Traffic transportation administrative law enforcement site auxiliary decision-making system based on augmented reality

Abstract

The invention relates to the technical field of computer-aided management, and particularly discloses a traffic transportation administrative law enforcement site auxiliary decision-making system based on augmented reality, which comprises an AR enhancement display module, a behavior recognition module, a data analysis module, an evidence management module, an auxiliary decision-making module, an event management module and a communication cooperation module. The AR enhanced display module is used for displaying field information in real time through AR equipment, combining real-time data with a virtual layer, and providing real-time visual flow information of a road or a channel; the behavior recognition module is used for automatically recognizing illegal behaviors on site by using computer vision and image recognition technologies, displaying illegal records in real time on site, giving an alarm and displaying related areas in a highlight manner; according to the method, the field information is displayed in real time through the AR equipment, the real-time data is combined with the virtual layer, the visual flow information of the road or the channel is visually provided, law enforcement officers are helped to rapidly know the traffic condition, and the law enforcement efficiency is prevented from being affected by information lag.

Description

Technical Field

[0001] The present invention belongs to the technical field of computer-aided management, and in particular relates to an on-site auxiliary decision-making system for transportation administrative law enforcement based on augmented reality. Background Art

[0002] With the development of social economy, the connection between regions is becoming increasingly close, and emergencies are gradually showing the characteristics of involving wider fields and expanding the scope of influence. Many regions have established emergency dispatch command platforms to control emergencies. Taking the transportation industry as an example, there are emergency platforms such as highway emergency command systems and public transportation dispatch emergency command platforms.

[0003] In recent years, the information construction of transportation administrative law enforcement management departments has continued to develop, promoting the gradual improvement of the level of transportation administrative law enforcement. Due to the continuous deepening of transportation supervision business and the diversification of illegal behaviors, transportation administrative law enforcement is still facing tremendous pressure, mainly manifested in: lack of unified command and dispatch. Due to the particularity of law enforcement departments, traditional telephone calls or other information methods cannot timely unify the command of personnel and vehicles, and cannot achieve the effect of one call and a hundred responses. In the process of law enforcement and punishment, the management of temporarily detained vehicles is a difficult problem. For various types of violations and detained vehicles, the current management methods are still at the manual registration stage, and the management efficiency is low. The lack of data analysis of law enforcement supervision data makes it difficult to provide effective data support for law enforcement management. Law enforcement evidence is an important material in the law enforcement process. At present, it is impossible to archive and scientifically store audio and video data in the law enforcement process, and it is impossible to provide guarantees for later reference.

[0004] Therefore, it is necessary to propose an on-site decision-making support system for transportation administrative law enforcement based on augmented reality to solve the problem of low efficiency of transportation administrative law enforcement in existing technologies.

[0005] The above information disclosed in this background technology is only used to increase the understanding of the background technology of the present invention and therefore, it may include information that does not constitute the prior art known to ordinary technicians in this field. Summary of the invention

[0006] The purpose of the present invention is to provide an on-site auxiliary decision-making system for transportation administrative law enforcement based on augmented reality to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The on-site decision-making support system for transportation administrative law enforcement based on augmented reality includes: AR enhanced display module, behavior recognition module, data analysis module, evidence management module, decision-making support module, event management module, and communication collaboration module;

[0009] The AR enhanced display module is used to display on-site information in real time through AR devices, and combines real-time data with virtual layers to provide real-time visual traffic information of roads or waterways;

[0010] The behavior recognition module is used to automatically identify illegal behaviors on site using computer vision and image recognition technology, instantly display violation records on site, issue warnings and highlight relevant areas;

[0011] The data analysis module is used to analyze the collected data in real time, combine the historical data of vehicles or ships with traffic models for intelligent deduction, and provide specific law enforcement suggestions;

[0012] The evidence management module is used to automatically record images, videos, and sound data at the scene through AR devices, and associate them with illegal behaviors and traffic accidents to generate a complete chain of evidence;

[0013] The decision-making support module is used to provide real-time decision support to law enforcement officers based on on-site conditions, and to give optimal law enforcement action suggestions or solutions through data visualization and scenario simulation;

[0014] The accident management module is used to automatically obtain the real-time data of the accident location, accident vehicles or ships on site through AR devices when a traffic accident occurs, and provide automatic analysis reports;

[0015] The communication and collaboration module is used to enable real-time communication between law enforcement officers and the command center and other law enforcement officers through AR devices and terminals, share on-site information, and allocate tasks and resources in real time.

[0016] Preferably, the data analysis module is also used to select an AR device according to the real scene of the road or waterway, and integrate the AR device with the transportation administrative law enforcement system;

[0017] AR devices perceive traffic data in real-world scenes through cameras and sensors, and scan and identify key elements of the scene in real time through object detection and recognition algorithms;

[0018] The AR device transmits real-time traffic data and real-world scene information to the cloud or local server through a communication interface connected to the cloud or local server;

[0019] Based on key elements, dynamic data fusion technology based on environmental perception is used to combine real-time collected traffic data with real scenes to generate virtual information;

[0020] Use data mining and association analysis methods to compare traffic data with historical data to discover potential patterns or trends.

[0021] Preferably, the behavior recognition module is also used to identify and analyze the behavior of vehicles or ships based on key elements through deep learning algorithms and image processing technology to infer whether there is any violation of traffic regulations;

[0022] When illegal behavior occurs, dynamic path planning is carried out in combination with real-time traffic data to generate enforcement routes to avoid congested sections or accident-prone areas;

[0023] Combine big data and AI technology to assess the impact of illegal activities and generate action recommendations.

[0024] Preferably, the AR enhanced display module is further used to overlay virtual information onto the real view through a transparent display layer of the AR device, and display content based on a personalized interface of the AR device;

[0025] When illegal behavior occurs, the AR device reminds law enforcement officers through visualization and voice broadcast, and highlights the corresponding virtual information;

[0026] Continuously update the display content based on real-time traffic data, and combine holographic technology to display multiple dimensions of traffic information in the same view;

[0027] The integrated transportation administrative enforcement system determines the severity of the violation and provides multi-level alerts based on the severity.

[0028] Preferably, the evidence management module is also used to record each illegal act in the cloud or local server and save it in a distributed storage manner according to the vehicle or ship;

[0029] For repeated violations, historical data is compared with new data to generate prompt information to remind law enforcement officers whether the vehicle or ship has a history of violations;

[0030] Uniquely number and mark each piece of evidence, including safe storage of physical evidence and encrypted storage of electronic evidence;

[0031] Introduce an evidence management system to centrally manage evidence and use biometric technology to prevent illegal access;

[0032] Evidence that has been processed shall be reviewed and archived regularly, and evidence that is no longer needed or has expired shall be destroyed in accordance with the law.

[0033] Preferably, the auxiliary decision-making module is also used to virtually reconstruct and simulate the accident scene in combination with the AR enhanced display module to assist in the analysis of the cause of the accident and the definition of responsibility;

[0034] And use virtual reality technology to conduct decision-making simulation tests to understand the effects and risks of different decision paths in advance;

[0035] By integrating machine learning and deep learning models, on-site conditions can be analyzed in real time, providing early warning of potential risks and problems;

[0036] The above analysis and processing are integrated through a global optimization algorithm to propose the optimal emergency response plan or action plan.

[0037] Preferably, the incident management module is further used to classify and prioritize illegal behaviors according to their types and severity, integrate automated tools to diagnose illegal behaviors, and identify the root causes of illegal behaviors;

[0038] Conduct trend analysis on illegal behaviors through big data, provide long-term improvement directions, and reduce the occurrence of the same type of illegal behaviors;

[0039] By continuously collecting real-time traffic data and feedback information, the management process of violations is continuously optimized.

[0040] Preferably, the communication collaboration module is also used to provide multiple interaction modes such as voice recognition, gesture control, and touch screen operation to obtain decision suggestions and operation guidance;

[0041] Use IoT technology to achieve real-time information sharing and collaborative work among multiple devices, and perceive environmental changes in real scenes in real time;

[0042] When violations occur, tasks are automatically assigned and progress is tracked, and the handling process is adjusted according to the type of violation.

[0043] Preferably, the system completes repetitive work tasks through automated workflows or RPA technology without intervening in law enforcement personnel, automatically adjusts the interface layout and function display according to the behavior data and interest preferences of law enforcement personnel, provides a personalized experience, provides cross-platform compatibility, interconnects with different types of devices, conducts real-time data transmission and control, and integrates external systems, third-party services or platforms through API interfaces to achieve data sharing and functional complementarity.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention uses AR devices to display on-site information in real time, and combines real-time data with virtual layers to intuitively provide visualized traffic information of roads or waterways, helping law enforcement personnel to quickly understand traffic conditions and avoid affecting law enforcement efficiency due to information lags; it uses computer vision and image recognition technology to automatically identify violations on the scene and instantly display violation records. At the same time, through real-time analysis of on-site data, combined with historical data and traffic models of vehicles or ships, it deduces and provides specific law enforcement suggestions to help law enforcement personnel make scientific and reasonable decisions.

[0046] During the law enforcement process, the images, videos and sound data of the scene are automatically recorded and associated with the illegal behavior or traffic accidents to form a complete chain of evidence, which improves the transparency and credibility of the law enforcement process. Through data visualization and scenario simulation, real-time decision-making support is provided to law enforcement personnel, allowing them to respond quickly to complex situations, helping law enforcement personnel to quickly and accurately assess the accident situation and take corresponding measures.

[0047] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a framework diagram of the on-site auxiliary decision-making system for transportation administrative law enforcement based on augmented reality of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] Embodiment 1:

[0051] See also Figure 1 As shown, the on-site decision-making support system for transportation administrative law enforcement based on augmented reality includes: AR enhanced display module, behavior recognition module, data analysis module, evidence management module, decision-making support module, event management module, and communication collaboration module;

[0052] The AR enhanced display module is used to display on-site information in real time through AR devices (such as AR glasses), and combines real-time data with virtual layers to provide real-time visual traffic information of roads or waterways, including traffic flow, waterway status, vehicle or ship information, intersection monitoring, accident hotspots, etc.;

[0053] The behavior recognition module is used to automatically identify illegal behaviors on site using computer vision and image recognition technology, such as overloading, illegal docking, unqualified ship navigation, road transport of goods that do not meet the labeling requirements, etc., and instantly display the violation records on site, issue warnings and highlight the relevant areas;

[0054] The data analysis module is used to analyze the collected data in real time, combine the historical data of vehicles or ships with traffic models for intelligent deduction, and provide specific law enforcement suggestions (such as the best action plan for handling violations, determining the cause of accidents, etc.);

[0055] The evidence management module is used to automatically record images, videos, and sound data on the scene through AR devices, and associate them with illegal behaviors and traffic accidents to generate a complete chain of evidence, automatically archive them, and generate a complete law enforcement report;

[0056] The decision-making support module is used to provide real-time decision support to law enforcement officers based on on-site conditions, and to give optimal law enforcement action suggestions or solutions through data visualization and scenario simulation;

[0057] The accident management module is used to automatically obtain the real-time data of the accident location, accident vehicles or ships on site through AR devices when a traffic accident occurs, and provide automatic analysis reports;

[0058] The communication and collaboration module is used to enable real-time communication between law enforcement officers and the command center and other law enforcement officers through AR devices and terminals, share on-site information, and allocate tasks and resources in real time.

[0059] The data analysis module is also used to select AR devices according to the real scene of the road or waterway, and integrate the AR devices with the transportation administrative law enforcement system;

[0060] AR devices perceive traffic data in real-world scenes through cameras and sensors, and scan and identify key elements of the scene in real time through object detection and recognition algorithms;

[0061] Analyze the historical behavior and violation frequency of ships or vehicles, assess whether their current behavior is risky, predict future traffic bottlenecks based on real-time road conditions or port traffic, and alert law enforcement officers;

[0062] The AR device transmits real-time traffic data and real-world scene information to the cloud or local server through a communication interface connected to the cloud or local server;

[0063] Based on key elements, dynamic data fusion technology based on environmental perception is used to combine real-time collected traffic data with real scenes to generate virtual information;

[0064] Dynamic data fusion technology based on environmental perception refers to the information obtained through multiple sensors or data sources in the environmental perception system, combined with dynamic data processing technology, to fuse information from different sources in real time to achieve comprehensive and accurate perception of the environment. Sensor fusion is one of the core technologies of dynamic data fusion. It involves combining data from different types of sensors (such as lidar, camera, temperature and humidity sensor, GPS, etc.) to overcome the limitations of a single sensor and provide more comprehensive and accurate environmental information.

[0065] In a dynamic environment, the environmental perception system must be able to effectively associate observation data at different times and spaces to achieve target tracking and state estimation. By matching the timestamps and spatial positions of different sensor data, it ensures that different observation data of the same target can be effectively associated. Combine Kalman filtering, particle filtering and other methods to track multiple targets and dynamically update the target position and state.

[0066] Through precise dynamic data fusion, law enforcement officers no longer need to rely on pre-set maps or layers and can obtain accurate information support in any scenario, greatly improving on-site decision-making efficiency.

[0067] Use data mining and association analysis methods to compare traffic data with historical data to discover potential patterns or trends.

[0068] The behavior recognition module is also used to identify and analyze the behavior of vehicles or ships based on key elements through deep learning algorithms and image processing technology to infer whether there are any violations of traffic regulations;

[0069] Automatically identify and record the license plate number of illegal vehicles through license plate recognition technology to determine whether there are any illegal behaviors such as overdue annual inspections and illegal parking;

[0070] Through deep learning algorithms, the movement trajectory, speed, and driving path of vehicles or ships can be identified and analyzed to determine whether there are behaviors such as speeding, changing lanes without turning on the lights, and violating traffic lights. Through image recognition technology, it can be determined whether the vehicle or ship is parked in a prohibited parking area or a place that affects the smooth flow of traffic. Through the fusion of multi-dimensional data such as GPS, sensors, and surveillance videos, multi-level and multi-angle behavior recognition can be carried out to reduce the occurrence of misjudgments and missed judgments.

[0071] When illegal behavior occurs, dynamic path planning is carried out in combination with real-time traffic data to generate enforcement routes to avoid congested sections or accident-prone areas;

[0072] Combine big data and AI technology to assess the impact of illegal activities and generate action recommendations.

[0073] The AR enhanced display module is also used to overlay virtual information onto the real view through the transparent display layer of the AR device, and display content based on the personalized interface of the AR device;

[0074] Display real-time vehicle or ship status information (such as license plate number, ship type, location, navigation speed, load, etc.), on-site dangerous areas, channel signs, parking space availability, etc.;

[0075] When an illegal act occurs, the AR device reminds law enforcement officers through visualization and voice broadcast, and highlights the corresponding virtual information, including the real-time location, speed, and sailing direction of the vehicle or ship, road conditions or waterway flow, traffic density, on-site violation marks (such as speeding, overloading, illegal parking, etc.), road or waterway signs, dangerous areas, accident hotspots, etc.;

[0076] Example: When law enforcement officers look at a vehicle, AR glasses will display the vehicle's license plate number, driving status, and whether it has violated laws such as overloading. When looking at a ship, its current location, speed, and other data will be displayed.

[0077] The display content is continuously updated based on real-time traffic data, and holographic technology is used to display traffic information in multiple dimensions in the same view. If a new traffic event (such as congestion or accident) occurs on site, the AR device will instantly update the relevant information and issue visual and auditory warnings.

[0078] Different traffic information (such as real-time data of ships or vehicles, traffic flow, accident hotspots, etc.) is displayed through transparent virtual information, and these data will dynamically adjust the display mode according to the actual scene and perspective. For example: If a truck is overloaded, AR glasses will display a red warning mark next to it to remind law enforcement officers to take action.

[0079] The integrated transportation administrative enforcement system determines the severity of the violation and provides multi-level alerts based on the severity.

[0080] The evidence management module is also used to record each violation in the cloud or local server and save it in a distributed storage method according to the vehicle or ship;

[0081] For repeated violations, historical data is compared with new data to generate prompt information to remind law enforcement officers whether the vehicle or ship has a history of violations;

[0082] Uniquely number and mark each piece of evidence, including safe storage of physical evidence and encrypted storage of electronic evidence;

[0083] Integrate and generate AR-based real-time law enforcement reports, including descriptions of illegal behaviors, images, video evidence, on-site recordings, etc., introduce evidence management systems to manage evidence in a unified manner, and use biometric technology to prevent illegal access;

[0084] In the case handling process, law enforcement agencies or experts will review, verify and analyze the evidence. The validity and authenticity of the evidence will be confirmed through scientific methods and professional tools. Evidence that has been processed will be regularly reviewed and filed, and evidence that is no longer needed or expired will be destroyed in accordance with the law.

[0085] A cloud platform is used to store large amounts of evidence data, and relevant evidence is automatically identified and recommended during the processing based on data mining and machine learning algorithms, helping investigators quickly locate key evidence points.

[0086] Embodiment 2:

[0087] See also Figure 1 As shown, this embodiment is basically the same as the above embodiment, except that the auxiliary decision module is also used to combine with the AR enhanced display module to virtually reconstruct and simulate the accident scene, allowing law enforcement officers to see the scene layout, dangerous areas, escape routes, fire source locations, etc. through AR glasses or mobile devices, to assist in accident cause analysis and responsibility definition;

[0088] Example: For specific scenarios (such as fire, traffic accident, illegal parking, etc.), possible action plans are pushed based on existing experience and expert knowledge base. For example, when a fire occurs, law enforcement personnel are automatically reminded of the location of the fire source and should take actions such as extinguishing the fire, evacuating, or calling for support.

[0089] And use virtual reality technology to conduct decision-making simulation tests to understand the effects and risks of different decision paths in advance;

[0090] By integrating machine learning and deep learning models, on-site conditions can be analyzed in real time, providing early warning of potential risks and problems;

[0091] At the scene of a traffic accident, the camera detects changes in traffic flow and the severity of the accident, and automatically generates emergency response suggestions. The system uses historical data and simulation results to continuously optimize decision-making suggestions. As decision-making data continues to accumulate, machine learning and deep learning models will continue to learn and adjust to achieve more accurate decision support.

[0092] For example, convolutional neural networks (CNN) can be used for image data analysis and processing, and recurrent neural networks (RNN) can be used for time series data modeling and prediction. Through training on a large amount of environmental data, machine learning and deep learning algorithms can automatically extract features and perform data fusion, improving the intelligence and accuracy of the system.

[0093] The above analysis and processing are integrated through a global optimization algorithm to propose the optimal emergency response plan or action plan.

[0094] The incident management module is also used to classify and prioritize violations according to their type and severity, integrate automated tools to diagnose violations, and identify the root causes of violations;

[0095] Conduct trend analysis on illegal behaviors through big data, provide long-term improvement directions, and reduce the occurrence of the same type of illegal behaviors;

[0096] By continuously collecting real-time traffic data and feedback information, the management process of violations can be continuously optimized. For example, once an incident or accident is effectively resolved, the record can be closed, and the relevant law enforcement personnel or departments can be notified. Accidents and incidents can be summarized, problems in the resolution process can be analyzed, and detailed reports can be generated to provide a basis for future improvements.

[0097] The communication and collaboration module is also used to provide multiple interactive modes such as voice recognition, gesture control, and touch screen operation to obtain decision suggestions and operation guidance;

[0098] Use IoT technology to achieve real-time information sharing and collaborative work among multiple devices, and perceive environmental changes in real scenes in real time;

[0099] Law enforcement officers interact through voice commands to directly obtain decision-making suggestions and operational guidance. In an emergency, law enforcement officers do not need to operate the equipment, but can directly ask "what should be done next" through voice, and the system will feedback the corresponding emergency plan through voice. Or if law enforcement officers report "suspicious persons are found on the east side", the system will push the response plan in time through NLP analysis and combined with surrounding monitoring data.

[0100] When violations occur, tasks are automatically assigned and progress is tracked, and the handling process is adjusted according to the type of violation.

[0101] The system uses automated workflows or RPA technology to complete repetitive tasks without intervening with law enforcement officers. It automatically adjusts the interface layout and function display based on the behavioral data and interest preferences of law enforcement officers, provides a personalized experience, offers cross-platform compatibility, and interconnects with different types of devices for real-time data transmission and control. It integrates external systems, third-party services or platforms through API interfaces to achieve data sharing and functional complementarity.

[0102] As can be seen from the above, the present invention uses AR devices to display on-site information in real time, and combines real-time data with virtual layers to intuitively provide visualized traffic information of roads or waterways, helping law enforcement personnel to quickly understand traffic conditions and avoid affecting law enforcement efficiency due to information lags; it uses computer vision and image recognition technology to automatically identify violations on the scene and instantly display violation records. At the same time, through real-time analysis of on-site data, combined with historical data and traffic models of vehicles or ships, it deduces and provides specific law enforcement suggestions to help law enforcement personnel make scientific and reasonable decisions.

[0103] During the law enforcement process, the images, videos and sound data of the scene are automatically recorded and associated with the illegal behavior or traffic accidents to form a complete chain of evidence, which improves the transparency and credibility of the law enforcement process. Through data visualization and scenario simulation, real-time decision-making support is provided to law enforcement personnel, allowing them to respond quickly to complex situations, helping law enforcement personnel to quickly and accurately assess the accident situation and take corresponding measures.

[0104] Embodiment 3:

[0105] The embodiment of the present invention also provides a computer-readable storage medium, on which is stored a program of the on-site decision-making support system for transportation administrative law enforcement based on augmented reality as described above, which, when executed by a processor, implements each process of the above data processing method embodiment and can achieve the same technical effect. To avoid repetition, it is not repeated here. Among them, the computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0106] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0107] In the drawings of the embodiments disclosed in the present invention, only the structures involved in the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0108] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0109] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The on-site decision-making support system for transportation administrative law enforcement based on augmented reality is characterized by: include: AR enhanced display module, behavior recognition module, data analysis module, evidence management module, decision support module, event management module, communication collaboration module; The AR enhanced display module is used to display on-site information in real time through an AR device, and to combine real-time data with a virtual layer to provide real-time visual traffic information of roads or waterways; The behavior recognition module is used to automatically identify illegal behaviors on site using computer vision and image recognition technology, instantly display violation records on site, issue warnings and highlight relevant areas; The data analysis module is used to analyze the collected data in real time, combine the historical data of vehicles or ships with traffic models for intelligent deduction, and provide specific law enforcement suggestions; The evidence management module is used to automatically record the images, videos, and sound data of the scene through the AR device, and associate them with illegal acts and traffic accidents to generate a complete chain of evidence; The auxiliary decision-making module is used to provide real-time decision-making support to law enforcement personnel according to the on-site situation, and give the best law enforcement action suggestions or processing solutions through data visualization and scenario simulation; The accident management module is used to automatically obtain the real-time data of the accident location, the accident vehicle or ship at the scene through the AR device when a traffic accident occurs, and provide an automatic analysis report; The communication collaboration module is used to enable real-time communication between law enforcement personnel and the command center and other law enforcement personnel through the AR device and terminal, share on-site information, and allocate tasks and resources in real time.

2. The on-site decision-making support system for transportation administrative law enforcement based on augmented reality according to claim 1 is characterized in that: The data analysis module is also used for: Selecting the AR device according to the real scene of the road or waterway, and integrating the AR device with the transportation administrative law enforcement system; The AR device senses the traffic data of the real scene through cameras and sensors, and scans and identifies key elements of the scene in real time through object detection and recognition algorithms; The AR device transmits the real-time acquired traffic data and real scene information to the cloud or local server via a communication interface connected to the cloud or local server; Based on the key elements, dynamic data fusion technology based on environmental perception is used to combine the traffic data collected in real time with the real scene to generate virtual information; The traffic data is compared with the historical data using data mining and association analysis methods to discover potential patterns or trends.

3. The on-site decision-making support system for transportation administrative law enforcement based on augmented reality according to claim 2 is characterized in that: The behavior recognition module is also used for: Based on the key elements, the behavior of the vehicle or ship is identified and analyzed through deep learning algorithms and image processing technology to infer whether there is any violation of traffic regulations; When the above-mentioned illegal behavior occurs, dynamic path planning is carried out in combination with the above-mentioned real-time traffic data to generate an enforcement route to avoid congested sections or accident-prone areas; Combine big data and AI technology to evaluate the impact of the illegal behavior and generate action recommendations.

4. The on-site decision-making support system for transportation administrative law enforcement based on augmented reality according to claim 3 is characterized in that: The AR enhanced display module is also used for: Overlaying the virtual information onto the real view through the transparent display layer of the AR device, and displaying the content based on the personalized interface of the AR device; When the illegal behavior occurs, the AR device reminds the law enforcement personnel through visualization and voice broadcast, and highlights the corresponding virtual information; The display content is continuously updated according to the real-time traffic data, and the holographic technology is combined to display multi-dimensional traffic information in the same view; The integrated transportation administrative enforcement system determines the severity of the violation and provides multi-level warnings based on the severity.

5. The on-site decision-making support system for transportation administrative law enforcement based on augmented reality according to claim 4 is characterized in that: The evidence management module is also used to: Recording each of the illegal acts in the cloud or local server and storing them in a distributed storage manner according to the vehicle or ship; For repeated violations, historical data is compared with new data to generate prompt information to remind the law enforcement personnel whether the vehicle or vessel has a historical violation record; Uniquely number and mark each piece of evidence, store physical evidence securely, and encrypt and store electronic evidence; Introduce an evidence management system to centrally manage the evidence and use biometric technology to prevent illegal access; The evidence that has been processed shall be reviewed and archived regularly, and the evidence that is no longer needed or expired shall be destroyed in accordance with the law.

6. The on-site decision-making support system for transportation administrative law enforcement based on augmented reality according to claim 5 is characterized in that: The auxiliary decision module is also used for: Combined with the AR enhanced display module, the accident scene is virtually reconstructed and simulated to assist in the analysis of the cause of the accident and the definition of responsibility; And use virtual reality technology to conduct decision-making simulation tests to understand the effects and risks of different decision paths in advance; By integrating machine learning and deep learning models, on-site conditions can be analyzed in real time, providing early warning of potential risks and problems; The above analysis and processing are integrated through a global optimization algorithm to propose the optimal emergency response plan or action plan.

7. The on-site decision-making support system for transportation administrative law enforcement based on augmented reality according to claim 6 is characterized in that: The incident management module is also used to: Classify and prioritize the violations according to their type and severity, integrate automated tools to diagnose the violations, and identify the root causes of the violations; Conduct trend analysis on the aforementioned illegal behaviors through big data, provide long-term improvement directions, and reduce the occurrence of the same type of illegal behaviors; By continuously collecting real-time traffic data and feedback information, the management process of the illegal behavior is continuously optimized.

8. The on-site decision-making support system for transportation administrative law enforcement based on augmented reality according to claim 7 is characterized in that: The communication cooperation module is also used for: Provide multiple interactive modes such as voice recognition, gesture control, and touch screen operation to obtain decision suggestions and operation guidance; Use IoT technology to achieve real-time information sharing and collaborative work among multiple devices, and perceive environmental changes in the real scene in real time; When such illegal behaviors occur, tasks are automatically assigned and progress is tracked, and the handling process is adjusted according to the type of illegal behaviors.

9. The on-site decision-making support system for transportation administrative law enforcement based on augmented reality according to claim 8 is characterized by: The system uses automated workflows or RPA technology to complete repetitive work tasks without intervening with the law enforcement officers. It automatically adjusts the interface layout and function display based on the law enforcement officers' behavioral data and interest preferences to provide a personalized experience.

10. The on-site decision-making support system for transportation administrative law enforcement based on augmented reality according to claim 9 is characterized by: The system provides cross-platform compatibility, interconnects with different types of devices, conducts real-time data transmission and control, and integrates external systems, third-party services or platforms through API interfaces to achieve data sharing and functional complementarity.

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