Data processing method and device, computer equipment, storage medium and program product

Abstract

The application provides a data processing method and device, computer equipment, a storage medium and a program product. The data processing method comprises: obtaining first detection data of a first entrance road section of an intersection; the first detection data comprises vehicle data of a plurality of first vehicles, the first vehicles passing through the first entrance road section and not reaching any exit road section of the intersection within a detection period; performing hierarchical road condition detection on the first entrance road section based on the first detection data to obtain L kinds of road condition information of the first entrance road section within the detection period; selecting a first exit road section from the intersection which has not formed a flow direction within the detection period; if the road condition information of the first exit road section matches the L kinds of road condition information successfully, setting the road condition information of the first exit road section as road condition information of a first flow direction formed by the first entrance road section and the first exit road section. The application can determine the road condition information of the flow direction level at the intersection.

Description

Technical Field

[0001] This application relates to the field of computer technology, and more particularly to the field of electronic map technology, specifically to a data processing method, a data processing device, a computer equipment, a computer-readable storage medium, and a computer program product. Background Technology

[0002] In electronic maps, traditional road-level traffic information is no longer sufficient for travel needs. More precise flow-level and lane-level traffic information is required to more accurately plan routes to avoid congestion and calculate travel time (ETA). However, lane-level traffic information only works in areas covered by high-precision map data based on RTK (Real-time Kinematic) technology. In most scenarios, flow-level traffic information is still the primary source. Flow-level traffic information is difficult to determine at congested intersections because vehicles entering a congested intersection cannot leave quickly. The vehicle's trajectory at the intersection is an incomplete trajectory (hereinafter referred to as the incomplete trajectory), and the flow direction cannot be determined from the incomplete trajectory, thus making it impossible to determine the flow-level traffic information at the intersection. Summary of the Invention

[0003] This application provides a data processing method, apparatus, computer equipment, storage medium, and program product that can determine traffic condition information at the flow direction level at intersections.

[0004] On one hand, embodiments of this application provide a data processing method, which includes:

[0005] Acquire first detection data of the first entrance road segment of the intersection; the intersection includes at least one entrance road segment and at least one exit road segment, and one entrance road segment and one exit road segment form a flow direction of the intersection; the first detection data includes vehicle data of multiple first vehicles, and the first vehicles have passed through the first entrance road segment within the detection cycle but have not yet reached any exit road segment of the intersection;

[0006] Based on the first detection data, the first entrance road segment is subjected to layered road condition detection to obtain the layered road condition detection results of the first entrance road segment. The layered road condition detection results are used to indicate the L types of road condition information that appear in the first entrance road segment within the detection period, where L is an integer greater than 1.

[0007] The system filters out the first exit road segment from the intersection that has not yet formed a flow direction within the detection period, and obtains the road condition information of the first exit road segment.

[0008] The road condition information of the first exit road segment is matched with L types of road condition information. If the match is successful, the road condition information of the first exit road segment is set as the road condition information of the first flow direction. The first flow direction is formed by the first entrance road segment and the first exit road segment.

[0009] Accordingly, embodiments of this application provide a data processing apparatus, which includes:

[0010] The acquisition unit is used to acquire first detection data of the first entrance road segment of the intersection; the intersection includes at least one entrance road segment and at least one exit road segment, and one entrance road segment and one exit road segment form a flow direction of the intersection; the first detection data includes vehicle data of multiple first vehicles, and the first vehicles have passed through the first entrance road segment within the detection cycle but have not yet reached any exit road segment of the intersection.

[0011] The processing unit is used to perform layered road condition detection on the first entrance road segment based on the first detection data, and obtain the layered road condition detection result of the first entrance road segment. The layered road condition detection result is used to indicate the L types of road condition information that appear in the first entrance road segment within the detection period, where L is an integer greater than 1.

[0012] The processing unit is also used to filter out the first exit road segment from the intersection that has not yet formed a flow direction within the detection cycle, and to obtain the road condition information of the first exit road segment.

[0013] The processing unit is also used to match the road condition information of the first exit road segment with L types of road condition information. If the match is successful, the road condition information of the first exit road segment is set as the road condition information of the first flow direction. The first flow direction is formed by the first entrance road segment and the first exit road segment.

[0014] In one implementation, the vehicle data of any first vehicle includes vehicle speed information of the corresponding first vehicle passing through the first entrance road segment within the detection period; the processing unit, used to perform layered road condition detection on the first entrance road segment based on the first detection data, specifically performs the following steps when obtaining the layered road condition detection result of the first entrance road segment:

[0015] Based on the distribution characteristics of vehicle speed information in the first detection data, the first detection data is processed into data layers to obtain L data layers; one of the L data layers corresponds to one type of road condition information.

[0016] The L types of road condition information corresponding to the L data layers are determined as the layered road condition detection results for the first entrance road segment.

[0017] In one implementation, the vehicle data of any first vehicle also includes vehicle time information of the corresponding first vehicle passing through the first entrance road segment within the detection period; the processing unit is used to perform data layering processing on the first detection data according to the distribution characteristics of the vehicle speed information in the first detection data, and when L data layers are obtained, it is specifically used to perform the following steps:

[0018] Based on the vehicle speed information in the first detection data, the vehicle data in the first detection data is classified into M first datasets. Each of the M first datasets corresponds to different road condition information, and M is an integer greater than 1.

[0019] Sort the M first datasets according to the number of vehicle data in each of the M first datasets;

[0020] Following a time window filtering strategy, the sorted M first datasets are filtered to obtain K second datasets. The time ranges of the K second datasets overlap, where K is an integer greater than 1 and less than or equal to M. The time range of any second dataset is determined based on the vehicle time information in the corresponding second dataset.

[0021] According to the adjacent road condition detection strategy, adjacent road condition detection is performed on K second datasets to obtain L third datasets. One of the L third datasets corresponds to a data layer. The speed difference between any two adjacent third datasets with corresponding road condition information in the L third datasets is greater than or equal to the difference threshold, and L is less than or equal to K.

[0022] In one implementation, the processing unit, used to filter the sorted M first datasets according to a time window filtering strategy to obtain K second datasets, specifically performs the following steps:

[0023] The time window is initialized using the time range corresponding to the first of the M sorted first datasets, and the second of the M sorted first datasets is used as the current dataset.

[0024] Perform intersection detection between the time window and the time range corresponding to the current dataset;

[0025] If the intersection detection result indicates that the time window intersects with the time range corresponding to the current dataset, then the time window is expanded according to the time range corresponding to the current dataset, and the current dataset is updated using the next first dataset of the current dataset;

[0026] If the intersection detection result indicates that the time window does not intersect with the time range corresponding to the current dataset, then the current dataset is deleted from the M first datasets, and the current dataset is updated with the next first dataset of the current dataset;

[0027] Repeatedly perform intersection detection and the operation triggered by the intersection detection result until the last first dataset in the M first datasets. Then, determine the remaining first datasets in the M first datasets excluding the deleted first datasets as K second datasets.

[0028] In one implementation, the processing unit, when performing adjacent road condition detection on K second datasets according to the adjacent road condition detection strategy to obtain L third datasets, specifically performs the following steps:

[0029] Based on the speed information corresponding to two adjacent second datasets of road condition information in K second datasets, calculate the first speed difference information between two adjacent second datasets of road condition information;

[0030] If the difference in the first speed information is less than the difference threshold, then the two adjacent second datasets corresponding to the road condition information are merged to obtain a new second dataset.

[0031] If the first speed difference information is greater than or equal to the difference threshold, then keep the two adjacent second datasets of the corresponding road condition information unchanged;

[0032] If there is a merged second dataset among the K second datasets, then the merged second datasets are determined as L third datasets; if there is no merged second dataset among the K second datasets, then the K second datasets are determined as L third datasets.

[0033] In one implementation, the processing unit, when classifying the vehicle data in the first detection data into M first datasets based on the vehicle speed information in the first detection data, specifically performs the following steps:

[0034] Obtain the speed thresholds corresponding to N types of road condition information, where N is an integer greater than 1;

[0035] Based on the speed thresholds corresponding to N types of road condition information and the vehicle speed information in the first detection data, the vehicle data in the first detection data is divided into N datasets, and one dataset in the N datasets corresponds to one type of road condition information among the N types of road condition information.

[0036] Select M valid datasets from N datasets and determine the M valid datasets as the M first datasets, where M is less than or equal to N; a valid dataset is a dataset from the N datasets that contains at least two vehicle data.

[0037] In one implementation, the processing unit, when matching the road condition information of the first exit road segment with L types of road condition information, specifically performs the following steps:

[0038] The traffic information for the first exit road segment is queried from the L types of traffic information to obtain the query results;

[0039] If the query results indicate that there is road condition information in type L that is the same as the road condition information of the first exit road segment, then the match is considered successful.

[0040] In one implementation, obtaining the unit is also used to perform the following steps:

[0041] Acquire second detection data for the first entrance road segment; the second detection data includes vehicle data for multiple second vehicles, which pass through the first entrance road segment and reach an exit road segment at the intersection within the detection cycle;

[0042] Based on the first entrance road segment and multiple exit road segments where second vehicles arrive, determine one or more second flow directions at the intersection;

[0043] The vehicle data of each second vehicle in the second detection data is divided into the dataset corresponding to the second flow direction to which the second vehicle belongs;

[0044] Based on the vehicle data in the dataset corresponding to each second flow direction, determine the road condition information for each second flow direction.

[0045] In one implementation, each vehicle data in the second detection data includes vehicle speed information corresponding to the second vehicle passing through the first entrance road segment; the processing unit, when determining the road condition information for each second flow direction based on the vehicle data in the dataset corresponding to each second flow direction, specifically performs the following steps:

[0046] Based on the vehicle speed information in the dataset corresponding to each second flow direction, determine the flow direction speed information for each second flow direction;

[0047] The road condition information mapped to the flow velocity information of each second flow direction is determined as the road condition information for each second flow direction.

[0048] In one implementation, the processing unit is further configured to perform the following steps:

[0049] Acquire different road condition information for the same flow direction within the detection period;

[0050] Different road condition information in the same direction of flow is fused to obtain target road condition information in the same direction of flow.

[0051] In one implementation, the processing unit is further configured to perform the following steps:

[0052] Traffic conditions for each direction of traffic at the intersection will be displayed on the electronic map.

[0053] Accordingly, embodiments of this application provide a computer device, which includes:

[0054] A processor is a tool for implementing computer programs.

[0055] A computer-readable storage medium storing a computer program adapted to be loaded by a processor and executed by the above-described data processing method.

[0056] Accordingly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when read and executed by a processor of a computer device, causes the computer device to perform the aforementioned data processing method.

[0057] Accordingly, this application provides a computer program product comprising a computer program stored in a computer-readable storage medium. A processor of a computer device reads the computer program from the computer-readable storage medium and executes the computer program, causing the computer device to perform the data processing method described above.

[0058] In this embodiment, for multiple first vehicles that have passed through the first entrance road segment of the intersection within the detection period but have not yet reached any exit road segment of the intersection, which is at least one vehicle that has not completed the intersection, the first entrance road segment can be subjected to layered road condition detection based on the vehicle data of the multiple first vehicles to obtain the layered road condition detection result of the first entrance road segment. The layered road condition detection result can be used to indicate L types of road condition information that appear in the first entrance road segment within the detection period, where L is an integer greater than 1. The first exit road segment that has not yet formed a flow direction within the detection period can be filtered from the intersection, and the road condition information of the first exit road segment can be matched with the L types of road condition information. If the match is successful, the road condition information of the first exit road segment can be set as the road condition information of the first flow direction formed by the first entrance road segment and the first exit road segment. As can be seen, this application embodiment, through layered road condition detection, can utilize the layered road condition information of vehicles that have not completed their journeys at intersections to match the road condition information of exit road segments that have not formed a flow direction within the detection period, in order to infer the flow direction to which the incomplete trajectory belongs, and the road condition information in that flow direction; thus, this application embodiment can determine the road condition information of the flow direction at the intersection, that is, the flow direction-level road condition information at the intersection, and in particular, can determine the road condition information of the flow direction to which the incomplete trajectory belongs. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a schematic diagram illustrating a method of representing roads in an electronic map according to an embodiment of this application;

[0061] Figure 2 This is a schematic diagram of the flow direction at an intersection provided in an embodiment of this application;

[0062] Figure 3 This is a schematic diagram of traffic information at an intersection provided in an embodiment of this application;

[0063] Figure 4 This is a schematic diagram of the architecture of a data processing system provided in an embodiment of this application;

[0064] Figure 5 This is a flowchart illustrating a data processing method provided in an embodiment of this application;

[0065] Figure 6 This is a schematic diagram illustrating the matching of layered road condition information with road condition information of exit road segments, provided in an embodiment of this application.

[0066] Figure 7 This is a flowchart illustrating another data processing method provided in an embodiment of this application;

[0067] Figure 8 This application is a schematic diagram of a layered road condition detection process provided in an embodiment;

[0068] Figure 9 This is a flowchart illustrating another data processing method provided in an embodiment of this application;

[0069] Figure 10 This is a schematic diagram illustrating a method for displaying traffic information according to an embodiment of this application;

[0070] Figure 11 This is a schematic diagram of different road condition information in the same direction of flow provided in an embodiment of this application;

[0071] Figure 12 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application;

[0072] Figure 13This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0073] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0074] To better understand the technical solutions provided in the embodiments of this application, the technical terms involved in the embodiments of this application are introduced here:

[0075] I. Electronic Map:

[0076] An electronic map is a map system that uses computer technology to store and view maps digitally. Electronic maps can provide functions such as navigation, route planning, and travel services (e.g., finding various places, querying travel routes, and public transportation information). In electronic maps, roads can be represented by road segments (links or segments), which are the basic units that make up roads in an electronic map. Figure 1 Road 1 shown can be represented by 3 road segments, which are: Figure 1 Road segment 1, road segment 2 and road segment 3.

[0077] II. Intersections:

[0078] An intersection is a point where roads converge. In an electronic map, an intersection can include at least one entrance road segment (at least one entrance road segment can be represented by m entrance links, where m is a positive integer) and at least one exit road segment (at least one exit road segment can be represented by n exit links, where n is a positive integer). An entrance road segment is the road segment that enters the intersection, and an exit road segment is the road segment that leaves the intersection. Entering an intersection from one entrance road segment, one can leave the intersection from one of the at least one exit road segment. An entrance road segment and an exit road segment can form a flow direction at the intersection (flow direction can be simply referred to as the direction of travel), for example, the flow direction can include U-turns, left turns, going straight, and right turns.

[0079] like Figure 2 The intersection shown includes one entrance road segment and three exit road segments. The one entrance road segment could be, for example, […]. Figure 2 In the middle of the road segment LA, the 3 exit road segments could be, for example, Figure 2The intersection is divided into road segments LB, LC, and LD. If a vehicle enters the intersection from road segment LA and exits from road segment LD, the vehicle's direction of travel can be determined as a left turn. Similarly, if a vehicle enters the intersection from road segment LA and exits from road segment LB, the vehicle's direction of travel can be determined as a straight-ahead direction. If a vehicle enters the intersection from road segment LA and exits from road segment LC, the vehicle's direction of travel can be determined as a right turn.

[0080] III. Road Condition Information:

[0081] Traffic information refers to information that characterizes road conditions, specifically the traffic flow on roads. Classified according to road conditions, traffic information can include various types, such as smooth traffic, slow traffic, congestion, and extreme congestion. In electronic maps, different traffic information can be displayed differently, for example, by using different colors to represent different traffic conditions. Typically, green represents smooth traffic, yellow represents slow traffic, red represents congestion, and dark red represents extreme congestion.

[0082] Traffic information can be categorized by level, resulting in multiple levels such as road-level, lane-level, and flow-direction-level information. Road-level information refers to traffic conditions defined on a road-by-road basis; at intersections, road-level information often integrates traffic conditions from multiple flow directions, such as... Figure 3 At the intersection shown, the traffic information for the flow direction entering the ramp from the right is congested, while the traffic information for straight traffic is unobstructed. Since the sample size of vehicles entering the ramp from the right is relatively large, the overall traffic information for the entire road is congested. It's clear that road-level traffic information is very coarse and has low accuracy. Lane-level traffic information refers to traffic information determined on a per-lane basis. Lane-level traffic information can only be determined in areas covered by high-precision map data and has high computational complexity, resulting in low accuracy. Flow-direction-level traffic information is traffic information determined on a per-direction basis. Multiple flow directions may form at an intersection, and flow-direction-level traffic information at an intersection can include the traffic information for each flow direction formed at the intersection. Traffic information for different flow directions at an intersection can sometimes differ significantly, for example… Figure 3 The traffic information for the straight-ahead flow direction is clear, while the traffic information for the right-hand entry ramp flow direction is congested.

[0083] Based on the above description of technical terms such as electronic maps, intersections, and traffic information, this application proposes a data processing method that can be used to determine traffic information at the flow direction level at intersections.

[0084] In detail, for incomplete intersection trajectories (incomplete trajectories refer to the trajectories traversed by vehicles that have not completed the intersection; vehicles that have not completed the intersection refer to vehicles that have passed through any entrance road segment of the intersection within the detection period but have not yet reached any exit road segment), this data processing method can utilize the layered multi-level road condition information on the incomplete trajectory to match it with the road condition information of the exit road segment that has not formed a flow direction within the detection period, in order to infer the flow direction of the incomplete trajectory and the road condition information in that flow direction. For completed intersection trajectories (completed trajectories refer to the trajectories traversed by vehicles that have completed the intersection; vehicles that have completed the intersection refer to vehicles that have passed through any entrance road segment of the intersection within the detection period and have reached an exit road segment), the completed trajectory can uniquely determine a flow direction, and the road condition information of that flow direction can be calculated using vehicle samples that have passed through that flow direction.

[0085] This data processing method can also publish the determined flow-level traffic information to the electronic map in real time. This allows the electronic map to refer to the real-time flow-level traffic information when providing navigation and route planning services, thereby improving the accuracy of the travel time ETA calculation function and the accuracy of the route avoidance function, and enhancing the user experience and product reputation of the electronic map.

[0086] This data processing method can significantly increase the number of traffic flow-level road information releases in congested scenarios. Specifically, when a major congestion occurs in a certain traffic flow direction at an intersection, vehicles in that direction may remain stuck for an extended period (exceeding the trajectory lifecycle of the electronic map, i.e., the detection period, for example, 5 or 10 minutes) without passing through the intersection. In this case, the electronic map cannot identify which vehicle routes belong to that traffic flow direction. That is, there are no available vehicle trajectories for that direction to participate in the calculation of traffic condition information, making it impossible to calculate the congestion information for that direction. This can easily lead to missed congestion reports (missed congestion reports refer to the inability to release traffic flow-level traffic condition information due to the inability to calculate it), thus affecting the effectiveness of route avoidance and ETA (Electronic Toll Collection) metrics. The data processing method provided in this application can determine traffic flow-level information for vehicles that have not completed their trajectories, compensating for the lag that requires waiting for vehicles to complete the intersection before determining traffic flow-level information, effectively reducing missed congestion reports.

[0087] The data processing method proposed in this application can be executed by a computer device, such as a server, which is located within a data processing system. The server will be described below in conjunction with the data processing system:

[0088] like Figure 4As shown, the data processing system may include a server 401 and a terminal device 402. The server 401 and the terminal device 402 may establish a direct communication connection through wired communication, or the server 401 and the terminal device 402 may establish an indirect communication connection through wireless communication. This application embodiment does not limit this. Figure 4 The number of terminal devices 402 is only one for example. The number of terminal devices 402 can be at least one, and this application embodiment does not limit this.

[0089] In the data processing system, a front-end for an electronic map (the front-end of the electronic map refers to the client of the electronic map) can be installed and run in terminal device 402. Terminal device 402 can be located in a vehicle, and the front-end of the electronic map can send the vehicle's positioning trajectory to server 401 through terminal device 402. The positioning trajectory can be determined based on a positioning system. This embodiment does not limit the type of positioning system; for example, the positioning system can be GPS (Global Positioning System) or BeiDou Navigation Satellite System, etc. Server 401 is the back-end of the electronic map. The server can receive the vehicle's positioning trajectory (the positioning trajectory can be formed by continuous positioning points), attach the positioning points in the positioning trajectory to the road, and determine the traffic flow information at the intersection level based on the vehicle's positioning trajectory. Attaching the positioning point trajectory to the road means matching positioning points that were not matched to the road due to positioning errors to the road. Server 401 can publish the traffic flow information to the electronic map, and the front-end of the electronic map in terminal device 402 can display the traffic flow information, for example, using different colors to distinguish different traffic conditions for different flow directions at intersections.

[0090] Figure 4 The data processing system shown in the embodiments is intended to more clearly illustrate the technical solutions of the embodiments of this application, and does not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0091] The client mentioned in this application refers to a program that provides services locally on a terminal device, and may include, but is not limited to, any of the following: application client, mini-program client, and web (World Wide Web) program client. The terminal device mentioned in this application may include, but is not limited to, any of the following: smartphone, tablet computer, laptop computer, desktop computer, smartwatch, smart home appliance, smart vehicle terminal, and aircraft. The server mentioned in this application may be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms, etc.

[0092] The following section, using the structure of server 401 as an example, describes the process for determining traffic information at the flow level. For instance... Figure 4 As shown, server 401 may include a single-vehicle speed calculation module, a multi-vehicle calculation and statistics module, and a traffic condition publishing module. Each module in server 401 participates in determining traffic condition information at the flow level. Specifically:

[0093] (1) Single vehicle speed calculation module:

[0094] The single-vehicle speed calculation module calculates the vehicle speed information for each road segment traversed within a detection cycle (e.g., 5 or 10 minutes in the current system time) based on the vehicle's positioning trajectory. It can also record whether the vehicle reaches the exit road segment after passing through the intersection. If so, it can also record the exit road segment reached. The vehicle speed information for a road segment refers to the speed at which the vehicle travels through that road segment. This speed information can be calculated based on the distance the vehicle travels on that road segment (which can be less than or equal to the road segment length) and the time spent on that segment. For example, the vehicle speed information for a road segment can be equal to the distance the vehicle travels on that road segment divided by the time spent on that segment.

[0095] During the detection period, each vehicle can calculate the vehicle data corresponding to each road segment it passes through. The vehicle data corresponding to any vehicle passing through any road segment can include at least a tuple.<inlink,outlink> Vehicle speed information and vehicle time information. Among them, the binary data...<inlink,outlink> This can be used to record the road segment identifiers (inlink) for entry road segments and (outlink) for exit road segments. The entry road segment is the road segment currently traversed by the vehicle. If the vehicle reaches the exit road segment after passing through the entry road segment to the intersection, the road segment identifier of the reached exit road segment can be recorded in the tuple. If the vehicle reaches the exit road segment but does not reach the exit road segment after passing through the entry road segment to the intersection, the road segment identifier of the exit road segment can be recorded as empty in the tuple. Vehicle speed information refers to the speed at which the vehicle travels through the road segment. Vehicle time information refers to the time the vehicle spends on the road segment (i.e., the time consumed by the vehicle on the road segment), which can include the start time point and the end time point. The start time point is the time when the vehicle enters the road segment, and the end time point is the time when the vehicle leaves the road segment (corresponding to the case where the vehicle leaves the road segment within the detection cycle) or the end time point of the detection cycle (corresponding to the case where the vehicle has not left the road segment within the detection cycle).

[0096] (2) Multi-vehicle calculation and statistics module:

[0097] The multi-vehicle calculation and statistics module can acquire vehicle data for all vehicles passing through the entrance road segment, using the entrance road segment as the unit. This module can examine the vehicle data for each vehicle, checking if it contains the exit road segment identifier. If it does, it indicates the vehicle has completed the intersection (i.e., the vehicle has completed the intersection), allowing determination of the vehicle's flow direction. If it does not contain the exit road segment identifier, it indicates the vehicle has not completed the intersection (i.e., the vehicle has not completed the intersection). The module can then categorize the vehicle data based on whether it contains the exit road segment identifier. It can aggregate the vehicle data corresponding to vehicles that have not completed the intersection to obtain the first detection data, and aggregate the vehicle data corresponding to vehicles that have completed the intersection to obtain the second detection data.

[0098] For the first detection data, if the vehicle speed information in the first detection data exhibits typical stratification (typical stratification may be due to different flow directions of vehicles passing through the entrance road segment, and different road conditions in different flow directions), it indicates that the entrance road segment has multiple layers of road condition information. For the second detection data, the flow direction of vehicles can be determined from the binary data in the second detection data—whether it's a U-turn, left turn, right turn, or going straight. Vehicle data can be organized according to different flow directions, and the flow velocity information in each flow direction can be calculated based on the vehicle speed information in each flow direction, thereby obtaining the road condition information for each flow direction.

[0099] (3) Traffic Information Module:

[0100] Iterate through each entry road segment with layered multi-level road condition information. For each exit road segment reachable from the intersection after passing through the entry road segment, check if the exit road segment has formed a flow direction with the entry road segment. Specifically, check if the road condition information of the flow direction formed by the entry road segment and the exit road segment has been determined. If no flow direction has been formed with the entry road segment, match the road condition information of the exit road segment with the layered multi-level road condition information. If the match is successful, the flow direction formed by the entry road segment and the exit road segment, as well as the road condition information of the flow direction, can be determined.

[0101] It can be seen that the single-vehicle speed calculation module, multi-vehicle calculation and statistics module, and traffic condition publishing module in the server work together to determine the traffic condition information of the flow direction of completed trajectories at intersections, as well as the traffic condition information of the flow direction of incomplete trajectories at intersections, thereby increasing the number of flow-level traffic condition information published at intersections. Furthermore, the server can determine and publish flow-level traffic condition information once per detection cycle, meaning that the server can periodically determine and publish flow-level traffic condition information, improving the real-time performance of flow-level traffic condition information publishing.

[0102] The data processing method provided in the embodiments of this application will be described in detail below.

[0103] This application provides a data processing method, which includes the process of determining road condition information at the flow direction level for routes that have not been completed. For example... Figure 5 As shown, the data processing method may include, but is not limited to, the following steps S501-S504:

[0104] S501, acquire the first detection data of the first entrance road segment of the intersection; the intersection includes at least one entrance road segment and at least one exit road segment, and one entrance road segment and one exit road segment form a flow direction of the intersection; the first detection data includes vehicle data of multiple first vehicles, and the first vehicles have passed through the first entrance road segment within the detection cycle but have not yet reached any of the exit road segments of the intersection.

[0105] In step S501, the intersection may include at least one entrance road segment and at least one exit road segment. An entrance road segment and an exit road segment can form a flow direction at the intersection. The first entrance road segment is any one of the entrance road segments of the intersection. The first detection data of the first entrance road segment may include vehicle data of multiple first vehicles. A first vehicle has passed through the first entrance road segment within the detection period but has not yet reached any exit road segment of the intersection; that is, the first vehicle is a vehicle that has not completed the intersection. The number of vehicle data in the first detection data is greater than or equal to a quantity threshold. If the number of vehicle data in the first detection data is less than the quantity threshold, the calculation can be abandoned, and the flow direction-level road condition information related to the first entrance road segment cannot be further determined. The quantity threshold can be set based on empirical values ​​(e.g., the quantity threshold can be 4). By using the quantity threshold, it can be ensured that there is enough vehicle data in the first detection data to participate in subsequent layered road condition detection, which can improve the accuracy and reliability of layered road condition detection.

[0106] The vehicle data for the first vehicle may include at least the road segment identifier of the first entrance road segment, the vehicle speed information of the first vehicle passing through the first entrance road segment within the detection cycle, and the vehicle time information of the first vehicle passing through the first entrance road segment within the detection cycle. The road segment identifier of the exit road segment in the vehicle data for the first vehicle is empty. The vehicle speed information refers to the speed of the first vehicle passing through the first entrance road segment. The vehicle time information refers to the time the first vehicle spends on the first entrance road segment (i.e., the time consumed by the first vehicle on the first entrance road segment), which may include a start time point and an end time point. The start time point is the time when the first vehicle enters the first entrance road segment, and the end time point is the time when the first vehicle leaves the first entrance road segment (corresponding to the case where the first vehicle leaves the first entrance road segment within the detection cycle) or the end time point of the detection cycle (corresponding to the case where the first vehicle has not left the first entrance road segment within the detection cycle).

[0107] S502, based on the first detection data, perform layered road condition detection on the first entrance road segment to obtain the layered road condition detection result of the first entrance road segment. The layered road condition detection result is used to indicate the L types of road condition information that appear in the first entrance road segment within the detection period, where L is an integer greater than 1.

[0108] In step S502, layered road condition detection refers to detecting whether the first entrance road segment possesses layered multiple road condition information. The detection method is to detect whether there are multiple data layers in the vehicle speed information in the first detection data; if multiple data layers exist, it indicates that the first entrance road segment possesses layered multiple road condition information, with one data layer corresponding to one type of road condition information; if only one data layer exists, it indicates that the first entrance road segment does not possess layered road condition information.

[0109] In detail, the process of layered road condition detection may include: based on the distribution characteristics of vehicle speed information in the first detection data, performing data layering processing on the first detection data to obtain L data layers; one of the L data layers corresponds to one type of road condition information; and determining the L types of road condition information corresponding to the L data layers as the layered road condition detection results of the first entrance road segment.

[0110] The distribution characteristics refer to the distribution of vehicle speed information in the first detection data. For example, if the distribution characteristics of vehicle speed information in the first detection data are clustered, then the vehicle speed information in the first detection data is distributed in one or more clusters. The differences between vehicle speed information within the same cluster are small, while the differences between vehicle speed information within different clusters are large. The first detection data can be stratified according to clusters, with one cluster corresponding to one data stratification.

[0111] The layered traffic condition detection results can indicate L types of traffic condition information that appear in the first entrance road segment within the detection period. For the L types of traffic condition information indicated by the layered traffic condition detection results, if L is an integer greater than 1, it indicates that the first entrance road segment has multiple types of layered traffic condition information, and steps S503-S504 in this embodiment can be triggered; if L equals 1, it indicates that the first entrance road segment does not have layered traffic condition information, and it is impossible to continue to determine the flow-level traffic condition information related to the first entrance road segment.

[0112] S503 filters out the first exit road segment from the intersection that has not yet formed a flow direction within the detection cycle, and obtains the road condition information of the first exit road segment.

[0113] In step S503, the first exit road segment refers to any exit road segment among at least one exit road segment of the intersection that has not yet formed a flow direction within the detection period; more specifically, the first exit road segment refers to any exit road segment that, among the exit road segments that the first entrance road segment may reach, has not yet formed a flow direction with the first entrance road segment within the detection period. The filtering process may include: obtaining the road segment identifiers of the exit road segments that the first entrance road segment may reach; obtaining the road segment identifiers of the exit road segments that have formed a flow direction with the first entrance road segment; deleting the road segment identifiers of the exit road segments that have formed a flow direction with the first entrance road segment from the road segment identifiers of the exit road segments that the first entrance road segment may reach; the exit road segments corresponding to the remaining road segment identifiers after filtering are the exit road segments that have not yet formed a flow direction with the first entrance road segment, and the first exit segment is any one of these exit road segments.

[0114] S504, match the road condition information of the first exit road segment with L types of road condition information. If the match is successful, set the road condition information of the first exit road segment as the road condition information of the first flow direction. The first flow direction is formed by the first entrance road segment and the first exit road segment.

[0115] In step S504, the road condition information of the first exit road segment can be determined based on the speed information corresponding to the first exit road segment. Specifically, different road condition information corresponds to different speed ranges. The road condition information corresponding to the first speed range to which the speed information of the first exit road segment belongs can be determined as the road condition information of the first exit road segment. The speed information of the first exit road segment can be calculated based on the vehicle speed information of at least one third vehicle passing through the first exit road segment during the detection period. For example, the speed information of the first exit road segment can be equal to the average of the vehicle speed information of at least one third vehicle.

[0116] Matching processing refers to checking whether there is road condition information in the L types of road condition information that is identical to the road condition information of the first exit road segment. The matching check process may include: querying the road condition information of the first exit road segment in the L types of road condition information to obtain the query results; if the query results indicate that there is road condition information in the L types of road condition information that is identical to the road condition information of the first exit road segment, then the matching can be determined to be successful; if the query results indicate that there is no road condition information in the L types of road condition information that is identical to the road condition information of the first exit road segment, then the matching can be determined to be unsuccessful. Further, if the matching is successful, the road condition information of the first exit road segment can be set as the road condition information of the first flow direction formed by the first entrance road segment and the first exit road segment; if the matching fails, it is impossible to continue to determine the road condition information of the flow direction related to the first exit road segment.

[0117] The matching check process is as follows Figure 6 As shown, the L types of road condition information of the first entrance road segment can be added to the first set, resulting in the first set = {S1, S2, ..., S...} L}, where S1, S2, ..., S L Representing L types of road condition information. Obtain P (P is a positive integer) possible exit road segments reachable from the first entrance road segment, denoted as {O1, O2, ..., O...}. P}, where O i Let represent the i-th exit road segment out of P exit road segments. By traversing the P exit road segments, we can determine whether the first entrance road segment is related to O. i Has the direction of the resulting flow been determined? Road condition information; if the first entrance road segment is adjacent to O... i If the direction of the resulting flow is uncertain and road condition information is unavailable, then O can be obtained. i The road condition information S; if S is in the first set, then it can be determined that the road segment from the first entrance and O are... i The resulting traffic information in the direction of flow is called traffic information S. If S is not in the first set, it can be processed according to O. i The processing logic for O i+1 Processing is required. If the first entrance road segment intersects with O... i The direction of the flow has been determined, and the road condition information can be followed according to O. i The processing logic for O i+1 Process the data. Continue traversing the P exit road segments until all P exit road segments have been traversed.

[0118] In this embodiment, by performing layered traffic condition detection on the incomplete trajectory of a vehicle (e.g., the first entrance road segment), and considering the availability of multiple layers of traffic condition information for the incomplete trajectory, this layered traffic condition information can be matched with the traffic condition information of the downstream exit road segment to infer the flow direction of the vehicle after traversing the incomplete trajectory. This allows for the issuance of traffic condition information specific to that flow direction, thus compensating for the lag in issuing flow-level traffic condition information only after the vehicle has completed the intersection, and preventing the omission of flow-level traffic condition information during intersection congestion. This embodiment is applicable to the field of traffic condition monitoring and can publish flow-level traffic condition information in real time.

[0119] This application provides a data processing method, which includes a layered road condition detection process. For example... Figure 7 As shown, the data processing method may include, but is not limited to, the following steps S701-S705:

[0120] S701, acquire the first detection data of the first entrance road segment of the intersection; the intersection includes at least one entrance road segment and at least one exit road segment, and one entrance road segment and one exit road segment form a flow direction of the intersection; the first detection data includes vehicle data of multiple first vehicles, and the first vehicles have passed through the first entrance road segment within the detection cycle but have not yet reached any of the exit road segments of the intersection.

[0121] In this embodiment, the execution process of step S701 is the same as described above. Figure 5 The execution process of step S501 in the illustrated embodiment is the same, and the execution process of step S701 can be found in the above description. Figure 5 The description of step S501 in the illustrated embodiment will not be repeated here.

[0122] S702, based on the distribution characteristics of vehicle speed information in the first detection data, perform data layering processing on the first detection data to obtain L data layers; one of the L data layers corresponds to one type of road condition information.

[0123] In step S702, the data layering process may include the following sub-steps s11-s14:

[0124] s11, based on the vehicle speed information in the first detection data, classify the vehicle data in the first detection data into M first datasets. Each of the M first datasets corresponds to different road condition information, and M is an integer greater than 1.

[0125] In sub-step s11, the classification process may include: obtaining speed thresholds corresponding to N types of road condition information, where N is an integer greater than 1; dividing the vehicle data in the first detection data into N datasets based on the speed thresholds corresponding to the N types of road condition information and the vehicle speed information in the first detection data, where one dataset in the N datasets corresponds to one type of road condition information among the N types of road condition information; selecting M valid datasets from the N datasets, and determining the M valid datasets as M first datasets, where M is less than or equal to N. Here, a valid dataset refers to a dataset among the N datasets that contains at least two vehicle data points.

[0126] In the above classification process:

[0127] ① The speed threshold refers to a speed range. The division rule is to classify vehicle data in the first detection data that belong to the same speed range (i.e., the speed threshold) into the corresponding dataset for that same speed range (i.e., the speed threshold). The road condition information corresponding to the dataset refers to the road condition information corresponding to the speed threshold of the dataset.

[0128] For example, the first detection data contains 5 vehicle data sets: vehicle data 1 through vehicle data 5. Vehicle data 1 has a speed of 5 km / h, vehicle data 2 has a speed of 2.5 km / h, vehicle data 3 has a speed of 1 km / h, vehicle data 4 has a speed of 50 km / h, and vehicle data 5 has a speed of 80 km / h. The speed thresholds for the four road condition information are: extremely congested (0 km / h, 10 km / h), congested (10 km / h, 20 km / h), slow-moving (20 km / h, 30 km / h), and unobstructed (30 km / h, unlimited). According to the partitioning rules, vehicle data 1, vehicle data 2, and vehicle data 3 can be grouped into the same dataset, which corresponds to extremely congested road condition information. Vehicle data 4 and vehicle data 5 can be grouped into the same dataset, which corresponds to unobstructed road condition information.

[0129] ② A valid dataset refers to a dataset containing at least two vehicle data points out of N datasets. In other words, invalid datasets containing only one vehicle data point or none at all are removed from the N datasets, resulting in M ​​first datasets. This is done because a dataset containing no vehicle data indicates that the corresponding road condition information for the first entry road segment did not appear during the detection period. For a dataset containing only one vehicle data point, the vehicle data in that dataset is likely incorrectly classified due to calculation errors in vehicle speed information, and the vehicle data in that dataset is not statistically significant. Removing these datasets improves the accuracy of layered road condition detection.

[0130] ③ If M is an integer greater than 1, then sub-step s12 can be executed. If M equals 1, then it is impossible to continue to determine the flow-level road condition information related to the first entrance road segment.

[0131] ④ Before dividing into N datasets, valid vehicle data can be selected from the first detection data. If the number of valid vehicle data is greater than or equal to a threshold (the threshold can be set based on experience, for example, a threshold of 4), then each valid vehicle data can be divided into N datasets based on the speed thresholds corresponding to the N types of road condition information and the vehicle speed information of each valid vehicle data. Valid vehicle data can include at least one of the following: vehicle data of the first vehicle with a stable positioning trajectory, and vehicle data of the first vehicle that is not involved in an accident. The first vehicle with a stable positioning trajectory refers to a positioning trajectory composed of positioning trajectory points on the road, or a positioning trajectory composed of positioning trajectory points on the same side of the road, rather than a positioning trajectory composed of positioning trajectory points that fluctuate between the left and right sides of the road.

[0132] In other words, multiple valid vehicle data points can be selected from the initial detection data. When the number of selected valid vehicle data points is greater than or equal to a threshold, layered road condition detection can be performed on the first entrance road segment based on these multiple valid vehicle data points. Valid vehicle data refers to accurate vehicle data. By filtering out inaccurate vehicle data from the initial detection data and selecting accurate vehicle data to participate in layered road condition detection, the accuracy of layered road condition detection can be improved. Furthermore, by using the threshold, a sufficient number of valid vehicle data points can be ensured to participate in layered road condition detection, which can improve the accuracy and reliability of layered road condition detection, thereby further improving the accuracy of layered road condition detection.

[0133] s12, sort the M first datasets according to the number of vehicle data in each of the M first datasets.

[0134] s13. According to the time window filtering strategy, the sorted M first datasets are filtered to obtain K second datasets. The time ranges of the K second datasets have an intersection, and K is an integer greater than 1 and K is less than or equal to M.

[0135] In sub-step s13, the filtering process of the time window filtering strategy refers to filtering out the first datasets that do not intersect with the time ranges of the other first datasets from the sorted M first datasets, and determining the remaining first datasets as K second datasets, so that the time ranges of the K second datasets intersect.

[0136] In this context, the time range corresponding to any second dataset is determined based on the vehicle time information within that dataset. Specifically, the vehicle time information includes a start time and an end time. The time range corresponding to the second dataset can include a start point and an end point. The start point of the time range can be determined based on the start time of each vehicle time information within the second dataset; for example, the start point could be the earliest start time among all the start times of the vehicle time information within the second dataset. Similarly, the end point of the time range can be determined based on the end time of each vehicle time information within the second dataset; for example, the end point could be the latest end time among all the end times of the vehicle time information within the second dataset.

[0137] For example, the second dataset includes five vehicle data points: vehicle data 1 through vehicle data 5. Vehicle data 1 has vehicle times of [15:02:25, 15:02:26], vehicle data 2 has vehicle times of [15:02:24, 15:02:25], vehicle data 3 has vehicle times of [15:02:24, 15:02:27], vehicle data 4 has vehicle times of [15:02:23, 15:02:24], and vehicle data 5 has vehicle times of [15:02:23, 15:02:25]. Therefore, the starting point of the time range in the second dataset is 15:02:23, and the ending point is 15:02:27. The corresponding time range for the second dataset is [15:02:23, 15:02:27].

[0138] The filtering process of the time window filtering strategy can include: initializing the time window using the time range corresponding to the first of the M sorted first datasets, and taking the second of the M sorted first datasets as the current dataset; performing intersection detection on the time range corresponding to the time window and the current dataset; if the intersection detection result indicates that the time window intersects with the time range corresponding to the current dataset, the time window can be expanded according to the time range corresponding to the current dataset, and the current dataset can be updated using the next first dataset; if the intersection detection result indicates that the time window does not intersect with the time range corresponding to the current dataset, the current dataset can be deleted from the M first datasets, and the current dataset can be updated using the next first dataset; repeating the intersection detection and the operation triggered by the intersection detection result until the last first dataset in the M first datasets, the remaining first datasets in the M first datasets excluding the deleted first datasets can be determined as K second datasets.

[0139] During the filtering process of the above time window filtering strategy:

[0140] ① A time window refers to the time range used for intersection detection. Intersection detection checks whether there is an intersection between the time window and the time range corresponding to the current dataset. Expanding the time window based on the time range corresponding to the current dataset means: determining the earlier start of the expanded time window's time range between the start of the current dataset's time range and the start of the time window's time range; and determining the later end of the expanded time window's time range between the end of the current dataset's time range and the end of the time window's time range. For example, if the time window is [15:02:23, 15:02:27], and the time range corresponding to the current dataset is [15:02:24, 15:02:28], then the time window intersects with the time range corresponding to the current dataset, and the expanded time window is [15:02:23, 15:02:28].

[0141] ② If K is an integer greater than 1, then sub-step s13 can be executed. If K equals 1, then it is impossible to continue to determine the flow-level road condition information related to the first entrance road segment.

[0142] ③ After the time window filtering strategy, the time ranges of the K second datasets overlap. This means the data stratification of the first detection data occurs within the same time range. This ensures that the various road condition information within each stratification occurs simultaneously due to the different flow directions of vehicles passing through the first entrance road segment, rather than because the vehicles themselves do not have different flow directions, but the resulting stratified road condition information changes over time. The time window filtering strategy avoids erroneous stratified road condition detection results caused by changes over time, ensuring that the various stratified road condition information is due to different flow directions, thereby improving the accuracy of stratified road condition detection.

[0143] s14. According to the adjacent road condition detection strategy, adjacent road condition detection is performed on K second datasets to obtain L third datasets. One of the L third datasets corresponds to a data layer. The speed difference between any two adjacent third datasets with corresponding road condition information in the L third datasets is greater than or equal to the difference threshold, and L is less than or equal to K.

[0144] In sub-step s14, the adjacent road condition detection strategy refers to detecting whether the speed difference between two adjacent second datasets of corresponding road condition information is less than a difference threshold. The various road condition information items have an order, and adjacent road condition information refers to two road condition items that are adjacent in their order. For example, if the order of the four road condition information items—smooth traffic, slow traffic, congested traffic, and extremely congested traffic—is smooth traffic, slow traffic, congested traffic, and extremely congested traffic, then smooth traffic and slow traffic are two adjacent road condition items, slow traffic and congested traffic are two adjacent road condition items, and congested traffic and extremely congested traffic are two adjacent road condition items. For cases where the difference is less than the difference threshold, the two adjacent second datasets of corresponding road condition information can be merged, and the road condition information corresponding to the merged new second dataset can be redefined. For cases where the difference is greater than or equal to the difference threshold, the two adjacent second datasets of corresponding road condition information can be retained.

[0145] The adjacent road condition detection process of the adjacent road condition detection strategy may include: calculating the first speed difference information between two adjacent second datasets based on the speed information corresponding to two adjacent second datasets of corresponding road condition information in K second datasets; if the first speed difference information is less than the difference threshold, the two adjacent second datasets of corresponding road condition information can be merged to obtain a new second dataset; if the first speed difference information is greater than or equal to the difference threshold, the two adjacent second datasets of corresponding road condition information can be kept unchanged; if there are merged second datasets among the K second datasets, the merged second datasets can be determined as L third datasets; if there are no merged second datasets among the K second datasets, the K second datasets can be determined as L third datasets.

[0146] During the adjacent road condition detection process of the above adjacent road condition detection strategy:

[0147] ① The speed information corresponding to the second dataset can be determined based on the speed information of each vehicle in the second dataset. For example, the speed information corresponding to the second dataset can be the average of the speed information of each vehicle in the second dataset.

[0148] ② The method for determining the traffic information corresponding to the new second dataset obtained by merging may include: determining the speed information corresponding to the new second dataset based on the speed information corresponding to two adjacent second datasets of the corresponding traffic information. For example, the average of the speed information corresponding to two adjacent second datasets of the corresponding traffic information can be determined as the speed information corresponding to the new second dataset; or the traffic information corresponding to the second speed range to which the speed information corresponding to the new second dataset belongs can be determined as the traffic information corresponding to the new second dataset.

[0149] ③ After adjacent road condition detection by the adjacent road condition detection strategy, the speed difference between any two adjacent third datasets with corresponding road condition information in the L third datasets is greater than or equal to the difference threshold. This ensures that there is a clear speed boundary between any two adjacent third datasets with corresponding road condition information in the L third datasets, thereby ensuring that the stratification of multiple road condition information in the stratified road condition detection results is obvious and improving the accuracy of stratified road condition detection.

[0150] S703, the L types of road condition information corresponding to the L data layers are determined as the layered road condition detection results of the first entrance road segment.

[0151] In summary, the contents of steps S702-S703 are as follows: Figure 8 As shown, the hierarchical road condition detection process can be summarized as follows: Valid vehicle data is selected from the first detection data. If the number of valid vehicle data is greater than or equal to a threshold (e.g., the threshold could be 4), the speed thresholds corresponding to N types of road condition information can be obtained. Based on these speed thresholds, the valid vehicle data is divided into N datasets. M valid datasets (each containing at least two vehicle data points) are selected from the N datasets to form M first datasets. If M is an integer greater than 1, the M first datasets are sorted according to the number of vehicle data points in each dataset. Following a time window filtering strategy, the sorted M first datasets are filtered to obtain K second datasets, ensuring that the time ranges of the K second datasets overlap. If K is an integer greater than 1, the speed information corresponding to each second dataset is calculated. Following an adjacent road condition detection strategy, adjacent road condition detection is performed on the K second datasets to obtain L third datasets, ensuring that the speed difference between any two adjacent third datasets with corresponding road condition information is greater than or equal to a difference threshold. If L is an integer greater than 1, then the traffic information corresponding to the L third datasets can be determined as L types of hierarchical traffic information, triggering the matching of the L types of traffic information with the traffic information of the downstream exit road segment of the intersection to determine the flow-level traffic information.

[0152] If any of the following conditions exist: the number of valid vehicle data is less than the number threshold, M equals 1, K equals 1, and L equals 1, then the calculation is abandoned and the flow-level road condition information related to the first entrance road segment cannot be determined.

[0153] S704 filters out the first exit road segment from the intersection that has not yet formed a flow direction within the detection cycle, and obtains the road condition information of the first exit road segment.

[0154] In this embodiment, the execution process of step S704 is the same as described above. Figure 5The execution process of step S503 in the illustrated embodiment is the same, and the execution process of step S704 can be found in the above description. Figure 5 The description of step S503 in the illustrated embodiment will not be repeated here.

[0155] S705, match the road condition information of the first exit road segment with L types of road condition information. If the match is successful, set the road condition information of the first exit road segment as the road condition information of the first flow direction. The first flow direction is formed by the first entrance road segment and the first exit road segment.

[0156] In this embodiment, the execution process of step S705 is the same as described above. Figure 5 The execution process of step S504 in the illustrated embodiment is the same, and the execution process of step S705 can be found in the above description. Figure 5 The description of step S504 in the illustrated embodiment will not be repeated here.

[0157] In this embodiment, during the layered road condition detection process, the filtering process using a time window filtering strategy ensures that the various road condition information in each layer is generated within the same time frame due to different flow directions, rather than being generated by changes over time without flow direction differences. This improves the accuracy of layered road condition detection. Furthermore, during the layered road condition detection process, the adjacent road condition detection strategy ensures that the layering of various road condition information is clear, further enhancing the accuracy of layered road condition detection.

[0158] This application provides a data processing method, which includes the process of determining traffic condition information at the flow direction level for routes that have been completed. For example... Figure 9 As shown, the data processing method may include, but is not limited to, the following steps S901-S904:

[0159] S901, acquire second detection data of the first entrance road segment of the intersection; the second detection data includes vehicle data of multiple second vehicles, which pass through the first entrance road segment and arrive at an exit road segment of the intersection within the detection cycle.

[0160] In step S901, the second detection data for the first entrance road segment may include vehicle data for multiple second vehicles. A second vehicle passes through the first entrance road segment and reaches an exit road segment at the intersection within the detection cycle; that is, the second vehicle has completed its journey through the intersection. The number of vehicle data in the second detection data is greater than or equal to a quantity threshold. If the number of vehicle data in the second detection data is less than the quantity threshold, the calculation can be abandoned, and the flow-level traffic information related to the first entrance road segment cannot be further determined. By using the quantity threshold, it can be ensured that sufficient vehicle data in the second detection data participates in the determination of flow-level traffic information, thereby improving the accuracy and reliability of the flow-level traffic information determination.

[0161] The vehicle data for the second vehicle may include at least the road segment identifier of the first entrance road segment, the road segment identifier of the exit road segment reached by the second vehicle, the vehicle speed information of the second vehicle during the detection period when passing through the first entrance road segment, and the vehicle time information of the second vehicle during the detection period when passing through the first entrance road segment. The vehicle speed information refers to the speed of the second vehicle when passing through the first entrance road segment. The vehicle time information refers to the time the second vehicle spends on the first entrance road segment (i.e., the time consumed by the second vehicle on the first entrance road segment), which may include a start time point and an end time point. The start time point is the time when the second vehicle enters the first entrance road segment, and the end time point is the time when the second vehicle leaves the first entrance road segment.

[0162] S902, based on the first entrance road segment and multiple exit road segments where second vehicles arrive, determines one or more second flow directions at the intersection.

[0163] In step S902, the determination of one or more second flow directions may include: determining one or more exit road segments to which at least one second vehicle has arrived based on the road segment identifiers of the exit road segments appearing in the vehicle data in the second detection data. A second flow direction is determined for each first entrance road segment and each arrived exit road segment, resulting in one or more second flow directions. For example, based on the road segment identifiers of the exit road segments appearing in the vehicle data in the second detection data, it can be determined that at least one second vehicle has arrived at exit road segments 1, 2, and 3. The first entrance road segment and exit road segment 1 form a second flow direction (which can be represented as second flow direction 1), the first entrance road segment and exit road segment 2 form a second flow direction (which can be represented as second flow direction 2), and the first entrance road segment and exit road segment 3 form a second flow direction (which can be represented as second flow direction 3), thus determining three second flow directions.

[0164] S903, the vehicle data of each second vehicle in the second detection data is divided into the dataset corresponding to the second flow direction to which the second vehicle belongs.

[0165] In step S903, the second flow direction to which any second vehicle belongs refers to the second flow direction formed by the first entrance road segment passed by the second vehicle and the exit road segment passed by the second vehicle. The vehicle data of the second vehicle can be assigned to the dataset corresponding to the second flow direction.

[0166] S904, based on the vehicle data in the dataset corresponding to each second flow direction, determine the road condition information for each second flow direction.

[0167] In step S904, the process of determining the road condition information for each second flow direction based on the vehicle data in the dataset corresponding to each second flow direction may include: determining the flow direction speed information for each second flow direction based on the vehicle speed information in the dataset corresponding to each second flow direction; for example, the flow direction speed information for each second flow direction may be the average value of the vehicle speed information in the dataset corresponding to the second flow direction. The road condition information mapped by the flow direction speed information for each second flow direction can be determined as the road condition information for each second flow direction; wherein, the road condition information mapped by the flow direction speed information refers to determining the road condition information corresponding to the third speed range to which the flow direction speed information belongs as the road condition information mapped by the flow direction speed information.

[0168] Optionally, similar to the processing of the first detection data, valid vehicle data can be selected from the second detection data. If the number of valid vehicle data is greater than or equal to a threshold (the threshold can be set based on experience, for example, a threshold of 4), one or more second flow directions at the intersection can be determined based on the road segment identifiers of the first entrance road segment and the exit road segments in each valid vehicle data set. Each valid vehicle data set can be assigned to the dataset corresponding to its respective second flow direction. Based on the vehicle data in the dataset corresponding to each second flow direction, the road condition information for each second flow direction can be determined. Valid vehicle data can include at least one of the following: vehicle data of second vehicles with stable positioning trajectories, and vehicle data of second vehicles that have not been involved in accidents. A second vehicle with a stable positioning trajectory refers to a positioning trajectory composed of positioning trajectory points on the road, or a positioning trajectory composed of positioning trajectory points on the same side of the road, rather than a positioning trajectory composed of positioning trajectory points that fluctuate between the left and right sides of the road. In other words, valid vehicle data is accurate vehicle data. By filtering out inaccurate vehicle data from the second detection data and selecting accurate vehicle data to participate in the calculation of road condition information for the flow direction of the completed trajectory, the accuracy of the calculation of road condition information for the flow direction of the completed trajectory can be improved.

[0169] Optionally, traffic information for each flow direction at the intersection can be published to the electronic map. Furthermore, the traffic information for each flow direction at the intersection is periodically determined and published on a detection cycle basis. For example, at the end of the current detection cycle, the traffic information for each flow direction at the intersection determined for the current detection cycle is published; at the end of the next detection cycle, the traffic information for each flow direction at the intersection determined for the next detection cycle is published. "Publishing" means that various functions of the electronic map can obtain and use the traffic information for each flow direction at the intersection; for example, the electronic map's display module can obtain and display the traffic information for each flow direction at the intersection; the electronic map's navigation module can obtain and display the traffic information for each flow direction at the intersection when navigating to the intersection; and the electronic map's route planning function can obtain the traffic information for each flow direction at relevant intersections during route planning, and refer to the traffic information for each flow direction at relevant intersections to plan routes, in order to avoid congested flow directions as much as possible and to plan the optimal route.

[0170] Furthermore, in electronic maps, traffic information is displayed differently for different types of traffic conditions. This differentiation can include any of the following: different traffic conditions can be represented by different colors, different traffic conditions can be represented by different text, and so on. For example, in... Figure 10At the intersection, three traffic flows were formed: left turn, straight ahead, and right turn. The traffic information for each flow direction was different and distinguished using different text, for example... Figure 10 In the middle, left turns are slow, straight traffic is congested, and right turns are unimpeded.

[0171] Optionally, within the detection period, multiple different road condition information may be determined for the same flow direction, for example, in Figure 11 In this process, the road condition information for the first flow direction is determined based on the first entrance road segment of the intersection, and the road condition information for the first flow direction is determined based on the second entrance road segment of the intersection, thus becoming the second road condition information. In this case, different road condition information for the same flow direction can be fused to obtain the final target road condition information for that same flow direction.

[0172] In detail, the fusion processing method may include: counting the number of times each type of road condition information appears in different road condition information in the same direction of flow; if the count of the occurrence of each type of road condition information is the same, then one type of road condition information can be randomly selected from the different road condition information in the same direction of flow and determined as the target road condition information in the same direction of flow; if the count of the occurrence of each type of road condition information is different, then the road condition information with the most occurrences can be selected from the different road condition information in the same direction of flow and determined as the target road condition information in the same direction of flow.

[0173] It is easy to see that by fusing different traffic condition information in the same flow direction, we can avoid the confusion caused by different traffic condition information published in the same flow direction. For example, different traffic condition information in the same flow direction may be displayed incorrectly; similarly, different traffic condition information in the same flow direction may lead to route planning errors. Therefore, by fusing different traffic condition information in the same flow direction, we can determine the accuracy of traffic condition information publication at the flow direction level.

[0174] In this embodiment, for a completed trajectory, the flow direction of the completed trajectory can be determined. Vehicle data can be organized according to the flow direction, and road condition information for that flow direction can be calculated, thereby quickly determining the road condition information for the flow direction of the completed trajectory. Furthermore, for different road condition information determined for the same flow direction within the same detection cycle, this embodiment fuses the different road condition information to determine the final target road condition information for the same flow direction. This avoids publishing incorrect road condition information for the same flow direction and improves the accuracy of flow-level road condition information dissemination.

[0175] Figure 5 and Figure 7 The illustrated embodiment describes the process of determining road condition information at the flow direction level for routes that have not been completed. Figure 9 The illustrated embodiment describes the process of determining road condition information at the flow direction level for routes that have been completed. Figure 9 The execution order of the illustrated embodiment is prior to Figure 5 and Figure 7 The execution order of the illustrated embodiment. This is because in Figure 5 and Figure 7 In the illustrated embodiment, it is necessary to base on Figure 9 The second flow direction determined in the illustrated embodiment is used to filter out the first exit road segment that has not yet formed a flow direction. This will be explained here.

[0176] The methods of the embodiments of this application have been described in detail above. In order to facilitate better implementation of the above solutions of the embodiments of this application, the apparatus of the embodiments of this application is provided below.

[0177] Please see Figure 12 , Figure 12 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application. The data processing device can be installed in the computer equipment provided in the embodiment of this application. The computer equipment can be, for example, the backend (i.e., server) of an electronic map. Figure 12 The data processing device shown can be a computer program running on a computer device, which can be used to execute... Figure 5 , Figure 7 or Figure 9 Some or all of the steps in the method embodiments shown. Please refer to [link / reference]. Figure 12 The data processing apparatus may include the following units:

[0178] The acquisition unit 1201 is used to acquire first detection data of the first entrance road segment of the intersection; the intersection includes at least one entrance road segment and at least one exit road segment, and one entrance road segment and one exit road segment form a flow direction of the intersection; the first entrance road segment is any one of the entrance road segments of the intersection; the first detection data includes vehicle data of multiple first vehicles, and the first vehicles have passed through the first entrance road segment within the detection cycle but have not yet reached any one of the exit road segments of the intersection;

[0179] The processing unit 1202 is used to perform layered road condition detection on the first entrance road segment based on the first detection data, and obtain the layered road condition detection result of the first entrance road segment. The layered road condition detection result is used to indicate the L types of road condition information that appear in the first entrance road segment within the detection period, where L is an integer greater than 1.

[0180] The processing unit 1202 is also used to filter out the first exit road segment from the intersection that has not yet formed a flow direction within the detection cycle, and to obtain the road condition information of the first exit road segment.

[0181] The processing unit 1202 is also used to match the road condition information of the first exit road segment with L types of road condition information. If the matching is successful, the road condition information of the first exit road segment is set as the road condition information of the first flow direction. The first flow direction is formed by the first entrance road segment and the first exit road segment.

[0182] In one implementation, the vehicle data of any first vehicle includes the vehicle speed information of the corresponding first vehicle passing through the first entrance road segment within the detection period; the processing unit 1202 is used to perform layered road condition detection on the first entrance road segment based on the first detection data, and when obtaining the layered road condition detection result of the first entrance road segment, it is specifically used to perform the following steps:

[0183] Based on the distribution characteristics of vehicle speed information in the first detection data, the first detection data is processed into data layers to obtain L data layers; one of the L data layers corresponds to one type of road condition information.

[0184] The L types of road condition information corresponding to the L data layers are determined as the layered road condition detection results for the first entrance road segment.

[0185] In one implementation, the vehicle data of any first vehicle also includes vehicle time information of the corresponding first vehicle passing through the first entrance road segment within the detection period; the processing unit 1202 is used to perform data layering processing on the first detection data according to the distribution characteristics of the vehicle speed information in the first detection data, and to obtain L data layers, specifically to perform the following steps:

[0186] Based on the vehicle speed information in the first detection data, the vehicle data in the first detection data is classified into M first datasets. Each of the M first datasets corresponds to different road condition information, and M is an integer greater than 1.

[0187] Sort the M first datasets according to the number of vehicle data in each of the M first datasets;

[0188] Following a time window filtering strategy, the sorted M first datasets are filtered to obtain K second datasets. The time ranges of the K second datasets overlap, where K is an integer greater than 1 and less than or equal to M. The time range of any second dataset is determined based on the vehicle time information in the corresponding second dataset.

[0189] According to the adjacent road condition detection strategy, adjacent road condition detection is performed on K second datasets to obtain L third datasets. One of the L third datasets corresponds to a data layer. The speed difference between any two adjacent third datasets with corresponding road condition information in the L third datasets is greater than or equal to the difference threshold, and L is less than or equal to K.

[0190] In one implementation, the processing unit 1202, when filtering the sorted M first datasets according to a time window filtering strategy to obtain K second datasets, specifically performs the following steps:

[0191] The time window is initialized using the time range corresponding to the first of the M sorted first datasets, and the second of the M sorted first datasets is used as the current dataset.

[0192] Perform intersection detection between the time window and the time range corresponding to the current dataset;

[0193] If the intersection detection result indicates that the time window intersects with the time range corresponding to the current dataset, then the time window is expanded according to the time range corresponding to the current dataset, and the current dataset is updated using the next first dataset of the current dataset;

[0194] If the intersection detection result indicates that the time window does not intersect with the time range corresponding to the current dataset, then the current dataset is deleted from the M first datasets, and the current dataset is updated with the next first dataset of the current dataset;

[0195] Repeatedly perform intersection detection and the operation triggered by the intersection detection result until the last first dataset in the M first datasets. Then, determine the remaining first datasets in the M first datasets excluding the deleted first datasets as K second datasets.

[0196] In one implementation, the processing unit 1202, when performing adjacent road condition detection on K second datasets according to the adjacent road condition detection strategy to obtain L third datasets, specifically performs the following steps:

[0197] Based on the speed information corresponding to two adjacent second datasets of road condition information in K second datasets, calculate the first speed difference information between two adjacent second datasets of road condition information;

[0198] If the difference in the first speed information is less than the difference threshold, then the two adjacent second datasets corresponding to the road condition information are merged to obtain a new second dataset.

[0199] If the first speed difference information is greater than or equal to the difference threshold, then keep the two adjacent second datasets of the corresponding road condition information unchanged;

[0200] If there is a merged second dataset among the K second datasets, then the merged second datasets are determined as L third datasets; if there is no merged second dataset among the K second datasets, then the K second datasets are determined as L third datasets.

[0201] In one implementation, the processing unit 1202, when classifying the vehicle data in the first detection data into M first datasets based on the vehicle speed information in the first detection data, specifically performs the following steps:

[0202] Obtain the speed thresholds corresponding to N types of road condition information, where N is an integer greater than 1;

[0203] Based on the speed thresholds corresponding to N types of road condition information and the vehicle speed information in the first detection data, the vehicle data in the first detection data is divided into N datasets, and one dataset in the N datasets corresponds to one type of road condition information among the N types of road condition information.

[0204] Select M valid datasets from N datasets and determine the M valid datasets as the M first datasets, where M is less than or equal to N; a valid dataset is a dataset from the N datasets that contains at least two vehicle data.

[0205] In one implementation, the processing unit 1202, when matching the road condition information of the first exit road segment with L types of road condition information, specifically performs the following steps:

[0206] The traffic information for the first exit road segment is queried from the L types of traffic information to obtain the query results;

[0207] If the query results indicate that there is road condition information in type L that is the same as the road condition information of the first exit road segment, then the match is considered successful.

[0208] In one implementation, the acquisition unit 1201 is also used to perform the following steps:

[0209] Acquire second detection data for the first entrance road segment; the second detection data includes vehicle data for at least one second vehicle, which passes through the first entrance road segment and arrives at an exit road segment of the intersection within the detection cycle;

[0210] Based on the first entrance road segment and at least one exit road segment where a second vehicle arrives, determine one or more second flow directions at the intersection;

[0211] The vehicle data of each second vehicle in the second detection data is divided into the dataset corresponding to the second flow direction to which the second vehicle belongs;

[0212] Based on the vehicle data in the dataset corresponding to each second flow direction, determine the road condition information for each second flow direction.

[0213] In one implementation, each vehicle data in the second detection data includes vehicle speed information corresponding to the second vehicle passing through the first entrance road segment; the processing unit 1202, when determining the road condition information for each second flow direction based on the vehicle data in the dataset corresponding to each second flow direction, specifically performs the following steps:

[0214] Based on the vehicle speed information in the dataset corresponding to each second flow direction, determine the flow direction speed information for each second flow direction;

[0215] The road condition information mapped to the flow velocity information of each second flow direction is determined as the road condition information for each second flow direction.

[0216] In one implementation, the processing unit 1202 is further configured to perform the following steps:

[0217] Acquire different road condition information for the same flow direction within the detection period;

[0218] Different road condition information in the same direction of flow is fused to obtain target road condition information in the same direction of flow.

[0219] In one implementation, the processing unit 1202 is further configured to perform the following steps:

[0220] Traffic conditions for each direction of traffic at the intersection will be displayed on the electronic map.

[0221] According to one embodiment of this application, Figure 12 The data processing apparatus shown can be constructed by combining each unit individually or entirely into one or more other units, or one or more of the units can be further divided into multiple functionally smaller units. This achieves the same operation without affecting the technical effects of the embodiments of this application. The above-mentioned units are based on logical function division. In practical applications, the function of one unit can be implemented by multiple units, or the function of multiple units can be implemented by one unit. In other embodiments of this application, the data processing apparatus may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented collaboratively by multiple units.

[0222] According to another embodiment of this application, the following can be achieved by running on a general-purpose computing device, such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM), a device capable of performing operations such as... Figure 5, Figure 7 or Figure 9 Computer programs for the steps involved in some or all of the methods shown, to construct, for example... Figure 12 The data processing apparatus shown herein, and the data processing method for implementing the embodiments of this application, are described. A computer program may be recorded on, for example, a computer-readable storage medium, loaded onto the aforementioned computing device via the computer-readable storage medium, and executed therein.

[0223] In this embodiment, for multiple first vehicles that have passed through the first entrance road segment of the intersection within the detection period but have not yet reached any exit road segment of the intersection, which is at least one vehicle that has not completed the intersection, the first entrance road segment can be subjected to layered road condition detection based on the vehicle data of the multiple first vehicles to obtain the layered road condition detection result of the first entrance road segment. The layered road condition detection result can be used to indicate L types of road condition information that appear in the first entrance road segment within the detection period, where L is an integer greater than 1. The first exit road segment that has not yet formed a flow direction within the detection period can be filtered from the intersection, and the road condition information of the first exit road segment can be matched with the L types of road condition information. If the match is successful, the road condition information of the first exit road segment can be set as the road condition information of the first flow direction formed by the first entrance road segment and the first exit road segment. As can be seen, this application embodiment, through layered road condition detection, can utilize the layered road condition information of vehicles that have not completed their journeys at intersections to match the road condition information of exit road segments that have not formed a flow direction within the detection period, in order to infer the flow direction to which the incomplete trajectory belongs, and the road condition information in that flow direction; thus, this application embodiment can determine the road condition information of the flow direction at the intersection, that is, the flow direction-level road condition information at the intersection, and in particular, can determine the road condition information of the flow direction to which the incomplete trajectory belongs.

[0224] Based on the above methods and apparatus embodiments, this application provides a computer device. Please refer to... Figure 13 , Figure 13 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Figure 13 The computer device shown includes at least a processor 1301, an input interface 1302, an output interface 1303, and a computer-readable storage medium 1304. The processor 1301, input interface 1302, output interface 1303, and computer-readable storage medium 1304 can be connected via a bus or other means.

[0225] The computer-readable storage medium 1304 can be stored in the memory of a computer device. The computer-readable storage medium 1304 is used to store computer programs, which include computer instructions. The processor 1301 is used to execute the computer program stored in the computer-readable storage medium 1304. The processor 1301 (or CPU (Central Processing Unit)) is the computing and control core of the computer device. It is suitable for implementing computer programs, specifically for loading and executing computer programs to achieve corresponding methods or functions.

[0226] This application also provides a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space for storing the operating system of the computer device. Furthermore, the storage space also stores computer programs suitable for loading and execution by a processor. It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device; optionally, it can also be at least one computer-readable storage medium located remotely from the aforementioned processor.

[0227] The computer device can be the backend (i.e., server) of the electronic map. Specifically, the processor 1301 can load and execute the computer program stored in the computer-readable storage medium 1304 to achieve the aforementioned... Figure 5 , Figure 7 or Figure 9 The corresponding steps in the method shown. In a specific implementation, the computer program in the computer-readable storage medium 1304 is loaded by the processor 1301 and executed as follows:

[0228] Acquire the first detection data of the first entrance road segment of the intersection; the intersection includes at least one entrance road segment and at least one exit road segment, and one entrance road segment and one exit road segment form a flow direction of the intersection; the first entrance road segment is any one of the entrance road segments of the intersection; the first detection data includes vehicle data of multiple first vehicles, and the first vehicles have passed through the first entrance road segment within the detection cycle but have not yet reached any one of the exit road segments of the intersection;

[0229] Based on the first detection data, the first entrance road segment is subjected to layered road condition detection to obtain the layered road condition detection results of the first entrance road segment. The layered road condition detection results are used to indicate the L types of road condition information that appear in the first entrance road segment within the detection period, where L is an integer greater than 1.

[0230] The system filters out the first exit road segment from the intersection that has not yet formed a flow direction within the detection period, and obtains the road condition information of the first exit road segment.

[0231] The road condition information of the first exit road segment is matched with L types of road condition information. If the match is successful, the road condition information of the first exit road segment is set as the road condition information of the first flow direction. The first flow direction is formed by the first entrance road segment and the first exit road segment.

[0232] In one implementation, the vehicle data of any first vehicle includes vehicle speed information of the corresponding first vehicle passing through the first entrance road segment within the detection period; when the computer program in the computer-readable storage medium 1304 is loaded and executed by the processor 1301 to perform layered road condition detection on the first entrance road segment based on the first detection data and obtain the layered road condition detection result of the first entrance road segment, it is specifically used to perform the following steps:

[0233] Based on the distribution characteristics of vehicle speed information in the first detection data, the first detection data is processed into data layers to obtain L data layers; one of the L data layers corresponds to one type of road condition information.

[0234] The L types of road condition information corresponding to the L data layers are determined as the layered road condition detection results for the first entrance road segment.

[0235] In one implementation, the vehicle data of any first vehicle also includes vehicle time information of the corresponding first vehicle passing through the first entrance road segment within the detection period; the computer program in the computer-readable storage medium 1304 is loaded and executed by the processor 1301, and when performing data layering processing on the first detection data according to the distribution characteristics of the vehicle speed information in the first detection data to obtain L data layers, it is specifically used to perform the following steps:

[0236] Based on the vehicle speed information in the first detection data, the vehicle data in the first detection data is classified into M first datasets. Each of the M first datasets corresponds to different road condition information, and M is an integer greater than 1.

[0237] Sort the M first datasets according to the number of vehicle data in each of the M first datasets;

[0238] Following a time window filtering strategy, the sorted M first datasets are filtered to obtain K second datasets. The time ranges of the K second datasets overlap, where K is an integer greater than 1 and less than or equal to M. The time range of any second dataset is determined based on the vehicle time information in the corresponding second dataset.

[0239] According to the adjacent road condition detection strategy, adjacent road condition detection is performed on K second datasets to obtain L third datasets. One of the L third datasets corresponds to a data layer. The speed difference between any two adjacent third datasets with corresponding road condition information in the L third datasets is greater than or equal to the difference threshold, and L is less than or equal to K.

[0240] In one implementation, when the computer program in the computer-readable storage medium 1304 is loaded and executed by the processor 1301 to filter the sorted M first datasets according to a time window filtering strategy to obtain K second datasets, it specifically performs the following steps:

[0241] The time window is initialized using the time range corresponding to the first of the M sorted first datasets, and the second of the M sorted first datasets is used as the current dataset.

[0242] Perform intersection detection between the time window and the time range corresponding to the current dataset;

[0243] If the intersection detection result indicates that the time window intersects with the time range corresponding to the current dataset, then the time window is expanded according to the time range corresponding to the current dataset, and the current dataset is updated using the next first dataset of the current dataset;

[0244] If the intersection detection result indicates that the time window does not intersect with the time range corresponding to the current dataset, then the current dataset is deleted from the M first datasets, and the current dataset is updated with the next first dataset of the current dataset;

[0245] Repeatedly perform intersection detection and the operation triggered by the intersection detection result until the last first dataset in the M first datasets. Then, determine the remaining first datasets in the M first datasets excluding the deleted first datasets as K second datasets.

[0246] In one implementation, when the computer program in the computer-readable storage medium 1304 is loaded and executed by the processor 1301 to perform adjacent road condition detection on K second datasets according to the adjacent road condition detection strategy to obtain L third datasets, it is specifically used to perform the following steps:

[0247] Based on the speed information corresponding to two adjacent second datasets of road condition information in K second datasets, calculate the first speed difference information between two adjacent second datasets of road condition information;

[0248] If the difference in the first speed information is less than the difference threshold, then the two adjacent second datasets corresponding to the road condition information are merged to obtain a new second dataset.

[0249] If the first speed difference information is greater than or equal to the difference threshold, then keep the two adjacent second datasets of the corresponding road condition information unchanged;

[0250] If there is a merged second dataset among the K second datasets, then the merged second datasets are determined as L third datasets; if there is no merged second dataset among the K second datasets, then the K second datasets are determined as L third datasets.

[0251] In one implementation, when the computer program in the computer-readable storage medium 1304 is loaded and executed by the processor 1301 to classify the vehicle data in the first detection data into M first datasets based on the vehicle speed information in the first detection data, it specifically performs the following steps:

[0252] Obtain the speed thresholds corresponding to N types of road condition information, where N is an integer greater than 1;

[0253] Based on the speed thresholds corresponding to N types of road condition information and the vehicle speed information in the first detection data, the vehicle data in the first detection data is divided into N datasets, and one dataset in the N datasets corresponds to one type of road condition information among the N types of road condition information.

[0254] Select M valid datasets from N datasets and determine the M valid datasets as the M first datasets, where M is less than or equal to N; a valid dataset is a dataset from the N datasets that contains at least two vehicle data.

[0255] In one implementation, when the computer program in the computer-readable storage medium 1304 is loaded and executed by the processor 1301 to match the road condition information of the first exit road segment with L types of road condition information, it specifically performs the following steps:

[0256] The traffic information for the first exit road segment is queried from the L types of traffic information to obtain the query results;

[0257] If the query results indicate that there is road condition information in type L that is the same as the road condition information of the first exit road segment, then the match is considered successful.

[0258] In one implementation, the computer program in the computer-readable storage medium 1304 is loaded by the processor 1301 and is also used to perform the following steps:

[0259] Acquire second detection data for the first entrance road segment; the second detection data includes vehicle data for at least one second vehicle, which passes through the first entrance road segment and arrives at an exit road segment of the intersection within the detection cycle;

[0260] Based on the first entrance road segment and at least one exit road segment where a second vehicle arrives, determine one or more second flow directions at the intersection;

[0261] The vehicle data of each second vehicle in the second detection data is divided into the dataset corresponding to the second flow direction to which the second vehicle belongs;

[0262] Based on the vehicle data in the dataset corresponding to each second flow direction, determine the road condition information for each second flow direction.

[0263] In one implementation, each vehicle data in the second detection data includes vehicle speed information corresponding to the second vehicle passing through the first entrance road segment; when the computer program in the computer-readable storage medium 1304 is loaded and executed by the processor 1301 to determine the road condition information for each second flow direction based on the vehicle data in the dataset corresponding to each second flow direction, it is specifically used to perform the following steps:

[0264] Based on the vehicle speed information in the dataset corresponding to each second flow direction, determine the flow direction speed information for each second flow direction;

[0265] The road condition information mapped to the flow velocity information of each second flow direction is determined as the road condition information for each second flow direction.

[0266] In one implementation, the computer program in the computer-readable storage medium 1304 is loaded by the processor 1301 and is also used to perform the following steps:

[0267] Acquire different road condition information for the same flow direction within the detection period;

[0268] Different road condition information in the same direction of flow is fused to obtain target road condition information in the same direction of flow.

[0269] In one implementation, the computer program in the computer-readable storage medium 1304 is loaded by the processor 1301 and is also used to perform the following steps:

[0270] Traffic conditions for each direction of traffic at the intersection will be displayed on the electronic map.

[0271] In this embodiment, for multiple first vehicles that have passed through the first entrance road segment of the intersection within the detection period but have not yet reached any exit road segment of the intersection, which is at least one vehicle that has not completed the intersection, the first entrance road segment can be subjected to layered road condition detection based on the vehicle data of the multiple first vehicles to obtain the layered road condition detection result of the first entrance road segment. The layered road condition detection result can be used to indicate L types of road condition information that appear in the first entrance road segment within the detection period, where L is an integer greater than 1. The first exit road segment that has not yet formed a flow direction within the detection period can be filtered from the intersection, and the road condition information of the first exit road segment can be matched with the L types of road condition information. If the match is successful, the road condition information of the first exit road segment can be set as the road condition information of the first flow direction formed by the first entrance road segment and the first exit road segment. As can be seen, this application embodiment, through layered road condition detection, can utilize the layered road condition information of vehicles that have not completed their journeys at intersections to match the road condition information of exit road segments that have not formed a flow direction within the detection period, in order to infer the flow direction to which the incomplete trajectory belongs, and the road condition information in that flow direction; thus, this application embodiment can determine the road condition information of the flow direction at the intersection, that is, the flow direction-level road condition information at the intersection, and in particular, can determine the road condition information of the flow direction to which the incomplete trajectory belongs.

[0272] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. A processor of a computer device reads the computer program from the computer-readable storage medium and executes the computer program, causing the computer device to perform the data processing method described above.

[0273] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0274] In this application embodiment, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0275] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0276] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A data processing method, characterized in that, include: Acquire first detection data of the first entrance road segment of the intersection; the intersection includes at least one entrance road segment and at least one exit road segment, one entrance road segment and one exit road segment form a flow direction of the intersection; the first detection data includes vehicle data of multiple first vehicles, the first vehicles passing through the first entrance road segment within the detection period and not yet reaching any exit road segment of the intersection; Based on the first detection data, the first entrance road segment is subjected to layered road condition detection to obtain the layered road condition detection result of the first entrance road segment. The layered road condition detection result is used to indicate the L types of road condition information that appear in the first entrance road segment within the detection period, where L is an integer greater than 1. The first exit road segment that has not yet formed a flow direction within the detection period is selected from the intersection, and the road condition information of the first exit road segment is obtained; The road condition information of the first exit road segment is matched with the L types of road condition information. If the match is successful, the road condition information of the first exit road segment is set as the road condition information of the first flow direction. The first flow direction is formed by the first entrance road segment and the first exit road segment.

2. The method as described in claim 1, characterized in that, The vehicle data for any one of the first vehicles includes vehicle speed information for the corresponding first vehicle passing through the first entrance road segment within the detection period; the step of performing layered road condition detection on the first entrance road segment based on the first detection data to obtain the layered road condition detection result of the first entrance road segment includes: Based on the distribution characteristics of vehicle speed information in the first detection data, the first detection data is subjected to data layering processing to obtain L data layers; one of the L data layers corresponds to a type of road condition information. The L types of road condition information corresponding to the L data layers are determined as the layered road condition detection results of the first entrance road segment.

3. The method as described in claim 2, characterized in that, The vehicle data for any of the first vehicles also includes vehicle time information corresponding to the first vehicle passing through the first entrance road segment within the detection period; the step of performing data layering processing on the first detection data based on the distribution characteristics of the vehicle speed information in the first detection data to obtain L data layers includes: Based on the vehicle speed information in the first detection data, the vehicle data in the first detection data is classified into M first datasets. Each of the M first datasets corresponds to different road condition information, and M is an integer greater than 1. The M first datasets are sorted according to the number of vehicle data in each of the M first datasets; According to the time window filtering strategy, the sorted M first datasets are filtered to obtain K second datasets; the time ranges corresponding to the K second datasets have an intersection, K is an integer greater than 1 and K is less than or equal to M; the time range corresponding to any second dataset is determined based on the vehicle time information in the corresponding second dataset; According to the adjacent road condition detection strategy, adjacent road condition detection is performed on the K second datasets to obtain L third datasets. One of the L third datasets corresponds to one of the data layers. The speed difference information between any two adjacent third datasets with corresponding road condition information in the L third datasets is greater than or equal to the difference threshold, and L is less than or equal to K.

4. The method as described in claim 3, characterized in that, The sorted M first datasets are filtered according to a time window filtering strategy to obtain K second datasets, including: The time window is initialized using the time range corresponding to the first first dataset among the M sorted first datasets, and the second first dataset among the M sorted first datasets is taken as the current dataset; Intersection detection is performed between the time window and the time range corresponding to the current dataset; If the intersection detection result indicates that the time window intersects with the time range corresponding to the current dataset, then the time window is expanded according to the time range corresponding to the current dataset, and the current dataset is updated using the next first dataset of the current dataset; If the intersection detection result indicates that the time window does not intersect with the time range corresponding to the current dataset, then the current dataset is deleted from the M first datasets, and the current dataset is updated with the next first dataset of the current dataset; Repeat the intersection detection and the operation triggered by the intersection detection result until the last first dataset in the M first datasets is determined as the K second datasets.

5. The method as described in claim 3, characterized in that, The adjacent road condition detection strategy is used to perform adjacent road condition detection on the K second datasets to obtain L third datasets, including: Based on the speed information corresponding to two adjacent second datasets of the corresponding road condition information in the K second datasets, calculate the first speed difference information between the two adjacent second datasets of the corresponding road condition information; If the first speed difference information is less than the difference threshold, then the two adjacent second datasets of the corresponding road condition information are merged to obtain a new second dataset; If the first speed difference information is greater than or equal to the difference threshold, then the two second datasets adjacent to the corresponding road condition information remain unchanged; If there is a merged second dataset among the K second datasets, then each merged second dataset is determined as the L third datasets; if there is no merged second dataset among the K second datasets, then the K second datasets are determined as the L third datasets.

6. The method as described in claim 3, characterized in that, The step of classifying the vehicle data in the first detection data into M first datasets based on the vehicle speed information in the first detection data includes: Obtain the speed thresholds corresponding to N types of road condition information, where N is an integer greater than 1; Based on the speed thresholds corresponding to the N types of road condition information and the vehicle speed information of the vehicle data in the first detection data, the vehicle data in the first detection data is divided into N datasets, and one of the N datasets corresponds to one type of road condition information among the N types of road condition information. Select M valid datasets from the N datasets, and determine the M valid datasets as the M first datasets, where M is less than or equal to N; the valid datasets refer to the datasets in the N datasets that contain at least two vehicle data.

7. The method according to any one of claims 1-6, characterized in that, The process of matching the road condition information of the first exit road segment with the L types of road condition information includes: The road condition information of the first exit road segment is queried from the L types of road condition information to obtain the query results; If the query result indicates that there is road condition information in the L types of road condition information that is the same as the road condition information of the first exit road segment, then the match is determined to be successful.

8. The method according to any one of claims 1-6, characterized in that, The method further includes: Acquire second detection data for the first entrance road segment; the second detection data includes vehicle data for multiple second vehicles, which pass through the first entrance road segment and arrive at an exit road segment of the intersection within the detection cycle; Based on the first entrance road segment and the exit road segments to which the plurality of second vehicles arrive, determine one or more second flow directions at the intersection; The vehicle data of each second vehicle in the second detection data is divided into the dataset corresponding to the second flow direction to which the second vehicle belongs; Based on the vehicle data in the dataset corresponding to each second flow direction, determine the road condition information for each second flow direction.

9. The method as described in claim 8, characterized in that, Each vehicle data in the second detection data includes the vehicle speed information of the corresponding second vehicle as it passes through the first entrance road segment; The step of determining the road condition information for each second flow direction based on the vehicle data in the dataset corresponding to each second flow direction includes: Based on the vehicle speed information in the dataset corresponding to each second flow direction, determine the flow direction speed information for each second flow direction; The road condition information mapped to the flow velocity information of each second flow direction is determined as the road condition information for each second flow direction.

10. The method according to any one of claims 1-6, characterized in that, The method further includes: Acquire different road condition information for the same flow direction within the detection period; Different road condition information in the same flow direction is fused to obtain the target road condition information in the same flow direction.

11. The method according to any one of claims 1-6, characterized in that, The method further includes: Traffic information for each direction of flow at the intersection will be published on the electronic map.

12. A data processing apparatus, characterized in that, include: An acquisition unit is used to acquire first detection data of a first entrance road segment of an intersection; the intersection includes at least one entrance road segment and at least one exit road segment, one entrance road segment and one exit road segment forming a flow direction of the intersection; the first detection data includes vehicle data of a plurality of first vehicles, wherein the first vehicles have passed through the first entrance road segment within the detection period and have not yet reached any exit road segment of the intersection; The processing unit is configured to perform layered traffic condition detection on the first entrance road segment based on the first detection data, and obtain the layered traffic condition detection result of the first entrance road segment. The layered traffic condition detection result is used to indicate the L types of traffic condition information that appear in the first entrance road segment within the detection period, where L is an integer greater than 1. The processing unit is also used to filter out the first exit road segment from the intersection that has not yet formed a flow direction within the detection period, and to obtain the road condition information of the first exit road segment. The processing unit is further configured to match the road condition information of the first exit road segment with the L types of road condition information. If the match is successful, the road condition information of the first exit road segment is set as the road condition information of the first flow direction. The first flow direction is formed by the first entrance road segment and the first exit road segment.

13. A computer device, characterized in that, The computer device includes: A processor is a tool for implementing computer programs. A computer-readable storage medium storing a computer program adapted to be loaded by the processor and executed as described in any one of claims 1-11.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and executed as described in any one of claims 1-11.

15. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the data processing method as described in any one of claims 1-11.

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