Traffic situation research and judgment early warning method, device and system

Abstract

The invention provides a traffic situation research and judgment early warning method, device and system. The method comprises the following steps: acquiring equipment original data frames of passing subjects on each section of a preset traffic road section, wherein the passing subjects comprise motor vehicles, non-motor vehicles and pedestrians; performing holographic fusion processing on the state parameters of each passing main body on the equipment original data frame to obtain a holographic fusion data frame, and performing passing track dynamic modeling on the holographic fusion data frame to obtain a real-time driving track of each passing main body; and according to the holographic fusion data frame, the real-time driving track of each passing main body and the constructed passing parameter change characteristic indexes of each passing main body at different positions, carrying out research and judgment early warning on the trend of a traffic abnormal event. According to the invention, through holographic data fusion and dynamic modeling, in combination with the traffic parameter change characteristic indexes, accurate trend pre-judgment and early warning of traffic abnormal events are realized, and the accuracy and timeliness of traffic situation perception are significantly improved.

Description

Technical Field

[0001] This application relates to the field of traffic safety, and in particular to a method, device and system for assessing and warning about traffic conditions. Background Technology

[0002] With the continuous development of transportation systems, real-time perception and early warning of road traffic conditions have become crucial for improving road safety and traffic efficiency. Currently, several traffic situation detection and accident tracing methods based on radar-visual fusion have been proposed.

[0003] However, current technologies are all designed for the detection and analysis of specific events. The data used for each event is relatively fixed, and a universal event judgment method has not been formed. It is not flexible enough and it is difficult to adapt to various actual business scenarios of road traffic conditions. Summary of the Invention

[0004] Therefore, it is necessary to provide a traffic situation assessment and early warning method, device and system to achieve trend prediction and accurate early warning of abnormal traffic events.

[0005] A traffic situation assessment and early warning method includes:

[0006] Acquire the original data frames of the equipment at each section of the preset traffic segment for the main traffic entities, including motor vehicles, non-motor vehicles and pedestrians;

[0007] The original data frame of the device is subjected to holographic fusion processing of the state parameters of each passing subject to obtain a holographic fused data frame. The passage trajectory is dynamically modeled on the holographic fused data frame to obtain the real-time driving trajectory of each passing subject.

[0008] Based on the holographic fusion data frames, the real-time driving trajectories of each traffic entity, and the constructed characteristic indicators of traffic parameter changes of each traffic entity at different locations, the trend of abnormal traffic events is analyzed and early warning is issued.

[0009] In another embodiment, the step of judging and issuing early warnings about the trend of abnormal traffic events based on the holographic fusion data frame, the real-time driving trajectories of each traffic entity, and the constructed characteristic indicators of traffic parameter changes of each traffic entity at different locations includes:

[0010] An event analysis model is constructed based on the holographic fusion data frame and the real-time driving trajectories of each traffic entity.

[0011] Based on the characteristics of traffic parameter changes and event analysis models, the trends of abnormal traffic events are analyzed and early warnings are issued.

[0012] In another embodiment, the step of judging and issuing early warnings about the trend of abnormal traffic events based on the traffic parameter change characteristic indicators and the event judgment model includes:

[0013] The current passage parameter determination features are calculated based on the passage parameter change characteristic indicators, and the passage parameter determination features are input into the event analysis model;

[0014] If the traffic parameter determination features satisfy all the preset features in the event analysis model, the corresponding traffic anomaly event is output; or, if the traffic parameter determination features satisfy at least one preset feature in the event analysis model, the corresponding traffic anomaly event is output; or, if some of the traffic parameters satisfy the preset features in the event analysis model, and another part of the traffic parameter change feature indicators satisfy at least one preset feature in the event analysis model, the corresponding traffic anomaly event is output.

[0015] In another embodiment, the step of judging and issuing early warnings about the trend of abnormal traffic events based on the traffic parameter change characteristic indicators and the event judgment model includes:

[0016] The current passage parameter determination features are calculated based on the passage parameter change characteristic indicators, and the passage parameter determination features are input into the event analysis model;

[0017] Based on the weights of each feature assigned in the event judgment model, the weight value of the judgment model corresponding to the current passage parameter judgment feature is calculated.

[0018] If the weight value meets the preset conditions, the corresponding traffic anomaly event will be output.

[0019] In another embodiment, the traffic parameter change characteristic indicators include the operating speed change characteristic indicators, operating location change characteristic indicators, and number change characteristic indicators of each traffic entity at different traffic locations; the construction process of the traffic parameter change characteristic indicators of each traffic entity at different locations includes:

[0020] Statistical analysis was conducted on each traffic entity in the pre-defined area of ​​the traffic segment to construct characteristic indicators of the change in operating speed and the change in the number of traffic entities for each entity.

[0021] Statistical analysis was conducted on the main traffic entities in the driving lanes to construct characteristic indicators of changes in operating speed, operating position, and number of traffic entities in the lanes and adjacent lanes.

[0022] Statistical analysis is performed on the main vehicles traveling within the preset cross-section to construct characteristic indicators of the change in the operating speed and the change in the number of main vehicles within the preset cross-section.

[0023] In another embodiment, the preset traffic segment includes a tunnel; the acquisition of the device's original data frames at each cross-section of the preset traffic segment includes:

[0024] Acquire data frames collected by the holographic device at each cross-section within the tunnel, as well as tracking data of each passing entity within the tunnel;

[0025] The identification device acquires identification data of each passing entity at the entrance and exit of the tunnel;

[0026] The data frame is processed based on the tracking data and the identification data of each passing entity to obtain the original data frame.

[0027] In another embodiment, the step of performing holographic fusion processing on the original data frame of the device to obtain a holographic fused data frame carrying the state parameters of each passing subject includes:

[0028] Based on the original data frame of the device, a digital twin simulation is performed by integrating GIS spatial analysis and a three-dimensional digital model of transportation infrastructure to obtain the holographic fusion data frame.

[0029] A traffic situation analysis and early warning device, comprising:

[0030] The data acquisition module is used to acquire the original data frames of the equipment at each section of the preset traffic segment, where the traffic entities include motor vehicles, non-motor vehicles and pedestrians.

[0031] The data simulation module is used to perform holographic fusion processing on the original data frame of the device to obtain a holographic fused data frame, and to perform dynamic modeling of the passage trajectory on the holographic fused data frame to obtain the real-time driving trajectory of each passage subject.

[0032] The analysis module is used to analyze and warn of trends in abnormal traffic events based on the holographic fusion data frame, the real-time driving trajectory of each traffic subject, and the constructed characteristic indicators of traffic parameter changes of each traffic subject at different locations.

[0033] In another embodiment, the analysis and early warning server further includes:

[0034] The data display module is used to display the real-time driving trajectories of at least each traffic entity and to output traffic anomaly events.

[0035] An analysis and early warning system includes:

[0036] Multiple recognition devices are installed at the entrances and exits of preset traffic sections to collect images of passing subjects at the entrances and exits and extract their features.

[0037] Multiple holographic devices are deployed at preset intervals within a preset traffic segment to collect dynamic data of various traffic entities within the preset traffic segment.

[0038] The analysis and early warning equipment is used to acquire raw data frames of traffic entities at various cross-sections of a preset traffic segment. The traffic entities include motor vehicles, non-motor vehicles, and pedestrians. The raw data frames are then subjected to holographic fusion processing of the state parameters of each traffic entity to obtain holographic fused data frames. Dynamic trajectory modeling is performed on the holographic fused data frames to obtain the real-time driving trajectory of each traffic entity. Based on the holographic fused data frames, the real-time driving trajectories of each traffic entity, and the constructed characteristic indicators of traffic parameter changes of each traffic entity at different locations, the trend of abnormal traffic events is analyzed and early warning is issued.

[0039] The aforementioned traffic situation assessment and early warning method, device, and system acquire the original data frames of the equipment at each section of a preset traffic segment, perform holographic data fusion and dynamic modeling of the state parameters of each traffic entity in the original data frames, and combine the characteristic indicators of the traffic parameter changes of each traffic entity at different locations to achieve accurate trend prediction and early warning of abnormal traffic events, significantly improving the accuracy and timeliness of traffic situation perception. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 Here is a flowchart of a traffic situation assessment and early warning method according to one embodiment;

[0042] Figure 2 Here is a flowchart of a traffic situation assessment and early warning method according to another embodiment;

[0043] Figure 3 Here is a flowchart of a traffic situation assessment and early warning method according to another embodiment;

[0044] Figure 4 Here is a flowchart of a traffic situation assessment and early warning method according to another embodiment;

[0045] Figure 5 Here is a flowchart of a traffic situation assessment and early warning method according to another embodiment;

[0046] Figure 6 This is a module connection diagram of a traffic situation analysis and early warning device according to one embodiment;

[0047] Figure 7 This is a module connection diagram of a traffic situation assessment and early warning device according to another embodiment;

[0048] Figure 8 Here is a block diagram of a traffic situation assessment and early warning system according to another embodiment;

[0049] Figure 9 This is an internal structural diagram of a computer device according to one embodiment.

[0050] Explanation of reference numerals in the attached diagram: 601, Data acquisition module; 602, Data simulation module; 603, Analysis and judgment module; 701, Data display module; 801, Identification device; 802, Holographic device; 803, Analysis and early warning device. Detailed Implementation

[0051] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0053] It is understood that the term "connection" in the following embodiments should be understood as "electrical connection," "communication connection," etc., if the connected circuits, modules, units, etc., have electrical signal or data transmission with each other.

[0054] It is understandable that "at least one" refers to one or more, and "multiple" refers to two or more. "At least a part of an element" refers to part or all of an element.

[0055] When used herein, the singular forms of “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising / including” or “having,” etc., specify the presence of the stated features, wholes, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0056] Existing technologies for traffic incident assessment rely on relatively fixed data usage and lack a universal approach, making them inflexible and difficult to adapt to various real-world road traffic situations. Therefore, this application provides a traffic situation assessment and early warning method, device, and system.

[0057] like Figure 1 As shown, a traffic situation assessment and early warning method in one embodiment includes steps 101, 102, and 103.

[0058] 101. Obtain the raw data frames from the equipment at each cross-section of the preset traffic segment. The traffic entities include motor vehicles, non-motor vehicles, and pedestrians. The equipment output includes, but is not limited to, GIS data (latitude and longitude), license plate numbers, lane numbers, lane change signs, stop signs, vehicle license plate numbers, vehicle models, front / side images of vehicles, and hazardous materials labels. This step cleans and filters the equipment output to finally obtain the raw equipment data frames.

[0059] 102. The original data frames from the equipment undergo holographic fusion processing to obtain holographic fused data frames, which are then used for dynamic trajectory modeling of the traffic entities, resulting in real-time driving trajectories for each entity. The holographic fusion process involves linking observation data from different devices targeting the same object to form a unified and more comprehensive data view. This fusion process ultimately yields holographic fused data frames, providing a more complete and consistent set of state parameters for each traffic entity compared to the original data frames. Based on these temporally continuous holographic fused data frames, the system tracks and models the trajectory of each traffic entity. Through a trajectory tracking algorithm, the target's position and speed information at different times are linked together to generate a real-time driving trajectory that reflects its historical path, instantaneous motion state, and predicts its short-term future position. This trajectory reflects the historical driving path and real-time dynamics of the traffic entity on the road segment.

[0060] 103. Based on the holographic fusion data frames, the real-time driving trajectories of each traffic entity, and the constructed characteristic indicators of the changes in traffic parameters of each traffic entity at different locations, the trend of abnormal traffic events is analyzed and early warning is issued.

[0061] The trends of abnormal traffic events include, but are not limited to: traffic flow, congestion, and sudden traffic incidents. Traffic flow includes, but is not limited to, pedestrians running into traffic, non-motorized vehicles running into traffic, excessive speeding, and excessively slow driving; congestion includes light congestion, moderate congestion, and severe congestion; and sudden traffic incidents include, but are not limited to, sudden acceleration, sudden deceleration, sudden lane changes, continuous lane changes, sudden abnormalities, abnormal parking, and vehicles driving in the wrong direction.

[0062] This step is based on holographic fusion data frames. By statistically analyzing and modeling the speed change characteristics of traffic subjects at different locations and in different statistical dimensions, it constructs traffic parameter change characteristic indicators for each traffic subject at different locations. Based on the traffic parameter change characteristic indicators, holographic fusion data frames, and the real-time driving trajectories of each traffic subject, it identifies abnormal patterns in traffic flow, thereby enabling trend prediction and early warning of abnormal traffic events.

[0063] This embodiment uses holographic fusion processing and dynamic modeling of traffic trajectories to collect raw data frames from various cross-sections, thereby constructing a continuous digital model of traffic conditions. This model can accurately identify subtle changes in traffic flow, such as the onset of local congestion, lane changes, and abnormal deceleration or acceleration of vehicles. This allows for a more accurate assessment of the current traffic situation and provides a reliable basis for subsequent decision-making, thus enabling trend prediction and early warning of abnormal traffic events.

[0064] In another embodiment, such as Figure 2 As shown, step 103 includes: 201, configuring an event analysis model based on the holographic fusion data frame and the real-time driving trajectory of each traffic subject; 202, analyzing and issuing early warnings about the trend of abnormal traffic events based on the traffic parameter change characteristic indicators and the event analysis model.

[0065] 201. Construct an event analysis model based on holographic fusion data frames and the real-time driving trajectories of each traffic entity; wherein, the analysis model is the decision rule base used in the system to diagnose abnormal traffic events. This step first defines the set of input features required by the model based on the analysis of holographic fusion data frames and the real-time driving trajectories of each traffic entity, and then combines the input features into triggering conditions for different events by configuring the core judgment logic of the model. The system supports continuous optimization and adjustment of the constructed analysis model, and the adjustment methods include but are not limited to manual adjustment, which is not limited in this embodiment.

[0066] 202. Based on the traffic parameter change characteristic indicators and the event analysis model, the trend of abnormal traffic events is analyzed and early warning is issued. Specifically, the real-time judgment characteristics are obtained based on the traffic parameter change characteristic indicators, the judgment characteristics are input into the event analysis model, and the event analysis model performs calculations and matching according to its internal preset judgment logic, and outputs the corresponding traffic abnormal event analysis results and early warning information.

[0067] This embodiment provides an event analysis method implemented by configuring an event analysis model. The model is configured based on holographic fusion data frames and the real-time driving trajectories of various traffic entities, ensuring that event analysis is based on solid evidence. Furthermore, traffic management personnel can optimize the identification rules for abnormal traffic situations by adjusting the model's parameters, making the method applicable to different traffic sections and expanding its application scenarios.

[0068] In another embodiment, step 202 includes the following steps: step 301 and step 302.

[0069] 301. Calculate the current traffic parameter judgment features based on the traffic parameter change characteristic indicators, and input the traffic parameter judgment features into the event analysis model.

[0070] 302. If the traffic parameter determination features satisfy all the preset features in the event analysis model, output the corresponding traffic abnormality event; or, if the traffic parameter determination features satisfy at least one preset feature in the event analysis model, output the corresponding traffic abnormality event; or, if some traffic parameters satisfy the preset features in the event analysis model and another part of the traffic parameter change feature indicators satisfy at least one preset feature in the event analysis model, output the corresponding traffic abnormality event.

[0071] Step 302 can be understood as the decision-making method based on the judgment features can be divided into "AND operation mode" and "OR operation mode".

[0072] In the operation mode, check whether all the input judgment features meet the preset features; or in the operation mode, check whether at least one of the input judgment features meets the preset features.

[0073] In some traffic anomalies, the following judgment modes exist: some traffic parameters use an "AND operation mode," while others use an "OR operation mode." For example, the event model requires features T1, T2, T3, and T4. The corresponding traffic anomaly is output when T1 and T2 are both satisfied (i.e., AND operation mode), and either T3 or T4 is satisfied (i.e., OR operation mode). This embodiment introduces two logical judgment modes—AND and OR—to realize the function of judging traffic anomalies. The AND operation mode can combine multiple features to jointly verify traffic anomalies, reducing the false alarm rate; the OR operation mode can cover a wider range, reducing the missed alarm rate. By using two different logical judgment modes or a combination of both, the system's applicability is ensured while maintaining the accuracy of early warnings.

[0074] In another embodiment, such as Figure 4 As shown, step 202 includes: step 401, step 402, and step 403.

[0075] 401. Calculate the current traffic parameter judgment features based on the traffic parameter change characteristic indicators, and input the traffic parameter judgment features into the event analysis model.

[0076] 402. Based on the weights of each feature assigned in the event analysis model, calculate the weight value of the judgment model corresponding to the current passage parameter judgment feature.

[0077] 403. If the weight value meets the preset conditions, output the corresponding traffic anomaly event.

[0078] This embodiment employs a "weighted judgment" mode. Specifically, the system checks which preset judgment features the current real-time data meets based on the current access parameters, and accumulates the weight values ​​of these met judgment features to obtain a comprehensive weight score. The system compares this comprehensive weight score with a preset weight threshold to determine whether an event has occurred and its severity level.

[0079] This embodiment introduces a weighted judgment mode, which enables quantitative and hierarchical assessment of complex traffic situations. It is suitable for the analysis of events with continuously evolving characteristics, such as "congestion," making early warning information more accurate and providing more refined basis for traffic managers' decision-making.

[0080] The characteristics for determining the passage parameters in the above-mentioned case examples can be referred to in the following embodiments.

[0081] In another embodiment, the traffic parameter change characteristic indicators include the operating speed change characteristic indicators, operating location change characteristic indicators, and number change characteristic indicators for each traffic entity at different traffic locations; the construction process of the traffic parameter change characteristic indicators for each traffic entity at different locations includes:

[0082] Statistical analysis is performed on each traffic entity within a pre-defined area of ​​the traffic segment to construct speed change characteristic indicators for each entity. These indicators, including speed change characteristic indicators and traffic entity quantity change characteristic indicators, include but are not limited to: speed indicators, such as the average speed of the traffic entity over a period of time; and acceleration indicators, i.e., the rate of change of speed of the traffic entity over a period of time. Based on these basic indicators, traffic parameter judgment characteristics are calculated, including but not limited to: slow speed, fast speed, acceleration, deceleration, stopping, and driving against traffic.

[0083] Statistical analysis is performed on the traffic entities in the driving lanes to construct characteristic indicators of traffic parameter changes for traffic entities in and adjacent lanes. These traffic parameter change characteristic indicators include, but are not limited to, basic indicators, including average lane speed and lane displacement. Based on the basic indicators, traffic parameter judgment characteristics are calculated, including, but not limited to, slow lane changes, rapid lane changes, continuous lane changes, decrease in average lane speed, and increase in average lane speed.

[0084] Statistical analysis is performed on the traffic entities traveling within a preset cross-section to construct characteristic indicators of speed change and number change of traffic entities within the preset cross-section. These indicators include, but are not limited to, basic indicators such as the number of vehicles in the cross-section, average speed of the cross-section, number of pedestrians in the cross-section, and number of non-motorized vehicles in the cross-section. Traffic parameter judgment characteristics are calculated based on these basic indicators, including, but not limited to, an increase in the number of vehicles in the cross-section, a decrease in the number of vehicles in the cross-section, the appearance of pedestrians, the appearance of non-motorized vehicles, a decrease in average speed in the cross-section, a rapid decrease in average speed in the cross-section, and an increase in average speed in the cross-section.

[0085] This embodiment calculates feature indicators across three dimensions: points (individual traffic entities), lines (lanes), and areas (cross-sectional regions). It covers the speed variation patterns of different lanes, different time periods, and different vehicle types. Combined with dynamic modeling of the trajectories of each traffic entity, it can comprehensively capture the traffic status of traffic entities within the tunnel, enabling accurate assessment of the current traffic situation and improving the foresight, accuracy, and decision support value of early warnings.

[0086] In another embodiment, the preset traffic segment includes a tunnel; step 101 includes: step 501, step 502, and step 503.

[0087] 501. Acquire data frames collected by the holographic devices at each cross-section within the tunnel, as well as tracking data of each passing entity within the tunnel. These devices can assign an anonymous internal tracking ID to each detected target (motor vehicle, non-motor vehicle, pedestrian) and output dynamic tracking data bound to that ID.

[0088] 502. Acquire identification data of each entity passing through the tunnel at the entrance and exit. Dedicated identification devices deployed before the tunnel entrance and after the exit are triggered when entities pass through. Taking vehicles as an example, the dedicated identification devices capture images of the vehicle's front and / or side views, and extract and output vehicle information, such as license plate number, vehicle model, body color, and hazardous materials markings, using image recognition algorithms. This step establishes identity registration and verification checkpoints at both ends of the tunnel.

[0089] 503. Process the data frame according to the tracking data and the identification data of each passing entity to obtain the original data frame.

[0090] Due to their enclosed physical structure and unfavorable electromagnetic environment, tunnels are prone to issues such as satellite positioning signal failure and unstable wireless communication signals. This embodiment establishes a continuous and unique digital identity for each user within the tunnel, from entrance to exit, even when GPS and wireless communication are unavailable. This allows for precise warnings down to the individual user, making emergency response plans more targeted and providing highly reliable data for post-incident tracing.

[0091] In another embodiment, step 102 includes:

[0092] Based on the original data frame of the equipment, a digital twin simulation is performed by integrating spatial analysis of Geographic Information System (GIS) and a three-dimensional digital model of transportation infrastructure to obtain a holographic fused data frame.

[0093] In practical applications, due to factors such as limited sensor accuracy and environmental interference, the position of the main traffic vehicle given by the original data frame is not stable and smooth enough, but fluctuates within a small range. To ensure the display effect of the trajectory, a correction is made through GIS spatial calculation to keep the trajectory line as straight as possible within a lane. However, when the device is at a relatively far distance (such as in dim lighting), a small number of data points may be lost. To increase redundancy, the system will perform path missing inference and completion based on existing data when data is lost.

[0094] This embodiment introduces GIS spatial analysis and a 3D digital model of traffic infrastructure to construct a high-precision digital twin simulation environment, reducing equipment errors and achieving lane-level precise positioning and real trajectory restoration, providing a more accurate data foundation for subsequent situation analysis.

[0095] Based on the same inventive concept as the above method, such as Figure 6 As shown, this application proposes a traffic situation assessment and early warning device, including a data acquisition module 601, a data simulation module 602, and an assessment and analysis module 603, which are used to perform the steps corresponding to the above method. Further description of each module can be found in the description of the above embodiments.

[0096] The data acquisition module 601 is used to acquire the original data frames of the equipment at each section of the preset traffic segment, wherein the traffic subjects include motor vehicles, non-motor vehicles and pedestrians.

[0097] The data simulation module 602, connected to the data acquisition module 601, is used to perform holographic fusion processing on the original data frame of the device to obtain the state parameters of each passing subject, and to perform dynamic modeling of the passage trajectory on the holographic fusion data frame to obtain the real-time driving trajectory of each passing subject.

[0098] The analysis module 603, connected to the data simulation module 602, is used to analyze and warn of the trend of abnormal traffic events based on the holographic fusion data frame, the real-time driving trajectory of each traffic subject, and the constructed characteristic indicators of the change of traffic parameters of each traffic subject at different locations.

[0099] This device hardwareizes the analysis process through a modular architecture, enabling parallel processing and functional decoupling of data acquisition, fusion simulation, and intelligent analysis. This improves system processing efficiency and real-time performance, and enhances system stability, maintainability, and scalability.

[0100] In another embodiment, the traffic situation assessment and early warning device also includes: 701, a data display module.

[0101] The data display module 701 and the analysis module 603 are connected within the same network to display at least the real-time driving trajectories of each traffic entity and output traffic anomaly events. For example, it displays the real-time driving trajectories of each traffic entity output by the data simulation module 602, as well as detailed information on traffic anomaly events output by the analysis module 603.

[0102] In this embodiment, by integrating a data display module, the complex data processing and analysis results in the background are transformed into graphical information that front-end users can intuitively understand, thereby improving the efficiency of human-computer interaction and providing traffic management personnel with a comprehensive situational awareness and an efficient command and decision-making interface.

[0103] Based on the same inventive concept as the above-mentioned methods and devices, this application proposes a judgment and early warning system, including: at least two identification devices 801, at least two holographic devices 802 and judgment and early warning device 803.

[0104] The recognition device 801 is installed at the entrance and exit of a preset traffic segment to collect images of passing subjects at the entrance and exit, and extract their features. Optionally, the recognition device 801 may include a license plate recognition camera, a vehicle model recognition camera, etc.

[0105] The holographic device 802 is deployed at preset intervals within a preset traffic segment to collect dynamic data of various traffic entities within the preset traffic segment. Optionally, to achieve recognition accuracy and cost control, the preset interval can be 100-200 meters, such as 100 meters, 150 meters, and 200 meters. Optionally, the holographic device 802 may include lidar, millimeter-wave radar, thermal imaging cameras, etc.

[0106] The analysis and early warning device 803 is used to acquire the original data frames of the device at each section of a preset traffic segment. The traffic subjects include motor vehicles, non-motor vehicles, and pedestrians. The original data frames of the device are subjected to holographic fusion processing of the state parameters of each traffic subject to obtain holographic fused data frames. The traffic trajectory is dynamically modeled on the holographic fused data frames to obtain the real-time driving trajectory of each traffic subject. Based on the holographic fused data frames, the real-time driving trajectory of the vehicles, and the constructed characteristic indicators of the change of traffic parameters of each traffic subject at different locations, the trend of abnormal traffic events is analyzed and early warning is given.

[0107] Optionally, the identification device 801 and the holographic device 802 are connected to the analysis and early warning device 803 via a switch. The communication protocol between them can be Hypertext Transfer Protocol (HTTP), WebSocket protocol, or Message Queuing Telemetry Transport (MQTT), etc.

[0108] This system collects the identity and dynamic data of various traffic entities through a network of front-end devices, including identification and holographic devices. The data is then fused and analyzed by back-end central equipment, including analysis and early warning devices, using model assessments. This system achieves unified perception of motor vehicles, non-motor vehicles, and pedestrians, and can automatically determine abnormal traffic trends, improving the objectivity and timeliness of early warnings.

[0109] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure may be as follows: Figure 9 As shown. This computer device includes a processor, memory, input / output interfaces, communication interfaces, a display unit, and input devices.

[0110] The computer device comprises a processor, memory, and input / output interfaces connected via a system bus. A communication interface, display unit, and input devices are also connected to the system bus via input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a traffic situation assessment and early warning method. The display unit is used to generate a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0111] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to this application and does not constitute a limitation on the computer device to which the solution of this application is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0112] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in the above method embodiment.

[0113] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the above method embodiment.

[0114] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described in the above method embodiments.

[0115] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0116] In the description of this specification, references to terms such as "some embodiments," "other embodiments," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative descriptions of the above terms do not necessarily refer to the same embodiments or examples.

[0117] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0118] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A traffic situation assessment and early warning method, characterized in that, include: Acquire the original data frames of the equipment at each section of the preset traffic segment for the main traffic entities, including motor vehicles, non-motor vehicles and pedestrians; The original data frame of the device is subjected to holographic fusion processing of the state parameters of each passing subject to obtain a holographic fused data frame. The passage trajectory is dynamically modeled on the holographic fused data frame to obtain the real-time driving trajectory of each passing subject. Based on the holographic fusion data frames, the real-time driving trajectories of each traffic entity, and the constructed characteristic indicators of traffic parameter changes of each traffic entity at different traffic locations, the trend of abnormal traffic events is analyzed and early warning is issued.

2. The method according to claim 1, characterized in that, The step of analyzing and issuing early warnings about the trend of abnormal traffic events based on the holographic fusion data frames, the real-time driving trajectories of each traffic entity, and the constructed characteristic indicators of traffic parameter changes for each traffic entity at different traffic locations includes: An event analysis model is constructed based on the holographic fusion data frame and the real-time driving trajectories of each traffic entity. Based on the traffic parameter change characteristic indicators and event analysis model, the trend of abnormal traffic events is analyzed and early warning is issued.

3. The method according to claim 2, characterized in that, The step of analyzing and issuing early warnings about the trends of abnormal traffic events based on the traffic parameter change characteristic indicators and event analysis model includes: The current passage parameter determination features are calculated based on the passage parameter change characteristic indicators, and the passage parameter determination features are input into the event analysis model; If the traffic parameter determination features satisfy all the preset features in the event analysis model, the corresponding traffic anomaly event is output; or, if the traffic parameter change feature indicators satisfy at least one preset feature in the event analysis model, the corresponding traffic anomaly event is output; or, if some of the traffic parameters satisfy the preset features in the event analysis model and another part of the traffic parameter change feature indicators satisfy at least one preset feature in the event analysis model, the corresponding traffic anomaly event is output.

4. The method according to claim 2, characterized in that, The step of analyzing and issuing early warnings about the trends of abnormal traffic events based on the traffic parameter change characteristic indicators and event analysis model includes: The current passage parameter determination features are calculated based on the passage parameter change characteristic indicators, and the determination features are input into the event analysis model; Based on the weights of each feature assigned in the event judgment model, the weight value of the judgment model corresponding to the current passage parameter judgment feature is calculated. If the weight value meets the preset conditions, the corresponding traffic anomaly event will be output.

5. The method according to claim 1, characterized in that, The traffic parameter change characteristics include the operating speed change characteristics of each traffic entity at different traffic locations, the operating location change characteristics, and the number of traffic entities change characteristics. The process of constructing the characteristic indicators of traffic parameter changes for each traffic entity at different locations includes: Statistical analysis was conducted on each traffic entity in the pre-defined area of ​​the traffic segment to construct characteristic indicators of the change in operating speed and the change in the number of traffic entities for each entity. Statistical analysis was conducted on the main traffic entities in the driving lanes to construct characteristic indicators of changes in operating speed, operating position, and number of traffic entities in the lanes and adjacent lanes. Statistical analysis is performed on the main vehicles traveling within the preset cross-section to construct characteristic indicators of the change in the operating speed and the change in the number of main vehicles within the preset cross-section.

6. The method according to claim 1, characterized in that, The preset traffic segment includes a tunnel; the acquisition of raw data frames from the device at each cross-section of the passing subject within the preset traffic segment includes: Acquire data frames collected by the holographic device at each cross-section within the tunnel, as well as tracking data of each passing entity within the tunnel; The identification device acquires identification data of each passing entity at the entrance and exit of the tunnel; The data frame is processed based on the tracking data and the identification data of each passing entity to obtain the original data frame.

7. The method according to claim 6, characterized in that, The step of performing holographic fusion processing on the original data frames of the device to obtain holographic fused data frames carrying the state parameters of each passing subject includes: Based on the original data frame of the equipment, a digital twin simulation is performed by integrating GIS spatial analysis and a three-dimensional digital model of transportation infrastructure to obtain the holographic fusion data frame.

8. A traffic situation analysis and early warning device, characterized in that, include: The data acquisition module is used to acquire the original data frames of the equipment at each section of the preset traffic segment by the traffic subjects, including motor vehicles, non-motor vehicles and pedestrians; The data simulation module is used to perform holographic fusion processing on the original data frame of the device to obtain a holographic fused data frame, and to perform dynamic modeling of the traffic trajectory on the holographic fused data frame to obtain the real-time driving trajectory of each traffic subject. The analysis module is used to analyze and warn of trends in abnormal traffic events based on the holographic fusion data frame, the real-time driving trajectory of each traffic subject, and the constructed characteristic indicators of traffic parameter changes of each traffic subject at different locations.

9. The apparatus according to claim 8, characterized in that, Also includes: The data display module is used to display at least the real-time driving trajectories of each of the aforementioned traffic entities, and to output traffic anomaly events.

10. A judgment and early warning system, characterized in that, include: Multiple recognition devices are installed at the entrances and exits of preset traffic sections to collect images of passing subjects at the entrances and exits and extract their features. Multiple holographic devices are deployed at preset intervals within a preset traffic segment to collect dynamic data of various traffic entities within the preset traffic segment. The analysis and early warning equipment is used to acquire the original data frames of the equipment at each section of a preset traffic segment, wherein the traffic subjects include motor vehicles, non-motor vehicles and pedestrians; The original data frame of the device is subjected to holographic fusion processing of the state parameters of each passing subject to obtain a holographic fused data frame; Dynamic modeling of the traffic trajectory is performed on the holographic fusion data frame to obtain the real-time driving trajectory of each traffic subject; Based on the holographic fusion data frame, the real-time driving trajectory of the traffic subject, and the constructed characteristic indicators of the traffic parameter changes of each traffic subject at different locations, the trend of abnormal traffic events is analyzed and early warning is issued.

Images