Method and system for digitally generating traffic incident road occupation early warning signal

Abstract

According to the traffic incident road occupation early warning signal digital generation method provided by the invention, the digital data structure of the traffic incident road occupation early warning signal which can be publicly propagated with public service information is defined, no privacy data is involved, and it is ensured that early warning signals from different sources can be transmitted in a cross-platform manner; receiving and understanding by an intelligent driving machine or system are supported; a unified digital signal acquisition and release system is set as a cross-domain and cross-industry system platform interconnection and intercommunication channel, unified data fusion processing is performed on acquired early warning signals, and the reliability of the early warning signals is ensured; because the data structure of the early warning signal is uniform, the early warning signal can be conveniently converted into a digital signal capable of supporting the understanding of a machine according to a preset definition form in a digital signal acquisition and publishing system to be published.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation and vehicle networking technology, specifically to a method and system for digitally generating traffic incident lane occupancy warning signals. Background Technology

[0002] In modern urban transportation, traffic accidents, road construction, and traffic control often result in the closure of sections of road, affecting traffic flow. With the widespread use of intelligent connected vehicles, especially autonomous vehicles, route planning has become particularly important. Whether route planning is based on vehicle intelligent driving systems or on automaker platforms, it requires real-time acquisition of data on these road closure events.

[0003] In existing technologies, intelligent vehicles primarily acquire lane occupancy data through onboard perception systems, such as visual cameras and millimeter-wave radar, to identify traffic signs and safety barriers. This perception method is highly susceptible to environmental factors, making it difficult to guarantee accuracy and reliability. Furthermore, lane occupancy information can only be collected when the vehicle has already reached the occupied section of road, resulting in a certain degree of lag.

[0004] Meanwhile, existing public-facing traffic incident information collection primarily targets driver warnings and other application scenarios, mostly based on uploaded visual formats such as text, images, videos, and audio. Dissemination methods include radio / television, internet websites, road guidance screens and variable message signs, SMS, and manual notifications, resulting in diverse information content and formats. These driver-oriented visual signals cannot be directly used by vehicle intelligent systems; they must be converted into digital signals after recognition before being applied to intelligent vehicle systems. This makes it difficult for intelligent connected vehicles to obtain the much-needed spatiotemporal warning information about lane occupancy caused by traffic incidents in a timely and accurate manner. Even if some devices support uploading digital information, information security concerns prevent direct data exchange between different types of systems.

[0005] In summary, the application of lane occupancy data to intelligent vehicle path planning in existing technologies faces three core challenges: (1) Insufficient single-vehicle perception capabilities, such as inaccurate identification and delayed response; (2) Structural defects in information interaction and coverage, such as communication gaps and coverage blind spots, making it impossible to achieve effective full-domain, full-brand, and all-time service of traffic incident lane occupancy warning digital signals; (3) Unstructured, non-standardized, and insecure information content and release mechanisms, failing to accurately reach intelligent connected vehicles and affecting their path planning and behavioral decisions. These problems all prevent the direct application of lane occupancy information to intelligent vehicle systems. Summary of the Invention

[0006] To address the problem in existing technologies that prevent the direct application of lane occupancy data to intelligent vehicle systems, this invention provides a method for digitally generating lane occupancy warning signals for traffic incidents. This method can generate digital warning signals for lane occupancy incidents related to the spatiotemporal impact of road traffic, while ensuring efficient acquisition, accurate dissemination, unified representation, and safe application of the digital signals. This application also discloses a digital signal acquisition and dissemination system for lane occupancy warning signals for traffic incidents.

[0007] The technical solution of this invention is as follows: a method for digitally generating traffic incident lane occupancy warning signals, characterized by comprising the following steps: S1: Defines the digital data structure for traffic incident lane occupancy warning signals; The data structure of the digital road occupancy warning signal includes: event type, geographical location, affected space, and affected time. The event types include: traffic accidents, road construction, traffic congestion, and severe weather; The geographical location includes: latitude, longitude, and elevation; The time of impact includes: start time and expected end time; The affected space includes: the list of affected lane numbers and the length of the affected road segment; S2: Construct a unified digital signal acquisition and dissemination system for traffic incident lane occupancy warning digital signals, serving as an interconnection channel for cross-domain and cross-industry system platforms; S3: Define the data acquisition mode; Specify the data collector and collection mode, and upload the data to the collection and publishing system according to the specified data structure. The data collection modes include: mobile micro-application collection, intelligent security facility collection, system and cloud platform collection, and manual collection; The actual operator of the mobile micro-application is a user in a designated industry, and the designated mobile micro-application is mounted on the mobile handheld terminal of the user in the designated industry. When a traffic incident occurs during the production operation and affects road traffic, the user uses a mobile handheld terminal at the work site to upload the digital road occupancy warning signal for this operation in the specified form in the micro-application. The intelligent safety facilities refer to the equipment used in the process of handling traffic incidents to monitor the status of road construction areas in real time; all intelligent safety facilities are connected to the digital signal acquisition and release system through a unified communication interface to upload the digital road occupancy warning signal data in real time. The system and cloud platform acquisition mode refers to setting up a message middleware module between the designated system and platform and the digital signal acquisition and release system to enable platform-level data docking between the designated system and platform and the digital signal acquisition and release system; the designated system and platform transmit internal traffic event lane occupancy warning signals to the digital signal acquisition and release system in a specified format based on the middleware module; The manual data collection refers to the manual input module set up in the digital signal acquisition and release system, where business personnel from designated industries manually input traffic incident road occupancy warning information. S4: All data uploaded from all acquisition modes are uniformly aggregated into the digital signal acquisition and dissemination system. The system cleans the data and saves the cleaned early warning data. The data cleaning includes: deduplicating duplicate reports of the same traffic accident warning data; correcting or removing obviously erroneous data; and supplementing and improving incomplete information. S5: Perform data fusion on early warning data; The data fusion includes: integrating different information reported by multiple collection terminals for the same traffic event into a complete traffic event lane occupancy warning signal; S6: Extract data from the warning signal that meets the data structure requirements of the digital road occupancy warning signal, and convert it into a digital signal form that can be understood by the intelligent system; S7: The digital signal acquisition and release system pushes the generated traffic incident lane occupancy warning digital signal to the digital signal usage platform.

[0008] Its further features are: In step S3, the designated industries include: vehicle manufacturers, municipal operations departments, and traffic management departments; the designated systems and platforms include: traffic management department data system, municipal operations department management system, and vehicle manufacturer cloud platform. Set confidence levels for different data sources; The manually collected content includes: uploading work plans, which refers to allowing business personnel in designated industries to manually enter spatiotemporal information of traffic incident lane occupancy warnings that have not yet occurred; after receiving the work plan, the system compares it with the warning data stored in the system, and after completing data cleaning and fusion, extracts the traffic incident lane occupancy warning signal information from the work plan content according to the specified data structure, converts it into digital signals, and releases it; The data cleaning includes the following operations: a1: Establish data validity verification rules to automatically remove abnormal data; The abnormal data includes: data whose location information is significantly deviated from the road range or whose event timestamp is significantly deviated from the current time. a2: Duplicate data identification and deduplication are achieved through spatiotemporal feature comparison, specifically including the following steps: a21: Extract key data features from digital road occupancy warning signals, including: event type, geographical location, affected space, and affected time; All key data features are concatenated to obtain the concatenated data features; a22: Calculate the Euclidean distance between the spliced ​​data features corresponding to the two early warning signals; When the Euclidean distance between the spliced ​​data features of two warning signals is less than a preset threshold, the two are determined to be duplicate data. a23: Compare the confidence levels of two duplicate data points, retain the warning signal data with higher confidence level, and perform subsequent processing; Data with low confidence levels is stored in the system; a3: Supplement and improve the data for missing key fields; The data fusion includes merging multi-source reported information on related traffic incidents, specifically including the following steps: b1: Extract key data features from the reported early warning data. The key data features include: event type, geographical location, impact space, and impact time. b2: If multiple warning data meet the following conditions at the same time, they are judged as multi-source reports of related traffic events, and the related warning data are recorded as: multi-source data; Condition 1: The spaces affected overlap; Condition 2: The time periods of influence overlap; Condition 3: The distance between geographical locations is less than the preset distance; Condition 4: Different event types; b3: Integrate spatiotemporally related traffic events into a single lane occupancy warning signal operation; The fusion operation includes: taking the union of the influence space as the fused influence space, taking the union of the influence time as the fused influence time, taking the union of the event types as the fused event types; and taking the geographical location of the early warning data with the highest confidence as the fused geographical location. b4: Store and publish data that merges related traffic events from all multi-source data into a single warning signal; simultaneously, create a complete event source information archive containing structured attributes and unstructured attachments for the merged warning signal data; It also includes security policies, which include: personnel and device authentication, interactive system authentication, and interactive data security authentication; The personnel and device authentication includes: when an individual user logs into the system, a multi-factor authentication method is used, which includes: a combination of ID card, mobile phone number, SMS verification code and facial recognition authentication; when a device accesses the system, it uses a digital certificate to log in; the devices accessing the system include: intelligent security facilities that automatically upload data and mobile devices used by individuals. The identity authentication of the interactive system includes: issuing a digital certificate based on the hardware security module storage for each system, platform and module accessing the digital signal acquisition and release system, and performing two-way identity authentication before interaction; The interactive data security authentication includes: in the data transmission stage, user-entered data is transmitted through an HTTPS encrypted channel throughout the entire process; the system uses a digital certificate to digitally sign the sent data, and the recipient verifies the signature; for data involving privacy, the system adopts de-identification processing and encrypted transmission methods; in the publishing stage, the publishing module digitally signs the data, and the recipient applies the data after verifying the signature; when the system displays data, all privacy information is de-identified before being presented.

[0009] A digital signal acquisition and dissemination system for traffic incident lane occupancy warning signals, characterized in that it comprises: a digital signal acquisition subsystem for traffic incident lane occupancy warning signals, a data aggregation subsystem, a data processing subsystem, a storage subsystem, and a dissemination subsystem; The data processing subsystem, the data aggregation subsystem, the storage subsystem, and the publishing subsystem are deployed within the vehicle network. The data acquisition subsystem adopts a distributed data acquisition architecture, deployed in the vehicle network or the industry private network according to the distribution of acquisition sources; there are more than one acquisition subsystem in the same city; the acquisition subsystem receives data uploaded from various data acquisition modes allowed by the system; the acquisition subsystem has a built-in data cleaning module, authentication module, and manual input module; the manual input module is used to receive spatiotemporal information of traffic incident lane occupancy warnings manually entered by business personnel of designated industries; the authentication module verifies the identity of connected devices and users, and the data cleaning module performs data cleaning processing on the collected warning data; the cleaned warning dataset is sent to the data processing submodule; The data processing subsystem is deployed in each city, with one data processing subsystem set up in each city to process the data collected by the acquisition subsystem in each city. The data processing submodule determines whether data fusion processing is required for early warning signals from various sources. Data that does not require processing is directly sent to the storage subsystem and the publishing subsystem. Data that requires fusion processing is processed and then sent to the data aggregation subsystem. The data aggregation subsystem is located one level above the data processing subsystem. After aggregating the data processed by all collection subsystems, it forwards it to the publishing subsystem. Simultaneously, the data aggregation subsystem interfaces with all vehicle manufacturer platforms, distributing data from all vehicle manufacturer cloud platforms to the collection subsystems for processing according to city. The data aggregation subsystem includes a message middleware module, which is positioned between the data aggregation subsystem and external platforms and systems. This message middleware module includes a hardware security module for identity authentication, providing authentication support for identity authentication and encrypted transmission for each data transmission. The publishing subsystem performs unified data publishing with all digital signal usage platforms; the publishing subsystem extracts data that meets the data structure requirements of digital road occupancy warning signals from the warning signals, converts it into a digital signal form that can be understood by the intelligent system; and pushes the generated traffic event road occupancy warning digital signal to the digital signal usage platform. The storage subsystem saves the data.

[0010] Its further features are: It also includes: mobile micro-applications and smart security facilities; The mobile micro-application includes: an application APP and an H5 connection mounted on an existing application in a specified industry. The mobile micro-application runs on various types of mobile handheld terminals and receives user-uploaded warning signal information based on a graphic interface. Industry users log in to the mobile micro-application through a preset multi-factor authentication mechanism and access the data collection subsystem to upload data. After being powered on, the intelligent security facility connects to the data acquisition subsystem, logs in using a digital certificate, and uploads its own location data in real time. The intelligent safety facilities include: intelligent traffic cones, digital signs, vehicle-mounted terminals, and vehicle-mounted apps; The intelligent safety facility also includes: mobile facilities, which include: low-speed operating vehicles and mobile intelligent operating facilities; The mobile facility is equipped with positioning sensors or a vehicle-mounted terminal. After the mobile facility is connected to the digital signal acquisition and dissemination system, it transmits its location information back to the acquisition subsystem in real time. The acquisition subsystem tracks the dynamic changes in the location of the mobile facility in real time and, in conjunction with the roadside unit and the cloud platform, dynamically updates the spatial information of the construction area. The digital signal usage platforms include: an integrated urban vehicle-road-cloud control platform, an industry vehicle management platform, a vehicle manufacturer cloud platform, and an internet navigation platform.

[0011] This application provides a method for digitally generating traffic incident lane occupancy warning signals. It defines a digital data structure for these signals, which serve as publicly disseminated public service information, ensuring no privacy data is involved and guaranteeing cross-platform transmission of warning signals from different sources, while also supporting reception and understanding by intelligent driving machines or systems. A unified digital signal acquisition and dissemination system is established as an interconnection channel between cross-domain and cross-industry system platforms, performing unified data fusion processing on the acquired warning signals to ensure their reliability. Because the warning signal data structure is unified, it can be easily converted into machine-understandable digital signals according to a preset definition within the digital signal acquisition and dissemination system. This method limits the uploaders and upload formats of the warning signals, ensuring reliable data sources. Uploading warning signals through specified data acquisition methods and preset data formats reduces the difficulty of subsequent data cleaning and improves data processing efficiency. This method, based on reliable sources, acquires warning signals and then disseminates them in digital form, enabling broader, more timely, and accurate acquisition of various traffic incident lane occupancy warning digital signals, reducing the omission and delay of warning information. Attached Figure Description

[0012] Figure 1 A schematic diagram of the overall system for collecting and disseminating digital signals for traffic incident lane occupancy warnings; Figure 2 A hierarchical diagram of the digital signal acquisition and dissemination system for traffic incident lane occupancy warning signals; Figure 3 This is an example of the collection and dissemination of digital signals for traffic incident lane occupancy warnings. Detailed Implementation

[0013] This invention aims to address the problems existing in the collection and dissemination of traffic incident lane occupancy warning information, such as non-standard collection modes, unfocused content, low collection accuracy, inconsistent information expression, limited dissemination methods, and lack of information security. It provides a method and system for generating digital signals for traffic incident lane occupancy warnings based on intelligent network interaction, enabling efficient collection, accurate dissemination, unified expression, and safe application of digital signals for lane occupancy warnings of traffic incidents affecting the time and space of road traffic, thereby improving the intelligence level of traffic travel and user experience.

[0014] like Figure 1As shown, this application constructs a digital signal acquisition and dissemination system for traffic incident lane occupancy warning signals, which includes: a digital signal acquisition subsystem, a data aggregation subsystem, a data processing subsystem, a storage subsystem, and a dissemination subsystem for traffic incident lane occupancy warning signals. The data processing subsystem, data aggregation subsystem, storage subsystem, and dissemination subsystem are deployed within a vehicle-to-everything (V2X) network. The acquisition subsystem adopts a distributed data acquisition architecture, and is deployed in either the V2X network or a dedicated industry network according to the distribution of the acquisition sources.

[0015] The subsystems in this system are arranged hierarchically, such as... Figure 2 As shown, the data acquisition subsystem and data processing subsystem are deployed separately for each city. Only one data aggregation subsystem and one data publishing subsystem are configured within the system.

[0016] Within the same city, different industries have their own intranets and public networks, so a separate data collection subsystem is deployed in each network, resulting in more than one data collection subsystem in the same city. The data collection subsystem receives data uploaded from various systems using permitted data collection modes. It includes a data cleaning module, an authentication module, and a manual data entry module. The manual data entry module receives spatiotemporal information about traffic incidents and road occupancy warnings manually entered by business personnel from designated industries. The authentication module verifies the identity of connected devices and users, and the data cleaning module cleans the collected warning data. The cleaned warning dataset is then sent to the data processing subsystem.

[0017] The data acquisition subsystem adopts a distributed data acquisition architecture, capable of processing information input from massive data sources simultaneously. The system incorporates an intelligent filtering algorithm that automatically identifies and removes duplicate reports and false information, ensuring the accuracy and reliability of the collected data. Simultaneously, the system supports direct access to various third-party platform systems or terminals via H5 links, collecting standardized and regulated traffic incident lane occupancy warning digital signal data from the source. It also uses digital certificate technology to authenticate the personnel and equipment involved in data collection, and digital signature technology to protect information from tampering during transmission.

[0018] Compared to the internet-based data collection subsystem, the industry-specific network data collection subsystem places greater emphasis on data accuracy and legal validity. All data collected through this subsystem comes with complete metadata, including collection time, location, and responsible police officer, ensuring data traceability. The system also supports data exchange with other business systems within the industry, enabling the rapid flow of traffic incident lane occupancy warning digital signals within the industry system.

[0019] The data processing subsystem is deployed in each city, with one data processing subsystem set up in each city to process the data collected by the data collection subsystem in each city.

[0020] The data processing submodule determines whether data fusion processing is required for early warning signals from various sources. Data that does not require processing is directly sent to the storage subsystem and the publishing subsystem. Data that requires fusion processing is processed and then sent to the data aggregation subsystem.

[0021] The data aggregation subsystem sits one level above the data processing subsystem. It aggregates data processed by all acquisition subsystems and forwards it to the publishing subsystem. Simultaneously, the data aggregation subsystem interfaces with all automotive enterprise platforms, distributing data from all automotive enterprise cloud platforms to the acquisition subsystems for processing based on city. The data aggregation subsystem includes a message middleware module, which acts as an intermediary between the data aggregation subsystem and external platforms and systems. This module includes a hardware security module for identity authentication, providing authentication support for each data transmission and ensuring encrypted transmission. Each message middleware module adaptively defines its data connection method based on the specific data and requirements of the connected platform. The implementation can be based on existing technologies.

[0022] The publishing subsystem publishes data in a unified manner with all digital signal usage platforms; the publishing subsystem extracts data that meets the data structure requirements of digital road occupancy warning signals from the warning signals, converts it into a digital signal form that can be understood by the intelligent system, and pushes the generated traffic event road occupancy warning digital signal to the digital signal usage platform.

[0023] The storage subsystem saves the data. After cleaning, the data enters the classification and storage stage, employing a four-dimensional classification model of "event type - severity - geographic location - impact time". In the event type dimension, it distinguishes more than ten standard categories such as traffic accidents, road construction, and traffic control; the severity dimension uses a five-level grading standard, from minor to extremely serious; the geographic location dimension incorporates a GIS system; and a hierarchical spatial index of "administrative region - road - road segment - specific location - impact time" is established based on the approximate impact time assessed according to the time severity. For example, when multiple traffic accidents occur in a certain area, the system can quickly retrieve all relevant events in that area, providing data support for traffic management decisions.

[0024] Traffic incident data is primarily stored in three storage layers: file storage, cache, and relational database. The file storage layer stores multimedia materials related to traffic incidents, such as accident scene photos and surveillance video clips. The cache layer temporarily stores data on frequently occurring traffic incidents. The relational database stores structured data of all traffic incident lane occupancy warning digital signals. The database design fully considers the characteristics of traffic incidents, supporting efficient querying and analysis of spatiotemporal data.

[0025] Considering that most of the sources for collecting traffic incident lane occupancy warning digital signals are located on the Internet and other industry-specific networks, to improve the efficiency of collecting and disseminating these signals and reduce latency, the main system components (collection subsystem, storage and processing system, and dissemination subsystem) are deployed on the vehicle-to-everything (V2X) network / Internet (private network). Specifically, the main business processes of the system and the data collected for traffic incident lane occupancy warning digital signals are stored on the V2X / Internet (private network). A collection subsystem for gathering information such as traffic accidents from traffic management departments is also deployed on the industry-specific network. Simultaneously, all traffic incident warning data is transmitted back to the industry-specific network to support traffic management operations and is stored as a business log. This architecture design fully considers the multi-source collection requirements of traffic incident lane occupancy warning digital signals while ensuring efficient data processing and timely information dissemination.

[0026] The system also includes mobile micro-applications for data collection and intelligent security facilities; Mobile micro-applications include: apps and H5 links attached to existing applications in designated industries. These mobile micro-applications run on various types of mobile handheld terminals and receive user-uploaded warning signal information based on a graphic interface. Industry users log in to the mobile micro-applications through preset multi-factor authentication mechanisms and access the data collection subsystem to upload data.

[0027] After being powered on and easily configured by the user, the intelligent safety facilities connect to the data collection subsystem, log in using digital certificates, and upload their own location data in real time. These intelligent safety facilities include: intelligent traffic cones, digital signs, low-speed work vehicles, vehicle-mounted terminals, and vehicle-mounted apps. The digital signal usage platforms include: an integrated urban vehicle-road-cloud control platform, an industry vehicle management platform, a vehicle manufacturer cloud platform, and an internet navigation platform.

[0028] A method for digitally generating traffic incident lane occupancy warning signals based on the above system includes the following steps.

[0029] S1: Defines the digital data structure for traffic incident lane occupancy warning signals; The data structure of the digital road occupancy warning signal includes: event type, geographical location, affected space, and affected time. Event types include: traffic accidents, road construction, traffic congestion, and severe weather; when signals are digitally represented, event types are encoded, such as using a numerical form based on 0 to 3 to represent each event type; Geographical location includes: latitude, longitude, and elevation; The affected time includes: start time and expected end time; The affected space includes: a list of affected lane numbers and the length of the affected road segment. In practice, lane numbers are numbered from 1 outwards from the road centerline. Combining the geographical location and the affected space allows for the precise location of the specific road segment and lane.

[0030] The data structure of the digital road occupancy warning signal constructed in this application allows for convenient structured encoding and digital representation. The digital encoding method in this embodiment is as follows: each feature field is represented according to a predetermined field length, and then all feature fields are concatenated in a predetermined order to form a complete digital warning signal.

[0031] S2: Construct a unified digital signal acquisition and dissemination system for traffic incident lane occupancy warning digital signals, serving as an interconnection channel for cross-domain and cross-industry system platforms.

[0032] The traffic incident lane occupancy early warning digital signal acquisition in this application adopts a multi-terminal collaborative, precise and efficient acquisition mode.

[0033] S3: Define the data acquisition mode; Specify the data collector and collection mode, and upload the data to the collection and publishing system according to the specified data structure.

[0034] Data collection modes include: mobile micro-application collection, intelligent security facility collection, system and cloud platform collection, and manual collection.

[0035] The actual operators of the mobile micro-application data collection are users in designated industries. The designated mobile micro-application is mounted on the mobile handheld terminals of these users. When a traffic incident occurs during production operations that affects road traffic, the users use their mobile handheld terminals at the work site to upload a digital road occupancy warning signal in the specified format within the micro-application. Designated industries include: vehicle manufacturers, municipal work departments, and traffic management departments.

[0036] Specifically, by developing a mobile application (APP) or H5 link for collecting digital signals for traffic incident lane occupancy warnings and integrating it into existing industry applications, such as mini-programs and official accounts, when traffic incidents caused by industry users' production operations affect road traffic—for example, partial lane closures or work vehicles occupying lanes at low speeds—mobile handheld terminals can be used at the work site. After identity authentication, users can submit standardized and digitized incident lane occupancy warning information, including the incident type, start location, affected lanes and length, start time, and expected end time. This information can be quickly reported through mobile micro-applications, forming a collectively intelligent data collection network. This approach expands monitoring coverage and reduces equipment deployment costs, making it particularly suitable for the standardized collection and unified dissemination of digital signals for traffic incident lane occupancy warnings.

[0037] Intelligent safety facilities refer to equipment used in the process of handling traffic incidents to monitor the status of road construction areas in real time; all intelligent safety facilities are connected to the digital signal acquisition and dissemination system through a unified communication interface to upload digital road occupancy warning signal data in real time.

[0038] By utilizing new intelligent facilities such as smart cones, digital signs, and vehicle-mounted terminals / apps, real-time perception and dynamic monitoring of road construction areas are achieved. Smart cones, equipped with built-in positioning and wireless communication modules, automatically sense the surrounding environment, upload location information, and transmit the data back to the platform in real time. Digital signs dynamically display and publish construction warnings, and can remotely update warning information based on construction progress, improving information delivery efficiency. Automatic data uploads through intelligent safety facilities effectively enhance the safety and management efficiency of temporary road occupancy construction. For low-speed operating vehicles and mobile construction scenarios, positioning sensors or vehicle-mounted terminals are installed in the intelligent facilities. After the mobile facilities are connected to the digital signal acquisition and dissemination system, the vehicle-mounted facilities transmit location information back to the acquisition subsystem in real time. The acquisition subsystem tracks the dynamic changes in vehicle location in real time, and, combined with roadside units and the cloud platform, dynamically updates the spatial information affecting the construction area, ensuring real-time updates of road occupancy information during mobile construction. This effectively supports dynamic modeling and safety warnings for the construction area, strengthening traffic guidance and safety protection capabilities in mobile construction scenarios.

[0039] In this method, to ensure the reliability of the data source, only designated systems and platforms can access the early warning signal digital signal acquisition and dissemination system. These designated systems and platforms include: traffic management department data systems, municipal operations department management systems, and vehicle manufacturer cloud platforms.

[0040] The system and cloud platform acquisition mode refers to setting up a message middleware module between the designated system and platform and the digital signal acquisition and release system to enable platform-level data docking between the designated system and platform and the digital signal acquisition and release system; the designated system and platform transmit internal traffic event lane occupancy warning signals to the digital signal acquisition and release system in a specified format based on the middleware module.

[0041] In this embodiment, the specified systems and platforms include: traffic accident and traffic control systems / platforms of traffic management departments, which automatically collect corresponding lane occupancy warning signal data, such as the start and expected end times of accidents or controls, affected lanes, and lengths; infrastructure status data transmitted by bridge health monitoring systems and tunnel structure monitoring equipment of road management departments, such as construction, maintenance, and closure; and traffic event information detected and transmitted back by intelligent connected vehicles. After preliminary aggregation and verification of data transmitted from multiple vehicles, the vehicle manufacturer's cloud platform pushes the confirmed traffic event information to the traffic event lane occupancy warning signal generation system. Through the fusion of data from multiple systems, a nationally unified digital signal database for traffic event lane occupancy warnings is constructed and uniformly released through multiple channels.

[0042] Manual data collection refers to the inclusion of a manual input module within the digital signal acquisition and dissemination system. This module serves as an effective supplement to automation, allowing designated industry personnel to manually input traffic incident lane occupancy warning information. These personnel use the system's manual input module to record such information, supplementing automation and suitable for scenarios involving blind spots in equipment coverage or specific work schedules. Municipal work departments such as road maintenance, pipeline construction, and landscaping maintenance, or traffic management departments (dispatch management, emergency response, etc.), manually input the spatiotemporal information of lane occupancy warnings according to the actual work plan. This enables accurate collection and standardized dissemination of lane occupancy warning information corresponding to various planned work operations.

[0043] The manually collected content includes: work plan upload, which refers to allowing business personnel in designated industries to manually enter spatiotemporal information of traffic incident lane occupancy warnings that have not yet occurred; after the system receives the work plan, it compares it with the warning data stored in the system, performs data cleaning and fusion, extracts the traffic incident lane occupancy warning signal information from the work plan content according to the specified data structure, converts it into digital signals and releases it.

[0044] Because data comes from various sources, such as Figure 3As shown, for the same traffic incident involving lane obstruction caused by a car accident, accident handling personnel will upload lane obstruction warning data, which is then connected to the digital signal acquisition and dissemination system via an industry platform. Simultaneously, passing vehicles will also collect lane obstruction information and upload it to the vehicle manufacturer's data platform, which is then connected to the digital signal acquisition and dissemination system via the vehicle manufacturer's cloud platform. A single traffic incident can originate from different sources, and the specific content of the data may differ. After the system determines it to be the same traffic incident, it needs to decide which data source to use. This method sets different confidence levels for different data sources; for example, for traffic incidents caused by car accidents, the confidence level of data uploaded by the traffic management industry will be higher than other data sources. For lane obstruction traffic incidents caused by municipal work, the confidence level of data from the municipal platform will be higher than other data sources.

[0045] S4: All data uploaded from all acquisition modes are uniformly aggregated into the digital signal acquisition and dissemination system, which cleans the data and saves the cleaned early warning data. Data cleaning includes: deduplicating duplicate reports of the same traffic accident warning data; correcting or removing obviously erroneous data; and supplementing and improving incomplete information.

[0046] Data cleaning includes the following operations: a1: Establish data validity verification rules to automatically remove abnormal data; Abnormal data includes: data whose location information deviates significantly from the road area or whose event timestamp deviates significantly from the current time. a2: Duplicate data identification and deduplication are achieved through spatiotemporal feature comparison, specifically including the following steps: a21: Extract key data features from digital road occupancy warning signals. Key data features include: event type, geographical location, space of impact, and time of impact. All key data features are concatenated to obtain the concatenated data features; a22: Calculate the Euclidean distance between the spliced ​​data features corresponding to the two early warning signals; When the Euclidean distance between the spliced ​​data features of two warning signals is less than a preset threshold, the two are determined to be duplicate data. a23: Compare the confidence levels of two duplicate data points, retain the warning signal data with higher confidence level, and perform subsequent processing; Data with low confidence levels is stored in the system; a3: Supplement and improve the data for missing key fields.

[0047] S5: Perform data fusion on early warning data; Data fusion includes integrating different information reported by multiple data collection terminals for related traffic events into a complete traffic event lane occupancy warning signal.

[0048] Data fusion includes merging multi-source reported information on related traffic incidents; specifically, it includes the following steps: b1: Extract key data features from the reported early warning data. Key data features include: event type, geographical location, impact area, and impact time. b2: If multiple warning data simultaneously meet the following conditions, they are judged as multi-source reports of related traffic events, and the related warning data are recorded as: multi-source data; specific related traffic events, for example: a traffic accident occurred in a road construction section, then road construction and traffic accident are related traffic events; Condition 1: The spaces affected overlap; Condition 2: The time periods of influence overlap; Condition 3: The distance between geographical locations is less than the preset distance; Condition 4: Different event types; b3: Integrate spatiotemporally related traffic events into a single lane occupancy warning signal operation; The fusion operation includes: taking the union of the impact space as the fused impact space, taking the union of the impact time as the fused impact time, taking the union of the event types as the fused event types; and taking the geographical location of the warning data with the highest confidence as the fused geographical location. b4: Store and publish data that merges related traffic events from all multi-source data into a single warning signal; simultaneously, create a complete event source information archive containing structured attributes and unstructured attachments for the merged warning signal data.

[0049] like Figure 3 In the illustrated embodiment, two types of traffic events occupying the same lane exist: municipal construction and traffic accidents. Warning signals related to municipal construction are reported by the municipal department, including: lane ## closure, starting latitude and longitude, length range, start time, and expected end time. Traffic accidents are reported by traffic management personnel handling the accidents, including: lane ## closure, starting latitude and longitude, and start time. If the vehicle involved in the accident is an intelligent vehicle, the vehicle occupying the lane will upload lane occupancy data to the vehicle manufacturer's platform: lane ## closure, starting latitude and longitude location information, and start time.

[0050] The digital signal acquisition and dissemination system will simultaneously receive three sets of warning signal data from different sources. First, during data cleaning, the data uploaded by the vehicles occupying the road and the data uploaded by traffic management authorities are treated as duplicates and deduplicated after calculating their sum of similarities because they share the same time and spatial impact range, as well as the same event type. Since the confidence level of the traffic management data is higher than that of the vehicle-uploaded data for road occupancy caused by traffic accidents, the traffic management data is retained. However, in the data fusion stage, the traffic management data and the municipal data, because they overlap in both spatial and temporal impact but differ in event type, will be merged.

[0051] The integrated early warning signal is uniformly published to the city cloud control platform, major car manufacturer platforms, and navigation websites by the publishing subsystem. When each car manufacturer platform plans routes for vehicles that may pass through this section of road, it will adjust the driving route, control and adjust the vehicle speed, and issue early warning information.

[0052] S6: Extract data from the warning signal that meets the data structure requirements of the digital road occupancy warning signal, and convert it into a digital signal form that can be understood by the intelligent system.

[0053] S7: The digital signal acquisition and release system pushes the traffic incident lane occupancy warning digital signal generated to the digital signal usage platform.

[0054] The method for releasing digital traffic event road occupancy warning signals in this application includes at least the following two information release methods. One is to directly release the standardized traffic event road occupancy warning digital signals, after proper processing, to urban cloud control platforms, vehicle manufacturer cloud platforms, industry vehicle management platforms, etc., and then indirectly push them to various intelligent connected vehicles, industry vehicles, or intelligent connected unmanned equipment, etc.

[0055] Second, traffic incident lane occupancy warning digital signals will be released to Internet navigation platforms such as Gaode and Baidu Maps, and applied to various intelligent connected vehicles and terminal equipment using navigation platform services.

[0056] By building standardized information sets and dissemination channels, and breaking down data barriers between professional applications and public services, the consistency and interoperability of the same early warning signal across different systems and platforms nationwide are ensured. This is a key step in realizing the large-scale, nationwide application of digital signals for road occupancy warnings.

[0057] Because the digital warning signals published in this application are data directly provided to the autonomous driving or navigation software of intelligent vehicles, the accuracy of the data must be guaranteed. To ensure data reliability, this application specifies the data source; only data from designated industries, platforms, and systems can be used as data sources. Furthermore, to prevent erroneous data from entering the system, a security strategy with an information security protection mechanism is also implemented. This information security protection mechanism establishes a comprehensive information security protection system at every stage of the collection, transmission, processing, and publication of traffic incident lane occupancy warning digital signals.

[0058] Security strategies include: personnel and device authentication, interactive system authentication, and interactive data security authentication; Personnel and device authentication includes: multiple authentication methods are used when individual users log in to the system, including: using ID card, mobile phone number, SMS verification code and facial recognition authentication methods simultaneously; when devices access the system, they use digital certificates to log in; devices accessing the system include: intelligent security facilities that automatically upload data and mobile devices used by individuals. Interactive system identity authentication includes: issuing digital certificates based on hardware security module storage for each system, platform and module (such as event system acquisition module, vehicle enterprise cloud platform docking module, map navigation platform docking module) connected to the digital signal acquisition and release system, and performing two-way identity authentication before interaction; Interactive data security authentication includes: During data transmission, user-entered data is transmitted entirely through an HTTPS encrypted channel; the system digitally signs the sent data using a digital certificate, and the recipient verifies the signature to ensure data authenticity. For privacy-related data, such as the mobile phone number and ID card number used by the visitor during login, the system employs anonymization and encrypted transmission methods. During the publishing phase, the publishing module digitally signs the data, and the recipient must verify the signature before using the data; when the system displays data, all privacy information is anonymized before presentation.

[0059] This mechanism employs a multi-layered protection strategy: For identity authentication, on-site data collection personnel must undergo multiple verifications using "ID card number + mobile phone number + SMS verification code + facial recognition," while automated data collection devices utilize digital certificate authentication. Central data entry personnel ensure legitimate access through "username + strong password + IP whitelist." Regarding system interaction security, each module (such as event collection and vehicle manufacturer cloud platform integration) uses hardware-based security modules for two-way authentication with digital certificates to prevent unauthorized access. For data security, sensitive information is transmitted using HTTPS encryption, and critical business data (such as early warning information reporting and release) is digitally signed to ensure integrity and non-repudiation. Privacy data (such as ID card numbers and mobile phone numbers) is anonymized. Furthermore, through strict access control, log auditing, and regular vulnerability scanning and patching, the system's security capabilities are continuously improved, effectively preventing data leakage, tampering, and unauthorized access risks, ensuring the safe and stable operation of the traffic incident lane occupancy warning digital signal system.

[0060] This method improves the efficiency and accuracy of digital signal acquisition for traffic incident lane occupancy warnings: through cross-domain and cross-industry system platform interconnection and multi-channel, multi-terminal, secure, and efficient acquisition methods, it can acquire various traffic incident lane occupancy warning digital signals more broadly, timely, and accurately, reducing the omission and delay of warning information and providing a more reliable data foundation for subsequent traffic management and travel services. It also achieves standardized information expression and efficient dissemination: the concise, standardized, and unified digital information expression method is easy to understand and use, and also facilitates information exchange and sharing between different systems, promoting the rapid circulation and effective utilization of traffic incident lane occupancy warning digital signals throughout the intelligent transportation system.

[0061] By using the technical solution of this invention, a cross-domain and cross-industry system platform interconnection channel is constructed. It adopts a standardized traffic incident lane occupancy warning signal data format, the content of which covers structured information such as incident type, spatial location, affected lane, start and end time, ensuring cross-regional and cross-system mutual recognition and interoperability, realizing seamless connection and data interaction of traffic information systems in different fields and industries, breaking down information silos, and forming a unified traffic incident lane occupancy warning digital signal collection and release system, laying the foundation for large-scale application nationwide.

Claims

1. A method for digitally generating traffic incident lane occupancy warning signals, characterized in that, It includes the following steps: S1: Defines the digital data structure for traffic incident lane occupancy warning signals; The data structure of the digital road occupancy warning signal includes: event type, geographical location, affected space, and affected time. The event types include: traffic accidents, road construction, traffic congestion, and severe weather; The geographical location includes: latitude, longitude, and elevation; The time of impact includes: start time and expected end time; The affected space includes: the list of affected lane numbers and the length of the affected road segment; S2: Construct a unified digital signal acquisition and dissemination system for traffic incident lane occupancy warning digital signals, serving as an interconnection channel for cross-domain and cross-industry system platforms; S3: Define the data acquisition mode; Specify the data collector and collection mode, and upload the data to the collection and publishing system according to the specified data structure. The data collection modes include: mobile micro-application collection, intelligent security facility collection, system and cloud platform collection, and manual collection; The actual operator of the mobile micro-application is a user in a designated industry, and the designated mobile micro-application is mounted on the mobile handheld terminal of the user in the designated industry. When a traffic incident occurs during the production operation and affects road traffic, the user uses a mobile handheld terminal at the work site to upload the digital road occupancy warning signal for this operation in the specified form in the micro-application. The intelligent safety facilities refer to the equipment used in the process of handling traffic incidents to monitor the status of road construction areas in real time; all intelligent safety facilities are connected to the digital signal acquisition and release system through a unified communication interface to upload the digital road occupancy warning signal data in real time. The system and cloud platform acquisition mode refers to setting up a message middleware module between the designated system and platform and the digital signal acquisition and release system to enable platform-level data docking between the designated system and platform and the digital signal acquisition and release system; the designated system and platform transmit internal traffic event lane occupancy warning signals to the digital signal acquisition and release system in a specified format based on the middleware module; The manual data collection refers to the manual input module set up in the digital signal acquisition and release system, where business personnel from designated industries manually input traffic incident road occupancy warning information. S4: All data uploaded from all acquisition modes are uniformly aggregated into the digital signal acquisition and dissemination system. The system cleans the data and saves the cleaned early warning data. The data cleaning includes: deduplicating duplicate reports of the same traffic accident warning data; correcting or removing obviously erroneous data; and supplementing and improving incomplete information. S5: Perform data fusion on early warning data; The data fusion includes: integrating different information reported by multiple collection terminals for the same traffic event into a complete traffic event lane occupancy warning signal; S6: Extract data from the warning signal that meets the data structure requirements of the digital road occupancy warning signal, and convert it into a digital signal form that can be understood by the intelligent system; S7: The digital signal acquisition and release system pushes the generated traffic incident lane occupancy warning digital signal to the digital signal usage platform.

2. The method for digitally generating traffic incident lane occupancy warning signals according to claim 1, characterized in that: In step S3, the designated industries include: vehicle manufacturers, municipal operations departments, and traffic management departments; the designated systems and platforms include: traffic management department data system, municipal operations department management system, and vehicle manufacturer cloud platform. Set confidence levels for different data sources.

3. The method for digitally generating traffic incident lane occupancy warning signals according to claim 1, characterized in that: The manually collected content includes: uploading work plans, which refers to allowing business personnel in designated industries to manually enter spatiotemporal information of traffic incidents that have not yet occurred and cause road occupancy warnings; after receiving the work plan, the system compares it with the warning data stored in the system, and after completing data cleaning and fusion, extracts the traffic incident road occupancy warning signal information from the work plan content according to the specified data structure, converts it into digital signals, and releases it.

4. The method for digitally generating traffic incident lane occupancy warning signals according to claim 1, characterized in that: The data cleaning includes the following operations: a1: Establish data validity verification rules to automatically remove abnormal data; The abnormal data includes: data whose location information is significantly deviated from the road range or whose event timestamp is significantly deviated from the current time. a2: Duplicate data identification and deduplication are achieved through spatiotemporal feature comparison, specifically including the following steps: a21: Extract key data features from digital road occupancy warning signals, including: event type, geographical location, affected space, and affected time; All key data features are concatenated to obtain the concatenated data features; a22: Calculate the Euclidean distance between the spliced ​​data features corresponding to the two early warning signals; When the Euclidean distance between the spliced ​​data features of two warning signals is less than a preset threshold, the two are determined to be duplicate data. a23: Compare the confidence levels of two duplicate data points, retain the warning signal data with higher confidence level, and perform subsequent processing; Data with low confidence levels is stored in the system; a3: Supplement and improve the data for missing key fields.

5. The method for digitally generating traffic incident lane occupancy warning signals according to claim 1, characterized in that: The data fusion includes merging multi-source reported information on related traffic incidents, specifically including the following steps: b1: Extract key data features from the reported early warning data. The key data features include: event type, geographical location, impact space, and impact time. b2: If multiple warning data meet the following conditions at the same time, they are judged as multi-source reports of related traffic events, and the related warning data are recorded as: multi-source data; Condition 1: The spaces affected overlap; Condition 2: The time periods of influence overlap; Condition 3: The distance between geographical locations is less than the preset distance; Condition 4: Different event types; b3: Integrate spatiotemporally related traffic events into a single lane occupancy warning signal operation; The fusion operation includes: taking the union of the influence space as the fused influence space, taking the union of the influence time as the fused influence time, taking the union of the event types as the fused event types; and taking the geographical location of the early warning data with the highest confidence as the fused geographical location. b4: Store and publish data that merges related traffic events from all multi-source data into a single warning signal; simultaneously, create a complete event source information archive containing structured attributes and unstructured attachments for the merged warning signal data.

6. The method for digitally generating traffic incident lane occupancy warning signals according to claim 1, characterized in that: It also includes security policies, which include: personnel and device authentication, interactive system authentication, and interactive data security authentication; The personnel and device authentication includes: when an individual user logs into the system, a multi-factor authentication method is used, which includes: authentication combining ID card, mobile phone number, SMS verification code and facial recognition; when a device accesses the system, it uses a PKI digital certificate to log in; the devices accessing the system include: intelligent security facilities that automatically upload data and mobile devices used by individuals. The identity authentication of the interactive system includes: issuing a digital certificate based on the hardware security module storage for each system, platform and module accessing the digital signal acquisition and release system, and performing two-way identity authentication before interaction; The interactive data security authentication includes: in the data transmission stage, user-entered data is transmitted through an HTTPS encrypted channel throughout the entire process; the system uses a digital certificate to digitally sign the sent data, and the recipient verifies the signature; for data involving privacy, the system adopts de-identification processing and encrypted transmission methods; in the publishing stage, the publishing module digitally signs the data, and the recipient applies the data after verifying the signature; when the system displays data, all privacy information is de-identified before being presented.

7. A digital signal acquisition and dissemination system for traffic incident lane occupancy early warning signals, characterized in that, It includes: The system comprises a traffic incident lane occupancy warning digital signal acquisition subsystem, a data aggregation subsystem, a data processing subsystem, a storage subsystem, and a dissemination subsystem; The data processing subsystem, the data aggregation subsystem, the storage subsystem, and the publishing subsystem are deployed within the vehicle network. The data acquisition subsystem adopts a distributed data acquisition architecture and is deployed in the vehicle network or the industry private network according to the distribution of the data acquisition sources. There are more than one data collection subsystem in the same city; the data collection subsystem receives data uploaded from various data collection modes allowed by the system; the data collection subsystem has a built-in data cleaning module, authentication module and manual data entry module; The manual input module is used to receive spatiotemporal information of traffic incident road occupancy warnings manually entered by business personnel in designated industries. The authentication module verifies the identity of connected devices and users, and the data cleaning module cleans the collected early warning data. The cleaned early warning dataset is sent to the data processing submodule. The data processing subsystem is deployed in each city, with one data processing subsystem set up in each city to process the data collected by the acquisition subsystem in each city. The data processing submodule determines whether data fusion processing is required for early warning signals from various sources. Data that does not require processing is directly sent to the storage subsystem and the publishing subsystem. Data that requires fusion processing is processed and then sent to the data aggregation subsystem. The data aggregation subsystem is located one level above the data processing subsystem. After aggregating the data processed by all collection subsystems, it forwards it to the publishing subsystem. Simultaneously, the data aggregation subsystem interfaces with all vehicle manufacturer platforms, distributing data from all vehicle manufacturer cloud platforms to the collection subsystems for processing according to city. The data aggregation subsystem includes a message middleware module, which is positioned between the data aggregation subsystem and external platforms and systems. This message middleware module includes a hardware security module for identity authentication, providing authentication support for identity authentication and encrypted transmission for each data transmission. The publishing subsystem performs unified data publishing with all digital signal usage platforms; the publishing subsystem extracts data that meets the data structure requirements of digital road occupancy warning signals from the warning signals, converts it into a digital signal form that can be understood by the intelligent system; and pushes the generated traffic event road occupancy warning digital signal to the digital signal usage platform. The storage subsystem saves the data.

8. The digital signal acquisition and dissemination system for traffic incident lane occupancy early warning signals according to claim 7, characterized in that: It also includes: mobile micro-applications and smart security facilities; The mobile micro-application includes: an application APP and an H5 connection mounted on an existing application in a specified industry. The mobile micro-application runs on various types of mobile handheld terminals and receives user-uploaded warning signal information based on a graphic interface. Industry users log in to the mobile micro-application through a preset multi-factor authentication mechanism and access the data collection subsystem to upload data. After being powered on, the intelligent security facility connects to the data acquisition subsystem, logs in using a digital certificate, and uploads its own location data in real time. The intelligent safety facilities include: intelligent traffic cones, digital signs, vehicle-mounted terminals, and vehicle-mounted apps.

9. The traffic incident lane occupancy early warning signal digital signal acquisition and dissemination system according to claim 8, characterized in that: The intelligent safety facility also includes: mobile facilities, which include: low-speed operating vehicles and mobile intelligent operating facilities; The mobile facility is equipped with positioning sensors or a vehicle-mounted terminal. After the mobile facility is connected to the digital signal acquisition and dissemination system, it transmits its location information back to the acquisition subsystem in real time. The acquisition subsystem tracks the dynamic changes in the location of the mobile facility in real time and, in conjunction with the roadside unit and the cloud platform, dynamically updates the spatial information of the construction area.

10. The traffic incident lane occupancy early warning signal digital signal acquisition and dissemination system according to claim 7, characterized in that: The digital signal usage platforms include: an integrated urban vehicle-road-cloud control platform, an industry vehicle management platform, a vehicle manufacturer cloud platform, and an internet navigation platform.

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