Traffic transportation emergency command and dispatch system based on big data resources

Abstract

The invention discloses a traffic transportation emergency command and dispatch system based on big data resources, which is constructed based on the big data resources and mainly comprises a data acquisition module, a big data processing and analysis module, an event early warning and prediction module, an emergency command and dispatch module, an information display and interaction module and the like. Various devices (a sensor, a camera, a GPS and the like) are utilized to comprehensively collect data such as traffic flow, road conditions, weather, events and the like. Acquisition has the characteristics of high frequency and high precision, supports a plurality of data transmission modes and protocols, and can integrate and format the acquired data to guarantee data quality and convenience of subsequent processing. According to the system, effective utilization of traffic data is realized through collaborative operation of all the modules, and the traffic transportation emergency management capability is improved from multiple aspects, including improvement of management efficiency, enhancement of emergency response capability, improvement of decision support, enhancement of information transparency, optimization of resource allocation, reduction of economic loss and improvement of public safety.

Description

Technical Field

[0001] The present invention belongs to the technical field related to transportation, and specifically relates to a transportation emergency command and dispatch system based on big data resources. Background Art

[0002] With the acceleration of urbanization, the complexity of transportation networks continues to increase, and the impact of various emergencies (such as traffic accidents, natural disasters, public health emergencies, etc.) on the transportation system has become increasingly significant. Traditional traffic emergency command and dispatch systems often rely on manual decision-making and local information, and it is difficult to quickly obtain global data, resulting in slow response speed, unreasonable resource allocation, and inability to effectively respond to emergencies. Therefore, it is imperative to develop an emergency command and dispatch system that can process and analyze big data in real time. Summary of the invention

[0003] The purpose of the present invention is to provide a transportation emergency command and dispatch system based on big data resources to solve the problem that the traditional transportation emergency command and dispatch system proposed in the above background technology often relies on manual decision-making and local information, and it is difficult to quickly obtain global data, resulting in slow response speed, unreasonable resource allocation, and inability to effectively deal with emergencies.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A transportation emergency command and dispatch system based on big data resources, the system includes

[0006] Data acquisition module:

[0007] Collect data on traffic flow, road conditions, weather, and event information in real time through sensors, cameras, GPS positioning systems, and other devices;

[0008] Big data processing and analysis module:

[0009] Using data mining and machine learning techniques, we clean, integrate and analyze the massive amounts of data collected to identify potential traffic events and trends;

[0010] Event warning and prediction module:

[0011] Based on the analysis results, an event warning model is built to identify and predict potential traffic congestion and accident risks in advance and issue alerts in a timely manner;

[0012] Emergency command and dispatch module:

[0013] Intelligently dispatch traffic police, rescue resources and other emergency resources based on real-time data and prediction results to optimize dispatch decisions;

[0014] Information display and interaction module:

[0015] Traffic conditions, event information and dispatch decisions are displayed through a visual interface, enabling multi-terminal information sharing and interaction, and supporting commanders and the public to obtain information in a timely manner.

[0016] Preferably, the data acquisition module mainly includes

[0017] Data collection from multiple sources:

[0018] Traffic flow monitoring: Use ground sensors, cameras, radars and other equipment to monitor traffic flow, speed and vehicle type information in real time.

[0019] Real-time event monitoring: Obtain information on emergencies such as traffic accidents, road construction, and weather changes through cameras, sensors, and radio frequency identification (RFID) technology.

[0020] Environmental data collection: Integrate weather stations and air quality monitoring equipment to obtain real-time weather conditions, temperature, humidity and other environmental data.

[0021] Data collection frequency and accuracy:

[0022] Ensure that data collection frequency is high (seconds or milliseconds) to achieve real-time monitoring and dynamic analysis.

[0023] Improve the accuracy of data collection and ensure that the collected information can accurately reflect traffic conditions.

[0024] Information integration and formatting:

[0025] Integrate data from different types of sensors and unify the data format to facilitate subsequent analysis and processing.

[0026] Realize multi-dimensional data integration and support parallel processing of various information such as traffic, environment, and events.

[0027] Data transmission:

[0028] Use stable and reliable communication protocols (such as MQTT, HTTP, WebSocket, etc.) to transmit the collected data to the data processing center in real time.

[0029] It supports both wireless (such as 4G / 5G) and wired (such as fiber optic networks) data transmission methods to ensure stable data access in various situations.

[0030] Preferably, the big data processing and analysis module includes

[0031] Data storage and management:

[0032] Distributed storage system: Use Hadoop, HDFS and other distributed storage technologies to store large-scale data to ensure data security and scalability.

[0033] Data management platform: Use data management tools to classify, index and back up stored data to support efficient data retrieval and maintenance.

[0034] Data cleaning and preprocessing:

[0035] Through data cleaning technology, redundant, duplicate and erroneous data are removed to ensure data quality.

[0036] Standardize the data to eliminate format differences between data from different sources and facilitate subsequent analysis.

[0037] Data analysis and mining:

[0038] Real-time data analysis: Use stream processing frameworks (such as Apache Storm and Apache Flink) to instantly analyze data collected in real time to identify traffic anomalies, accidents, etc.

[0039] Historical data analysis: Perform in-depth analysis of historical data using batch processing tools such as Apache Spark to identify traffic patterns, trends, and potential risks.

[0040] Machine learning and prediction: Use machine learning algorithms (such as regression analysis, classification algorithms, cluster analysis, etc.) to build traffic incident prediction models to improve the predictability of emergency response.

[0041] Data Visualization:

[0042] The analysis results are displayed in the form of intuitive charts through data visualization tools (such as Tableau, D3.js, etc.), helping commanders to quickly understand and make decisions.

[0043] Provide the public with real-time information visualization and improve information transparency.

[0044] Preferably, event warning and prediction module

[0045] Real-time monitoring and early warning:

[0046] Abnormal traffic detection: Real-time monitoring of traffic flow data, using threshold setting and trend analysis to automatically identify abnormal or unexpected events, such as congestion, accidents, etc.

[0047] Intelligent event warning: Based on monitoring data and preset rules, event warning notifications are automatically generated and transmitted to commanders, drivers and relevant departments in a timely manner.

[0048] Prediction model building:

[0049] Historical data analysis: Use historical traffic data to analyze the patterns of events and identify key factors related to traffic accidents and congestion.

[0050] Machine learning and statistical analysis: Combine machine learning algorithms (such as decision trees, random forests, support vector machines, etc.) to build a traffic event prediction model to predict future traffic conditions.

[0051] Risk assessment and emergency response recommendations:

[0052] Risk level assessment: Based on the prediction results, the risk level of possible events is assessed to help commanders develop corresponding emergency strategies.

[0053] Response action recommendations: In the event of an emergency, an optimized emergency response plan is given based on prediction and analysis, including measures such as traffic diversion and accident handling.

[0054] User-customized early warning service:

[0055] Personalized warning settings: Allow users (such as traffic management departments and the public) to customize warning parameters and notification methods according to specific needs to ensure targeted and timely information delivery.

[0056] Preferably, emergency command and dispatch module

[0057] Incident Response Management:

[0058] Event confirmation and classification: Receive event warnings from the monitoring system, conduct preliminary review and classification, and determine the nature and urgency of the event.

[0059] Quick search of emergency plans: According to the type of incident, relevant emergency plans can be quickly retrieved to provide a basis for command decisions.

[0060] Resource Scheduling and Allocation:

[0061] Real-time resource monitoring: Real-time monitoring of the status and location of on-site emergency resources (such as ambulances, fire trucks, traffic police, etc.) to ensure the effectiveness of dispatching resources.

[0062] Dynamic Scheduling: Automatically or manually adjust the allocation and scheduling of emergency resources based on the scale and nature of the incident to ensure timely response.

[0063] Decision support and instruction issuance:

[0064] Data-supported decision making: Provide decision support to commanders based on data analysis and prediction results, including possible consequences and best courses of action.

[0065] Command issuance and feedback: Issue commands through the communication system and receive feedback information in real time to ensure the smooth flow of the command chain.

[0066] Multi-party coordination and information sharing:

[0067] Collaborative combat platform: Establish a multi-party collaborative combat platform for emergency command to support real-time collaboration among multiple departments such as transportation, medical care, and public security.

[0068] Information Sharing Mechanism: To achieve information sharing among participating departments and surrounding communities to ensure global awareness and information consistency.

[0069] Post-evaluation and summary:

[0070] Incident handling effectiveness evaluation: Evaluate the incident handling process and effectiveness, and summarize the success and room for improvement of emergency response.

[0071] Experience feedback and optimization: Feedback the evaluation results into the emergency plan, optimize the emergency response plan in a timely manner, and improve the efficiency of future responses.

[0072] Preferably, information display and interaction module

[0073] Data Visualization:

[0074] Dynamic map display: Through GIS technology, traffic flow, accident location, emergency resource location and other information are displayed in real time to provide commanders with a geographic spatial perspective.

[0075] Data panel: Provides a variety of data panels, such as accident statistics, resource usage, incident response time, etc., so that users can quickly obtain key information.

[0076] Information Query and Retrieval:

[0077] Event query: Users can query historical event records and processing status by keywords, time periods, and other conditions.

[0078] Resource settings: Allow users to query and manage emergency resources, including vehicles, personnel, facilities and other information.

[0079] Interactive features:

[0080] User feedback system: Set up a feedback portal so that users can provide feedback on the system’s user experience, information accuracy, etc., to help optimize the system.

[0081] Command issuance and confirmation: Provides an interface for commanders to issue commands and confirm responses, ensuring effective communication in the chain of command.

[0082] Real-time information updates:

[0083] Alarm information push: timely push event warnings, accident updates, emergency response status and other information to relevant users to ensure the timeliness of the information.

[0084] Announcement release: Supports the release of announcements and notifications for the public and specific users to ensure information transparency.

[0085] User-defined settings:

[0086] Personalized dashboard: allows users to customize information display content, layout and theme to suit personal usage habits and needs.

[0087] Warning and notification settings: Users can set personalized warning notification conditions and methods as needed to ensure timely acquisition of key information.

[0088] Compared with the prior art, the present invention provides a transportation emergency command and dispatch system based on big data resources, which has the following beneficial effects:

[0089] Improve traffic management efficiency: Through real-time data collection and analysis, the system can quickly respond to traffic events, optimize traffic flow management, and reduce congestion;

[0090] Enhance emergency response capabilities: The event warning and prediction module can identify potential risks in advance, so that emergency resources can be dispatched to the right location at the first time, improving the speed and effectiveness of emergency response;

[0091] Improve decision support: The deep insights and predictive models provided by the big data processing and analysis module help commanders make more scientific and reasonable decisions;

[0092] Enhance information transparency: The information display and interaction module ensures that all relevant parties, including the public and emergency service personnel, can obtain traffic conditions and emergency information in a timely manner, improving the efficiency of information sharing;

[0093] Optimize resource allocation: The emergency command and dispatch module ensures optimal use of resources by dynamically dispatching traffic police, rescue resources and other emergency resources;

[0094] Reduce economic losses: By reducing traffic congestion and improving emergency response efficiency, the system helps reduce economic losses caused by traffic accidents and other emergencies;

[0095] Improve public safety: The system's real-time monitoring and rapid response capabilities help protect public safety and reduce the incidence and severity of traffic accidents;

[0096] It reflects the important role of the transportation emergency command and dispatch system based on big data resources in improving the operating efficiency of the transportation system, enhancing emergency management capabilities, improving decision-making quality and ensuring public safety. DETAILED DESCRIPTION

[0097] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0098] The present invention provides a technical solution:

[0099] A transportation emergency command and dispatch system based on big data resources, the system includes

[0100] Data acquisition module:

[0101] Collect data on traffic flow, road conditions, weather, and event information in real time through sensors, cameras, GPS positioning systems, and other devices;

[0102] Big data processing and analysis module:

[0103] Using data mining and machine learning techniques, we clean, integrate and analyze the massive amounts of data collected to identify potential traffic events and trends;

[0104] Event warning and prediction module:

[0105] Based on the analysis results, an event warning model is built to identify and predict potential traffic congestion and accident risks in advance and issue alerts in a timely manner;

[0106] Emergency command and dispatch module:

[0107] Intelligently dispatch traffic police, rescue resources and other emergency resources based on real-time data and prediction results to optimize dispatch decisions;

[0108] Information display and interaction module:

[0109] Traffic conditions, event information and dispatch decisions are displayed through a visual interface, enabling multi-terminal information sharing and interaction, and supporting commanders and the public to obtain information in a timely manner.

[0110] The data acquisition module mainly includes

[0111] Data collection from multiple sources:

[0112] Traffic flow monitoring: Use ground sensors, cameras, radars and other equipment to monitor traffic flow, speed and vehicle type information in real time.

[0113] Real-time event monitoring: Obtain information on emergencies such as traffic accidents, road construction, and weather changes through cameras, sensors, and radio frequency identification (RFID) technology.

[0114] Environmental data collection: Integrate weather stations and air quality monitoring equipment to obtain real-time weather conditions, temperature, humidity and other environmental data.

[0115] Data collection frequency and accuracy:

[0116] Ensure that data collection frequency is high (seconds or milliseconds) to achieve real-time monitoring and dynamic analysis.

[0117] Improve the accuracy of data collection and ensure that the collected information can accurately reflect traffic conditions.

[0118] Information integration and formatting:

[0119] Integrate data from different types of sensors and unify the data format to facilitate subsequent analysis and processing.

[0120] Realize multi-dimensional data integration and support parallel processing of various information such as traffic, environment, and events.

[0121] Data transmission:

[0122] Use stable and reliable communication protocols (such as MQTT, HTTP, WebSocket, etc.) to transmit the collected data to the data processing center in real time.

[0123] It supports both wireless (such as 4G / 5G) and wired (such as fiber optic networks) data transmission methods to ensure stable data access in various situations.

[0124] Implementation Technology

[0125] Sensors and devices:

[0126] Video surveillance equipment: Monitor traffic conditions, including traffic flow, traffic accidents, etc., through high-definition cameras.

[0127] Ground sensors: Use pressure sensors and magnetic induction sensors to monitor vehicle conditions on the road.

[0128] Weather Station: Use micro weather stations to obtain accurate weather data for traffic environment analysis.

[0129] Data Integration Platform:

[0130] Use data integration tools (such as Apache Kafka, etc.) to perform real-time stream processing of data, bringing together data from different sources to form a complete view of traffic information.

[0131] Edge computing:

[0132] Set up edge computing devices near the data collection point to conduct preliminary analysis of real-time data, reduce transmission burden, and improve system responsiveness.

[0133] Big data processing and analysis modules include

[0134] Data storage and management:

[0135] Distributed storage system: Use Hadoop, HDFS and other distributed storage technologies to store large-scale data to ensure data security and scalability.

[0136] Data management platform: Use data management tools to classify, index and back up stored data to support efficient data retrieval and maintenance.

[0137] Data cleaning and preprocessing:

[0138] Through data cleaning technology, redundant, duplicate and erroneous data are removed to ensure data quality.

[0139] Standardize the data to eliminate format differences between data from different sources and facilitate subsequent analysis.

[0140] Data analysis and mining:

[0141] Real-time data analysis: Use stream processing frameworks (such as Apache Storm and Apache Flink) to instantly analyze data collected in real time to identify traffic anomalies, accidents, etc.

[0142] Historical data analysis: Perform in-depth analysis of historical data using batch processing tools such as Apache Spark to identify traffic patterns, trends, and potential risks.

[0143] Machine learning and prediction: Use machine learning algorithms (such as regression analysis, classification algorithms, cluster analysis, etc.) to build traffic incident prediction models to improve the predictability of emergency response.

[0144] Data Visualization:

[0145] The analysis results are displayed in the form of intuitive charts through data visualization tools (such as Tableau, D3.js, etc.), helping commanders to quickly understand and make decisions.

[0146] Provide the public with real-time information visualization and improve information transparency.

[0147] Implementation Technology

[0148] Data processing framework:

[0149] Use Apache Spark as the big data processing framework, supporting batch processing and stream processing to meet the needs of efficient data analysis.

[0150] Leverage Apache Kafka to achieve high-throughput processing of data streams to support real-time data analytics.

[0151] Machine Learning Platform:

[0152] Use machine learning platforms (such as TensorFlow, PyTorch, etc.) to build and train predictive models to support event warning and risk assessment.

[0153] Data Visualization Tools:

[0154] Integrate visualization tools (such as Power BI, Grafana, etc.) to achieve dynamic display and monitoring of data analysis results.

[0155] Event warning and prediction module

[0156] Real-time monitoring and early warning:

[0157] Abnormal traffic detection: Real-time monitoring of traffic flow data, using threshold setting and trend analysis to automatically identify abnormal or unexpected events, such as congestion, accidents, etc.

[0158] Intelligent event warning: Based on monitoring data and preset rules, event warning notifications are automatically generated and transmitted to commanders, drivers and relevant departments in a timely manner.

[0159] Prediction model building:

[0160] Historical data analysis: Use historical traffic data to analyze the patterns of events and identify key factors related to traffic accidents and congestion.

[0161] Machine learning and statistical analysis: Combine machine learning algorithms (such as decision trees, random forests, support vector machines, etc.) to build a traffic event prediction model to predict future traffic conditions.

[0162] Risk assessment and emergency response recommendations:

[0163] Risk level assessment: Based on the prediction results, the risk level of possible events is assessed to help commanders develop corresponding emergency strategies.

[0164] Response action recommendations: In the event of an emergency, an optimized emergency response plan is given based on prediction and analysis, including measures such as traffic diversion and accident handling.

[0165] User customized warning service:

[0166] Personalized warning settings: Allow users (such as traffic management departments and the public) to customize warning parameters and notification methods according to specific needs to ensure targeted and timely information delivery.

[0167] Implementation Technology

[0168] Data collection and processing:

[0169] Use data processing frameworks (such as Apache Spark) to clean and preprocess data to ensure data quality.

[0170] Use data stream processing tools (such as Apache Kafka) to achieve efficient flow of real-time data.

[0171] Machine Learning Algorithms:

[0172] Develop machine learning models for event prediction using programming languages ​​such as Python or Java and frameworks such as Scikit-learn and TensorFlow.

[0173] Decision Support System:

[0174] Establish an algorithm-based decision support system, use data visualization technology to display prediction results and risk assessment results, and help decision makers quickly implement emergency measures.

[0175] Emergency command and dispatch module

[0176] Incident Response Management:

[0177] Event confirmation and classification: Receive event warnings from the monitoring system, conduct preliminary review and classification, and determine the nature and urgency of the event.

[0178] Quick search of emergency plans: According to the type of incident, relevant emergency plans can be quickly retrieved to provide a basis for command decisions.

[0179] Resource Scheduling and Allocation:

[0180] Real-time resource monitoring: Real-time monitoring of the status and location of on-site emergency resources (such as ambulances, fire trucks, traffic police, etc.) to ensure the effectiveness of dispatching resources.

[0181] Dynamic Scheduling: Automatically or manually adjust the allocation and scheduling of emergency resources based on the scale and nature of the incident to ensure timely response.

[0182] Decision support and instruction issuance:

[0183] Data-supported decision making: Provide decision support to commanders based on data analysis and prediction results, including possible consequences and best courses of action.

[0184] Command issuance and feedback: Issue commands through the communication system and receive feedback information in real time to ensure the smooth flow of the command chain.

[0185] Multi-party coordination and information sharing:

[0186] Collaborative combat platform: Establish a multi-party collaborative combat platform for emergency command to support real-time collaboration among multiple departments such as transportation, medical care, and public security.

[0187] Information Sharing Mechanism: To achieve information sharing among participating departments and surrounding communities to ensure global awareness and information consistency.

[0188] Post-evaluation and summary:

[0189] Incident handling effectiveness evaluation: Evaluate the incident handling process and effectiveness, and summarize the success and room for improvement of emergency response.

[0190] Experience feedback and optimization: Feedback the evaluation results into the emergency plan, optimize the emergency response plan in a timely manner, and improve the efficiency of future responses.

[0191] Implementation Technology

[0192] Command system design:

[0193] An integrated command and dispatch system is used in combination with GIS (geographic information system) technology to achieve spatial management and dispatch of resources.

[0194] Use a unified communication platform to support real-time transmission of voice, video, hydrological and other information.

[0195] Data analysis and decision support:

[0196] Use data analysis tools (such as data visualization software) to provide intuitive support for command decisions and ensure quick judgment.

[0197] Apply scientific decision-making models to evaluate the advantages and disadvantages of different response plans and help commanders make the best decisions.

[0198] Mobile devices and apps:

[0199] Develop mobile applications to support commanders' real-time command and dispatch on site and improve dispatch flexibility.

[0200] Information display and interaction module

[0201] Data Visualization:

[0202] Dynamic map display: Through GIS technology, traffic flow, accident location, emergency resource location and other information are displayed in real time to provide commanders with a geographic spatial perspective.

[0203] Data panel: Provides a variety of data panels, such as accident statistics, resource usage, incident response time, etc., so that users can quickly obtain key information.

[0204] Information Query and Retrieval:

[0205] Event query: Users can query historical event records and processing status by keywords, time periods, and other conditions.

[0206] Resource settings: Allow users to query and manage emergency resources, including vehicles, personnel, facilities and other information.

[0207] Interactive features:

[0208] User feedback system: Set up a feedback portal so that users can provide feedback on the system’s user experience, information accuracy, etc., to help optimize the system.

[0209] Command issuance and confirmation: Provides an interface for commanders to issue commands and confirm responses, ensuring effective communication in the chain of command.

[0210] Real-time information updates:

[0211] Alarm information push: timely push event warnings, accident updates, emergency response status and other information to relevant users to ensure the timeliness of the information.

[0212] Announcement release: Supports the release of announcements and notifications for the public and specific users to ensure information transparency.

[0213] User-defined settings:

[0214] Personalized dashboard: allows users to customize information display content, layout and theme to suit personal usage habits and needs.

[0215] Warning and notification settings: Users can set personalized warning notification conditions and methods as needed to ensure timely acquisition of key information.

[0216] Implementation Technology

[0217] Front-end development technology:

[0218] Use modern front-end technologies like HTML5, CSS3, JavaScript frameworks like React or Vue.js to build the user interface and make it friendly and intuitive.

[0219] Use data visualization libraries (such as D3.js, ECharts, etc.) to achieve dynamic display of data charts and maps.

[0220] Backend service support:

[0221] The backend uses APIs (such as RESTful APIs) to provide data support and interact with the database to achieve real-time data capture and information updates.

[0222] Use WebSocket technology to achieve real-time communication and ensure that information can be pushed to the front end in a timely manner.

[0223] User Experience Design (UX / UI):

[0224] Design a friendly user experience and ensure the ease of use and efficiency of the module through user research and usability testing.

[0225] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A transportation emergency command and dispatch system based on big data resources, characterized by: System includes Data acquisition module: Collect data on traffic flow, road conditions, weather, and event information in real time through sensors, cameras, GPS positioning systems, and other devices; Big data processing and analysis module: Using data mining and machine learning techniques, we clean, integrate and analyze the massive amounts of data collected to identify potential traffic events and trends; Event warning and prediction module: Based on the analysis results, an event warning model is built to identify and predict potential traffic congestion and accident risks in advance and issue alerts in a timely manner; Emergency command and dispatch module: Intelligently dispatch traffic police, rescue resources and other emergency resources based on real-time data and prediction results to optimize dispatch decisions; Information display and interaction module: Traffic conditions, event information and dispatch decisions are displayed through a visual interface, enabling multi-terminal information sharing and interaction, and supporting commanders and the public to obtain information in a timely manner.

2. A transportation emergency command and dispatch system based on big data resources according to claim 1, characterized in that: The data acquisition module mainly includes: Data collection from multiple sources: Traffic flow monitoring: Use ground sensors, cameras, radars and other equipment to monitor traffic flow, speed and vehicle type information in real time. Real-time event monitoring: Obtain emergency information such as traffic accidents, road construction, and weather changes through cameras, sensors, and radio frequency recognition technology. Environmental data collection: Integrate weather stations and air quality monitoring equipment to obtain real-time weather conditions, temperature, humidity and other environmental data; Data collection frequency and accuracy: Ensure high frequency of data collection to enable real-time monitoring and dynamic analysis; Improve data collection accuracy to ensure that the collected information can accurately reflect traffic conditions; Information integration and formatting: Integrate data from different types of sensors and unify the data format to facilitate subsequent analysis and processing; Realize multi-dimensional data integration and support parallel processing of various information such as traffic, environment, and events; Data transmission: Use stable and reliable communication protocols to transmit the collected data to the data processing center in real time; Supports both wireless and wired data transmission modes to ensure stable data access in various situations.

3. The transportation emergency command and dispatch system based on big data resources according to claim 1 is characterized by: Big data processing and analysis modules include: Data storage and management: Distributed storage system: Use Hadoop and HDFS distributed storage technology to store large-scale data to ensure data security and scalability; Data management platform: Use data management tools to classify, index and back up stored data, and support efficient data retrieval and maintenance; Data cleaning and preprocessing: Use data cleaning technology to remove redundant, duplicate and erroneous data to ensure data quality; Standardize the data to eliminate format differences between data from different sources to facilitate subsequent analysis; Data analysis and mining: Real-time data analysis: Use the stream processing framework to analyze the real-time collected data and identify traffic anomalies and accidents; Historical data analysis: In-depth analysis of historical data through batch processing tools to discover traffic patterns, trends and potential risks; Machine learning and prediction: Use machine learning algorithms to build traffic incident prediction models to improve the predictability of emergency response; Data Visualization: The analysis results are presented in an intuitive chart form through data visualization tools to help commanders quickly understand and make decisions; Provide the public with real-time information visualization and improve information transparency.

4. The transportation emergency command and dispatch system based on big data resources according to claim 1 is characterized by: Event warning and prediction module Real-time monitoring and early warning: Abnormal traffic detection: Real-time monitoring of traffic flow data, using threshold setting and trend analysis to automatically identify abnormal or unexpected events, such as congestion and accidents; Intelligent event warning: Automatically generate event warning notifications based on monitoring data and preset rules, and send them to commanders, drivers and relevant departments in a timely manner; Prediction model building: Historical data analysis: Utilize historical traffic data to analyze the patterns of events and identify key factors related to traffic accidents and congestion; Machine learning and statistical analysis: Combine machine learning algorithms to build a traffic event prediction model to predict future traffic conditions; Risk assessment and emergency response recommendations: Risk level assessment: Based on the prediction results, the risk level of possible events is assessed to help commanders formulate corresponding emergency strategies; Response action recommendations: In the event of an emergency, an optimized emergency response plan is given based on prediction and analysis, including measures such as traffic diversion and accident handling; User-customized early warning service: Personalized warning settings: Allow users to customize warning parameters and notification methods according to specific needs to ensure targeted and timely information delivery.

5. The transportation emergency command and dispatch system based on big data resources according to claim 1 is characterized by: The emergency command and dispatch module includes: Incident Response Management: Event confirmation and classification: Receive event warnings from the monitoring system, conduct preliminary review and classification, and determine the nature and urgency of the event; Quick search of emergency plans: according to the type of incident, quickly retrieve relevant emergency plans to provide a basis for command decision-making; Resource Scheduling and Allocation: Real-time resource monitoring: Real-time monitoring of the status and location of on-site emergency resources to ensure the effectiveness of dispatching resources; Dynamic scheduling: Automatically or manually adjust the allocation and scheduling of emergency resources based on the scale and nature of the incident to ensure timely response; Decision support and instruction issuance: Data-supported decision-making: Provide decision-making support to commanders based on data analysis and prediction results, including possible consequences and best courses of action; Command issuance and feedback: Issue commands through the communication system and receive feedback information in real time to ensure the smooth flow of the command chain; Multi-party coordination and information sharing: Collaborative Operations Platform: Establish a multi-party collaborative operations platform for emergency command to support real-time collaboration among multiple departments such as transportation, medical care, and public security; Information sharing mechanism: To achieve information sharing among participating departments and surrounding communities, ensuring global awareness and information consistency; Post-evaluation and summary: Incident handling effect evaluation: Evaluate the incident handling process and effect, and summarize the success and room for improvement of emergency response; Experience feedback and optimization: Feedback the evaluation results into the emergency plan, optimize the emergency response plan in a timely manner, and improve the efficiency of future responses.

6. The transportation emergency command and dispatch system based on big data resources according to claim 1 is characterized by: Information display and interaction modules include: Data Visualization: Dynamic map display: Through GIS technology, real-time display of information on traffic flow, accident locations, and emergency resource locations, providing commanders with a geographic spatial perspective; Data panel: provides a variety of data panels, such as accident statistics, resource usage, event response time, etc., so that users can quickly obtain key information; Information Query and Retrieval: Event query: Users can query historical event records and processing status by keywords, time periods, etc. Resource settings: Allow users to query and manage emergency resources, including information on vehicles, personnel, and facilities; Interactive features: User feedback system: set up a feedback portal so that users can provide feedback on the system’s user experience, information accuracy, etc., to help optimize the system; Command issuance and confirmation: Provides an interface for commanders to issue commands and confirm responses, ensuring effective communication in the chain of command; Real-time information updates: Alarm information push: timely push event warnings, accident updates, emergency response status and other information to relevant users to ensure the timeliness of the information; Announcement release: supports the release of announcements and notifications for the public and specific users to ensure information transparency; User-defined settings: Personalized dashboard: allows users to customize information display content, layout and theme to meet personal usage habits and needs; Warning and notification settings: Users can set personalized warning notification conditions and methods as needed to ensure timely acquisition of key information.