Methods, devices and electronic equipment for determining road congestion, and storage media

Abstract

This application discloses a method, apparatus, electronic device, and storage medium for determining road congestion. The method includes performing a first compensation process on abnormal data in digital twin data to obtain first digital twin data; determining a first location information of a target vehicle based on the first digital twin data, wherein the target vehicle is associated with intersection information through a second compensation process; calculating the distance information of the target vehicle from the intersection based on the first location information of the target vehicle and the associated intersection information; incrementally aggregating the target vehicle based on the distance information of the target vehicle from the intersection to obtain the intersection passage time, wherein the intersection passage time is further processed by a third compensation process to obtain a final intersection passage time; and determining the road congestion situation based on the final intersection passage time. The method in this application achieves compensation for digital twin data, ensuring the accuracy and real-time nature of road congestion determination.

Description

Technical Field

[0001] This application relates to the field of digital twin technology, and in particular to a method, apparatus, electronic device, and storage medium for determining road congestion. Background Technology

[0002] Digital twins are a comprehensive application of information technologies such as sensing, computing, and modeling. Through software definition, they describe, diagnose, predict, and make decisions about physical space, thereby achieving an interactive mapping between physical space and cyberspace (which can be understood as a digital virtual space). By processing and optimizing digital twin data, it can be used to calculate road congestion.

[0003] In related technologies, anomalies in the received digital twin data itself can lead to significant errors in subsequent calculations. Furthermore, anomalies in the data processing during the implementation of specific projects can also affect the calculation results for road congestion. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, and storage medium for determining road congestion, in order to compensate for digital twin data and thereby improve the accuracy of road congestion determination.

[0005] The embodiments of this application adopt the following technical solutions:

[0006] In a first aspect, embodiments of this application provide a method for determining road congestion, wherein the method includes:

[0007] The abnormal data in the digital twin data is subjected to the first compensation process to obtain the first digital twin data;

[0008] Based on the first digital twin data, the first location information of the target vehicle is determined, and the target vehicle is associated with intersection information through a second compensation process;

[0009] Based on the first location information of the target vehicle and the associated intersection information, the distance information of the target vehicle from the intersection is calculated;

[0010] Based on the distance information of the target vehicle from the intersection, the target vehicle is incrementally aggregated to obtain the intersection passage time, and the intersection passage time is processed by a third compensation process to obtain the final intersection passage time.

[0011] The road congestion situation is determined based on the final intersection passage time.

[0012] In some embodiments, a three-layer caching system is pre-configured, and the incremental aggregation includes:

[0013] The first cache layer caches the location information of the target vehicle corresponding to the last point closest to the preset electronic fence;

[0014] The corresponding UUID within the preset electronic fence is recorded in the second cache layer;

[0015] The third cache layer records the corresponding UUID that appeared in the first cache layer and simultaneously appeared in the second cache layer, and then crossed the preset electronic fence;

[0016] Based on the first cache layer, the second cache layer, and the third cache layer, record the UUID that appears in the third cache layer and simultaneously obtain the latitude and longitude position of the current UUID in the first cache layer;

[0017] Based on the road network information and the latitude and longitude location of the current UUID in the first cache layer, obtain the location of the current UUID in the corresponding road network and the road information where it is located.

[0018] Based on the current UUID's location in the corresponding road network and the road information it is on, calculate the maximum average vehicle delay time in each lane of each intersection when the current UUID has completely passed through an intersection.

[0019] In some embodiments, the intersection passage time is processed by a third compensation method to obtain the final intersection passage time, including:

[0020] When the target vehicle is lost in the lane, the complete intersection passage time of the lost target vehicle is obtained after a third compensation process. The third compensation process includes calculating the time to pass through the corresponding intersection based on the direction of the vehicle's head and its speed at the last point in the trajectory of the lost target vehicle.

[0021] In some embodiments, the step of performing a first compensation process on the abnormal data in the digital twin data to obtain the first digital twin data includes:

[0022] Through the queue middleware, the fused abnormal data in the digital twin data is compensated to obtain the fused data, and the first digital twin data is obtained. The fused abnormal data includes at least the twin data loss or abnormality, the twin data interspersed with green belts, and the corresponding UUID in the twin data being lost.

[0023] And / or,

[0024] By requesting road network information, the latitude and longitude coordinates of the abnormal positioning data in the digital twin data are used to determine whether the target vehicle is driving normally in the lane. If it is not in the lane, filtering and compensation processing is performed to obtain the first digital twin data.

[0025] In some embodiments, the step of performing a first compensation process on the abnormal data in the digital twin data to obtain the first digital twin data further includes:

[0026] The first digital twin data is obtained by filtering out the data time window error caused by the polygon tiles being located on different servers.

[0027] In some embodiments, the target vehicle associates intersection information through a second compensation process, including:

[0028] Obtain the latitude and longitude information of the target vehicle;

[0029] The distance between the latitude and longitude information of the target vehicle and the center points of the two road entrances and exits is calculated, and the information of the nearest road entrance is associated with the corresponding target vehicle.

[0030] In some embodiments, the method further includes:

[0031] The digital twin data was processed using the Flink real-time stream computing framework.

[0032] Secondly, embodiments of this application also provide a compensation device for road congestion calculation, wherein the device includes:

[0033] The first compensation module is used to perform first compensation processing on the abnormal data in the digital twin data to obtain the first digital twin data.

[0034] The second compensation module is used to determine the first location information of the target vehicle based on the first digital twin data, and the target vehicle is associated with intersection information through the second compensation process;

[0035] The distance calculation module is used to calculate the distance information between the target vehicle and the intersection based on the first location information of the target vehicle and the associated intersection information;

[0036] The third compensation module is used to incrementally aggregate the target vehicle based on the distance information between the target vehicle and the intersection to obtain the intersection passage time, wherein the intersection passage time is processed by the third compensation to obtain the final intersection passage time.

[0037] The determination module is used to determine the road congestion situation based on the final intersection passage time.

[0038] Thirdly, embodiments of this application also provide an electronic device, including: a processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the above-described method.

[0039] Fourthly, embodiments of this application also provide a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform the above-described method.

[0040] The at least one technical solution adopted in this application embodiment can achieve the following beneficial effects: First, abnormal data in the digital twin data is subjected to a first compensation process to obtain first digital twin data. Then, based on the first digital twin data, the first location information of the target vehicle is determined. The target vehicle is associated with intersection information through a second compensation process. Based on the first location information of the target vehicle and the associated intersection information, the distance information of the target vehicle from the intersection is calculated. Finally, based on the distance information of the target vehicle from the intersection, the target vehicle is incrementally aggregated to obtain the intersection passage time. After calculating the final intersection passage time by compensating for the intersection passage time, the road congestion situation can be determined. By continuously compensating and optimizing during the road congestion calculation process, the final calculation result is not affected by related anomalies.

[0041] After acquiring digital twin data through a real-time computing framework in the cloud, a series of compensations need to be made for anomalies in the twin data based on real-world scenarios, and in accordance with national standards for determining road congestion. Attached Figure Description

[0042] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0043] Figure 1 This is a schematic diagram of the road congestion determination method in the embodiments of this application;

[0044] Figure 2 This is a schematic diagram illustrating the different caching layers recording target vehicles in the road congestion determination method of this application embodiments;

[0045] Figure 3 This is a schematic diagram of the road congestion determination device in the embodiments of this application;

[0046] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0048] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0049] This application provides a method for determining road congestion, such as... Figure 1 The diagram illustrates a road congestion determination method according to an embodiment of this application. The method includes at least the following steps S110 to S150:

[0050] Step S110: Perform a first compensation process on the abnormal data in the digital twin data to obtain the first digital twin data.

[0051] Digital twin data primarily originates from roadside equipment, which is equipped with image sensors and sensing devices such as LiDAR and millimeter-wave radar. These sensing devices collect image information from real-world scenes and calculate the relative 3D positions of vehicles, roads, and other elements within the 2D image information based on the intrinsic and extrinsic parameters of the image sensors.

[0052] After receiving the digital twin data in the cloud, a compensation process is first performed to obtain the first digital twin data. This compensation mainly involves preprocessing the merged digital twin data.

[0053] Preprocessing can include handling issues such as missing or anomalies in digital twin data. It may also include handling issues such as the disappearance of UUIDs within the digital twin data. Furthermore, it may include handling anomalies in the generated digital twin data, such as crossing greenbelts.

[0054] Step S120: Based on the first digital twin data, determine the first location information of the target vehicle, and the target vehicle is associated with intersection information through the second compensation process.

[0055] The first digital twin data, after undergoing the first compensation process, can be used to determine the location information of the target vehicle. The target vehicle includes multiple vehicles, and is typically determined based on vehicle queuing results.

[0056] The target vehicle needs to undergo a second compensation process. After the second compensation process, the distance between the target vehicle and the intersection can be calculated. Since the intersection information is associated after the second compensation process, the corresponding intersection can be identified and found through the target vehicle, so as to perform subsequent calculations.

[0057] For example, the digital twin data A after the second compensation processing includes the following associated information: {UUID, associated intersection, road name, road ID}. Furthermore, the digital twin data for each intersection on each road also contains the aforementioned associated information. It can be understood that each UUID corresponds to a target vehicle in the digital twin data. In addition to the associated information, information related to the digital twin data also includes vehicle heading angle, color, vehicle type, etc.

[0058] Step S130: Calculate the distance information between the target vehicle and the intersection based on the first location information of the target vehicle and the associated intersection information.

[0059] Based on the first location information of the target vehicle and the associated intersection information, the distance between the vehicle and the corresponding intersection center point is calculated.

[0060] It's understandable that the distance between the vehicle and the center point of the intersection is calculated directly to simplify the calculation. The center point of the intersection can be used as a priori value or an empirical value obtained in advance.

[0061] Step S140: Based on the distance information of the target vehicle from the intersection, the target vehicle is incrementally aggregated to obtain the intersection passage time, wherein the intersection passage time is processed by a third compensation process to obtain the final intersection passage time.

[0062] The target vehicles are processed using incremental aggregation within a cloud-based distributed computing framework. Based on the distance of each target vehicle from the intersection, the intersection passage time is determined. This intersection passage time refers to the time taken for all target vehicles in the incrementally aggregated data to travel from the stop line at the intersection to the next intersection.

[0063] The intersection crossing time may vary in actual engineering practice, so compensation is necessary. The final intersection crossing time is obtained after the third compensation step.

[0064] Step S150: Determine the road congestion situation based on the final intersection passage time.

[0065] Based on the final intersection transit time (e.g., maximum average vehicle delay time) from the previous step, combined with the national congestion index calculation standard, we can obtain information such as traffic congestion classification and road congestion status.

[0066] Unlike related technologies that directly process digital twin data to calculate road congestion, the method described above employs multiple compensation processing techniques to compensate the digital twin data before calculation, resulting in more accurate calculation results.

[0067] Using the methods described above, the current lane congestion situation can be calculated in near real-time in the cloud based on digital twin data. This information can also be displayed on smart city screens and simultaneously distributed to autonomous vehicles. Alternatively, the cloud can provide information notifications and serve as a data source for autonomous driving based on the location information of the vehicles.

[0068] In the above method, the first compensation process is used to obtain the first digital twin data and then use it to calculate the first location information of the target vehicle; the second compensation process is used to associate the target vehicle with the intersection information; the third compensation process is used to compensate for the intersection crossing time to obtain the final intersection crossing time. Through the above multiple compensation processes, the accuracy and real-time performance of the calculation results can be guaranteed.

[0069] In one embodiment of this application, three cache layers are pre-set. The incremental aggregation includes: caching the location information of the target vehicle corresponding to the last point near the preset electronic fence in the first cache layer; recording the corresponding UUID within the preset electronic fence in the second cache layer; recording the corresponding UUID that appeared in the first cache layer, appeared in the second cache layer, and then crossed the preset electronic fence in the third cache layer; based on the first cache layer, the second cache layer, and the third cache layer, recording the corresponding UUID appearing in the third cache layer and obtaining the latitude and longitude position of the current UUID in the first cache layer; based on the road network information and the latitude and longitude position of the current UUID in the first cache layer, obtaining the position of the current UUID in the corresponding road network and the road information it is located on; and calculating the maximum average vehicle delay time in each lane of each intersection when the current UUID has completely passed through an intersection, according to the position of the current UUID in the corresponding road network and the road information it is located on.

[0070] In Flink's cloud-based window functions, aggregation functions follow. These aggregation functions are further divided into incremental aggregation and full aggregation. Incremental aggregation refers to the aggregation of data within the current time window.

[0071] like Figure 2 As shown, it is necessary to calculate the statistical results of the target vehicles between times t1, t2, t3, and t4. That is, to calculate the times t2-t1, t3-t2, and t4-t3.

[0072] Specifically, for incremental aggregation: a three-layer cache is set up. The first layer is a continuously refreshed Caffeine cache, caching information about the last point near the geofence. The second layer records the corresponding UUIDs within the geofence. The third layer records the corresponding UUIDs that appeared in the first layer cache, appeared in the second layer cache, and finally crossed the geofence. For the UUID appearing in the third layer cache, its corresponding latitude and longitude points in the first layer cache are retrieved. Then, the road network information is called to obtain its location within the corresponding road network and the road information it is on.

[0073] Based on road network information and the latitude and longitude location of the current UUID in the first cache layer, the location of the current UUID in the corresponding road network and the road information it is on are obtained. Based on the location of the current UUID in the corresponding road network and the road information it is on, the maximum average vehicle delay time in each lane of each intersection is calculated when the current UUID has completely traversed an intersection. It can be understood that each UUID corresponds to a target vehicle and has a unique and definite relationship.

[0074] In one embodiment of this application, the intersection passage time is obtained after a third compensation process to obtain the final intersection passage time, including: when the target vehicle is lost in the lane, the complete intersection passage time of the lost target vehicle is obtained after the third compensation process. The third compensation process includes calculating the time to pass through the corresponding intersection based on the heading direction and driving speed of the last point in the trajectory of the lost target vehicle.

[0075] This calculation calculates the maximum average vehicle delay time for each lane at each intersection for a UUID that has completely traversed an intersection. The correction algorithm employed includes addressing the possibility of vehicle loss during the fusion process. To calculate the complete intersection crossing time for each vehicle, compensation is applied when a vehicle disappears in the middle of the road, based on its existing travel path. This compensation is calculated using the vehicle's heading and speed at the last point in its trajectory. The distance from the vehicle to the exit intersection is calculated, taking into account the corresponding angle and starting position, to the intersection's stop line. Finally, the travel time to the disappearance position is calculated based on its speed.

[0076] In addition, the above process uses the ray method to determine whether a point is in the middle of the intersection. A ray is drawn from the vehicle's latitude and longitude position to the center point of the intersection. If there are two intersections, it is considered to be outside the intersection, and if there is one intersection, it is considered to be inside the intersection.

[0077] Preferably, during the implementation of a specific digital twin project, window packaging is performed. The largest value is taken after aggregating the acquired incremental aggregation intermediate results. The corresponding congestion index is then mapped to the national standard based on the maximum crossing time at the intersection.

[0078] Preferably, during the implementation of a specific digital twin project, data within a five-second scrolling window corresponding to the event time is obtained by opening a window.

[0079] In one embodiment of this application, the step of performing a first compensation process on abnormal data in digital twin data to obtain first digital twin data includes: using a queue middleware to perform compensation processing on fused abnormal data in the digital twin data to obtain fused data, thereby obtaining the first digital twin data. The fused abnormal data includes at least twin data loss or abnormality, twin data interspersed with green belts, and loss of corresponding UUID in the twin data; and / or, by requesting road network information, determining whether the target vehicle is driving normally in the lane based on the latitude and longitude coordinates of the positioning abnormal data in the digital twin data, and if not, performing filtering compensation processing to obtain the first digital twin data.

[0080] The corresponding fusion data is obtained through the middleware Kafka queue to obtain the first digital twin data. The fusion abnormal data includes at least twin data loss or abnormality, twin data interspersed with green belts, corresponding UUID loss in twin data, etc.

[0081] In addition, road network information can be obtained by requesting it, and given latitude and longitude coordinates, it can be determined whether the current vehicle is driving normally in the lane. If it is not in the lane, it can be filtered.

[0082] In one embodiment of this application, the step of performing a first compensation process on the abnormal data in the digital twin data to obtain the first digital twin data further includes: filtering the data time window error caused by the polygon tiles being located on different servers to obtain the first digital twin data.

[0083] For compensation processing, filtering is also required to correct the scrambled data caused by time errors resulting from the hexagonal tiles being located on different servers.

[0084] For example, the corresponding city is divided into multiple hexagons of level 7 (or other levels) according to the H3 principle. The data for each hexagon is sent to different cloud servers for distributed processing. Since the processing time of different servers is different, there will be a time difference when the downstream obtains the data. Based on the data flowing into the computing engine, the time is sorted within each window, and the time of the current window is cached and compared with the time of the previous window to avoid out-of-order processing between windows and within windows.

[0085] In one embodiment of this application, the target vehicle is associated with intersection information through a second compensation process, including: obtaining the latitude and longitude information of the target vehicle; calculating the distance between the latitude and longitude information of the target vehicle and the center points of the road entrance and exit intersections, and associating the information of the nearest intersection with the corresponding target vehicle.

[0086] The distance is calculated by taking the latitude and longitude of the corresponding vehicle and the center point of the nearest intersection, and then the information of the nearest intersection is associated with the vehicle object.

[0087] The association process primarily corrects for location anomalies in tracking data caused by the fusion algorithm. The correction method involves calculating the distance between each vehicle's latitude and longitude and the center points of the two intersections at the road's entrance and exit. The information from the nearest intersection is then associated with the corresponding vehicle object to compensate for the impact of the fusion algorithm on the vehicles.

[0088] Preferably, the distance between the vehicle and the corresponding intersection center point is calculated. Data beyond 500 meters from the intersection is filtered out to reduce the computational load on the cloud server.

[0089] Preferably, the vehicles are grouped by intersection based on the intersection information they carry. This allows for the calculation of congestion conditions for each lane at each intersection at the lane level.

[0090] In one embodiment of this application, the method further includes: processing the digital twin data using the Flink real-time streaming computing framework.

[0091] Flink is a framework and distributed processing engine for stateful computation on both unrestricted and restricted datasets. Flink is designed to run in all common cluster environments, performing computations at memory speeds and any scale. Using the Flink real-time stream processing framework to process data, the maximum average vehicle delay time at intersections is ultimately obtained, and congestion is determined according to national standards.

[0092] This application embodiment also provides a road congestion calculation compensation device 200, such as... Figure 3 The diagram shows a structural schematic of a road congestion calculation compensation device according to an embodiment of this application. The device 300 includes at least: a first compensation module 310, a second compensation module 320, a distance calculation module 330, a third compensation module 340, and a determination module 350, wherein:

[0093] In one embodiment of this application, the first compensation module 310 is specifically used to: perform a first compensation process on the abnormal data in the digital twin data to obtain the first digital twin data.

[0094] Digital twin data primarily originates from roadside equipment, which is equipped with image sensors and sensing devices such as LiDAR and millimeter-wave radar. These sensing devices collect image information from real-world scenes and calculate the relative 3D positions of vehicles, roads, and other elements within the 2D image information based on the intrinsic and extrinsic parameters of the image sensors.

[0095] After receiving the digital twin data in the cloud, a compensation process is first performed to obtain the first digital twin data. This compensation mainly involves preprocessing the merged digital twin data.

[0096] Preprocessing can include handling issues such as missing or anomaly-related data in the digital twin. It can also include situations like the disappearance of UUIDs within the digital twin data. Furthermore, it may address anomalies in the generated digital twin data, such as data crossing greenbelts.

[0097] In one embodiment of this application, the second compensation module 320 is specifically used to: determine the first location information of the target vehicle based on the first digital twin data, wherein the target vehicle is associated with intersection information through a second compensation process.

[0098] The first digital twin data, after undergoing the first compensation process, can be used to determine the location information of the target vehicle. The target vehicle includes multiple vehicles, and is typically determined based on vehicle queuing results.

[0099] The target vehicle needs to undergo a second compensation process. After the second compensation process, the distance between the target vehicle and the intersection can be calculated. Since the intersection information is associated after the second compensation process, the corresponding intersection can be identified and found through the target vehicle, so as to perform subsequent calculations.

[0100] For example, the digital twin data A after the second compensation processing includes the following associated information: {UUID, associated intersection, road name, road ID}. Furthermore, the digital twin data for each intersection on each road also contains the aforementioned associated information. It can be understood that each UUID corresponds to a target vehicle in the digital twin data.

[0101] In one embodiment of this application, the distance calculation module 330 is specifically used to: calculate the distance information between the target vehicle and the intersection based on the first location information of the target vehicle and the associated intersection information.

[0102] Based on the first location information of the target vehicle and the associated intersection information, the distance between the vehicle and the corresponding intersection center point is calculated.

[0103] It's understandable that the distance between the vehicle and the center point of the intersection is calculated directly to simplify the calculation. The center point of the intersection can be used as a priori value or an empirical value obtained in advance.

[0104] In one embodiment of this application, the third compensation module 340 is specifically used to: incrementally aggregate the target vehicle based on the distance information of the target vehicle from the intersection to obtain the intersection passage time, wherein the intersection passage time is processed by the third compensation to obtain the final intersection passage time.

[0105] The target vehicles are processed using incremental aggregation within a cloud-based distributed computing framework. Based on the distance of each target vehicle from the intersection, the intersection passage time is determined. This intersection passage time refers to the time taken for all target vehicles in the incrementally aggregated data to travel from the stop line at the intersection to the next intersection.

[0106] The intersection crossing time may vary in actual engineering practice, so compensation is necessary. The final intersection crossing time is obtained after the third compensation step.

[0107] In one embodiment of this application, the determining module 350 is specifically used to: determine the road congestion situation based on the final intersection passage time.

[0108] Based on the final intersection transit time (e.g., maximum average vehicle delay time) from the previous step, combined with the national congestion index calculation standard, we can obtain information such as traffic congestion classification and road congestion status.

[0109] It is understood that the above-mentioned road congestion calculation compensation device can realize each step of the road congestion calculation compensation method provided in the foregoing embodiments. The relevant explanations of the road congestion calculation compensation method are applicable to the road congestion calculation compensation device, and will not be repeated here.

[0110] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Please refer to it. Figure 4 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0111] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0112] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0113] The processor reads the corresponding computer program from non-volatile memory into main memory and then runs it, forming a traffic congestion calculation and compensation device at the logical level. The processor executes the program stored in memory and specifically performs the following operations:

[0114] The abnormal data in the digital twin data is subjected to the first compensation process to obtain the first digital twin data;

[0115] Based on the first digital twin data, the first location information of the target vehicle is determined, and the target vehicle is associated with intersection information through a second compensation process;

[0116] Based on the first location information of the target vehicle and the associated intersection information, the distance information of the target vehicle from the intersection is calculated;

[0117] Based on the distance information of the target vehicle from the intersection, the target vehicle is incrementally aggregated to obtain the intersection passage time, and the intersection passage time is processed by a third compensation process to obtain the final intersection passage time.

[0118] The road congestion situation is determined based on the final intersection passage time.

[0119] The above is as stated in this application. Figure 1The method executed by the road congestion calculation compensation device disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0120] The electronic device can also perform Figure 1 The method for implementing a road congestion calculation compensation device, and the implementation of the road congestion calculation compensation device in... Figure 1 The functions of the embodiments shown are not described in detail here.

[0121] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform... Figure 1 The method executed by the road congestion calculation compensation device in the illustrated embodiment is specifically used to perform the following:

[0122] The abnormal data in the digital twin data is subjected to the first compensation process to obtain the first digital twin data;

[0123] Based on the first digital twin data, the first location information of the target vehicle is determined, and the target vehicle is associated with intersection information through a second compensation process;

[0124] Based on the first location information of the target vehicle and the associated intersection information, the distance information of the target vehicle from the intersection is calculated;

[0125] Based on the distance information of the target vehicle from the intersection, the target vehicle is incrementally aggregated to obtain the intersection passage time, and the intersection passage time is processed by a third compensation process to obtain the final intersection passage time.

[0126] The road congestion situation is determined based on the final intersection passage time.

[0127] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, 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.

[0128] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), 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.

[0129] 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.

[0130] 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 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0131] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0132] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0133] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0134] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0135] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0136] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for determining road congestion, wherein, The method includes: The abnormal data in the digital twin data is subjected to the first compensation process to obtain the first digital twin data; Based on the first digital twin data, the first location information of the target vehicle is determined, and the target vehicle obtains the associated intersection information after a second compensation process; Based on the first location information of the target vehicle and the associated intersection information, the distance information of the target vehicle from the intersection is calculated; Based on the distance information of the target vehicle from the intersection, the target vehicle is incrementally aggregated to obtain the intersection passage time, and the intersection passage time is processed by a third compensation process to obtain the final intersection passage time. The target vehicle is processed using incremental aggregation, and the exit time is determined based on the distance information of the target vehicle from the intersection. Three caching layers are pre-set. The incremental aggregation includes: The first cache layer caches the location information of the target vehicle corresponding to the last point closest to the preset electronic fence; The corresponding UUID within the preset electronic fence is recorded in the second cache layer; The third cache layer records the corresponding UUID that appeared in the first cache layer and simultaneously appeared in the second cache layer, and then crossed the preset electronic fence; Based on the first cache layer, the second cache layer, and the third cache layer, record the UUID that appears in the third cache layer and simultaneously obtain the latitude and longitude position of the current UUID in the first cache layer; Based on the road network information and the latitude and longitude location of the current UUID in the first cache layer, obtain the location of the current UUID in the corresponding road network and the road information where it is located. Based on the current UUID's location in the corresponding road network and the road information it is on, calculate the maximum average vehicle delay time in each lane of each intersection when the current UUID has completely passed through an intersection; The road congestion situation is determined based on the final intersection passage time.

2. The method as described in claim 1, wherein, The intersection passage time, after undergoing a third compensation process, yields the final intersection passage time, including: When the target vehicle is lost in the lane, the complete intersection passage time of the lost target vehicle is obtained after a third compensation process. The third compensation process includes calculating the time to pass through the corresponding intersection based on the direction of the vehicle's head and its speed at the last point in the trajectory of the lost target vehicle.

3. The method as described in claim 1, wherein, The first compensation process for abnormal data in the digital twin data to obtain the first digital twin data includes: Through the queue middleware, the fused abnormal data in the digital twin data is compensated to obtain the fused data, and the first digital twin data is obtained. The fused abnormal data includes at least the twin data loss or abnormality, the twin data interspersed with green belts, and the corresponding UUID in the twin data being lost. And / or, By requesting road network information, the latitude and longitude coordinates of the abnormal positioning data in the digital twin data are used to determine whether the target vehicle is driving normally in the lane. If it is not in the lane, filtering and compensation processing is performed to obtain the first digital twin data.

4. The method as described in claim 1, wherein, The step of performing a first compensation process on the abnormal data in the digital twin data to obtain the first digital twin data further includes: The first digital twin data is obtained by filtering out the data time window error caused by the polygon tiles being located on different servers.

5. The method as described in claim 1, wherein, The target vehicle uses the second compensation process to associate intersection information, including: Obtain the latitude and longitude information of the target vehicle; The distance between the latitude and longitude information of the target vehicle and the center points of the two road entrances and exits is calculated, and the information of the nearest road entrance is associated with the corresponding target vehicle.

6. The method according to any one of claims 1 to 5, wherein, The method further includes: The digital twin data was processed using the Flink real-time stream computing framework.

7. A compensation device for calculating road congestion, wherein, The device includes: The first compensation module is used to perform first compensation processing on the abnormal data in the digital twin data to obtain the first digital twin data. The second compensation module is used to determine the first location information of the target vehicle based on the first digital twin data, and the target vehicle obtains the associated intersection information after the second compensation process. The distance calculation module is used to calculate the distance information between the target vehicle and the intersection based on the first location information of the target vehicle and the associated intersection information; The third compensation module is used to incrementally aggregate the target vehicle based on the distance information between the target vehicle and the intersection to obtain the intersection passage time, wherein the intersection passage time is processed by the third compensation to obtain the final intersection passage time. The target vehicle is processed using incremental aggregation, and the exit time is determined based on the distance information of the target vehicle from the intersection. Three caching layers are pre-set. The incremental aggregation includes: The first cache layer caches the location information of the target vehicle corresponding to the last point closest to the preset electronic fence; The corresponding UUID within the preset electronic fence is recorded in the second cache layer; The third cache layer records the corresponding UUID that appeared in the first cache layer and simultaneously appeared in the second cache layer, and then crossed the preset electronic fence; Based on the first cache layer, the second cache layer, and the third cache layer, record the UUID that appears in the third cache layer and simultaneously obtain the latitude and longitude position of the current UUID in the first cache layer; Based on the road network information and the latitude and longitude location of the current UUID in the first cache layer, obtain the location of the current UUID in the corresponding road network and the road information where it is located. Based on the current UUID's location in the corresponding road network and the road information it is on, calculate the maximum average vehicle delay time in each lane of each intersection when the current UUID has completely passed through an intersection; The determination module is used to determine the road congestion situation based on the final intersection passage time.

8. An electronic device, comprising: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the method of any one of claims 1 to 6.

9. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the method of any one of claims 1 to 6.

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