that area at different times. This invention can automatically detect abnormal road conditions such as malicious acts of throwing sharp objects onto the road or sharp objects appearing on the road due to goods scattering during transportation, reducing safety hazards and ensuring driving safety.
[0083] Example 2
[0084] Figure 4 This is a flowchart of a road condition anomaly detection method provided in Embodiment 2 of the present invention. Based on the above embodiments, the present invention further specifies the process of determining whether a tire fault area is a road condition anomaly area.
[0085] like Figure 4 As shown, the method includes:
[0086] S210. Determine the location of the tire failure on the vehicle with the tire failure.
[0087] S220. Determine the tire failure area based on the tire failure locations of at least two vehicles with tire failures.
[0088] The process of determining the location of the tire failure of a vehicle, and the process of determining the tire failure area based on multiple tire failure locations, have been described in the above embodiments, and will not be repeated here.
[0089] S230. Determine the distribution of the number of vehicles with tire failures in the tire failure area at different times.
[0090] The form and determination process of the distribution of the number of vehicles with tire failures have been described in the above embodiments, and will not be repeated in this embodiment.
[0091] S240. Determine the risk coefficient of the tire failure area based on the distance between the tire repair location matching the tire failure area and the tire failure area.
[0092] The risk factor represents the probability that sharp objects may have been deliberately scattered in the area where the tire malfunctioned. Understandably, if the tire repair location is close to the tire malfunction area, the probability of sharp objects being deliberately scattered in that area is higher.
[0093] In an optional embodiment, different risk coefficients can be set for different distance ranges between the tire repair location and the tire failure area. For example, the risk coefficient is set to 0.9 for a distance of 0-100m; 0.8 for a distance of 100-500m; 0.7 for a distance of 500-1000m; and 0.6 for a distance of 1000-2000m. The risk coefficient for the tire failure area is determined based on the distance range between the nearest tire repair location and the tire failure area.
[0094] In another optional embodiment, different initial risk coefficient values can be set for different distance intervals between the tire repair location and the tire failure area. The method for setting the initial risk coefficient values is the same as the example above. The initial risk coefficient value for the tire failure area is determined based on the distance interval between the nearest tire repair location and the tire failure area. Different weighting coefficients are set for the number of different tire repair locations within this distance interval. For example, if there is only one tire repair location within the distance interval, the weighting coefficient is 1; if there are two, the weighting coefficient is 1.1; and if there are three or more, the weighting coefficient is 1.2. The product of the initial risk coefficient value and the weighting coefficient is used as the risk coefficient for the tire failure area.
[0095] In this embodiment, the distribution of the number of vehicles with tire failures is combined with the risk coefficient to jointly determine whether the tire failure area belongs to the area where the tire failure and abnormal tire pressure are caused by the spilling of sharp objects.
[0096] S250. Determine whether the distribution of the number of vehicles with tire malfunctions meets the condition of phased concentration. If yes, proceed to S260; otherwise, proceed to S2110.
[0097] The "phased concentration condition" refers to the situation where the number of vehicles with tire failures is concentrated in different phases at different times. "Concentration" can mean that the number of vehicles with tire failures exceeds a certain threshold.
[0098] In an optional embodiment, determining whether the distribution of vehicles with tire malfunctions meets the condition of phased density can be achieved by counting the number of vehicles with tire malfunctions for each minimum time unit. If the number of vehicles with tire malfunctions is greater than or equal to a preset threshold, a count is performed. If the count for a statistical time period is greater than or equal to a certain number of counts threshold, then the distribution of vehicles with tire malfunctions is determined to meet the condition of phased density. For example, the number of vehicles with tire malfunctions can be determined using days as the minimum time unit. If the number of vehicles with tire malfunctions is greater than or equal to the threshold, the count is incremented by 1. A statistical time period can be set to 10 days. Based on the final count and the number of counts threshold corresponding to 10 days, it is determined whether the distribution of vehicles with tire malfunctions meets the condition of phased density.
[0099] In another alternative embodiment, with Figure 3 Taking the provided line chart of tire failure vehicle distribution as an example, the distribution of tire failure vehicles meets the condition of phased density, which can also be defined as the number of peaks in the line chart exceeding a preset peak number threshold, for example, a peak number threshold of 3. Figure 3 As can be seen, there are 4 peaks in the line chart, therefore, Figure 3 The distribution of the number of vehicles with tire failures meets the condition of being concentrated in a certain phase.
[0100] In this embodiment, when the distribution of the number of vehicles with tire failures meets the condition of "phased density," it is considered that the tire failure area experiences high-frequency and phased occurrences of abnormal tire pressure. In this case, it is necessary to further consider the risk factor to determine whether the abnormal tire pressure is due to human error or an isolated incident.
[0101] Furthermore, if the distribution of vehicles with tire failures does not meet the criteria for a periodic high density, it can be assumed that although some tire failures and abnormal tire pressures have occurred in the tire failure area, the probability of these being due to abnormal road conditions is low. In this case, the tire failure area can be continuously monitored, and the criteria for a periodic high density can be reassessed based on the subsequent tire failures in that area.
[0102] S260. Determine whether the risk coefficient of the tire failure area is greater than or equal to the preset risk coefficient threshold. If yes, execute S270; otherwise, execute S2100.
[0103] In this embodiment, under the premise that the distribution of the number of vehicles with tire failures in the tire failure area meets the condition of phased density, the risk coefficient of the tire failure area is further judged. The advantage of this setting is that it can not only confirm that the tire failure area belongs to the abnormal road condition, but also analyze the causes of the abnormal road condition, so as to take corresponding measures to eliminate the factors causing the abnormal road condition.
[0104] S270. The tire failure area is determined to be a first type of abnormal road condition area.
[0105] The first type is used to indicate that the cause of the abnormality in the road condition area is human-caused.
[0106] In this embodiment, if the risk coefficient of the tire failure area is greater than or equal to the preset risk coefficient threshold, it is considered that the high frequency and periodic occurrence of tire failure and abnormal tire pressure (i.e., tire puncture) in the tire failure area is due to the human-caused spillage of sharp objects, and the tire failure area is identified as a road condition abnormality area caused by human factors.
[0107] Furthermore, abnormal road conditions can be eliminated by setting up barriers and clearing debris in a timely manner, thereby ensuring the driving safety of subsequent vehicles and reducing safety hazards.
[0108] S280. Based on the distribution of the number of vehicles with tire failures, determine the candidate time interval.
[0109] The number of vehicles with tire malfunctions at each time point within the candidate time interval is less than or equal to a preset second quantity threshold.
[0110] The candidate time interval is the time interval with sparse tire failures between periods of high frequency in the distribution of tire failures. When the distribution of tire failures is a line graph, the candidate time interval can be the time interval corresponding to the troughs in the line graph. Figure 3For example, t4-t5, t9-t11, and t19-t20 are all candidate time intervals. Furthermore, a time interval with a sparse number of vehicles experiencing tire failures that is closest to the current time can be selected as a candidate time interval.
[0111] Understandably, when it is determined that the tire failure area is due to road condition abnormalities caused by human error, taking the distribution of the number of vehicles with tire failures as an example as a line graph, the appearance of a new peak after the trough is because, within the time interval corresponding to the trough, there was a situation where sharp objects were deliberately scattered again. Therefore, in this embodiment, the advantage of determining the candidate time interval is that it can shorten the time interval for investigating the time point of deliberately scattering sharp objects and the perpetrator, thereby improving the investigation efficiency.
[0112] S290. Determine the time interval for which the road condition anomaly caused by human error occurred based on the first historical driving time corresponding to the vehicle with tire failure within the candidate time interval, and / or the second historical driving time corresponding to other vehicles besides the vehicle with tire failure.
[0113] Specifically, the vehicle with the earliest historical driving time within the candidate time interval whose tire malfunction occurred can be identified, and its historical driving time can be used as the first historical driving time. The vehicle with the latest historical driving time within the candidate time interval (i.e., all other vehicles besides the tire-malfunctioning vehicle) can be identified, and its historical driving time can be used as the second historical driving time. The time interval between the second historical driving time and the first historical driving time is considered the time interval for road condition anomalies caused by human error, i.e., the time interval in which sharp objects were intentionally dropped.
[0114] In this embodiment, the advantage of determining the time interval of abnormal road conditions is that it can further shorten the time interval for investigating the time point of deliberately dropping sharp objects and the perpetrator, thereby further improving efficiency.
[0115] Furthermore, facial recognition and motion analysis can be performed on video frames captured by cameras installed in the tire failure area during periods of abnormal road conditions to identify the person who dropped the sharp object.
[0116] In this embodiment, after determining that the tire failure area is a road condition abnormality caused by human factors, the time interval of deliberately scattering sharp objects can be determined based on the distribution of the number of vehicles with tire failures in the tire failure area, and the person who scattered the sharp objects can be identified, thereby preventing road condition abnormalities caused by human factors.
[0117] S2100, The tire failure area is determined to be a second type of abnormal road condition area.
[0118] The second type is used to indicate that the cause of the abnormality in the road condition area is an occasional cause.
[0119] In this embodiment, if the risk coefficient of the tire failure area is less than a preset risk coefficient threshold, then the frequent and intermittent occurrence of abnormal tire pressure (i.e., tire punctures) in the tire failure area is considered to be due to the accidental spillage of sharp objects, such as sharp objects scattered due to road maintenance in the surrounding area, or goods spilled from transport vehicles. The tire failure area is thus identified as a road condition abnormality area caused by an accidental reason.
[0120] Furthermore, the risk coefficient of the tire failure area calculated based on the tire repair location can be used as the first risk coefficient to determine the nearest repair section to the tire failure area. Then, based on the distance between the repair section and the tire failure area, a second risk coefficient for the tire failure area can be determined. The process of determining the second risk coefficient of the tire failure area based on the distance between the repair section and the tire failure area is similar to the process of calculating the risk coefficient of the tire failure area based on the tire repair location in the above embodiment, and will not be repeated here.
[0121] If the distribution of the number of vehicles with tire failures meets the condition of phased density, and the second risk coefficient of the tire failure area is greater than or equal to the preset risk coefficient threshold, then the tire failure area is determined to be an area of abnormal road conditions caused by road maintenance.
[0122] Furthermore, abnormal road conditions can be eliminated by setting up barriers and clearing debris in a timely manner, thereby ensuring the driving safety of subsequent vehicles and reducing safety hazards.
[0123] S2110, End.
[0124] The technical solution of this embodiment determines the time of tire failure of a vehicle based on tire pressure changes, and determines the location of the tire failure by combining movement trajectory and road network data. Spatially, the locations of multiple tire failures are aggregated and analyzed from a point-to-area perspective to obtain the tire failure area. Based on the distribution of the number of vehicles with tire failures in the tire failure area at different times, when tire failures occur with a high frequency in the tire failure area, the type of road condition anomaly in the surrounding environmental factors is further determined: if the risk coefficient of the tire repair locations around the tire failure area is high, it is considered to be a road condition anomaly caused by human factors; if the risk coefficient of the tire repair locations is low, or the risk coefficient of the repair section is high, it is considered to be a road condition anomaly caused by an isolated incident. Based on the determination that the road condition anomaly is caused by human factors, further, by identifying candidate time intervals with sparse distribution of the number of vehicles with tire failures, the time interval for road condition anomalies caused by the intentional spilling of sharp objects is determined, shortening the investigation cycle for such human-caused causes, and identifying the perpetrator of the intentional spilling of sharp objects based on the road condition anomaly time interval.
[0125] Example 3
[0126] Figure 5 This is a schematic diagram of a road condition anomaly detection device provided in Embodiment 3 of the present invention. Figure 5 As shown, the device includes: a tire failure location determination module 310, a tire failure area determination module 320, and a road condition anomaly judgment module 330, wherein:
[0127] Tire failure location determination module 310 is used to determine the location of the tire failure of a vehicle with a tire failure.
[0128] The tire failure area determination module 320 is used to determine the tire failure area based on the tire failure locations of at least two vehicles with tire failures.
[0129] The road condition anomaly judgment module 330 is used to determine the distribution of the number of vehicles with tire malfunctions in the tire malfunction area at different times, and to determine whether the tire malfunction area is a road condition anomaly area based on the distribution of the number of vehicles with tire malfunctions.
[0130] The technical solution of this invention determines the location of a tire failure by identifying the location of the tire failure on a vehicle, and by combining the locations of multiple tire failures, identifies a tire failure area. Furthermore, it determines whether the tire failure area represents an abnormal road condition based on the distribution of the number of vehicles with tire failures within that area at different times. This invention can automatically detect abnormal road conditions such as malicious acts of throwing sharp objects onto the road or sharp objects appearing on the road due to goods scattering during transportation, reducing safety hazards and ensuring driving safety.
[0131] Based on the above embodiments, the tire failure location determination module 310 includes:
[0132] The vehicle data determination unit is used to determine the vehicle information, movement trajectory, and current tire pressure of the faulty tire of the vehicle with the tire failure.
[0133] The tire pressure change curve determination unit is used to determine a tire pressure change curve that matches the tire failure vehicle based on the vehicle information.
[0134] The tire failure time determination unit is used to determine the tire failure time matching the vehicle with the tire failure based on the tire pressure change curve and the current tire pressure.
[0135] The tire failure location determination unit is used to determine the tire failure location of the vehicle based on the tire failure time, the movement trajectory, and road network data.
[0136] Based on the above embodiments, the tire failure location determination unit is specifically used for:
[0137] Based on the tire failure time and the movement trajectory, determine the road segment where the vehicle with the tire failure was located at the time of the tire failure;
[0138] Determine the average speed and ineffective driving time of the vehicle with the tire malfunction on the road segment;
[0139] The location of the tire failure of the vehicle is determined based on the road segment, the average driving speed, the invalid driving time, and the road network data.
[0140] Based on the above embodiments, the tire failure area determination module 320 includes:
[0141] An expanded area determination unit is used to determine the expanded area of the tire failure location for at least two vehicles with tire failures.
[0142] The first tire fault area determination unit is used to determine the intersection area of each intersecting expansion area as the tire fault area if the number of intersecting expansion areas is greater than or equal to a preset first number threshold.
[0143] The second tire failure area determination unit is used to define the expanded area as the tire failure area.
[0144] Based on the above embodiments, the road condition anomaly judgment module 330 includes:
[0145] The risk coefficient determination unit is used to determine the risk coefficient of the tire failure area based on the distance between the tire repair location matching the tire failure area and the tire failure area.
[0146] The first type of abnormal road condition area judgment unit is used to determine the tire failure area as a first type of abnormal road condition area if it is determined that the distribution of the number of vehicles with tire failure meets the condition of phased density, and the risk coefficient of the tire failure area is greater than or equal to a preset risk coefficient threshold.
[0147] The first type is used to indicate that the cause of the abnormality in the road condition area is human-caused.
[0148] Based on the above embodiments, the road condition anomaly judgment module 330 further includes:
[0149] The second type of abnormal road condition area judgment unit is used to determine the tire failure area as a second type of abnormal road condition area if it is determined that the distribution of the number of vehicles with tire failure meets the condition of staged density and the risk coefficient of the tire failure area is less than a preset risk coefficient threshold.
[0150] The second type is used to indicate that the cause of the abnormality in the road condition area is an occasional cause.
[0151] Based on the above embodiments, the device further includes:
[0152] The candidate time interval determination module is used to determine the candidate time interval based on the distribution of the number of vehicles with tire failures.
[0153] Among them, the number of vehicles with tire failures corresponding to each time within the candidate time interval is less than or equal to a preset second quantity threshold.
[0154] The abnormal road condition time interval determination module is used to determine the abnormal road condition time interval caused by the human factor based on the first historical driving time corresponding to the tire failure vehicle within the candidate time interval, and / or the second historical driving time corresponding to other vehicles besides the tire failure vehicle.
[0155] The road condition anomaly detection device provided in this embodiment of the invention can execute the road condition anomaly detection method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution.
[0156] Example 4
[0157] Figure 6A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.
[0158] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.
[0159] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.
[0160] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as road condition anomaly detection methods.
[0161] In some embodiments, the road condition anomaly detection method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the road condition anomaly detection method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the road condition anomaly detection method by any other suitable means (e.g., by means of firmware).
[0162] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.
[0163] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.
[0164] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0165] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).
[0166] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.
[0167] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.
[0168] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.
[0169] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.
Claims
1. A method for detecting abnormal road conditions, characterized in that, include: The location of the tire failure is determined by the point where the tire pressure of the faulty tire begins to become abnormal. There is a certain distance between the location of the tire failure and the point where the vehicle was identified as having a tire failure. The location of the tire failure is determined by working backward from the time when the tire failure was identified. Determine the tire failure area based on the tire failure locations of at least two vehicles with tire failures; Determine the distribution of the number of vehicles with tire malfunctions in the tire malfunction area at different times, and based on the distribution of the number of vehicles with tire malfunctions, determine whether the tire malfunction area is an area with abnormal road conditions, including: The risk factor of the tire failure area is determined based on the distance between the tire repair location matching the tire failure area and the tire failure area. If it is determined that the distribution of the number of vehicles with tire failures meets the condition of phased density, and the risk coefficient of the tire failure area is greater than or equal to a preset risk coefficient threshold, then the tire failure area is determined to be a first type of abnormal road condition area. The first type is used to indicate that the cause of the abnormality in the road condition area is human-caused.
2. The method according to claim 1, characterized in that, Determine the location of the tire failure on the vehicle, including: Determine the vehicle information, movement trajectory, and current tire pressure of the vehicle with the tire failure; Based on the vehicle information, determine the tire pressure change curve that matches the vehicle with the tire failure; Based on the tire pressure change curve and the current tire pressure, determine the tire failure time that matches the vehicle with the tire failure. Based on the tire failure time, the movement trajectory, and road network data, the location of the tire failure of the vehicle is determined.
3. The method according to claim 2, characterized in that, Based on the tire failure time, the movement trajectory, and road network data, the location of the tire failure of the vehicle is determined, including: Based on the tire failure time and the movement trajectory, determine the road segment where the vehicle with the tire failure was located at the time of the tire failure; Determine the average speed and ineffective driving time of the vehicle with the tire malfunction on the road segment; The location of the tire failure of the vehicle is determined based on the road segment, the average driving speed, the invalid driving time, and the road network data.
4. The method according to claim 1, characterized in that, Based on the locations of tire failures from at least two vehicles with tire failures, determine the tire failure area, including: Identify an expanded area for the tire failure locations of at least two vehicles with tire failures. If the number of intersecting expansion areas is greater than or equal to a preset first number threshold, then the intersecting area of each intersecting expansion area is taken as the tire fault area. Otherwise, the expanded area will be considered as the tire failure area.
5. The method according to claim 1, characterized in that, Based on the distribution of the number of vehicles with tire malfunctions, determining whether the tire malfunction area is an area with abnormal road conditions also includes: If it is determined that the distribution of the number of vehicles with tire failures meets the condition of phased density, and the risk coefficient of the tire failure area is less than the preset risk coefficient threshold, then the tire failure area is determined to be a second type of abnormal road condition area. The second type is used to indicate that the cause of the abnormality in the road condition area is an occasional cause.
6. The method according to claim 1, characterized in that, After determining that the tire failure area is a first-type road condition abnormality area, the following steps are also included: Based on the distribution of the number of vehicles with tire failures, candidate time intervals are determined; Among them, the number of vehicles with tire failures corresponding to each time within the candidate time interval is less than or equal to a preset second quantity threshold. The time interval for the road condition anomaly caused by human error is determined based on the first historical driving time corresponding to the vehicle with tire failure within the candidate time interval, and / or the second historical driving time corresponding to other vehicles besides the vehicle with tire failure.
7. A road condition anomaly detection device, characterized in that, include: The tire failure location determination module is used to determine the location of the tire failure of a vehicle. The tire failure location refers to the point where the tire pressure of the faulty tire of the vehicle begins to become abnormal. There is a certain distance between the tire failure location and the point where the vehicle is determined to have a tire failure. The tire failure location is obtained by reverse calculation based on the time when the tire failure was determined. The tire failure area determination module is used to determine the tire failure area based on the tire failure locations of at least two vehicles with tire failures. The road condition anomaly judgment module is used to determine the distribution of the number of vehicles with tire failures in the tire failure area at different times, and to determine whether the tire failure area is a road condition anomaly area based on the distribution of the number of vehicles with tire failures. The road condition anomaly detection module includes: The risk coefficient determination unit is used to determine the risk coefficient of the tire failure area based on the distance between the tire repair location matching the tire failure area and the tire failure area. The first type of abnormal road condition area judgment unit is used to determine the tire failure area as a first type of abnormal road condition area if it is determined that the distribution of the number of vehicles with tire failure meets the condition of phased density, and the risk coefficient of the tire failure area is greater than or equal to a preset risk coefficient threshold. The first type is used to indicate that the cause of the abnormality in the road condition area is human-caused.
8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the road condition anomaly detection method as described in any one of claims 1-6.
9. A storage medium for storing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the road condition anomaly detection method as described in any one of claims 1-6.