NLP-based traffic accident prevention and analysis method, device, equipment and medium

Abstract

According to the scheme, the method comprises the following steps: collecting and preprocessing traffic data of at least one dimension; extracting features related to traffic conditions from the text data through NLP; constructing a prediction model through the features extracted by the NLP and the traffic data; and predicting and analyzing real-time traffic data through the trained prediction model. According to the scheme, a large amount of traffic data can be analyzed and processed by applying the NLP technology, and powerful data support is provided for prevention and analysis of traffic accidents; the prediction model is constructed by combining the features extracted by the NLP and other traffic data, and the real-time traffic data is predicted and analyzed by using the trained model, so that a more intelligent and efficient traffic safety management mode is realized, and a safer and more convenient travel environment is provided for people.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a traffic accident prevention analysis method, device, equipment and medium based on NLP. Background Art

[0002] Traffic accidents have always been a serious problem in the field of traffic safety, which has caused a large number of casualties and property losses. In order to reduce traffic accidents as much as possible, traffic safety analysis can be used to interpret the driver's behavior and traffic conditions, so as to take corresponding measures.

[0003] Current research on traffic safety often only focuses on one aspect of traffic accidents, such as static road environment assessment or dynamic driving behavior analysis, without forming a framework for comprehensive understanding and response to traffic accidents.

[0004] Therefore, a more intelligent and efficient traffic safety management method is needed to provide people with a safer and more convenient travel environment. Summary of the invention

[0005] The embodiments of the present invention provide a traffic accident prevention analysis method, device, equipment and medium based on NLP to build a more intelligent and efficient traffic safety management method.

[0006] According to one aspect of the present invention, a traffic accident prevention analysis method based on NLP is provided, comprising:

[0007] Collect and preprocess traffic data in at least one dimension;

[0008] Extracting features related to traffic conditions from the text data through NLP;

[0009] Building a prediction model using features extracted by NLP and the traffic data;

[0010] The real-time traffic data is predicted and analyzed through the trained prediction model.

[0011] Optionally, the collecting of traffic data in at least one dimension includes:

[0012] Identify the data source, which may include one or more of traffic surveillance cameras, social media platforms, news reports, government-issued traffic accident reports, online forums, and blogs;

[0013] According to the selected data source, the traffic data is captured through an API interface or by writing a web crawler.

[0014] Optionally, the preprocessing of the traffic data of at least one dimension comprises one or more of the following steps:

[0015] storing the traffic data in a database;

[0016] Cleaning the traffic data in text format to remove noise, punctuation, HTML tags and special characters;

[0017] Segmenting the traffic data in the text format into words or tokens;

[0018] performing stop words removal on the traffic data, wherein the stop words are common words without actual information;

[0019] restoring words in the traffic data to their base forms;

[0020] The traffic data in the text format is labeled and classified.

[0021] Optionally, extracting features related to traffic conditions from the text data by using NLP includes:

[0022] Monitor the driver's emotional state in real time by analyzing their voice, facial expressions or text messages;

[0023] Detecting the negative emotion of the driver and executing a preset first measure to eliminate the negative emotion;

[0024] determining the passenger's emotions by monitoring the passenger's facial expressions, voice or biometric signals, and executing a preset second measure based on the emotions;

[0025] When a dangerous situation is detected, an early warning is triggered;

[0026] Provide personalized driving assistance suggestions based on the driver's personality and emotional state;

[0027] By collecting and analyzing the emotional data of drivers and passengers, the execution logic of the autonomous driving system can be adjusted.

[0028] Optionally, extracting features related to traffic conditions from the text data by using NLP includes:

[0029] Extract information related to traffic incidents from natural language text;

[0030] Identify entities related to traffic events from text, where the entities are basic elements constituting traffic events; and analyze the relationships between the entities;

[0031] Extract event information related to traffic incidents from text.

[0032] Optionally, the prediction model is constructed using the features extracted by NLP and the traffic data, including:

[0033] Constructing at least one prediction model using the features extracted by the NLP and the traffic data;

[0034] The predictive model is trained and adjusted through machine learning and deep learning to output intelligent traffic safety recommendations and real-time monitoring and response.

[0035] Optionally, the traffic data is obtained by one or more of the following methods:

[0036] Fusion of image data from traffic monitoring cameras and real-time data from sensors;

[0037] fusing real-time data from at least one source;

[0038] Data from at least one sensor is fused.

[0039] According to another aspect of the present invention, there is provided a traffic accident prevention analysis device based on NLP, comprising:

[0040] A collection unit, used for collecting and preprocessing traffic data of at least one dimension;

[0041] An extraction unit, used for extracting features related to traffic conditions from the text data through NLP;

[0042] A construction unit, used for constructing a prediction model through the features extracted by NLP and the traffic data;

[0043] The application unit is used to predict and analyze real-time traffic data through the trained prediction model.

[0044] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0045] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the NLP-based traffic accident prevention analysis method described in any embodiment of the present invention.

[0046] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the NLP-based traffic accident prevention analysis method described in any embodiment of the present invention when executed.

[0047] The solution of the embodiment of the present invention includes: collecting and preprocessing traffic data of at least one dimension; extracting features related to traffic conditions from the text data through NLP; building a prediction model through the features extracted by NLP and the traffic data; and predicting and analyzing real-time traffic data through the trained prediction model. The technical solution of the embodiment of the present invention can parse and process a large amount of traffic data by applying NLP technology, providing strong data support for the prevention and analysis of traffic accidents; building a prediction model by combining the features extracted by NLP and other traffic data, and using the trained model to predict and analyze real-time traffic data, thereby achieving a more intelligent and efficient traffic safety management method and providing people with a safer and more convenient travel environment.

[0048] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 is a flow chart of a traffic accident prevention analysis method based on NLP provided by an embodiment of the present invention;

[0051] Figure 2 This is a working principle diagram of an NLP provided by an embodiment of the present invention;

[0052] Figure 3 It is a framework diagram of a traffic accident prevention analysis method based on NLP provided by an embodiment of the present invention;

[0053] Figure 4 is a schematic structural diagram of a traffic accident prevention and analysis device based on NLP provided by an embodiment of the present invention;

[0054] Figure 5 It is a schematic diagram of the structure of an electronic device for implementing the NLP-based traffic accident prevention analysis method of an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0056] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0057] like Figure 1 As shown, an embodiment of the present invention provides a traffic accident prevention analysis method based on NLP, which may include the following steps:

[0058] S110: Collect and pre-process traffic data in at least one dimension.

[0059] The process of acquiring data from various sources such as databases, networks, sensors, etc. In traffic accident prevention, this may involve collecting historical accident reports, social media data, news reports, etc. The process of cleaning, transforming, and standardizing data before data analysis or machine learning. This may include steps such as noise removal, tokenization, conversion to lowercase, and removal of stop words.

[0060] In the embodiment of the present invention, step S110 specifically includes:

[0061] Identify the data source, which may include one or more of traffic surveillance cameras, social media platforms, news reports, government-issued traffic accident reports, online forums, and blogs;

[0062] According to the selected data source, the traffic data is captured through an API interface or by writing a web crawler.

[0063] Data collection and preprocessing, including:

[0064] Identify data sources: Data sources can be diverse, including but not limited to traffic surveillance cameras, social media platforms, news reports, government-issued traffic accident reports, online forums and blogs, etc. These sources provide rich multimodal data such as text, images, and videos.

[0065] Use API or crawler technology: Depending on the selected data source, you can use the API interface or write a web crawler to crawl the data. For social media platforms and online forums, you may need to follow their API usage rules and apply for corresponding access rights.

[0066] In an embodiment of the present invention, preprocessing the traffic data of at least one dimension includes one or more of the following steps:

[0067] storing the traffic data in a database;

[0068] Cleaning the traffic data in text format to remove noise, punctuation, HTML tags and special characters;

[0069] Segmenting the traffic data in the text format into words or tokens;

[0070] performing stop words removal on the traffic data, wherein the stop words are common words without actual information;

[0071] restoring words in the traffic data to their base forms;

[0072] The traffic data in the text format is labeled and classified.

[0073] Data storage: The collected data needs to be stored in an appropriate database for subsequent analysis and processing. You can choose a relational database (such as MySQL, PostgreSQL) or a non-relational database (such as MongoDB, Cassandra) to store the data.

[0074] Text cleaning: For text data, cleaning is required to remove unnecessary information such as noise, punctuation, HTML tags, special characters, etc. This can usually be done using regular expressions and text processing libraries (such as Python's NLTK library).

[0075] Word segmentation: Split text data into words or tokens for subsequent analysis and processing. Word segmentation is a key step in converting text data into basic units that can be understood by machines. For Chinese text, word segmentation is also required because there are no clear separators between Chinese words.

[0076] Stop word removal: Stop words are common words with no actual information, such as "de", "shi", "zai", etc. They are not very helpful for text analysis and processing, so they need to be removed from the text to reduce the size of the vocabulary.

[0077] Stemming and lemmatization: Reducing words to their basic forms to reduce lexical diversity. For example, reducing "running" to "run". This helps convert text data into a numerical representation that can be used in machine learning models.

[0078] Data tagging and labeling: For supervised learning tasks (such as text classification, named entity recognition, etc.), text data needs to be tagged and classified. This can be done through manual tagging or using pre-trained models for automatic tagging. The tagged data can be used to train machine learning models for automatic classification and recognition of text data.

[0079] S120. Extract features related to traffic conditions from the text data through NLP.

[0080] NLP (Natural Language Processing) is natural language processing, a branch discipline in the fields of artificial intelligence and linguistics, which studies various theories and methods that can enable effective communication between humans and computers using natural language. Figure 2 This is a schematic diagram of the working principle of an NLP provided by an embodiment of the present invention.

[0081] By combining NLP technology with traffic safety, we can gain a deeper understanding of the behaviors and intentions of traffic participants, predict potential traffic risks, and take corresponding preventive measures. This is not only a powerful supplement to existing traffic management systems but also an exploration and attempt for future intelligent transportation systems.

[0082] Figure 2 This is a schematic diagram of the working principle of NLP provided by an embodiment of the present invention. The following will elaborate on the working of NLP in detail:

[0083] In the embodiment of the present invention, step S120 specifically includes:

[0084] Real-time monitoring of the driver's emotional state by analyzing the driver's voice, facial expressions, or text information;

[0085] Detecting the driver's negative emotions and executing a preset first measure to eliminate the negative emotions;

[0086] Determining the passenger's emotion by monitoring the passenger's facial expressions, voice, or biometric signals and executing a preset second measure based on the emotion;

[0087] Triggering an alarm when a dangerous situation is detected;

[0088] Provide personalized driving assistance suggestions based on the driver's personality and emotional state;

[0089] By collecting and analyzing the emotional data of drivers and passengers, the execution logic of the autonomous driving system can be adjusted.

[0090] Specifically, sentiment analysis is achieved through NLP, including:

[0091] Driver emotion monitoring: By analyzing the driver's voice, facial expressions or text messages (such as social media posts, text messages, etc.), emotion analysis technology can monitor the driver's emotional state in real time. If negative emotions such as tension, anxiety, anger or fatigue are detected in the driver, the system can issue a warning or take corresponding measures, such as playing soothing music, suggesting a break or adjusting the in-car environment to help the driver regain a calm state.

[0092] Passenger emotion perception: In autonomous vehicles or public transportation, sentiment analysis can also be used to sense the emotions of passengers. By monitoring the passenger's facial expressions, voice or other biometric signals, the system can understand the passenger's comfort and satisfaction. If a passenger is found to be uncomfortable or nervous, the vehicle can automatically adjust the seat comfort, temperature, lighting or music to enhance the passenger's travel experience and indirectly improve safety.

[0093] Traffic environment sentiment analysis: Sentiment analysis is not limited to people in the car, but can also be applied to analyze the emotional atmosphere in the traffic environment. For example, through text data from social media or news reports, we can understand the public's reaction to specific traffic policies or events. This helps traffic management departments to adjust strategies in a timely manner to better meet public needs and reduce traffic problems caused by misunderstandings or dissatisfaction.

[0094] Early warning and emergency response: When the emotion analysis system detects a possible dangerous situation (such as the driver's extremely unstable emotions), it can trigger the early warning system to notify other vehicle occupants or external emergency services. In an emergency, this early warning can speed up the rescue response time, thereby reducing potential casualties and property losses.

[0095] Personalized driving assistance: Based on the driver's personality and emotional state, sentiment analysis technology can provide personalized driving assistance suggestions for each driver. For example, for drivers who are prone to anxiety, the system can provide more safety tips and driving guidance.

[0096] Data feedback and improvement: By collecting and analyzing emotional data from drivers and passengers, the autonomous driving system can continuously learn and improve to better adapt to the emotional needs and driving habits of different users.

[0097] In the embodiment of the present invention, step S120 specifically includes:

[0098] Extract information related to traffic incidents from natural language text;

[0099] Identify entities related to traffic events from text, where the entities are basic elements constituting traffic events; and analyze the relationships between the entities;

[0100] Extract event information related to traffic incidents from text.

[0101] Specifically, information extraction and event detection are achieved through NLP, including:

[0102] Information extraction is a text processing technology that extracts specified types of entity, relationship, event and other factual information from natural language text and forms structured data output. In the field of traffic safety, information extraction can help us automatically identify and extract key information related to traffic incidents.

[0103] Entity recognition: Identify entities related to traffic events from text, such as vehicles, drivers, passengers, roads, places, etc. These entities are the basic elements that constitute traffic events and are crucial to understanding the ins and outs of the events.

[0104] Relationship extraction: Analyze the relationships between entities in the text, such as the driving relationship between vehicles and drivers, the driving relationship between vehicles and roads, etc. These relationships can help us build a semantic network of traffic events and further understand the nature of the events.

[0105] Event extraction: Extract event information related to traffic incidents from text, such as accident type, time of occurrence, location, casualties, etc. This information is crucial for rapid response and handling of traffic incidents.

[0106] Event detection: Automatically identify and discover specific types of events in text, such as traffic accidents, traffic jams, etc. Through event detection, we can detect traffic incidents in a timely manner and take corresponding measures to reduce casualties and property losses.

[0107] Event type definition: Define the event types that need to be detected according to actual needs, such as traffic accidents, traffic congestion, road construction, etc. These event types should be able to fully cover the main events in the field of traffic safety.

[0108] Event trigger word recognition: Identify trigger words related to event types in the text, such as "crash", "congestion", "construction", etc. These trigger words are key clues for event detection.

[0109] Event argument extraction: After identifying the event trigger words, extract the argument information related to the event, such as the time, location, participants, etc. This argument information can help us understand the details of the event more comprehensively.

[0110] In addition, NLP can also achieve semantic understanding and reasoning, including:

[0111] Text classification and recognition: Through semantic understanding technology, we can classify and recognize a large amount of traffic-related text, such as traffic news, accident reports, traffic rules, etc. This helps us quickly filter out information related to traffic safety and conduct further analysis and processing.

[0112] Intention recognition: In autonomous driving or intelligent transportation systems, the intention of the driver or passenger is very important. Through semantic understanding technology, we can identify the intention of the driver or passenger, such as acceleration, deceleration, turning, etc., and perform corresponding control and operations based on these intentions to improve traffic safety.

[0113] Semantic role labeling: Semantic role labeling is a grammatical analysis method used to describe the relationship between predicates and arguments in a sentence. In the field of traffic safety, semantic role labeling can help us identify key information in a sentence, such as the responsible party for the accident, the cause of the accident, etc., so as to more accurately understand the nature of the accident and the attribution of responsibility.

[0114] Rule reasoning: Based on a series of pre-defined rules, the input text is analyzed and parsed to extract meaning from it, and the risks and consequences of traffic incidents are predicted through rule reasoning. For example, we can define a series of rules based on traffic rules and historical accident data. When the system detects that a vehicle has violated these rules, it can predict possible traffic incidents and take corresponding measures to avoid accidents.

[0115] Knowledge graph reasoning: Using the information such as entities, relationships and attributes in the knowledge graph, combined with the mining and analysis of large-scale text corpora, to reason and understand the semantics of the text. In the field of traffic safety, we can build a knowledge graph containing traffic entities, events and rules, and use knowledge graph reasoning to predict and evaluate the risks and consequences of traffic incidents. For example, when the system detects that a traffic accident has occurred on a certain road section, it can evaluate the risk level of the current accident by querying the historical accident data, traffic flow and other information of the road section in the knowledge graph, and take appropriate measures to reduce casualties and property losses.

[0116] S130: construct a prediction model using the features extracted by NLP and the traffic data.

[0117] In the embodiment of the present invention, step S130 specifically includes:

[0118] Constructing at least one prediction model using the features extracted by the NLP and the traffic data;

[0119] The predictive model is trained and adjusted through machine learning and deep learning to output intelligent traffic safety recommendations and real-time monitoring and response.

[0120] Combine the features extracted by NLP with other traffic data (such as traffic flow, speed, road conditions, etc.) to build one or more prediction models. These models can be implemented based on technologies such as machine learning and deep learning. Train and adjust the model to improve its prediction accuracy of traffic conditions. Use the trained model to predict and analyze real-time traffic data. This can be achieved by receiving real-time text data (such as real-time comments on social media, user feedback, etc.) and real-time docking with other traffic data sources. Based on the prediction results, generate corresponding reports or alerts to help traffic management departments, drivers, etc. understand the traffic conditions in a timely manner and take corresponding measures.

[0121] Regularly optimize and iterate the prediction model to adapt to changes in traffic conditions and the emergence of new data sources. This can be achieved by collecting more training data, adjusting model parameters, trying new algorithms, etc. Use evaluation metrics to evaluate the performance of machine learning or deep learning models. In classification tasks, common evaluation metrics include accuracy, recall, F1 score, etc. Continuously monitor the status of a system or process to detect and respond to any abnormalities in a timely manner.

[0122] In traffic accident prevention, real-time monitoring may involve continuous monitoring of traffic flow, road conditions, etc.

[0123] A notification or warning sent to relevant parties when an abnormal situation or potential risk is detected. In traffic accident prevention, an alarm may be used to alert drivers or traffic management departments to potential safety hazards.

[0124] Real-time monitoring and response, including:

[0125] Real-time traffic monitoring system: collects traffic data such as vehicle flow, speed, road conditions, etc. in real time through cameras, sensors and other equipment installed on the road.

[0126] Anomaly detection: Through machine learning or deep learning algorithms, abnormal behaviors or events that do not conform to normal traffic patterns are identified, such as sudden vehicle deceleration and abnormal road congestion.

[0127] Event recognition: NLP technology can help the system identify events related to traffic accidents, road construction, etc. from text information and determine their potential dangers.

[0128] Real-time alerts: Once a potential traffic safety hazard or emergency is detected, the system immediately generates an alert and notifies drivers, traffic management departments and the public through a variety of methods (such as SMS, APP push, traffic broadcasts, etc.).

[0129] Traffic light adjustment: Automatically adjust the timing of traffic lights based on traffic congestion or accidents to ease traffic pressure or guide vehicles to detour.

[0130] Emergency rescue dispatch: notify nearby emergency rescue teams (such as traffic police, fire, medical, etc.) to rush to the scene for rescue and treatment.

[0131] Information release: Release the latest traffic information to the public through traffic broadcasts, electronic display screens, etc., to guide drivers to choose the best driving routes.

[0132] Accident recording and analysis: Keep detailed records of traffic accidents, use NLP technology to conduct in-depth analysis of the causes of the accidents, and provide decision-making support for traffic management departments.

[0133] System optimization: Based on the results of real-time monitoring and response, the performance of NLP algorithms and traffic monitoring systems is continuously optimized to improve their ability to identify and handle traffic safety hazards and emergencies.

[0134] In software development or machine learning, iteration refers to repeatedly executing a process or algorithm in order to continually improve or optimize the results. In a traffic accident prevention project, iterations might involve continually refining models, improving data collection and processing methods, etc.

[0135] S140: Predict and analyze real-time traffic data using the trained prediction model.

[0136] In addition to text data, other types of traffic data can also be considered, such as images, videos, etc. By processing and analyzing these multimodal data through NLP technology, richer traffic information can be obtained, thereby further improving the accuracy of prediction and analysis.

[0137] In the embodiment of the present invention, the traffic data is obtained by one or more of the following methods:

[0138] Fusion of image data from traffic monitoring cameras and real-time data from sensors;

[0139] fusing real-time data from at least one source;

[0140] Data from at least one sensor is fused.

[0141] Specifically, multimodal data fusion includes:

[0142] Road condition perception: By fusing image data from traffic monitoring cameras and real-time data from sensors, accurate perception of road conditions can be achieved. For example, cameras can capture visual information such as road conditions, vehicle driving conditions, and traffic signs, while sensors can provide real-time data such as vehicle speed and location. Fusion of this data can achieve all-round perception of road conditions, thereby promptly discovering potential safety hazards.

[0143] Accident warning and emergency response: By integrating real-time data from multiple sources (such as traffic accident reports, eyewitness descriptions on social media, emergency calls, etc.), rapid warning and emergency response to traffic accidents can be achieved. The system can automatically analyze this data, determine the nature, scale and scope of the accident, and trigger corresponding emergency measures, such as notifying rescue teams, adjusting traffic lights, etc., to minimize casualties and property losses.

[0144] Intelligent navigation and traffic management: By integrating data from multiple sensors (such as traffic flow, vehicle speed, road congestion, etc.), intelligent navigation and traffic management can be achieved. The system can predict future traffic conditions based on real-time data and provide drivers with the best driving routes and traffic suggestions. At the same time, traffic management departments can also use this data to optimize traffic light control strategies, adjust road planning, etc., to improve road capacity and traffic safety levels.

[0145] The model can provide intelligent traffic safety recommendations, including:

[0146] Traffic condition analysis: Based on the results of NLP analysis and multimodal data fusion, traffic management departments can obtain comprehensive traffic condition information, including road congestion, accident rate, traffic flow, etc. This information can help management departments understand traffic conditions more accurately and make scientific decisions.

[0147] Decision-making suggestions: The system can provide decision-making suggestions to traffic management departments based on historical data and real-time data, such as optimizing traffic light control strategies, adjusting road planning, improving traffic infrastructure, etc. These suggestions can help management departments improve traffic conditions and reduce accident rates.

[0148] Early warning and emergency response: The system can monitor traffic events and potential dangers in real time and provide early warning information to management departments. Once a traffic accident or other emergency occurs, the system can quickly activate the emergency response mechanism, notify relevant departments and personnel, and provide necessary support and guidance.

[0149] Traffic rules promotion: Based on NLP technology, the system can analyze text information such as social media and news reports to understand the public's awareness and compliance with traffic rules. The system can promote traffic rules and safety knowledge to the public through various channels to improve the public's traffic safety awareness.

[0150] Travel suggestions: The system can provide travel suggestions to the public based on real-time road and traffic conditions, such as avoiding congested roads and choosing the best travel time. These suggestions can help the public better plan their travel routes and reduce travel time and safety risks.

[0151] Emergency assistance service: The system can provide the public with emergency assistance services, such as one-click alarm, traffic accident assistance, etc. Once an emergency occurs, the public can quickly contact relevant departments and personnel through the system to obtain necessary help and support.

[0152] Figure 3 This is a framework diagram of a traffic accident prevention analysis method based on NLP provided by an embodiment of the present invention. The traffic conditions are predicted and analyzed based on NLP technology, and its beneficial effects are mainly reflected in the following aspects:

[0153] Improve traffic safety: NLP technology can analyze and process a large amount of traffic data, including historical traffic flow, accident reports, weather information, etc., to predict potential traffic safety hazards. By early warning and taking corresponding preventive measures, the incidence of traffic accidents can be significantly reduced and the safety of road use can be improved.

[0154] Optimize traffic management: NLP technology can identify and analyze information such as traffic flow patterns and congestion conditions, and provide decision support for traffic management departments. Based on these predictions and analysis results, traffic management departments can formulate traffic management strategies more scientifically, such as adjusting traffic light timing and optimizing road planning, thereby improving traffic efficiency and alleviating traffic congestion.

[0155] Improve driving experience: NLP technology can analyze the driver's voice commands and text input in real time, understand their needs and intentions, and provide personalized driving suggestions. For example, based on real-time traffic conditions and historical data, it can plan the best driving route for the driver to avoid congested roads and potential dangerous areas. This can not only improve driving efficiency, but also reduce the driver's anxiety and stress, and improve the driving experience.

[0156] Realizing intelligent transportation: NLP technology is an important part of the intelligent transportation system. By combining with other technologies, such as the Internet of Things and big data, more intelligent and automated traffic management can be achieved. For example, through intelligent navigation, automatic driving and other functions, traffic efficiency can be greatly improved and traffic accidents caused by human factors can be reduced.

[0157] Promoting sustainable development: Through the prediction and analysis of traffic conditions by NLP technology, we can optimize the allocation of traffic resources, improve traffic efficiency, and reduce energy consumption and environmental pollution. This is of great significance to promoting the sustainable development of urban transportation.

[0158] like Figure 4 As shown, an embodiment of the present invention provides a traffic accident prevention analysis device based on NLP, which may include:

[0159] A collection unit 410, configured to collect and pre-process traffic data of at least one dimension;

[0160] An extraction unit 420, configured to extract features related to traffic conditions from the text data by using NLP;

[0161] A construction unit 430, configured to construct a prediction model using features extracted by NLP and the traffic data;

[0162] The application unit 440 is used to predict and analyze the real-time traffic data through the trained prediction model.

[0163] It is to be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the NLP-based traffic accident prevention analysis device. In other embodiments of the present invention, the NLP-based traffic accident prevention analysis device may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0164] The information interaction, execution process and other contents between the units in the above-mentioned device are based on the same concept as the embodiment of the method of the present invention. For specific contents, please refer to the description in the embodiment of the method of the present invention, and no further description is given here.

[0165] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0166] like Figure 5As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0167] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0168] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the traffic accident prevention analysis method based on NLP.

[0169] In some embodiments, the NLP-based traffic accident prevention analysis method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the NLP-based traffic accident prevention analysis method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the NLP-based traffic accident prevention analysis method by any other appropriate means (e.g., by means of firmware).

[0170] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0171] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0172] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0173] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0174] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0175] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0176] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0177] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. The traffic accident prevention analysis method based on NLP is characterized by: include: Collect and preprocess traffic data in at least one dimension; Extracting features related to traffic conditions from the text data through NLP; Building a prediction model using features extracted by NLP and the traffic data; The real-time traffic data is predicted and analyzed through the trained prediction model.

2. The method according to claim 1, characterized in that The collecting of traffic data in at least one dimension includes: Identify the data source, which may include one or more of traffic surveillance cameras, social media platforms, news reports, government-issued traffic accident reports, online forums, and blogs; According to the selected data source, the traffic data is captured through an API interface or by writing a web crawler.

3. The method according to claim 1, characterized in that The preprocessing of the traffic data of at least one dimension includes one or more of the following steps: storing the traffic data in a database; Cleaning the traffic data in text format to remove noise, punctuation, HTML tags and special characters; Segmenting the traffic data in the text format into words or tokens; performing stop words removal on the traffic data, wherein the stop words are common words without actual information; restoring words in the traffic data to their base forms; The traffic data in the text format is labeled and classified.

4. The method according to claim 1, characterized in that: The extracting features related to traffic conditions from the text data by NLP includes: Monitor the driver's emotional state in real time by analyzing their voice, facial expressions or text messages; Detecting the negative emotion of the driver and executing a preset first measure to eliminate the negative emotion; determining the passenger's emotions by monitoring the passenger's facial expressions, voice or biometric signals, and executing a preset second measure based on the emotions; When a dangerous situation is detected, an early warning is triggered; Provide personalized driving assistance suggestions based on the driver's personality and emotional state; By collecting and analyzing the emotional data of drivers and passengers, the execution logic of the autonomous driving system can be adjusted.

5. The method according to claim 1, characterized in that The extracting features related to traffic conditions from the text data by NLP includes: Extract information related to traffic incidents from natural language text; Identify entities related to traffic events from text, where the entities are basic elements constituting traffic events; and analyze the relationships between the entities; Extract event information related to traffic incidents from text.

6. The method according to claim 1, characterized in that The prediction model is constructed by using the features extracted by NLP and the traffic data, including: Constructing at least one prediction model using the features extracted by the NLP and the traffic data; The predictive model is trained and adjusted through machine learning and deep learning to output intelligent traffic safety recommendations and real-time monitoring and response.

7. The method according to claim 6, characterized in that The traffic data is obtained by one or more of the following methods: Fusion of image data from traffic monitoring cameras and real-time data from sensors; fusing real-time data from at least one source; Data from at least one sensor is fused.

8. A traffic accident prevention analysis device based on NLP, characterized in that: include: A collection unit, used for collecting and preprocessing traffic data of at least one dimension; An extraction unit, used for extracting features related to traffic conditions from the text data through NLP; A construction unit, used for constructing a prediction model through the features extracted by NLP and the traffic data; The application unit is used to predict and analyze real-time traffic data through the trained prediction model.

9. An electronic device, characterized in that include: at least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the NLP-based traffic accident prevention analysis method described in any one of claims 1-7.

10. A computer readable medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the NLP-based traffic accident prevention analysis method described in any one of claims 1-7 when executed.

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