Intelligent traffic condition lightning protection detection system

Abstract

The application relates to the technical field of lightning detection systems, and discloses an intelligent lightning detection system for traffic road conditions, which comprises a lightning sensor, a data acquisition module and a data processing server. The lightning sensor is arranged along the road, and the data acquisition module is connected with the lightning sensor through an electric signal. The data processing server of the application improves the decision-making efficiency. The application enables the data processing server to efficiently process large-scale data, supports decision-making through the analysis of historical and real-time data. This helps decision-makers to quickly understand the current situation, make wise strategies and policies to cope with the lightning risk on the road, thereby improving the decision-making efficiency. The big data analysis capability allows the server to timely detect the lightning activity risk and provide relevant warning and prediction information. This helps to improve the road safety, reduce the occurrence of lightning-related accidents, and protect the lives and property of drivers, passengers and road users.

Description

Technical Field

[0001] This invention belongs to the technical field of lightning protection detection systems, specifically, it relates to an intelligent lightning protection detection system for traffic conditions. Background Technology

[0002] Existing technologies include systems for detecting lightning activity, such as lightning sensors, radar, and weather satellites. These systems are typically used to monitor lightning clouds and provide lightning warnings. Traffic management systems utilize sensors, cameras, and real-time data to monitor road traffic conditions to provide traffic flow information, navigation services, and traffic control.

[0003] Existing lightning detection systems are typically standalone, focusing primarily on monitoring and predicting lightning activity without integrating them with traffic management systems. This creates information silos, making it difficult for traffic managers to effectively address lightning threats because they often lack access to timely lightning activity data. Current technologies also have limited capacity to integrate multiple data sources into a single system. This can lead to data fragmentation and inconsistencies, complicating big data analytics and reducing the accuracy of decision-making.

[0004] Traditional lightning detection and traffic management systems face challenges in terms of real-time performance. Lightning activity can occur in the blink of an eye, necessitating faster, more real-time data analysis and alerting systems to provide timely notifications. Existing technologies do not offer comprehensive decision support to help traffic managers optimize resource allocation and traffic flow in response to lightning activity. This can lead to inefficient and inappropriate responses.

[0005] In view of this, the present invention is proposed. Summary of the Invention

[0006] To solve the above-mentioned technical problems, the basic concept of the technical solution adopted by the present invention is as follows:

[0007] A smart lightning protection detection system for traffic conditions includes a data acquisition module and a data processing server. The data acquisition module includes a lightning sensor, an image sensor, and a 5G communication module, which are used to detect lightning electromagnetic waves, road images, and transmit data to the data processing server in real time, respectively. The lightning sensor is configured along the road, and the data acquisition module is electrically connected to the lightning sensor to collect road lightning activity data in real time.

[0008] The data processing server has big data analytics capabilities to receive, store, and analyze lightning activity data in order to assess road lightning risk in real time. The big data analytics capabilities include statistical analysis based on historical lightning data to identify trends and potential risks of lightning activity on roads.

[0009] The data processing server includes a data receiving module, a data storage module, a data cleaning and preprocessing module, a data analysis engine, a big data storage and processing framework, a visualization and reporting module, a predictive model module, and a data security and privacy module.

[0010] In a preferred embodiment of the present invention, the data processing server is configured with a prediction model for predicting the likelihood of road lightning activity and providing warning information.

[0011] In a preferred embodiment of the present invention, the intelligent traffic condition lightning protection detection system further includes a set of user terminals for receiving alarm information and sending alarm information to drivers or relevant agencies to assist in taking necessary safety measures.

[0012] In a preferred embodiment of the present invention, the user terminal has a navigation function to provide avoidance suggestions for areas at risk of lightning.

[0013] In a preferred embodiment of the present invention, the prediction results of the prediction model module of the data processing server include whether lightning activity occurs on the road, the duration of lightning activity, and prediction data of changes in lightning activity within the duration.

[0014] When the prediction result indicates that lightning activity has occurred on the road, the data processing server obtains the corresponding duration and sends it to the data acquisition module;

[0015] The data acquisition module counts down based on the duration. When the countdown time is less than a preset time threshold, the data acquisition module collects lightning activity data of the road according to a preset time interval, so as to obtain a number of lightning activity data before the countdown time reaches zero and send them to the data processing server.

[0016] The data processing server compares the plurality of lightning activity data with the lightning activity change prediction data within the duration. If the plurality of lightning activity data are all less than or equal to the change prediction data for the corresponding time, a safety message is issued; otherwise, an alarm message is issued.

[0017] In a preferred embodiment of the present invention, the data processing server has cloud storage capabilities to enable cross-regional and cross-device data sharing and collaborative analysis.

[0018] In a preferred embodiment of the present invention, the data receiving module is used to receive lightning activity data transmitted from the data acquisition module, including lightning electromagnetic waves and image information. The data receiving module needs to process data transmission and data formatting to ensure data integrity and availability. The data storage module is used to store the received lightning activity data. Big data analysis typically requires processing large amounts of data, so the data storage module usually includes a high-capacity hard drive or cloud storage solution to ensure long-term data preservation and easy access. The data cleaning and preprocessing module is used to detect and repair errors, missing values, or outliers in the data, and to convert the data into a format suitable for analysis. Preprocessing is used to normalize, and / or standardize, and / or denoise the data. The data analysis engine uses various algorithms and models to extract useful information from the data and perform statistical analysis, prediction, classification, and clustering tasks. These tasks can be used to identify lightning risk trends, generate predictive models, and generate alarm information.

[0019] In a preferred embodiment of the present invention, the visualization and reporting module presents the data to the end user in the form of visual charts, graphs, or reports. This module generates interactive reports, charts, and graphs to help users understand and utilize the analysis results. The predictive model module is used to predict future events or trends. The predictive model module includes the training and deployment of machine learning algorithms, deep learning models, or other predictive models. The data security and privacy module includes data encryption, access control, authentication, and auditing functions to ensure the confidentiality and integrity of the data.

[0020] In a preferred embodiment of the present invention, the prediction model includes a training module, a model evaluation module, a real-time prediction module, an alarm generation module, and a model update module. During model training, the data processing server uses historical lightning activity data for training. The training module continuously adjusts its internal parameters to fit the known data to the greatest extent possible, so as to accurately predict future lightning activity. After model training, its performance needs to be evaluated. The model evaluation module uses a portion of the data to test the model's performance to check whether the model can accurately predict lightning activity. The evaluation metrics may include accuracy, recall, and F1 score. Once the model training is complete and evaluated, it will be deployed to the data processing server for real-time prediction of lightning activity. The real-time prediction module continuously receives new data and uses the prediction model to analyze this data in real time to generate a probability prediction of lightning activity.

[0021] In a preferred embodiment of the present invention, if the prediction model detects a potential risk of lightning activity, the alarm generation module generates alarm information, which can be sent to relevant user terminals to notify drivers or relevant agencies to take necessary safety measures. The prediction model needs to be updated regularly to reflect new data and changed conditions. The model update module provides a model update strategy to ensure the accuracy and effectiveness of the model.

[0022] Compared with the prior art, the present invention has the following advantages:

[0023] 1. The data processing server includes a data receiving module, a data storage module, a data cleaning and preprocessing module, a data analysis engine, a big data storage and processing framework, a visualization and reporting module, a predictive model module, and a data security and privacy module.

[0024] 2. This invention improves road safety. By monitoring and analyzing lightning activity in real time, the system can quickly identify lightning risks on roads. When a potential lightning threat is detected, the system will issue an alarm, enabling drivers and relevant authorities to take necessary safety measures, such as slowing down, changing routes, or temporarily stopping. This will significantly improve road safety and reduce the occurrence of lightning-related accidents.

[0025] 3. This invention provides real-time lightning risk information. The system not only identifies lightning risks but also provides real-time lightning risk information based on historical data and big data analysis. This real-time information enables drivers to take action before lightning activity, thereby reducing the risk of being threatened by lightning. Furthermore, the system can provide avoidance suggestions for lightning-prone areas, helping drivers safely bypass potential lightning zones.

[0026] 4. This invention can optimize traffic flow. By analyzing lightning activity data, the system can also assist traffic management departments in optimizing traffic flow. For example, in areas with a high risk of lightning activity, traffic managers can implement traffic control measures to reduce traffic congestion and the risk of accidents. This will help improve road efficiency and reduce traffic delays.

[0027] 5. This invention can reduce economic losses. Lightning-induced accidents and damage not only threaten road safety but also lead to significant economic losses. The system of this invention can help reduce lightning-related accidents and damage through timely warnings and predictions, thereby saving on repair and medical costs. Furthermore, it helps protect vehicles, road infrastructure, and other property from lightning damage.

[0028] 6. This invention supports data-driven decision-making. The system's big data analytics capabilities enable traffic management departments to make more informed decisions based on historical data and real-time information. This includes identifying road improvement projects, planning for lightning-risk areas, and more effective emergency responses. Data-driven decision-making can improve the efficiency and safety of the overall transportation system.

[0029] 7. The data processing server of this invention improves decision-making efficiency. It enables the server to efficiently process large-scale data, supporting decision-making by analyzing historical and real-time data. This helps decision-makers quickly understand the current situation and formulate informed strategies and policies to address lightning risks on roads, thereby improving decision-making efficiency. Big data analytics capabilities allow the server to detect lightning activity risks in a timely manner and provide relevant alerts and forecasts. This helps improve road safety, reduce lightning-related accidents, and protect the lives and property of drivers, passengers, and road users. Data analysis improves the accuracy of lightning risk prediction. By collecting and analyzing large amounts of historical data, the system of this invention can better understand the trends and patterns of lightning activity, thus providing more accurate predictions. This helps to take preventative measures to reduce the threat of lightning.

[0030] The specific embodiments of the present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0031] In the attached diagram:

[0032] Figure 1 This is a schematic diagram of a traffic condition intelligent lightning protection detection system according to an embodiment of this application. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments will be clearly and completely described below with reference to the accompanying drawings. The following embodiments are used to illustrate the present invention.

[0034] It should be understood that the described embodiments are merely some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of the embodiments of this application.

[0035] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances. The singular forms "a," "the," and "the" used in this application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. The word "if" as used herein can be interpreted as "when," "when," or "in response to determination."

[0036] Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0037] Please see Figure 1 This is a schematic diagram of a traffic condition intelligent lightning protection detection system according to an embodiment of this application. It includes a data acquisition module and a data processing server. The data acquisition module includes a lightning sensor, an image sensor, and a 5G communication module, which are used to detect lightning electromagnetic waves, road images, and transmit data to the data processing server in real time, respectively. The lightning sensor is configured along the road, and the data acquisition module is electrically connected to the lightning sensor to collect road lightning activity data in real time.

[0038] The data processing server has big data analytics capabilities to receive, store, and analyze lightning activity data in order to assess road lightning risk in real time. The big data analytics capabilities include statistical analysis based on historical lightning data to identify trends and potential risks of lightning activity on roads.

[0039] The data processing server includes a data receiving module, a data storage module, a data cleaning and preprocessing module, a data analysis engine, a big data storage and processing framework, a visualization and reporting module, a predictive model module, and a data security and privacy module.

[0040] This invention improves road safety. By monitoring and analyzing lightning activity in real time, the system can quickly identify lightning risks on roads. When a potential lightning threat is detected, the system will issue an alarm, enabling drivers and relevant authorities to take necessary safety measures, such as slowing down, changing routes, or temporarily stopping. This will significantly improve road safety and reduce the occurrence of lightning-related accidents.

[0041] In other embodiments, those skilled in the art can use other communication modules to implement data transmission, such as WiFi communication modules.

[0042] This invention provides real-time lightning risk information. The system not only identifies lightning risks but also provides real-time lightning risk information based on historical data and big data analysis. This real-time information enables drivers to take action before lightning activity, thereby reducing the risk of being threatened by lightning. Furthermore, the system can provide avoidance suggestions for lightning-prone areas, helping drivers safely bypass potential lightning zones.

[0043] This invention can optimize traffic flow. By analyzing lightning activity data, the system can also assist traffic management departments in optimizing traffic flow. For example, in areas with a high risk of lightning activity, traffic managers can implement traffic control measures to reduce traffic congestion and the risk of accidents. This will help improve road efficiency and reduce traffic delays.

[0044] This invention can reduce economic losses. Lightning-induced accidents and damage not only threaten road safety but also lead to significant economic losses. The system of this invention can help reduce lightning-related accidents and damage through timely warnings and predictions, thereby saving repair and medical costs. Furthermore, it helps protect vehicles, road infrastructure, and other property from lightning damage.

[0045] This invention supports data-driven decision-making. The system's big data analytics capabilities enable traffic management departments to make more informed decisions based on historical data and real-time information. This includes identifying road improvement projects, planning for lightning-risk areas, and more effective emergency responses. Data-driven decision-making can improve the efficiency and safety of the overall transportation system.

[0046] The data processing server is equipped with a predictive model to predict the likelihood of road lightning activity and provide warning information. The intelligent traffic lightning protection detection system also includes a set of user terminals for receiving and sending warning information to drivers or relevant authorities to assist in taking necessary safety measures.

[0047] The user terminal has a navigation function to provide avoidance suggestions for areas at risk of lightning.

[0048] The data acquisition module and the data processing server use 5G communication technology for data transmission to achieve high-speed, low-latency data transmission.

[0049] The data processing server has cloud storage capabilities to enable data sharing and collaborative analysis across regions and devices.

[0050] The data receiving module receives lightning activity data transmitted from the data acquisition module, including lightning electromagnetic waves and image information. The data receiving module handles data transmission and formatting to ensure data integrity and availability. The data storage module stores the received lightning activity data. Since big data analysis typically involves processing large amounts of data, the data storage module usually includes high-capacity hard drives or cloud storage solutions to ensure long-term data preservation and easy access. The data cleaning and preprocessing module detects and repairs errors, missing values, or outliers in the data and converts the data into a format suitable for analysis. Preprocessing involves normalizing and / or standardizing the data, and / or denoising it. The data analysis engine uses various algorithms and models to extract useful information from the data, performing statistical analysis, prediction, classification, and clustering tasks. These tasks can be used to identify lightning risk trends, generate predictive models, and produce alert information.

[0051] The visualization and reporting module presents data to end users in the form of visual charts, graphs, or reports. This module generates interactive reports, charts, and graphs to help users understand and utilize the analysis results. The predictive model module is used to predict future events or trends. This module includes the training and deployment of machine learning algorithms, deep learning models, or other predictive models. The data security and privacy module includes data encryption, access control, authentication, and auditing functions to ensure the confidentiality and integrity of the data.

[0052] The data processing server of this invention improves decision-making efficiency by enabling it to efficiently process large-scale data and support decision-making through the analysis of historical and real-time data. This helps decision-makers quickly understand the current situation and formulate informed strategies and policies to address lightning risks on roads, thereby improving decision-making efficiency. Big data analytics capabilities allow the server to detect lightning activity risks in a timely manner and provide relevant alerts and forecasts. This helps improve road safety, reduce lightning-related accidents, and protect the lives and property of drivers, passengers, and road users. Data analysis improves the accuracy of lightning risk prediction. By collecting and analyzing large amounts of historical data, the system of this invention can better understand the trends and patterns of lightning activity, thus providing more accurate predictions. This helps to take preventative measures to reduce the threat of lightning.

[0053] The prediction model includes a training module, a model evaluation module, a real-time prediction module, an alarm generation module, and a model update module. During model training, the data processing server uses historical lightning activity data for training. The training module continuously adjusts its internal parameters to fit the known data to the greatest extent possible, so as to accurately predict future lightning activity. After model training, its performance needs to be evaluated. The model evaluation module uses a portion of the data to test the model's performance to check whether the model can accurately predict lightning activity. The evaluation metrics may include accuracy, recall, and F1 score. Once the model training is complete and has been evaluated, it will be deployed to the data processing server for real-time lightning activity prediction. The real-time prediction module continuously receives new data and uses the prediction model to analyze this data in real time to generate probability predictions of lightning activity.

[0054] Precision and recall are two metrics widely used in information retrieval and statistical classification to evaluate the quality of results.

[0055] The F1 score is a statistical metric used to measure the precision of a binary classification model. It considers both precision and recall. The F1 score can be viewed as a harmonic average of precision and recall, with a maximum value of 1 and a minimum value of 0.

[0056] If the predictive model detects a potential risk of lightning activity, the alarm generation module generates an alarm message, which can be sent to relevant user terminals to notify drivers or relevant agencies to take necessary safety measures. The predictive model needs to be updated regularly to reflect new data and changing conditions. The model update module provides model update strategies to ensure the accuracy and effectiveness of the model.

[0057] In one feasible embodiment, the prediction results of the prediction model module of the data processing server include whether lightning activity occurs on the road, the duration of lightning activity, and prediction data of changes in lightning activity within the duration.

[0058] When the prediction result indicates that lightning activity has occurred on the road, the data processing server obtains the corresponding duration and sends it to the data acquisition module;

[0059] The data acquisition module counts down based on the duration. When the countdown time is less than a preset time threshold, the data acquisition module collects lightning activity data of the road according to a preset time interval, so as to obtain a number of lightning activity data before the countdown time reaches zero and send them to the data processing server.

[0060] The data processing server compares the plurality of lightning activity data with the lightning activity change prediction data within the duration. If the plurality of lightning activity data are all less than or equal to the change prediction data for the corresponding time, a safety message is issued; otherwise, an alarm message is issued.

[0061] In this embodiment, if all of the lightning activity data are less than or equal to the predicted change data for the corresponding time, it indicates that the prediction model module of the data processing server has high accuracy. Since the lightning activity data includes activity data when the duration reaches zero, the lightning activity on the road has disappeared at this time, as predicted by the prediction model module of the data processing server, meaning the road is safe again, and a safety message can be issued. If any of the lightning activity data exceeds the predicted change data for the corresponding time, it indicates that the prediction model module of the data processing server has low accuracy. In this case, an alarm message needs to be issued again to remind and warn, preventing personnel from misjudging the lightning activity situation on the road.

[0062] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of the present invention.

[0063] The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without any inventive effort.

[0064] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0065] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function selected in one or more boxes.

[0066] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function selected in one or more boxes.

[0067] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0068] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0069] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0070] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0071] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A traffic road condition intelligent lightning protection detection system, characterized in that, The system includes a data acquisition module and a data processing server. The data acquisition module includes a lightning sensor, an image sensor, and a 5G communication module, which are used to detect lightning electromagnetic waves, road images, and transmit data to the data processing server in real time. The lightning sensor is configured along the road, and the data acquisition module is electrically connected to the lightning sensor to collect road lightning activity data in real time. The data processing server has big data analytics capabilities to receive, store, and analyze lightning activity data in order to assess road lightning risk in real time. The big data analytics capabilities include statistical analysis based on historical lightning data to identify trends and potential risks of lightning activity on roads. The data processing server includes a data receiving module, a data storage module, a data cleaning and preprocessing module, a data analysis engine, a big data storage and processing framework, a visualization and reporting module, a predictive model module, and a data security and privacy module. The prediction results of the prediction model module of the data processing server include whether lightning activity occurs on the road, the duration of lightning activity, and prediction data of changes in lightning activity within the duration. When the prediction result indicates that lightning activity has occurred on the road, the data processing server obtains the corresponding duration and sends it to the data acquisition module; The data acquisition module counts down based on the duration. When the countdown time is less than a preset time threshold, the data acquisition module collects lightning activity data of the road according to a preset time interval, so as to acquire a number of lightning activity data before the countdown time reaches zero and send them to the data processing server. The data processing server compares the plurality of lightning activity data with the lightning activity change prediction data within the duration. If the plurality of lightning activity data are all less than or equal to the change prediction data for the corresponding time, a safety message is issued; otherwise, an alarm message is issued.

2. The intelligent traffic condition lightning protection detection system of claim 1, wherein The data processing server is configured with a prediction model to predict the likelihood of lightning activity on roads and provide alert information. 3.The intelligent anti-mine detection system for traffic road conditions according to claim 1, characterized in that, The intelligent lightning protection detection system for traffic conditions also includes a set of user terminals, which are used to receive alarm information and send alarm information to drivers or relevant agencies to assist in taking safety measures.

4. The intelligent traffic condition lightning protection detection system of claim 3, wherein, The user terminal has a navigation function to provide avoidance suggestions for areas at risk of lightning.

5. The intelligent traffic condition lightning protection detection system of claim 1, wherein The data processing server has cloud storage capabilities to enable data sharing and collaborative analysis across regions and devices. 6.The intelligent anti-mine detection system for traffic road conditions according to claim 1, characterized in that, The data receiving module receives lightning activity data transmitted from the data acquisition module, including lightning electromagnetic waves and image information. The data receiving module handles data transmission and formatting to ensure data integrity and availability. The data storage module stores the received lightning activity data. Big data analysis requires processing large amounts of data; therefore, the data storage module includes high-capacity hard drives or cloud storage solutions to ensure long-term data preservation and easy access. The data cleaning and preprocessing module detects and repairs errors, missing values, or outliers in the data and converts the data into a format suitable for analysis. Preprocessing involves normalizing and / or standardizing the data, and / or denoising it. The data analysis engine uses algorithms and models to extract useful information from the data, performing statistical analysis, prediction, classification, and clustering tasks. These tasks are used to identify lightning risk trends, generate predictive models, and produce alert information. 7.The intelligent anti-mine detection system for traffic road conditions according to claim 1, characterized in that, The visualization and reporting module presents data to end users in the form of visual charts, graphs, or reports. It generates interactive reports, charts, and graphs to help users understand and utilize the analysis results. The predictive model module is used to predict future events or trends. The predictive model module includes the training and deployment of machine learning algorithms, deep learning models, or predictive models. The data security and privacy module includes data encryption, access control, authentication, and auditing functions to ensure the confidentiality and integrity of the data. 8.The intelligent anti-mine detection system for traffic road conditions according to claim 1, characterized in that, The prediction model includes a training module, a model evaluation module, a real-time prediction module, an alarm generation module, and a model update module. During model training, the data processing server uses historical lightning activity data for training. The training module continuously adjusts its internal parameters to fit the known data to the greatest extent possible, so as to accurately predict future lightning activity. After model training, its performance needs to be evaluated. The model evaluation module uses a portion of the data to test the model's performance to check whether the model can accurately predict lightning activity. The evaluation metrics include accuracy, recall, and F1 score. Once the model training is complete and has been evaluated, it will be deployed to the data processing server for real-time lightning activity prediction. The real-time prediction module continuously receives new data and uses the prediction model to analyze this data in real time to generate probability predictions of lightning activity. 9.The intelligent anti-mine detection system for traffic road conditions according to claim 8, characterized in that, If the predictive model detects a potential risk of lightning activity, the alarm generation module generates an alarm message, which is sent to the relevant user terminal to notify the driver or relevant agency to take safety measures. The predictive model needs to be updated regularly to reflect new data and changed conditions. The model update module provides model update strategies to ensure the accuracy and effectiveness of the model.

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