Intelligent management method for off-site law enforcement through unmanned aerial vehicle

Abstract

The embodiment of the invention provides an intelligent management method for off-site law enforcement through an unmanned aerial vehicle, which relates to the technical field of intelligent traffic management and comprises a data acquisition stage, a data processing and analysis stage and a data transmission and storage stage. According to the method, a highly integrated and intelligent full-process solution is constructed, an unmanned aerial vehicle is used as a core acquisition terminal, various technologies such as flight control, image acquisition and data transmission are fused, and seamless joint with a traffic management integrated command platform is realized. In the technical architecture aspect, the network level utilizes the Internet / mobile private network, the video private network and the security network, and the security transmission of data is ensured through a boundary access platform; the functional module layer covers data aggregation, core rule, business function and self-research platform modules, and all the modules work cooperatively. The method can be applied to scenes of daily traffic patrol and the like, efficiency and accuracy of traffic law enforcement are remarkably improved, and innovative technical support is provided for urban traffic management.

Description

Technical Field

[0001] This invention relates to the field of intelligent traffic management technology, and more specifically, to an intelligent management method that uses unmanned aerial vehicles (UAVs) for off-site enforcement. Background Technology

[0002] In the long-term practice of urban traffic management, traditional fixed monitoring equipment has played an important role, but its limitations have become increasingly apparent with changes in the traffic environment. Fixed monitoring equipment is typically installed in specific locations on roads, such as intersections and key road sections. This fixed installation method results in extremely limited coverage, making it difficult to achieve comprehensive coverage of the urban road network. For example, in old urban areas, narrow, winding alleys and back streets, lacking suitable installation conditions or not included in monitoring plans, have become havens for traffic violations. A survey in an old urban area of ​​a certain city found that illegal parking and driving against traffic due to monitoring blind spots occurred on average more than 200 times per month, seriously affecting residents' travel and regional traffic order. Furthermore, some monitoring equipment has blind spots due to unreasonable installation angles, which some vehicles exploit to evade capture of violations such as running red lights and illegal lane changes, significantly reducing the effectiveness of traffic regulations.

[0003] Existing problems in the application of drones in the traffic management industry: 1. Inadequate Data Links: Despite the excellent maneuverability and data acquisition capabilities of drones, inadequate data links are a key factor hindering their effectiveness in practical applications within the traffic management industry. Currently, there are numerous drone brands on the market, each with different data transmission protocols and interface standards, lacking unified industry standards. This leads to numerous challenges when transmitting data collected by drones to traffic management platforms, such as format conversion and protocol adaptation, resulting in low data transmission efficiency and poor stability.

[0004] 2. Reliance on Manual Processing of Violation Data: From data collection to final violation processing, the current process involves too many manual interventions, severely hindering enforcement efficiency. During data collection, staff manually operate drones for flight mission planning, data capture, and storage; in the data identification phase, manual review and filtering of numerous collected images and videos are required to identify violations; and in the data entry phase, manual input of violation information into the traffic management platform is also necessary. This end-to-end manual operation not only consumes significant manpower and time but is also prone to errors due to human factors, such as incorrect data entry and biased violation judgments, affecting the fairness and authority of enforcement. Statistics show that under the traditional manual processing model for violation data, processing 100 violation records takes an average of 2-3 working days, with an error rate as high as 5%-8%.

[0005] 3. Analysis of the three core pain points Data silos: Data captured by drones is often stored on independent storage devices or platforms, making seamless integration with existing integrated command systems used by traffic management departments impossible. The lack of effective data exchange interfaces and sharing mechanisms between these systems hinders timely data flow and integration, creating isolated information silos.

[0006] Coordinate conversion difficulties: As mobile imaging devices, drones present significant challenges in matching the coordinate information collected with the road codes and road segment information required by traffic management. Most existing Geographic Information Systems (GIS) lack dedicated geocoding for traffic management, making it impossible to directly convert the latitude and longitude coordinates captured by drones into specific road names, road segment numbers, and other traffic management information. Therefore, in practice, coordinate conversion must be performed manually through map comparison and on-site surveys, which is not only labor-intensive but also prone to errors. In a large-scale traffic violation crackdown, errors in coordinate conversion resulted in inaccurate location information for some violations, creating numerous difficulties for subsequent enforcement, evidence collection, and penalties.

[0007] Incomplete Data: When writing violation data collected by drones into the integrated command platform, it is necessary to accurately fill in key information such as road codes and road segment codes to achieve precise location and management of violation data. However, this information cannot be automatically obtained in existing data collection methods and must be manually selected and entered. Due to the complexity of the road network and the large number of road codes and road segment codes, manual entry is not only inefficient but also prone to omissions or errors. For example, when entering violation data, staff may mistakenly enter the wrong road code due to negligence or unfamiliarity with road information, resulting in the violation data not being accurately associated with the corresponding road and road segment, affecting subsequent data analysis and law enforcement decisions. Summary of the Invention

[0008] The present invention aims to provide, for example, an intelligent management method for non-on-site enforcement using drones. This method uses drones as the core data collection terminal and combines advanced data transmission, processing, and analysis technologies to streamline the entire chain from data collection, transmission, and processing to violation determination and enforcement. This enables the automation and intelligentization of violation processing, thereby significantly improving the efficiency and accuracy of traffic enforcement.

[0009] The embodiments of the present invention can be implemented as follows: In a first aspect, the present invention provides an intelligent management method for non-site law enforcement via drones, the method comprising the following steps: Step s100: Data collection phase; Traffic police officers use the task planning function of the self-developed platform to formulate patrol tasks for the drone, including setting flight routes, adjusting flight altitudes, and setting shooting time intervals; they control the drone equipped with a high-definition camera to take off according to the predetermined task. The drone uses the high-definition camera to collect images and video data of traffic scenes in real time. The built-in positioning module records the latitude and longitude coordinates of the flight position in real time and associates them with the collected data. The collected data is transmitted to the drone data access platform in real time through the drone handle using cloud API data access technology. The platform performs preliminary verification and storage of the data. Step s200: Data Processing and Analysis Stage; The UAV data monitoring program monitors the data storage directory in real time, and starts the processing flow after detecting new data uploads; Deep learning image recognition algorithms are used to perform license plate recognition on the collected images and video data, automatically detecting and recognizing vehicle license plate character information; Based on the traffic violation judgment rules and algorithm models built into the core rules module, the traffic behavior in the data is analyzed and judged in real time. If a violation is detected, key information such as the violation time, the structured violation location, violation type, and violation code obtained through the geographic information reverse coding service is automatically extracted; The coordinate acquisition program acquires the latitude and longitude coordinate information in the UAV-captured data in real time and transmits it to the geographic information reverse coding service. The geographic information reverse coding service, combined with a high-precision traffic geographic information database, converts the latitude and longitude coordinates into specific traffic location descriptions, achieving precise location of the violation; Step s300: Data transmission and storage stage; The data server formats the processed and analyzed illegal data to conform to the data interface specifications of the security network integrated command platform; Using the boundary access platform, the illegal data is securely transmitted from the video private network to the security network integrated command platform according to the established data transmission process. The boundary access platform performs strict security testing and auditing on the data; After receiving the illegal data, the security network integrated command platform enters the data into the six-in-one platform through the centralized command and storage program. The centralized command and storage program enters the data according to the data storage structure and business requirements of the six-in-one platform and verifies the integrity and accuracy of the data.

[0010] In an optional implementation, during the data collection phase, during morning and evening rush hours, for main roads with high traffic volume, the drone is set to conduct reciprocating flight patrols at a flight altitude of 50-100 meters and a shooting interval of 5-10 seconds; at night or during periods of low traffic volume, the flight altitude is increased and the shooting interval is extended.

[0011] In an optional implementation, during the data processing and analysis stage, the deep learning image recognition algorithm used for license plate recognition is a convolutional neural network algorithm, which can automatically adapt to complex scenes with different lighting conditions, shooting angles and vehicle speeds, and accurately extract license plate character information.

[0012] In an optional implementation, during the data transmission and storage phase, the boundary access platform adopts a one-way transmission mechanism to securely transmit data from the Internet to the video private network, ensuring that the data transmission process is not subject to external network attacks or data leakage risks.

[0013] In an optional implementation, the intelligent management method involves three major network platforms: the Internet / mobile private network, the video private network, and the security network. Secure data transmission is achieved through the boundary access platform. Data collected by the front-end drone is first transmitted to the boundary access platform via the Internet or mobile private network, then transmitted from the boundary access platform to the video private network to the security network integrated command platform, and finally uploaded to the six-in-one platform.

[0014] In an optional implementation, the intelligent management method is equipped with functional modules, which include a data aggregation module, a core rules module, a business function module, and a self-developed platform module. The data aggregation module is responsible for collecting various types of data uploaded by the drone and performing preliminary classification and organization. The core rules module has built-in traffic violation judgment rules and algorithm models to perform real-time analysis and filtering of data. The business function module covers core business functions such as license plate recognition, data monitoring, and violation information extraction. The self-developed platform module establishes a connection with the drone through a handle to realize remote control of the drone and data transmission, and utilizes cloud API interfaces to achieve rapid access and transmission of drone data.

[0015] In an optional implementation, the self-developed platform module adopts multiple protocols such as HTTP, MQTT and WEBSOCKET, and selects the most suitable transmission method according to different data types and transmission requirements to ensure the stability and efficiency of data transmission.

[0016] In an optional implementation, a drone data access platform developed based on the cloud API interface provided by the drone can receive images and video data collected by the drone in real time through seamless connection with the drone handle and transmit them to a designated data storage directory. The platform also has data encryption and verification functions.

[0017] In an optional implementation, the data monitoring and processing program developed based on the .NET 8 technology framework can monitor the data storage directory uploaded by the front-end drone in real time. After detecting new data upload, it automatically starts the data processing process, uses multi-threaded processing technology to process multiple data files in parallel, and has good cross-platform compatibility, and can run stably on various operating systems such as Windows, Linux and MacOS.

[0018] In an optional implementation, the independently developed geographic information reverse encoding service, combined with high-precision traffic geographic information data, transforms the latitude and longitude coordinates of the drone's shooting location into a structured traffic location description; the coordinate acquisition program is responsible for acquiring the latitude and longitude information in the drone's shooting data in real time and transmitting it to the geographic information reverse encoding service for processing.

[0019] The beneficial effects of the embodiments of the present invention include, for example: To address the limitations of traditional fixed monitoring equipment coverage, inadequate data links for drones in traffic management applications, and low efficiency of manual processing of violation data, this paper proposes an intelligent management method for off-site enforcement using drones. This method constructs a highly integrated and intelligent end-to-end solution, using drones as the core data acquisition terminal and integrating multiple technologies such as flight control, image acquisition, and data transmission to achieve seamless integration with the traffic management integrated command platform. In terms of technical architecture, the network layer utilizes the Internet / mobile private network, video private network, and security network, ensuring secure data transmission through a boundary access platform. The functional modules encompass data aggregation, core rules, business functions, and a self-developed platform module, with each module working collaboratively. The core module achieves several technological breakthroughs, such as data access based on DJI cloud APIs, a data monitoring program based on the .NET 8 framework, modification of the video network platform, and development of geographic information reverse encoding services and core algorithms. The implementation process is divided into three stages: data acquisition, processing and analysis, and data transmission to the database. During data acquisition, drone tasks are planned and data is transmitted according to requirements; the processing and analysis stage completes license plate recognition, violation judgment extraction, and precise location positioning; the data transmission to the database stage ensures that violation data is safely and compliantly entered into the security network integrated command platform and the six-in-one platform. This method can be applied to scenarios such as daily traffic patrols, significantly improving the efficiency and accuracy of traffic law enforcement and providing innovative technical support for urban traffic management. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a control flowchart of the intelligent management method for non-on-site law enforcement using drones in this embodiment; Figure 2 This is a network diagram of the intelligent management method for non-on-site law enforcement using drones, as described in this embodiment. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0023] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0024] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0025] In the description of this invention, it should be noted that if terms such as "upper," "lower," "inner," or "outer" are used to indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of this invention is usually placed, they are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0026] Furthermore, the terms "first" and "second" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0027] It should be noted that, where there is no conflict, the features in the embodiments of the present invention can be combined with each other.

[0028] Please combine Figures 1-2 In this embodiment, an intelligent management method for non-site law enforcement using drones is provided, which includes the following steps: Step s100: Data collection phase; Traffic police officers use the task planning function of the self-developed platform to formulate patrol tasks for the drone, including setting flight routes, adjusting flight altitudes, and setting shooting time intervals; they control the drone equipped with a high-definition camera to take off according to the predetermined task. The drone uses the high-definition camera to collect images and video data of traffic scenes in real time. The built-in positioning module records the latitude and longitude coordinates of the flight position in real time and associates them with the collected data. The collected data is transmitted to the drone data access platform in real time through the drone handle using cloud API data access technology. The platform performs preliminary verification and storage of the data. Step s200: Data Processing and Analysis Stage; The UAV data monitoring program monitors the data storage directory in real time, and starts the processing flow after detecting new data uploads; Deep learning image recognition algorithms are used to perform license plate recognition on the collected images and video data, automatically detecting and recognizing vehicle license plate character information; Based on the traffic violation judgment rules and algorithm models built into the core rules module, the traffic behavior in the data is analyzed and judged in real time. If a violation is detected, key information such as the violation time, the structured violation location, violation type, and violation code obtained through the geographic information reverse coding service is automatically extracted; The coordinate acquisition program acquires the latitude and longitude coordinate information in the UAV-captured data in real time and transmits it to the geographic information reverse coding service. The geographic information reverse coding service, combined with a high-precision traffic geographic information database, converts the latitude and longitude coordinates into specific traffic location descriptions, achieving precise location of the violation; Step s300: Data transmission and storage stage; The data server formats the processed and analyzed illegal data to conform to the data interface specifications of the security network integrated command platform; Using the boundary access platform, the illegal data is securely transmitted from the video private network to the security network integrated command platform according to the established data transmission process. The boundary access platform performs strict security testing and auditing on the data; After receiving the illegal data, the security network integrated command platform enters the data into the six-in-one platform through the centralized command and storage program. The centralized command and storage program enters the data according to the data storage structure and business requirements of the six-in-one platform and verifies the integrity and accuracy of the data.

[0029] As mentioned above, addressing the limitations of traditional fixed monitoring equipment coverage, inadequate data links for drones in traffic management applications, and low efficiency of manual processing of violation data, this paper proposes an intelligent management method for non-site enforcement using drones. This method constructs a highly integrated and intelligent end-to-end solution, using drones as the core data acquisition terminal and integrating multiple technologies such as flight control, image acquisition, and data transmission to achieve seamless integration with the traffic management integrated command platform. In terms of technical architecture, the network layer utilizes the Internet / mobile private network, video private network, and security network, ensuring secure data transmission through a boundary access platform. The functional modules encompass data aggregation, core rules, business functions, and a self-developed platform module, with each module working collaboratively. The core module achieves several technological breakthroughs, such as data access based on DJI cloud APIs, a data monitoring program based on the .NET 8 framework, modification of the video network platform, and development of geographic information reverse encoding services and core algorithms. The implementation process is divided into data acquisition, processing and analysis, and data transmission to the database. During data collection, drone missions are planned and data is transmitted according to requirements; in the processing and analysis phase, license plate recognition, violation judgment extraction, and precise location positioning are completed; in the data transmission and database entry phase, the illegal data is safely and compliantly integrated into the security network command platform and the six-in-one platform. This method can be applied to scenarios such as daily traffic patrols, significantly improving the efficiency and accuracy of traffic law enforcement and providing innovative technical support for urban traffic management.

[0030] The following embodiments illustrate the details of the intelligent management method for off-site law enforcement using drones as described in this application by way of examples.

[0031] Optionally, during the data collection phase, during morning and evening rush hours, for main roads with high traffic volume, drones are set to conduct reciprocating patrols at a flight altitude of 50-100 meters and a shooting interval of 5-10 seconds; at night or during periods of low traffic volume, the flight altitude is increased and the shooting interval is extended.

[0032] Optionally, in the data processing and analysis stage, the deep learning image recognition algorithm used for license plate recognition is a convolutional neural network algorithm, which can automatically adapt to complex scenes with different lighting conditions, shooting angles and vehicle speeds, and accurately extract license plate character information.

[0033] Optionally, during the data transmission and storage phase, the boundary access platform adopts a one-way transmission mechanism to securely transmit data from the Internet to the video private network, ensuring that the data transmission process is not subject to external network attacks or data leakage risks.

[0034] Optionally, the intelligent management method involves three major network platforms: Internet / mobile private network, video private network, and security network. Secure data transmission is achieved through the boundary access platform. Data collected by the front-end drone is first transmitted to the boundary access platform via the Internet or mobile private network, then transmitted from the boundary access platform to the video private network to the security network integrated command platform, and finally uploaded to the six-in-one platform.

[0035] Optionally, the intelligent management method is equipped with functional modules, including a data aggregation module, a core rules module, a business function module, and a self-developed platform module. The data aggregation module is responsible for collecting various types of data uploaded by drones and performing preliminary classification and organization. The core rules module has built-in traffic violation judgment rules and algorithm models to perform real-time analysis and filtering of data. The business function module covers core business functions such as license plate recognition, data monitoring, and violation information extraction. The self-developed platform module establishes a connection with the drone through a handle to realize remote control of the drone and data transmission, and uses cloud API interfaces to realize rapid access and transmission of drone data.

[0036] Optionally, the self-developed platform module adopts multiple protocols such as HTTP, MQTT and WEBSOCKET, and selects the most suitable transmission method according to different data types and transmission requirements to ensure the stability and efficiency of data transmission.

[0037] Optionally, a drone data access platform developed based on the cloud API interface provided by the drone can receive images and video data collected by the drone in real time through seamless connection with the drone handle and transmit them to a specified data storage directory. The platform also has data encryption and verification functions.

[0038] Optionally, a data monitoring and processing program developed based on the .NET 8 technology framework can monitor the data storage directory uploaded by the front-end drone in real time. After detecting new data upload, it automatically starts the data processing process, uses multi-threaded processing technology to process multiple data files in parallel, and has good cross-platform compatibility, and can run stably on various operating systems such as Windows, Linux and MacOS.

[0039] Optionally, the independently developed geographic information reverse coding service, combined with high-precision traffic geographic information data, transforms the latitude and longitude coordinates of the drone's shooting location into a structured traffic location description; the coordinate acquisition program is responsible for acquiring the latitude and longitude information in the drone's shooting data in real time and transmitting it to the geographic information reverse coding service for processing.

[0040] It should be understood that DJI drones can be used.

[0041] It should be understood that the technical framework of the intelligent management method for non-on-site law enforcement using drones in this embodiment is as follows: 1. Network Layer: The intelligent management method of this application involves three major network platforms: the Internet / mobile private network, the video private network, and the security network. Secure data transmission is achieved through a boundary access platform. Data collected by the front-end drone is first transmitted to the boundary access platform via the Internet or mobile private network. The boundary access platform employs a one-way transmission mechanism to securely transmit data from the Internet to the video private network, ensuring that data transmission is not subject to external network attacks or data leakage risks. Within the video private network, after preliminary processing and analysis, the data is again transmitted through the boundary access platform to the security network integrated command platform, and finally uploaded to the six-in-one platform. This layered network architecture and strict data transmission mechanism ensure both the real-time nature of the data and its security and compliance, meeting the stringent requirements of secure network data management.

[0042] 2. Functional module level Data aggregation module: Responsible for collecting various types of data uploaded by drones, including high-definition images, video streams, and flight status data. This module boasts strong compatibility, supporting data access in multiple formats and protocols, ensuring smooth transmission and storage of data collected by drones of different brands and models. Simultaneously, the data aggregation module also performs preliminary data classification and organization, providing a foundation for subsequent data processing and analysis.

[0043] The core rules module contains a comprehensive set of traffic violation determination rules and algorithm models. These rules and models are based on national traffic regulations and local traffic management ordinances, and have been optimized and improved to suit actual traffic scenarios. The core rules module performs real-time analysis and filtering of data transmitted from the data aggregation module, determining whether any violations exist based on preset rules. For example, it monitors and analyzes data such as vehicle trajectory, speed, and parking status in real time; once data meets the violation determination criteria, the violation processing procedure is immediately triggered.

[0044] Business Function Modules: These modules cover core business functions such as license plate recognition, data monitoring, and violation information extraction. The license plate recognition function utilizes advanced artificial intelligence image recognition technology, enabling it to quickly and accurately identify vehicle license plate information in complex traffic scenarios and compare it with a vehicle information database to obtain detailed vehicle information. The data monitoring function monitors the drone's flight status, data transmission, and system operation status in real time, promptly identifying and warning of potential problems and malfunctions. The violation information extraction function, after the core rules module determines that a violation has occurred, automatically extracts key violation information from the data, including the violation time, location, type, and vehicle information, providing complete and accurate data support for subsequent law enforcement processing.

[0045] Self-developed platform module: A dedicated drone data management and processing platform has been developed. It connects to the drone via a controller, enabling remote control and data transmission. This platform utilizes DJI's cloud API interface for rapid drone data access and transmission. Furthermore, it employs multiple protocols such as HTTP, MQTT, and WEBSOCKET, selecting the most suitable transmission method based on different data types and transmission requirements to ensure stable and efficient data transmission.

[0046] Furthermore, the core module technology breakthroughs of the intelligent management method for non-on-site law enforcement using drones in this application embodiment are as follows: 1. Cloud API Data Access: Based on the cloud API interfaces provided by DJI drones, a DJI drone data access platform has been developed. This platform, through seamless connection with the drone controller, can receive image and video data collected by the drone in real time and quickly transmit it to a designated data storage directory. The application of cloud API data access technology breaks the limitations of traditional data transmission methods, enabling real-time connection between drone data and the system platform. This technology significantly reduces data transmission latency and improves data transmission efficiency, saving valuable time for subsequent data processing and analysis. Simultaneously, the platform also features data encryption and verification functions to ensure the security and integrity of data during transmission, preventing data theft or tampering.

[0047] 2. Drone Data Monitoring Program: A high-performance data monitoring and processing program was developed based on the .NET 8 framework. This program features strong real-time performance, high automation, and cross-platform compatibility. It can monitor the data storage directory uploaded by the drone in real time, and automatically initiates the data processing flow upon detecting new data uploads. During data processing, multi-threading technology is employed, enabling parallel processing of multiple data files simultaneously, significantly improving processing efficiency. Furthermore, because the program is developed based on .NET 8, it possesses excellent cross-platform compatibility, running stably on various operating systems such as Windows, Linux, and macOS, adapting to different hardware and software environments, and greatly facilitating system deployment and application.

[0048] 3. Upgrade the Video Network Traffic Management Platform: The existing video network traffic management platform underwent a comprehensive upgrade and transformation, adding support for drone data. The upgraded platform can seamlessly connect with data transmitted from the drone data access platform, enabling real-time preview, storage, and management of images and video data collected by drones. Simultaneously, it deeply integrates drone-captured violation information with the platform's existing enforcement processes, achieving unified management and processing of violation data.

[0049] 4. Development of Geographic Information Reverse Encoding Service and Coordinate Acquisition Program: Addressing the shortcomings of traditional geographic information reverse encoding in traffic management applications, a novel geographic information reverse encoding service and coordinate acquisition program were independently developed. Traditional reverse encoding typically returns address information only accurate to broad areas such as neighborhoods and streets, failing to meet the stringent requirements of traffic management for precise descriptions of violation locations. The geographic information reverse encoding service developed in this invention, combined with high-precision traffic geographic information data, can convert the latitude and longitude coordinates of drone-captured locations into structured traffic location descriptions, such as "<Main Road> and <Intersection> Intersection <Direction> <Distance> meters". The coordinate acquisition program is responsible for acquiring the latitude and longitude information from the drone-captured data in real time and accurately transmitting it to the geographic information reverse encoding service for processing. This technological breakthrough enables precise location tracking of violation locations, providing accurate and detailed location information for traffic enforcement, effectively solving the problem of difficult traditional coordinate conversion, and improving the accuracy and credibility of enforcement.

[0050] 5. Core Algorithms: A series of core algorithms were designed and implemented to calculate key information such as the distance between the violation location and the benchmark point, and the extent of the violation area.

[0051] The application examples of the intelligent management method for off-site law enforcement using drones provided in this embodiment are as follows: Step s100: Data acquisition phase.

[0052] In practical applications, traffic police officers use the task planning function of a self-developed platform to create detailed patrol missions for drones based on traffic management needs. Mission planning includes parameters such as flight route settings, flight altitude adjustments, and shooting interval settings. For example, during morning and evening rush hours, for main roads with high traffic volume, officers can set the drone to fly back and forth at a lower altitude (e.g., 50-100 meters) and a shorter shooting interval (e.g., 5-10 seconds) to ensure comprehensive and real-time collection of traffic scene data. At night or during periods of lower traffic volume, the flight altitude and shooting interval can be appropriately increased to save energy and improve patrol efficiency.

[0053] Law enforcement officers piloted DJI drones equipped with high-definition cameras to conduct patrol missions according to pre-defined tasks. During flight, the drones used their high-definition cameras to collect real-time images and video data of traffic scenes. Simultaneously, the drones' built-in positioning module recorded the latitude and longitude coordinates of their flight position and correlated them with the collected data. The collected data was transmitted in real-time to the DJI drone data access platform via the drone's controller using cloud API data access technology. Upon receiving the data, the data access platform performed preliminary verification and storage, storing it in a designated data storage directory for subsequent data processing and analysis.

[0054] Step s200: Data processing and analysis stage.

[0055] 1. License Plate Recognition and Vehicle Information Association: The drone data monitoring program monitors the designated data storage directory in real time. When new data is detected, the data processing flow is immediately initiated. First, license plate recognition processing is performed on the collected image and video data. The program uses advanced deep learning image recognition algorithms, such as convolutional neural networks (CNN), to automatically detect and recognize vehicle license plates in images and videos. During the recognition process, the algorithm can automatically adapt to complex scenarios such as different lighting conditions, shooting angles, and vehicle speeds, accurately extracting license plate character information and providing complete vehicle information support for subsequent violation processing.

[0056] 2. Violation Information Judgment and Extraction: Simultaneously with license plate recognition, the data monitoring program analyzes and judges traffic behavior in real time based on the traffic violation judgment rules and algorithm models built into the core rules module. For example, for vehicle parking status data, combined with geographic information data of permitted parking areas, it determines whether the vehicle has engaged in illegal parking. If a violation is detected, the program automatically extracts relevant information, including the violation time (accurate to the second), violation location (a structured location description obtained through geographic information reverse coding services), violation type (such as illegal parking), and violation code (the standard code corresponding to traffic regulations), among other key information.

[0057] 3. Precise Location of Violations: Geographic information reverse coding services and coordinate acquisition programs play a crucial role in determining the location of violations. Images and videos captured by drones contain the latitude and longitude coordinates of the shooting location. The coordinate acquisition program acquires this coordinate information in real time and transmits it to the geographic information reverse coding service. This service, combined with a high-precision traffic geographic information database, transforms the latitude and longitude coordinates into a specific traffic location description. For example, the coordinates "(109.861948, 40.658367)" are transformed into "50 meters south of the intersection of Fuqiang Road and Gangtie Street". This precise coordinate transformation and location description ensures the accuracy of the violation location, providing a reliable basis for subsequent law enforcement evidence collection and penalties.

[0058] Step s300: Data transmission and storage stage.

[0059] Illegal data extracted during the data processing and analysis phase is centrally managed and transmitted through a data server. First, the data server formats the illegal data to conform to the data interface specifications of the security network integrated command platform. Then, using the boundary access platform, the illegal data is securely transmitted from the video private network to the security network integrated command platform according to a predetermined data transmission process. Simultaneously, the boundary access platform performs rigorous security checks and audits on the data to ensure that only legal and compliant data can enter the security network.

[0060] After receiving violation data, the integrated command platform of the safety net enters the data into the six-in-one platform through the centralized data entry program. Based on the data storage structure and business requirements of the six-in-one platform, the centralized data entry program accurately enters the violation data into the corresponding database tables. During the entry process, the centralized data entry program also verifies the completeness and accuracy of the data to ensure that the entered data meets system requirements. Once the data is successfully entered into the six-in-one platform, the violation data can proceed with subsequent processing in the traffic management system, such as generating violation processing notices, notifying vehicle owners via SMS or APP, and reviewing and executing violation penalties.

[0061] The practical application scenario of the intelligent management method for non-on-site law enforcement using drones in this embodiment can be daily traffic patrols: In daily urban traffic management, drones play a crucial role in off-site enforcement and intelligent management. During morning and evening rush hours, drones conduct focused patrols of areas with high traffic volume and frequent violations, such as main roads, transportation hubs, areas around schools, and commercial centers. Real-time transmission technology sends video and images back to the platform for further processing and penalties for violations.

[0062] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An intelligent management method for non-on-site law enforcement via drones, characterized in that, The method includes the following steps: Step s100: Data collection phase; Traffic police officers use the task planning function of the self-developed platform to formulate patrol tasks for the drone, including setting flight routes, adjusting flight altitudes, and setting shooting time intervals; they control the drone equipped with a high-definition camera to take off according to the predetermined task. The drone uses the high-definition camera to collect images and video data of traffic scenes in real time. The built-in positioning module records the latitude and longitude coordinates of the flight position in real time and associates them with the collected data. The collected data is transmitted to the drone data access platform in real time through the drone handle using cloud API data access technology. The platform performs preliminary verification and storage of the data. Step s200: Data processing and analysis stage; The drone data monitoring program monitors the data storage directory in real time and initiates the processing flow upon detecting new data uploads. Deep learning image recognition algorithms are used to identify license plates in the collected images and videos, automatically detecting and recognizing vehicle license plate character information; based on the traffic violation judgment rules and algorithm models built into the core rules module, traffic behavior in the data is analyzed and judged in real time. If a violation is detected, key information such as the violation time, the structured violation location obtained through geographic information reverse coding service, the violation type, and the violation code are automatically extracted. The coordinate acquisition program acquires latitude and longitude coordinates from drone-captured data in real time and transmits them to the geographic information inverse encoding service. The geographic information inverse encoding service, combined with a high-precision traffic geographic information database, converts the latitude and longitude coordinates into specific traffic location descriptions, enabling precise location of violations. Step s300: Data transmission and storage stage; The data server formats the processed and analyzed illegal data to conform to the data interface specifications of the security network integrated command platform; Using the boundary access platform, the illegal data is securely transmitted from the video private network to the security network integrated command platform according to the established data transmission process. The boundary access platform performs strict security testing and auditing on the data; After receiving the illegal data, the security network integrated command platform enters the data into the six-in-one platform through the centralized command and storage program. The centralized command and storage program enters the data according to the data storage structure and business requirements of the six-in-one platform and verifies the integrity and accuracy of the data.

2. The intelligent management method for non-site law enforcement via drones according to claim 1, characterized in that: During the data collection phase, during morning and evening rush hours, drones are set to patrol back and forth on main roads with high traffic volume at a flight altitude of 50-100 meters and a shooting interval of 5-10 seconds; at night or during periods of low traffic volume, the flight altitude is increased and the shooting interval is extended.

3. The intelligent management method for non-site law enforcement via drones according to claim 1, characterized in that: In the data processing and analysis stage, the deep learning image recognition algorithm used for license plate recognition is a convolutional neural network algorithm, which can automatically adapt to complex scenes with different lighting conditions, shooting angles and vehicle speeds, and accurately extract license plate character information.

4. The intelligent management method for non-site law enforcement via drones according to claim 1, characterized in that: During the data transmission and storage phase, the boundary access platform adopts a one-way transmission mechanism to securely transmit data from the Internet to the video private network, ensuring that the data transmission process is not subject to external network attacks or data leakage risks.

5. The intelligent management method for non-site law enforcement via drones according to claim 1, characterized in that: The intelligent management method involves three major network platforms: Internet / mobile private network, video private network, and security network. Secure data transmission is achieved through the boundary access platform. Data collected by the front-end drone is first transmitted to the boundary access platform via the Internet or mobile private network, then transmitted from the boundary access platform to the video private network to the security network integrated command platform, and finally uploaded to the six-in-one platform.

6. The intelligent management method for non-site law enforcement via drones according to claim 1, characterized in that: The intelligent management method is equipped with functional modules, including a data aggregation module, a core rules module, a business function module, and a self-developed platform module. The data aggregation module is responsible for collecting various types of data uploaded by drones and performing preliminary classification and organization. The core rules module has built-in traffic violation judgment rules and algorithm models to perform real-time analysis and filtering of data. The business function module covers core business functions such as license plate recognition, data monitoring, and violation information extraction. The self-developed platform module establishes a connection with the drone through a handle to realize remote control of the drone and data transmission, and utilizes cloud API interfaces to achieve rapid access and transmission of drone data.

7. The intelligent management method for non-site law enforcement via drones according to claim 6, characterized in that: The self-developed platform module adopts multiple protocols such as HTTP, MQTT and WEBSOCKET, and selects the most suitable transmission method according to different data types and transmission requirements to ensure the stability and efficiency of data transmission.

8. The intelligent management method for non-site law enforcement via drones according to claim 1, characterized in that: The drone data access platform, developed based on the cloud API interface provided by drones, can receive images and video data collected by drones in real time through seamless connection with drone controllers and transmit them to a designated data storage directory. The platform also has data encryption and verification functions.

9. The intelligent management method for non-site law enforcement via drones according to claim 1, characterized in that: This data monitoring and processing program, developed based on the .NET 8 technology framework, can monitor the data storage directory uploaded by front-end drones in real time. It automatically starts the data processing flow after detecting new data uploads, uses multi-threaded processing technology to process multiple data files in parallel, and has good cross-platform compatibility, running stably on various operating systems such as Windows, Linux, and MacOS.

10. The intelligent management method for non-site law enforcement via drones according to claim 1, characterized in that: The independently developed geographic information reverse encoding service, combined with high-precision traffic geographic information data, transforms the latitude and longitude coordinates of the drone's shooting location into a structured traffic location description; the coordinate acquisition program is responsible for acquiring the latitude and longitude information in the drone's shooting data in real time and transmitting it to the geographic information reverse encoding service for processing.

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