Traffic congestion discrimination method and device based on map coordinates and vehicle big data

Abstract

The embodiment of the application provides a traffic congestion discrimination method and device based on map coordinates and vehicle big data, fully utilizes high-precision coordinate information provided by modern map services and massive vehicle driving data, and realizes rapid and accurate discrimination of a traffic congestion state through comprehensive analysis of multidimensional data such as vehicle driving directions and speeds, thereby providing timely and reliable decision support for a traffic management department, optimizing traffic flow management, improving road passing efficiency, and reducing traffic delay and environmental pollution. The method innovatively combines map geometric analysis and vehicle big data processing, overcomes many limitations of traditional methods, and has remarkable application value and popularization prospect.

Description

Technical Field

[0001] This application relates to the field of data processing, specifically to a method and apparatus for traffic congestion identification based on map coordinates and vehicle big data. Background Technology

[0002] With the acceleration of urbanization and the rapid development of the automotive industry, urban traffic congestion has become increasingly serious, bringing many negative impacts on people's daily lives, economic development, and environmental quality. Against this backdrop, accurately and promptly identifying congested road sections has become a key issue in the field of intelligent traffic management.

[0003] Traditional traffic congestion assessment methods primarily rely on fixed traffic flow sensors and cameras. These devices are costly to deploy, have limited coverage, and offer infrequent data updates, making them insufficient for real-time traffic management. Furthermore, while some methods based solely on vehicle counts can reflect traffic volume to some extent, they often fail to accurately determine congestion levels due to a lack of comprehensive consideration of key information such as road geometry and vehicle direction. For example, on two-way roads, simply counting vehicles cannot distinguish between traffic flows from different directions, easily leading to misjudgments. Similarly, at complex overpasses or curves, the diversity of vehicle directions makes traditional methods ineffective in identifying congested sections.

[0004] To address the aforementioned problems, this invention proposes a traffic congestion identification method and apparatus based on map coordinates and vehicle big data. By fully utilizing the high-precision coordinate information provided by modern map services and massive amounts of vehicle driving data, it achieves rapid and accurate identification of traffic congestion status, providing timely and reliable decision support for traffic management departments to optimize traffic flow management, improve road traffic efficiency, and reduce traffic delays and environmental pollution. This method innovatively combines map geometric analysis with vehicle big data processing, overcoming many limitations of traditional methods and possessing significant application value and promising prospects for widespread adoption. Summary of the Invention

[0005] To address the problems in existing technologies, this application provides a traffic congestion identification method and device based on map coordinates and vehicle big data, in order to solve the problems of high management costs and difficulty in dynamic expansion when deploying xxl-jobs separately, and to realize the automatic discovery and dynamic scheduling of scheduled tasks in a microservice environment.

[0006] To solve at least one of the above problems, this application provides the following technical solution:

[0007] Firstly, this application provides a traffic congestion identification method based on map coordinates and vehicle big data, including:

[0008] Obtain the total route of the vehicle, which is divided into multiple segments based on selected points on the map; calculate the direction angle of the segment and the vehicle's direction of travel on the segment based on the starting and ending coordinates of the segment.

[0009] Acquire all capture devices on the road segment, and obtain the distance of each capture device to the road segment and its capture direction; determine the capture devices whose distance is less than a set threshold and whose capture direction is consistent with the driving direction of vehicles on the road segment as the devices belonging to the road segment.

[0010] Calculate the distance between the equipment belonging to a road segment and the starting point of the road segment, and sort all the equipment belonging to the road segment in ascending order of distance to obtain the trajectory route of the vehicle in the road segment.

[0011] The travel time and speed of all vehicles on the trajectory route are obtained from the information collected by the capture devices on the trajectory route. When the travel speed of a vehicle is less than a first threshold and the proportion of vehicles with a travel speed less than the first threshold is greater than a second threshold, the road segment is determined to be congested.

[0012] Furthermore, the step of calculating the direction angle of the road segment based on the starting and ending coordinates of the road segment includes:

[0013] Convert the latitude and longitude coordinates of the starting and ending points of the road segment into radian values;

[0014] Using the four-quadrant arctangent function, and based on the stated radian value, calculate the direction angle in radians from the starting point coordinates to the ending point coordinates of the road segment;

[0015] The direction angle in radians is converted into standard latitude and longitude angles, and the direction angle of the road segment is determined based on the standard latitude and longitude angles.

[0016] Furthermore, the step of calculating the direction angle of the road segment based on the starting and ending coordinates of the road segment includes:

[0017] If the calculation result is negative, add 360° to keep the direction angle range between 0° and 360°.

[0018] Set the maximum allowable deviation distance for road segments. When a road segment deviates from its straight segment by more than this deviation distance, it is split into segments, and the direction angle is calculated for each straight segment separately.

[0019] Furthermore, the step of obtaining the distance from each capture device to the road segment includes:

[0020] The distance D from the camera P to the road segment AB is calculated based on the relationship between the camera P and the two endpoints A and B of the road segment AB:

[0021] like Then the angle between AP and BP is an obtuse angle, and the distance D is the perpendicular distance from the camera P to the road segment AB.

[0022] like Then the angle between AP and AB is obtuse, and the distance D is the distance to AP;

[0023] like If AB and BP form an obtuse angle, then the distance D is the distance from BP.

[0024] Furthermore, the step of obtaining the distance from each capture device to the road segment includes:

[0025] GeoHash encoding is used to convert the coordinates of the capture devices into string hash values, and the capture devices are grouped according to the first few bits of the GeoHash encoding.

[0026] The cameras near the road segment are grouped, and the distance to the cameras within each group is calculated.

[0027] The method for determining whether the capture direction is consistent with the travel direction of vehicles on that road section is as follows:

[0028] Define the threshold angle between the capture direction and the road segment direction, and set the angle = abs(capture direction - road segment direction angle). If the angle is greater than 180°, then the angle = 360° - angle; if the angle is within ±30°, then it is determined that the capture direction matches the direction angle of the road segment, that is, it is consistent with the driving direction of the vehicles on the road segment.

[0029] Furthermore, the step of calculating the distance between the equipment belonging to the road segment and the starting point of the road segment includes:

[0030] Calculate the perpendicular point from the equipment belonging to the road segment to the road segment. Use the cumulative distance from the perpendicular point to the starting point of the road segment as the position parameter of the equipment belonging to the road segment on that road segment, and sort the equipment belonging to the road segment according to this position parameter.

[0031] Furthermore, the step of obtaining the travel time and speed of all vehicles on the trajectory route based on the information collected by the capture device on the trajectory route includes:

[0032] Acquire all vehicle data collected by the capture devices along the trajectory route;

[0033] Query the vehicle passage data of each vehicle at the first and last capture devices along its trajectory;

[0034] Calculate the speed of all vehicles on the trajectory based on travel time and distance.

[0035] Secondly, this application provides a traffic congestion identification device based on map coordinates and vehicle big data, comprising:

[0036] The road segment acquisition module is used to acquire the total route of the vehicle, which is divided into multiple road segments based on the selected points on the map; the direction angle of the road segment and the driving direction of the vehicle on the road segment are calculated based on the starting coordinates and ending coordinates of the road segment.

[0037] The device acquisition module is used to acquire all the capture devices on the road segment, and to acquire the distance of each capture device from the road segment and its capture direction; the capture devices whose distance is less than a set threshold and whose capture direction is consistent with the driving direction of the vehicles on the road segment are identified as the devices belonging to the road segment.

[0038] The trajectory acquisition module is used to calculate the distance between the equipment belonging to a road segment and the starting point of the road segment, and sort all the equipment belonging to the road segment in order of increasing distance to obtain the trajectory route of the vehicle in the road segment.

[0039] The congestion judgment module is used to obtain the travel time and speed of all vehicles on the trajectory route based on the information collected by the capture device on the trajectory route, and to determine that the road segment is congested when the vehicle speed is less than a first threshold and the proportion of vehicles with a speed less than the first threshold is greater than a second threshold.

[0040] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the traffic congestion determination method based on map coordinates and vehicle big data.

[0041] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the traffic congestion determination method based on map coordinates and vehicle big data.

[0042] Fifthly, this application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the traffic congestion determination method based on map coordinates and vehicle big data.

[0043] As can be seen from the above technical solution, this application provides a traffic congestion identification method and device based on map coordinates and vehicle big data. It fully utilizes the high-precision coordinate information provided by modern map services and massive amounts of vehicle driving data. Through comprehensive analysis of multi-dimensional data such as vehicle driving direction and speed, it achieves rapid and accurate identification of traffic congestion status, providing timely and reliable decision support for traffic management departments to optimize traffic flow management, improve road traffic efficiency, and reduce traffic delays and environmental pollution. This method innovatively combines map geometric analysis with vehicle big data processing, overcoming many limitations of traditional methods, and has significant application value and promising prospects for widespread application. Attached Figure Description

[0044] 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating the traffic congestion identification method based on map coordinates and vehicle big data in the embodiments of this application;

[0046] Figure 2 This is a schematic diagram of map road point connection for the traffic congestion judgment method based on map coordinates and vehicle big data in the embodiments of this application;

[0047] Figure 3 This is a structural diagram of the traffic congestion identification device based on map coordinates and vehicle big data in the embodiments of this application;

[0048] Figure 4 This is a schematic diagram of the structure of the electronic device in the embodiments of this application.

[0049] Figure label:

[0050] Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver storage unit 9144, antenna 9111, speaker 9131, microphone 9132. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, 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, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0052] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0053] In view of the problems existing in the prior art, this application provides a method and device for traffic congestion identification based on map coordinates and vehicle big data. It fully utilizes the high-precision coordinate information provided by modern map services and massive amounts of vehicle driving data. Through comprehensive analysis of multi-dimensional data such as vehicle driving direction and speed, it achieves rapid and accurate identification of traffic congestion status, providing timely and reliable decision support for traffic management departments to optimize traffic flow management, improve road traffic efficiency, and reduce traffic delays and environmental pollution. This method innovatively combines map geometric analysis with vehicle big data processing, overcoming many limitations of traditional methods, and has significant application value and promising prospects for widespread application.

[0054] To address the problem that existing methods cannot determine traffic congestion in a timely and accurate manner, this application provides an embodiment of a traffic congestion identification method based on map coordinates and big data on vehicles. See [link to embodiment]. Figures 1-2 The traffic congestion identification method based on map coordinates and vehicle big data specifically includes the following:

[0055] Step S101: Obtain the total route of the vehicle's journey. The total route is divided into multiple road segments based on the selected points on the map. Calculate the direction angle of the road segment and the vehicle's direction of travel on that road segment based on the starting and ending coordinates of the road segment.

[0056] refer to Figure 2 As shown in this embodiment, the map points are ABC. Two main routes can be formed between AC, namely, starting from A and ending at C, and starting from C and ending at A. This embodiment will use one of them as an example for explanation. The main route is divided into two segments: from point A to point B and from point B to point C.

[0057] Optionally, in this embodiment, the direction angle of the road segment is calculated based on the starting and ending coordinates of the road segment, specifically as follows:

[0058] (1) Convert the latitude and longitude coordinates of the starting point S (LonS, LatS) and ending point E (LonE, LatE) of the road segment into radian values:

[0059]

[0060] (2) Calculate the direction angle from the starting point coordinate S to the ending point coordinate E, as shown in the following formula:

[0061]

[0062] Among them, atan2(y, x) is the four-quadrant arctangent function, which determines the quadrant of the angle based on the signs of x and y. The returned angle range is π (-180°~180°), usually with due north as the reference (i.e., north is 0 degrees, and increases clockwise).

[0063] (3) Convert the direction angle in radians to standard latitude and longitude angles:

[0064] Angle value = θ ×

[0065] The method for determining the direction of a road segment using standard latitude and longitude angle values ​​is as follows:

[0066] 0° to 22.5° and 337.5° to 360°: The direction of the road segment is due north (N).

[0067] 22.5° to 67.5°: The road section is oriented northeast (NE).

[0068] 67.5° to 112.5°: The direction of the road segment is due east (E).

[0069] 112.5° to 157.5°: The road section is southeast (SE).

[0070] 157.5° to 202.5°: The direction of the road segment is due south (S).

[0071] 202.5° to 247.5°: The road section is oriented southwest (SW).

[0072] 247.5° to 292.5°: The direction of the road segment is due west (W).

[0073] 292.5° to 337.5°: The road section is oriented northwest (NW).

[0074] For example:

[0075] Suppose there is a route with the starting point coordinates S (LonS=116.397528, LatS=39.908735) and the ending point coordinates E (LonE=116.407528, LatE=39.909735).

[0076] 1. Convert the latitude and longitude of the starting and ending points to radians:

[0077] LatS radians = 39.908735 × ≈0.6968

[0078] LonS radians = 116.397528 × ≈2.0306

[0079] LatE radians = 39.909735 × ≈0.6969

[0080] LonE radians = 116.407528 × ≈2.0311

[0081] 2. Calculate the direction angle:

[0082] Lon radians = 2.0311 − 2.0306 = 0.0005

[0083] y=sin(0.0005)×cos(0.6969)≈0.00049999×0.7660≈0.000383

[0084] x=cos(0.6968)×sin(0.6969)−sin(0.6968)×cos(0.6969)×cos(0.0005)

[0085] Calculate the approximate values ​​for each part:

[0086] cos(0.6968)≈0.7661

[0087] sin(0.6969)≈0.6445

[0088] sin(0.6968)≈0.6444

[0089] cos(0.6969)≈0.7660

[0090] cos(0.0005)≈0.99999994

[0091] The expression to be substituted into x:

[0092] x≈0.7661×0.6445−0.6444×0.7660×0.99999994≈0.5−0.5≈0.0 (This is a simplified calculation; in reality, there will be a small non-zero value.)

[0093] Since the value of x is very small and close to zero, while y is positive, the direction angle θ = atan2(0.000383, 0) ≈ ≈1.5708 radians.

[0094] 3. Convert the radian values ​​to standard angle values:

[0095] Angle value = 1.5708 × ≈90°

[0096] This indicates that the direction angle from the starting point S to the ending point E is approximately 90°, i.e., due east. However, in actual calculations, due to slight differences in coordinates and calculation precision, the results may vary slightly, but they generally reflect that the approximate direction of this route is eastward. Through such calculations, the direction angle of each segment of the route on the map can be determined, providing accurate directional information for subsequent operations such as traffic congestion assessment.

[0097] Optionally, in this embodiment, an angle normalization step can be added when calculating the direction angle from the starting point coordinate S to the ending point coordinate E. If the calculation result is negative, 360° is added to ensure that the angle range is [0, 360°]. For example, if the calculated direction angle is -45°, it will be normalized to 315°.

[0098] Optionally, in this embodiment, to avoid polar region errors, the improved Vincenty formula can also be used to calculate the orientation angle:

[0099] θ=arctan2(sinΔLon·cosLatE, cosLatS·sinLatE- sinLatS·cosLatE·cos Lon)

[0100] in, Lon is the longitude difference between the starting point S and the ending point E. If the calculated direction angle θ is less than 0, then θ = θ + 2π, ultimately making the output direction angle range from 0° to 360°, with true north as 0° and increasing clockwise.

[0101] Optionally, in this embodiment, for complex roads (such as S-shaped curves), the route can be divided into multiple straight segments. Specifically, the splitting point can be determined by setting a maximum allowable deviation distance (e.g., 10 meters). When the route deviates from a straight segment by more than this deviation distance, methods such as Bézier curve interpolation can be used to segment the curved road, and then the direction angle is calculated for each straight segment. Simultaneously, the direction angle difference between adjacent segments can be detected. If it is greater than 45°, it is marked as a curve, and the segmentation density is increased at the curve to more accurately reflect changes in the route's direction.

[0102] Optionally, in this embodiment, the calculated road segments can also be verified by filtering invalid routes. If the length of the road segment is less than 5 meters, or the direction angle calculation fails (such as the starting point and the ending point coinciding, resulting in the inability to calculate the direction angle), the road segment is discarded and not included in the subsequent analysis.

[0103] Optionally, in this embodiment, the topological relationship of road segments can also be constructed: while generating the direction angle of road segments, the connection relationship between adjacent road segments is recorded to provide basic data for trajectory construction in step S103, so that adjacent line segments can be correctly connected when constructing the trajectory route. Specifically:

[0104] Node extraction: Identifying key nodes in the road network, such as intersections, junctions, and entrances / exits. These nodes are the connection points of road segments and also the turning points of vehicle travel paths. Nodes can be extracted by analyzing the intersections of road segments, that is, the intersection points of intersecting road segments are taken as nodes.

[0105] Edge Determination: Based on the extracted nodes, the edges in the road network are determined. Each edge represents a continuous road segment connecting two adjacent nodes. Edge attributes include the starting node, ending node, road type, length, number of lanes, and design speed.

[0106] Connection construction: Establishing connections between nodes and edges, that is, determining the edges connected to each node, as well as the starting and ending nodes connected to each edge. This can be achieved by finding the spatial relationships between nodes and edges. For example, for each node, find the edges that are within a certain threshold (such as 1 meter) and establish connections between these edges and the node.

[0107] Adjacency relationship construction: Constructing the adjacency relationships between edges, that is, determining the connection method and direction between adjacent edges. This can be achieved by analyzing the start and end nodes of the edges. If two edges share a common node, they are adjacent. At the same time, record the directional relationship between adjacent edges, such as forward connection (vehicles can travel directly from one edge to another) or reverse connection (vehicles need to turn around to travel from one edge to another).

[0108] Path connectivity check: This checks the connectivity of the road network to ensure that all edges and nodes form a connected network. For disconnected sections, further analysis and processing are required, as these may be dead ends caused by missing or incorrect data, necessitating data supplementation or correction.

[0109] Data Structure Selection: Choosing a suitable data structure to store topological relationships is crucial. Common data structures include adjacency lists, adjacency matrices, and linked lists. Adjacency lists are a commonly used data structure. They maintain an edge list for each node, recording information about all edges connected to that node. They also record the starting and ending nodes of each edge, as well as information about its adjacent edges.

[0110] Data storage implementation: The established topological relationships are stored in a database or file for subsequent querying and analysis. Relational databases (such as MySQL, PostgreSQL, etc.) or non-relational databases (such as MongoDB, Neo4j, etc.) can be used to store topological relationship data. When storing, a reasonable table structure or document structure needs to be designed to efficiently store and query nodes, edges, and the topological relationships between them.

[0111] Dynamic data updates: Updated road network data is periodically retrieved from map data providers to dynamically update the topology. This includes adding roads, modifying road attributes, and deleting abandoned roads. During the update process, data preprocessing, node and edge identification, and the establishment and storage of topology relationships need to be performed again.

[0112] Topology verification: After each update, the topology is verified to ensure that the updated topology remains correct and connected. A series of verification rules and algorithms can be run to check the integrity of the topology, such as checking for isolated nodes, duplicate edges, self-loops, etc., and any problems found can be corrected promptly.

[0113] By constructing topological relationships, an accurate, complete, and efficient road network topology model can be provided for traffic congestion identification methods, thereby supporting subsequent applications such as traffic flow analysis, route planning, and congestion identification.

[0114] Step S102: Obtain all capture devices on the road segment, and obtain the distance of each capture device to the road segment and its capture direction; determine the capture devices whose distance is less than a set threshold and whose capture direction is consistent with the driving direction of vehicles on the road segment as the devices belonging to the road segment.

[0115] Optionally, in this embodiment, the step of obtaining the distance from each capture device to the road segment is as follows: using a multi-threaded approach to determine the distance between the device and the path segment based on a set distance threshold, and initially determining the device on the path; for example, if there are approximately 40,000 device data, the number of threads is set to 20, and the distance threshold can be set to 10 meters, that is, if the distance between the device and the path segment is less than 10 meters, then the device is considered to be on the path.

[0116] The distance D between the device and the path segment is determined based on the relationship between the latitude and longitude of the device point P (LonP, LatP) and the two endpoints A (LonA, LatA) and B (LonB, LatB) of the path segment. The calculation is performed in three cases:

[0117] (1) When the angle between AP and BP is obtuse, then distance D is the perpendicular distance from point P to the path segment AB. If Therefore, the angle between AP and BP is obtuse. The perpendicular distance can be calculated using Heron's formula, as shown below:

[0118]

[0119] Where AB, BP, and AP are the line segments formed by the device point and the two endpoints of the path segment, respectively, D is the distance from the device point to the path segment, and S is the area of ​​triangle PAB.

[0120] (2) When the angle between AP and AB is obtuse, then the distance D is the distance to AP. If Therefore, the angle between AP and AB is obtuse. The formula for calculating the distance between device point P and endpoint A of the path segment is as follows:

[0121]

[0122] Where a = LatA - LatP is the difference in latitude between the two points, b = LonA - LonP is the difference in longitude between the two points, and 6378.137 is the radius of the Earth's equator, in kilometers.

[0123] (3) When the angle between AB and BP is obtuse, then the distance D is the distance to BP. If If the angle between AP and AB is obtuse, then the angle between AP and AB is obtuse. The formula for calculating the distance between device point P and the endpoint B of the path segment is the same as the formula for calculating distance D in (2).

[0124] Optionally, in this embodiment, spatial indexing technology can also be introduced to quickly retrieve device locations by converting geographic coordinates into string hash values, thereby optimizing distance calculation. Specifically, device location information can be converted into GeoHash encoding. For example, a grid precision of level 6 corresponds to a side length of approximately 10 meters, matching the distance threshold. 40,000 devices are stored in Redis according to their GeoHash encoding, where the key is the GeoHash value and the value is a list of device IDs. Then, the devices are grouped according to the first few digits of their GeoHash encoding. Devices within the same group are geographically closer. This allows for the initial selection of device groups likely near the route when calculating the distance from a device to a route segment, followed by precise distance calculation, thus improving retrieval efficiency.

[0125] Furthermore, an R-tree index can be built in memory, with each node's minimum bounding rectangle (MBR) containing at least 50 device coordinates. During a query, the MBR of the target road segment AB is extended by 10 meters (consistent with the distance threshold), and a range search is performed to quickly find devices that may be near the road segment.

[0126] Optionally, in this embodiment, the device direction matching rule can be set as follows: define the threshold of the angle between the direction of the capture device and the route direction as ±30°. For example, if the direction angle of a certain road segment is 90° (due east), then the direction of the capture device within the range of 60° - 120° is considered valid. Specifically, when calculating the angle between the device direction and the route direction, the formula can be used: Angle = abs(Device direction angle - Route direction angle). If the angle is greater than 180°, then the angle = 360° - angle. If the angle is within ±30°, then the device direction is considered to match the route direction, that is, consistent with the travel direction of vehicles on that road segment.

[0127] Step S103: Calculate the distance between the equipment belonging to the road segment and the starting point of the road segment, and sort all the equipment belonging to the road segment in order of increasing distance to obtain the trajectory route of the vehicle in the road segment.

[0128] Optionally, in this embodiment, a linear referencing system can be used to project the equipment belonging to the road segment onto the route segment. Specifically, the perpendicular point from the equipment to the route segment is calculated, and the cumulative distance from the perpendicular point to the starting point of the route is used as the equipment's position parameter on the route. The equipment is then sorted according to this parameter. For example, if the starting point of the route is A, and the distance from the perpendicular point of equipment P to point A is d, then the equipment is sorted according to the magnitude of d. Alternatively, the equipment coordinates P are projected onto the road segment AB, and the normalized position parameter t of the projection point P' is calculated. Specifically, t = [(PA)·(BA)] / |BA|², where t ranges from [0,1]. Based on the value of t, the equipment is arranged in ascending order, with the actual sorting criterion being S = A + t. (BA).

[0129] Simultaneously, for branch road segments, the topology map constructed in step S101 can be used to match the shortest reasonable path using Dijkstra's algorithm, thereby constructing a complete trajectory route. Specifically:

[0130] First, the road network data (including nodes, edges, and connections) provided by S101 is used to construct the trajectory route. This data is stored in a database or file, and S103 can directly query and use this data when constructing the trajectory.

[0131] Then, the device's coordinates are projected onto the nearest road. Using the edge information (such as the starting and ending coordinates of the road) in the topology constructed in S101, the perpendicular point from the device to each road is calculated, and the position of the perpendicular point in the road network is determined. Alternatively, the normalized position parameter t of the device on the edge is calculated.

[0132] Then, after determining the devices' projection onto the road network, the devices are sorted using the connectivity relationships in the topology to reflect the actual driving order of vehicles on the road, ensuring that the device sorting conforms to the road connectivity. For example, the connectivity relationships between nodes and edges in the topology can be used, and the sorting can be based on the cumulative distance (or normalized location parameters) of the devices' projection points on the road.

[0133] In complex road networks, since vehicles may change direction at intersections or branches, the branch and intersection information in the topology can be used to identify intersection nodes, and the most likely driving path of the vehicle can be determined based on the adjacency relationship in the topology. Then, a path planning algorithm (such as Dijkstra's algorithm) is used to match the shortest reasonable path to ensure the continuity and accuracy of the trajectory.

[0134] When constructing a trajectory route, if there are missing devices or discontinuous data, the adjacency and connectivity information in the topology can be used to interpolate and correct the trajectory to ensure its continuity and integrity.

[0135] Optionally, processing for abnormal data can be added. For example, the device coordinates can be determined to be drifted by calculating the changes in device position across multiple consecutive snapshots, and the drift can be corrected. For instance, the maximum offset tolerance between the device and the projection point P' can be set to 15 meters. That is, when calculating the distance from the device to the route segment, if the device position is found to be more than 15 meters off from the previously recorded position and exceeds the normal error range, it is marked as an anomaly. In this case, the historical average position can be used to replace the current coordinates to ensure data accuracy. Regarding missing road segments, continuous trajectories can be supplemented by Bézier curve fitting. Based on existing device position data, the control points of the Bézier curve are determined, and a smooth curve is generated to supplement the missing road segment trajectory.

[0136] Step S104: Based on the information collected by the capture device on the trajectory route, obtain the travel time and speed of all vehicles on the trajectory route, and determine that the road segment is congested when the vehicle speed is less than the first threshold and the proportion of vehicles with a speed less than the first threshold is greater than the second threshold.

[0137] Optionally, in this embodiment, the step of obtaining the travel time and speed of all vehicles on the trajectory route based on the information collected by the capture device on the trajectory route is specifically as follows:

[0138] The Flink sliding window computation engine is used to obtain information on all devices along the trajectory route. Vehicle passage data (passing the first and last devices) is queried from HDFS to construct time and distance sets, and the vehicle's speed along the trajectory route is calculated. For example, the Flink sliding window size is set to 5 minutes, and the sliding step is 1 minute. This method enables timely updating and processing of vehicle travel data, achieving real-time monitoring and analysis of traffic congestion. Simultaneously, ValueState is used to store the latest congestion level for each road segment, avoiding redundant calculations, improving computational efficiency, and ensuring the system can quickly respond to changes in traffic conditions.

[0139] The formula for calculating vehicle speed is: Where S_k is the device coordinate, and t_first and t_last are the times of the first and last capture of the vehicle, respectively. This formula calculates the vehicle's average speed by dividing the distance traveled along the trajectory by the travel time.

[0140] Furthermore, to ensure the accuracy of vehicle matching, a time window constraint can be used to match only data for the same vehicle within the time range of [t_first, t_first + road segment length / minimum speed limit]. For example, if the road segment length is 1km and the minimum speed limit is 30km / h, then the time window is 2 minutes. This avoids matching vehicle data unrelated to the current road segment, improving the accuracy of congestion detection.

[0141] Optionally, in this embodiment, a dynamic congestion threshold can also be set, that is, based on the relationship between vehicle speed and road speed limit, the congestion level is divided into four categories: smooth traffic, light congestion, heavy congestion, and abnormal state. Details are as follows:

[0142] Unobstructed: When the driving speed v is greater than or equal to 70% of the speed limit, the road is in an unobstructed state, and no alarm will be triggered at this time.

[0143] Mild congestion: When the driving speed v is between 50% and 70% of the speed limit, it is judged as mild congestion, triggering a yellow warning and recording relevant log information for subsequent analysis and processing.

[0144] Severe congestion: When the driving speed v is less than 50% of the speed limit, it is judged as severe congestion, triggering a red alert, and the congestion information is pushed to the traffic management platform to promptly notify relevant departments to carry out traffic management.

[0145] Abnormal Status: If the driving speed v exceeds 120% of the speed limit or falls below 5 km / h, it is considered an abnormal status. This situation may be caused by equipment failure, abnormal vehicle driving, or other reasons leading to data anomalies. In this case, the relevant data should be discarded, and an equipment maintenance alarm should be triggered for timely inspection and maintenance of the equipment.

[0146] This application presents a traffic congestion identification method based on map coordinates and vehicle big data. It fully utilizes the high-precision coordinate information and massive amounts of vehicle driving data provided by modern map services. Through comprehensive analysis of multi-dimensional data such as vehicle direction and speed, it achieves rapid and accurate identification of traffic congestion status, providing timely and reliable decision support for traffic management departments to optimize traffic flow management, improve road efficiency, and reduce traffic delays and environmental pollution. This innovative method combines map geometric analysis with vehicle big data processing, overcoming many limitations of traditional methods and possessing significant application value and promising prospects for wider application.

[0147] To address the problem that existing methods cannot determine traffic congestion in a timely and accurate manner, this application provides an embodiment of a traffic congestion discrimination device based on map coordinates and vehicle big data, which implements all or part of the aforementioned traffic congestion discrimination method based on map coordinates and vehicle big data. See [link to embodiment]. Figure 3 The traffic congestion identification device based on map coordinates and vehicle big data specifically includes the following components:

[0148] The road segment acquisition module 10 is used to acquire the total route of the vehicle, which is divided into multiple road segments according to the selected points on the map; the direction angle of the road segment and the driving direction of the vehicle on the road segment are calculated according to the starting coordinates and ending coordinates of the road segment.

[0149] The device acquisition module 20 is used to acquire all the capture devices on the road segment, and to acquire the distance of each capture device from the road segment and its capture direction; the capture devices whose distance is less than a set threshold and whose capture direction is consistent with the driving direction of the vehicles on the road segment are identified as the devices belonging to the road segment.

[0150] The trajectory acquisition module 30 is used to calculate the distance between the equipment belonging to the road segment and the starting point of the road segment, and sort all the equipment belonging to the road segment in order of increasing distance to obtain the trajectory route of the vehicle in the road segment.

[0151] The congestion judgment module 40 is used to obtain the travel time and speed of all vehicles on the trajectory route based on the information collected by the capture device on the trajectory route, and to determine that the road segment is congested when the vehicle speed is less than a first threshold and the proportion of vehicles with a speed less than the first threshold is greater than a second threshold.

[0152] As described above, the traffic congestion identification device based on map coordinates and vehicle big data provided in this application fully utilizes the high-precision coordinate information and massive amounts of vehicle driving data provided by modern map services. Through comprehensive analysis of multi-dimensional data such as vehicle driving direction and speed, it achieves rapid and accurate identification of traffic congestion status, providing timely and reliable decision support for traffic management departments to optimize traffic flow management, improve road traffic efficiency, and reduce traffic delays and environmental pollution. This method innovatively combines map geometric analysis with vehicle big data processing, overcoming many limitations of traditional methods and possessing significant application value and promising prospects for wider application.

[0153] From a hardware perspective, in order to address the problem that existing methods cannot determine traffic congestion in a timely and accurate manner, this application provides an embodiment of an electronic device for implementing all or part of the traffic congestion determination method based on map coordinates and vehicle big data. The electronic device specifically includes the following components:

[0154] The system comprises a processor, memory, a communication interface, and a bus; wherein the processor, memory, and communication interface communicate with each other via the bus; the communication interface is used to realize information transmission between the traffic congestion identification device based on map coordinates and vehicle big data and core business systems, user terminals, and related databases and other related devices; the logic controller can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the logic controller can be implemented with reference to the embodiments of the traffic congestion identification method based on map coordinates and vehicle big data, and the embodiments of the traffic congestion identification device based on map coordinates and vehicle big data in the embodiments, the contents of which are incorporated herein, and repeated details will not be described again.

[0155] It is understood that the user terminal may include smartphones, tablet computers, network set-top boxes, portable computers, desktop computers, personal digital assistants (PDAs), in-vehicle devices, smart wearable devices, etc. Among these, the smart wearable devices may include smart glasses, smartwatches, smart bracelets, etc.

[0156] In practical applications, the traffic congestion identification method based on map coordinates and vehicle big data can be partially executed on the electronic device side as described above, or all operations can be completed in the client device. The choice can be made based on the processing power of the client device and the limitations of the user's usage scenario. This application does not impose any limitations on this. If all operations are completed in the client device, the client device may further include a processor.

[0157] The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed server structure.

[0158] Figure 4 This is a schematic block diagram illustrating the system configuration of the electronic device 9600 according to an embodiment of this application. Figure 4 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that... Figure 4 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0159] In one embodiment, the traffic congestion identification method based on map coordinates and vehicle big data can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control:

[0160] Step S101: Obtain the total route of the vehicle, which is divided into multiple segments based on the selected points on the map; calculate the direction angle of the segment and the driving direction of the vehicle on the segment based on the starting and ending coordinates of the segment.

[0161] Step S102: Obtain all capture devices on the road segment, and obtain the distance of each capture device to the road segment and its capture direction; determine the capture devices whose distance is less than a set threshold and whose capture direction is consistent with the driving direction of vehicles on the road segment as the devices belonging to the road segment.

[0162] Step S103: Calculate the distance between the equipment belonging to the road segment and the starting point of the road segment, and sort all the equipment belonging to the road segment in order of increasing distance to obtain the trajectory route of the vehicle in the road segment.

[0163] Step S104: Based on the information collected by the capture device on the trajectory route, obtain the travel time and speed of all vehicles on the trajectory route, and determine that the road segment is congested when the vehicle speed is less than the first threshold and the proportion of vehicles with a speed less than the first threshold is greater than the second threshold.

[0164] As described above, the electronic device provided in this application fully utilizes the high-precision coordinate information and massive amounts of vehicle driving data provided by modern map services. Through comprehensive analysis of multi-dimensional data such as vehicle driving direction and speed, it achieves rapid and accurate judgment of traffic congestion status, providing timely and reliable decision support for traffic management departments to optimize traffic flow management, improve road traffic efficiency, and reduce traffic delays and environmental pollution. This method innovatively combines map geometric analysis with vehicle big data processing, overcoming many limitations of traditional methods and possessing significant application value and promising prospects for widespread adoption.

[0165] In another embodiment, the traffic congestion identification device based on map coordinates and vehicle big data can be configured separately from the central processing unit 9100. For example, the traffic congestion identification device based on map coordinates and vehicle big data can be configured as a chip connected to the central processing unit 9100, and the traffic congestion identification method based on map coordinates and vehicle big data can be implemented through the control of the central processing unit.

[0166] like Figure 4 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily need to include these components. Figure 4 All components shown; in addition, the electronic device 9600 may also include Figure 4 For components not shown, please refer to existing technologies.

[0167] like Figure 4 As shown, the central processing unit 9100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device, which receives inputs and controls the operation of various components of the electronic device 9600.

[0168] The memory 9140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 9100 may execute the program stored in the memory 9140 to perform information storage or processing, etc.

[0169] Input unit 9120 provides input to central processing unit 9100. Input unit 9120 may be, for example, a keypad or touch input device. Power supply 9170 provides power to electronic device 9600. Display 9160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0170] The memory 9140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 via the central processing unit 9100.

[0171] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0172] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processing unit 9100 to provide input signals and receive output signals, which is the same as in a conventional mobile communication terminal.

[0173] Based on different communication technologies, multiple communication modules 9110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby realizing typical telecommunications functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 9130 is coupled to a central processing unit 9100, enabling on-device recording via the microphone 9132 and on-device playback of stored audio via the speaker 9131.

[0174] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the traffic congestion identification method based on map coordinates and vehicle big data, where the execution subject is a server or client, as described in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the traffic congestion identification method based on map coordinates and vehicle big data, where the execution subject is a server or client, as described in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0175] Step S101: Obtain the total route of the vehicle, which is divided into multiple segments based on the selected points on the map; calculate the direction angle of the segment and the driving direction of the vehicle on the segment based on the starting and ending coordinates of the segment.

[0176] Step S102: Obtain all capture devices on the road segment, and obtain the distance of each capture device to the road segment and its capture direction; determine the capture devices whose distance is less than a set threshold and whose capture direction is consistent with the driving direction of vehicles on the road segment as the devices belonging to the road segment.

[0177] Step S103: Calculate the distance between the equipment belonging to the road segment and the starting point of the road segment, and sort all the equipment belonging to the road segment in order of increasing distance to obtain the trajectory route of the vehicle in the road segment.

[0178] Step S104: Based on the information collected by the capture device on the trajectory route, obtain the travel time and speed of all vehicles on the trajectory route, and determine that the road segment is congested when the vehicle speed is less than the first threshold and the proportion of vehicles with a speed less than the first threshold is greater than the second threshold.

[0179] As described above, the computer-readable storage medium provided in this application fully utilizes the high-precision coordinate information and massive amounts of vehicle driving data provided by modern map services. Through comprehensive analysis of multi-dimensional data such as vehicle driving direction and speed, it achieves rapid and accurate identification of traffic congestion status, providing timely and reliable decision support for traffic management departments to optimize traffic flow management, improve road traffic efficiency, and reduce traffic delays and environmental pollution. This method innovatively combines map geometric analysis with vehicle big data processing, overcoming many limitations of traditional methods and possessing significant application value and promising prospects for wider application.

[0180] Embodiments of this application also provide a computer program product capable of implementing all steps of the traffic congestion identification method based on map coordinates and vehicle big data, where the execution subject is a server or client, as described in the above embodiments. When executed by a processor, this computer program / instruction implements the steps of the traffic congestion identification method based on map coordinates and vehicle big data. For example, the computer program / instruction implements the following steps:

[0181] Step S101: Obtain the total route of the vehicle, which is divided into multiple segments based on the selected points on the map; calculate the direction angle of the segment and the driving direction of the vehicle on the segment based on the starting and ending coordinates of the segment.

[0182] Step S102: Obtain all capture devices on the road segment, and obtain the distance of each capture device to the road segment and its capture direction; determine the capture devices whose distance is less than a set threshold and whose capture direction is consistent with the driving direction of vehicles on the road segment as the devices belonging to the road segment.

[0183] Step S103: Calculate the distance between the equipment belonging to the road segment and the starting point of the road segment, and sort all the equipment belonging to the road segment in order of increasing distance to obtain the trajectory route of the vehicle in the road segment.

[0184] Step S104: Based on the information collected by the capture device on the trajectory route, obtain the travel time and speed of all vehicles on the trajectory route, and determine that the road segment is congested when the vehicle speed is less than the first threshold and the proportion of vehicles with a speed less than the first threshold is greater than the second threshold.

[0185] As described above, the computer program product provided in this application fully utilizes the high-precision coordinate information and massive amounts of vehicle driving data provided by modern map services. Through comprehensive analysis of multi-dimensional data such as vehicle driving direction and speed, it achieves rapid and accurate identification of traffic congestion status, providing timely and reliable decision support for traffic management departments to optimize traffic flow management, improve road traffic efficiency, and reduce traffic delays and environmental pollution. This method innovatively combines map geometric analysis with vehicle big data processing, overcoming many limitations of traditional methods and possessing significant application value and promising prospects for widespread adoption.

[0186] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0187] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0188] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0189] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0190] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A traffic congestion discrimination method based on map coordinates and vehicle big data, characterized by, The method comprises: acquiring a total route of vehicle driving, the total route being divided into multiple road segments according to point-to-point connection of a map; calculating a direction angle of the road segment and a driving direction of the vehicle on the road segment according to a starting point coordinate and an ending point coordinate of the road segment; acquiring all snapshot devices on the road segment, and acquiring a distance of each snapshot device to the road segment and a snapshot direction of each snapshot device; determining a road segment belonging device of the road segment as a snapshot device whose distance to the road segment is less than a set threshold value and whose snapshot direction is consistent with the driving direction of the vehicle on the road segment; the step of acquiring the distance of each snapshot device to the road segment comprises: converting a coordinate of the snapshot device into a string hash value by using GeoHash encoding, and grouping the snapshot devices according to the first several bits of the GeoHash encoding; screening out a snapshot device group near the road segment, and calculating the distance of the snapshot devices in the group; calculating a distance between the road segment belonging device and the starting point of the road segment, and sorting the road segment belonging devices of the road segment in an order from small to large according to the distance, to obtain a trajectory route of the vehicle driving on the road segment; acquiring driving time and driving speed of all vehicles on the trajectory route according to information collected by the snapshot devices on the trajectory route, and determining that the road segment is congested when the driving speed of the vehicle is less than a first threshold value and a proportion of the vehicles whose driving speed is less than the first threshold value is greater than a second threshold value. 2.The traffic congestion discrimination method based on map coordinates and vehicle big data according to claim 1, characterized in that, The step of calculating the direction angle of the road segment according to the starting point coordinate and the ending point coordinate of the road segment comprises: converting the longitude and latitude of the starting point coordinate and the ending point coordinate of the road segment into radian values; calculating a direction angle radian value of the road segment from the starting point coordinate to the ending point coordinate by using a four-quadrant arctangent function and according to the radian values; converting the direction angle radian value into a standard longitude and latitude angle value, and determining the direction angle of the road segment according to the standard longitude and latitude angle value. 3.The traffic congestion discrimination method based on map coordinates and vehicle big data according to claim 1, characterized in that, The step of calculating the direction angle of the road segment according to the starting point coordinate and the ending point coordinate of the road segment comprises: if the calculation result is a negative value, adding 360° to the calculation result, so that the range of the direction angle is kept in 0° to 360°; setting a maximum allowed deviation distance of the road segment, splitting the road segment when the road segment deviates from the straight line segment by more than the deviation distance, and calculating the direction angle of each straight line segment. 4.The traffic congestion discrimination method based on map coordinates and vehicle big data according to claim 1, wherein, The step of acquiring the distance of each snapshot device to the road segment comprises: the distance D of the snapshot device P to the road segment AB is calculated according to the relationship between the snapshot device P and the two end points A and B of the road segment AB: If then the angle between AP and BP is obtuse and the distance D is the perpendicular distance from the snapping device P to the road segment AB. If then the angle between AP and AB is obtuse and the distance D is the distance from AP. If then the angle between AB and BP is obtuse and the distance D is the distance from BP. 5.The traffic congestion discrimination method based on map coordinates and vehicle big data according to claim 1, characterized in that, The judgment method that the snapshot direction is consistent with the driving direction of the vehicle on the road segment comprises: defining an included angle threshold value of the snapshot direction and the direction angle of the road segment, and setting included angle = abs(snapshot direction - direction angle of the road segment), if the included angle is greater than 180°, then included angle = 360° - included angle; if the included angle is within ±30°, then it is determined that the snapshot direction matches the direction angle of the road segment, that is, the snapshot direction is consistent with the driving direction of the vehicle on the road segment. 6.The traffic congestion discrimination method based on map coordinates and vehicle big data according to claim 1, wherein, The step of calculating the distance between the road segment belonging device and the starting point of the road segment comprises: A vertical foot point of a device belonging to a road segment to the road segment is calculated, a cumulative distance from the vertical foot point to a starting point of the road segment is taken as a position parameter of the device belonging to the road segment on the road segment, and the device belonging to the road segment is sorted according to the position parameter. 7.The traffic congestion discrimination method based on map coordinates and vehicle big data according to claim 1, characterized in that, The step of obtaining the driving time and the driving speed of all vehicles on the trajectory route according to the information collected by the snapshot device on the trajectory route comprises: Obtaining all vehicle data collected by the snapshot device on the trajectory route; Inquiring the passing data of each vehicle through the first snapshot device and the last snapshot device on the trajectory route; Calculating the driving speed of all vehicles on the trajectory route according to the driving time and the road segment distance.

8. A traffic congestion discrimination device based on map coordinates and vehicle big data, characterized by, The device comprises: A road segment obtaining module, configured to obtain a total route of vehicle driving, wherein the total route is divided into a plurality of road segments according to selected points of a map; and calculate a direction angle of the road segment and a driving direction of the vehicle on the road segment according to starting point coordinates and end point coordinates of the road segment; A device obtaining module, configured to obtain all snapshot devices on the road segment, and obtain a distance of each snapshot device to the road segment and a snapshot direction of each snapshot device; determine a snapshot device with a distance less than a set threshold value and a snapshot direction consistent with the driving direction of the vehicle on the road segment as a road segment belonging device of the road segment; the step of obtaining the distance of each snapshot device to the road segment comprises: converting the coordinates of the snapshot device into a string hash value by using GeoHash encoding, and grouping the snapshot devices according to the first few bits of the GeoHash encoding; screening out the snapshot device groups near the road segment, and calculating the distances of the snapshot devices in the groups; A trajectory obtaining module, configured to calculate the distance between the road segment belonging device and the starting point of the road segment, and sort the order of all road segment belonging devices of the road segment in ascending order of distance to obtain a trajectory route of the vehicle driving on the road segment; A congestion judging module, configured to obtain the driving time and the driving speed of all vehicles on the trajectory route according to the information collected by the snapshot device on the trajectory route, and determine that the road segment is congested when the driving speed of the vehicle is less than a first threshold value, and the proportion of the vehicle with the driving speed less than the first threshold value is greater than a second threshold value.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to realize the steps of the traffic congestion discrimination method based on map coordinates and vehicle big data in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the traffic congestion discrimination method based on map coordinates and vehicle big data in any one of claims 1 to 7.

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