Method and system for constructing freezing rain distribution map based on Internet of Vehicles data analysis

Abstract

The invention relates to the technical field of Internet of Vehicles big data analysis, and discloses a method and system for constructing a freezing rain distribution map based on Internet of Vehicles data analysis, and the method comprises the steps: obtaining Internet of Vehicles data, data reporting time, rainfall information, temperature information and humidity information collected by each vehicle terminal, and position information of the vehicle terminal triggering an anti-lock braking system or an electronic control suspension system; based on the rainfall information, the temperature information and the humidity information, predicting the probability of occurrence of freezing rain; based on the position information, the number of vehicles triggering an anti-lock braking system or an electronic control suspension system is determined; based on the data reporting time, the probability of the occurrence of the freezing rain and the number of the vehicles, determining the time of the occurrence of the freezing rain and an area influenced by the freezing rain; and constructing a freezing rain distribution map based on the freezing rain occurrence time and the region influenced by the freezing rain. And the freezing rain distribution is displayed in a visual and understandable manner, so that a decision maker can quickly understand and deal with the freezing rain.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis of the Internet of Vehicles, and particularly to a method and system for constructing a freezing rain distribution map based on the analysis of Internet of Vehicles data. Background Art

[0002] The meteorological observation network has developed into a three-dimensional observation system covering ground-based, air-based and space-based. Although the prediction technology of weather forecasting is constantly improving, due to the complexity of atmospheric motion and the limitation of human understanding of the mechanism of atmospheric motion, there are still certain errors in the prediction accuracy of weather forecasting, especially the identification of freezing rain is particularly difficult. Summary of the Invention

[0003] In view of this, the present invention provides a method and system for constructing a freezing rain distribution map based on the analysis of Internet of Vehicles data to solve the problems in the related art that there are errors in weather forecasting and it is difficult to identify the situation of freezing rain.

[0004] In a first aspect, the present invention provides a method for constructing a freezing rain distribution map based on the analysis of Internet of Vehicles data. The method includes: obtaining Internet of Vehicles data, where the Internet of Vehicles data includes the data reporting time, the rainfall information, temperature information, humidity information collected by each vehicle terminal, and the location information of the vehicle terminals that trigger the anti-lock braking system or the electronic control suspension system; predicting the probability of freezing rain occurrence based on the rainfall information, the temperature information and the humidity information; determining the number of vehicles that trigger the anti-lock braking system or the electronic control suspension system based on the location information; determining the time of freezing rain occurrence and the area affected by freezing rain based on the data reporting time, the probability of freezing rain occurrence and the number of vehicles; and constructing a freezing rain distribution map based on the time of freezing rain occurrence and the area affected by freezing rain.

[0005] In an optional implementation manner, the determining the time of freezing rain occurrence and the area affected by freezing rain based on the data reporting time, the probability of freezing rain occurrence and the number of vehicles includes: determining the confidence level of the probability of freezing rain occurrence based on the number of vehicles; determining the area affected by freezing rain based on the confidence level and the location information; and determining the time of freezing rain occurrence based on the data reporting time corresponding to the triggering of the anti-lock braking system or the electronic control suspension system.

[0006] In an alternative embodiment, determining the confidence level of the probability of freezing rain occurrence based on the number of vehicles includes: If the number of vehicles is greater than or equal to a first preset threshold, the confidence level is a first confidence level, and if the number of vehicles increases, the first confidence level increases by a first confidence increment; If the number of vehicles is less than the first preset threshold and greater than or equal to a second preset threshold, the confidence level is a second confidence level, and if the number of vehicles increases, the second confidence level increases by a second confidence increment, where the first confidence increment is less than the second confidence increment.

[0007] In an alternative embodiment, predicting the probability of freezing rain occurrence based on the rainfall information, temperature information, and humidity information includes: Extracting rainfall features based on the rainfall information; Extracting temperature features based on the temperature information; Extracting humidity features based on the humidity information; Constructing a feature vector based on the rainfall features, temperature features, and humidity features; Inputting the feature vector into a trained freezing rain prediction model to generate the probability of freezing rain occurrence.

[0008] In an alternative embodiment, the method further includes: If the rainfall represented by the rainfall information is less than a first rainfall threshold, increasing the frequency of obtaining the rainfall information; If the rainfall represented by the rainfall information is greater than or equal to the first rainfall threshold and less than a second rainfall threshold, performing normalization processing on the rainfall information; If the rainfall represented by the rainfall information is greater than the second rainfall threshold, performing data calibration and correction on the rainfall information.

[0009] In an alternative embodiment, constructing a freezing rain distribution map based on the time of freezing rain occurrence and the area affected by freezing rain includes: Based on the time of freezing rain occurrence, superimposing a weather layer, a temperature and humidity layer, and a layer of the area affected by freezing rain in the form of layers on the map, where the weather layer and the temperature and humidity layer are generated based on the probability of freezing rain occurrence.

[0010] In an alternative embodiment, the method further includes preprocessing the vehicle networking data, including: Deleting duplicate data, error data, and abnormal data in the vehicle networking data to obtain cleaned data; Performing standardization processing on the cleaned data to obtain standardized data; Determining the time of freezing rain occurrence and the area affected by freezing rain based on the standardized data.

[0011] Second aspect, the present invention provides a system for constructing a freezing rain distribution map based on vehicle networking data analysis. The system includes: an acquisition module, configured to acquire vehicle networking data, where the vehicle networking data includes data reporting time, rainfall information, temperature information, humidity information collected by each vehicle terminal, and location information of vehicle terminals that trigger the anti-lock braking system or the electronic control suspension system; a prediction module, configured to predict the probability of freezing rain occurrence based on the rainfall information, the temperature information, and the humidity information; determine the number of vehicles that trigger the anti-lock braking system or the electronic control suspension system based on the location information; determine the time of freezing rain occurrence and the area affected by freezing rain based on the data reporting time, the probability of freezing rain occurrence, and the number of vehicles; a prediction module, configured to construct a freezing rain distribution map based on the time of freezing rain occurrence and the area affected by freezing rain.

[0012] Third aspect, the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the method for constructing a freezing rain distribution map based on vehicle networking data analysis according to the first aspect or any corresponding implementation manner thereof.

[0013] Fourth aspect, the present invention provides a computer-readable storage medium, on which computer instructions are stored. The computer instructions are used to cause a computer to execute the method for constructing a freezing rain distribution map based on vehicle networking data analysis according to the first aspect or any corresponding implementation manner thereof.

[0014] Fifth aspect, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the method for constructing a freezing rain distribution map based on vehicle networking data analysis according to the first aspect or any corresponding implementation manner thereof.

[0015] Based on vehicle networking data from numerous vehicle terminals, the freezing rain conditions in different regions can be captured. Through the location information of vehicle terminals that trigger the anti-lock braking system or the electronic control suspension system, the areas affected by freezing rain can be accurately located. Combining multiple data sources such as rainfall, temperature, humidity, and vehicle behavior provides comprehensive information support for freezing rain prediction and distribution map construction. The robustness and accuracy of freezing rain identification and prediction can be improved through the fusion analysis of multi-source data. Through map visualization technology, the freezing rain distribution is presented in an intuitive and easy-to-understand manner, facilitating decision-makers to quickly understand and respond. Description of the Drawings

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the related art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 The flowchart shows the method for constructing a freezing rain distribution map based on vehicle networking data analysis provided by an embodiment of the present invention;

[0018] Figure 2 The flowchart shows another method for constructing a freezing rain distribution map based on vehicle networking data analysis according to an embodiment of the present invention;

[0019] Figure 3 The structural diagram shows a system for constructing a freezing rain distribution map based on vehicle networking data analysis;

[0020] Figure 4 It is a hardware structural diagram of a computer device according to an embodiment of the present invention. Specific Embodiments

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0022] The meteorological observation network has developed into a three-dimensional observation system covering ground-based, air-based, and space-based. Despite the continuous progress of weather forecast prediction technology, due to the complexity of atmospheric motion and the limitations of human understanding of the mechanism of atmospheric motion, there are still certain errors in the prediction accuracy of weather forecasts, especially for the identification of freezing rain.

[0023] In addition, due to time constraints, the risk information in areas such as rural and remote areas that are greatly affected by the weather has not been sufficiently presented in weather forecasts, resulting in these areas being unable to obtain sufficient useful weather information from weather forecasts.

[0024] Moreover, there is a certain interval problem in the data update of weather forecasts, so the latest weather conditions may not be obtained in a timely manner; there are still omissions for small and medium-scale weather systems, and there are errors in the observation data.

[0025] According to an embodiment of the present invention, there is provided an embodiment of a method for constructing a freezing rain distribution map based on vehicle networking data analysis. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0026] In this embodiment, a method for constructing a freezing rain distribution map based on vehicle networking data analysis is provided, which can be used for mobile terminals such as mobile phones, tablet computers, vehicle intelligent cockpits, etc. Figure 1 The flowchart of the method for constructing a freezing rain distribution map based on vehicle networking data analysis provided by the embodiment of the present invention is shown, as Figure 1 shown, the process includes the following steps:

[0027] Step S101, obtain vehicle networking data, which includes the data reporting time, the rainfall information, temperature information, humidity information collected by each vehicle terminal, and the location information of the vehicle terminal that triggers the anti-lock braking system or the electronic control suspension system.

[0028] In this step, a reliable vehicle networking data source can be selected, and the data source can be provided by the vehicle networking of each vehicle enterprise. The vehicle networking data can be obtained from each data source in real time through an Application Programming Interface (API for short). In addition to the data reporting time, the rainfall information, temperature information, humidity information collected by each vehicle terminal, and the location information of the vehicle terminal that triggers the anti-lock braking system or the electronic control suspension system, the vehicle networking data can also include other data information.

[0029] Specifically, the vehicle networking data format is shown in Table 1:

[0030] Table 1 Schematic table of vehicle networking data format

[0031]

[0032] Among them, the temperature information corresponding to serial number 8 can be collected through the temperature sensors inside and outside the vehicle body. The data reporting time, that is, the signal triggering time, can adopt the time format of YYYYMMDDTTMMSS. The rainfall information can be obtained by sensing the number and time interval of raindrops through the infrared rain gauge or the optical rain sensor set on the vehicle windshield wiper.

[0033] With the growth of the number of intelligent vehicles, more and more data is collected by the vehicle networking. Among them, some information can obtain the distribution and influence area information related to freezing rain through data integration and analysis.

[0034] Step S102: Predict the probability of freezing rain based on rainfall information, temperature information, and humidity information; determine the number of vehicles that trigger the anti-lock braking system or the electronic control suspension system based on location information; determine the time of freezing rain occurrence and the area affected by freezing rain based on the data reporting time, the probability of freezing rain occurrence, and the number of vehicles.

[0035] In this step, the data reporting time, the probability of freezing rain occurrence, and the number of vehicles can be input into the trained freezing rain prediction model to obtain the time of freezing rain occurrence and the area affected by freezing rain. Among them, the trained freezing rain prediction model can accurately predict and classify future weather conditions based on a large amount of historical vehicle networking data and environmental factors, using complex algorithms and computing technologies. The time of freezing rain occurrence and the affected area range can be judged through the location and number of vehicles where the ABS intervenes. It plays an important role in vehicle systems or environmental factors such as windshield wipers, fog lights, and temperature. Through real-time processing and analysis of relevant data, the algorithm can intelligently identify the weather environment.

[0036] Specifically, the freezing rain prediction model can adopt deep learning technology to achieve high-precision prediction of future weather by training a large amount of vehicle networking data. Based on the data results of vehicle networking data and weather forecasts, learn to predict the weather.

[0037] A time series model can also be used to predict future weather conditions based on the exponential smoothing method and the changing trend of historical vehicle networking data.

[0038] Step S103: Construct a freezing rain distribution map based on the time of freezing rain occurrence and the area affected by freezing rain.

[0039] In this step, based on the determined area affected by freezing rain and the time of freezing rain occurrence, use GIS software or related tools to construct a freezing rain distribution map. Mark the area affected by freezing rain on the map, and different colors, symbols, etc. can be used to represent different probabilities of freezing rain occurrence or degrees of influence. At the same time, auxiliary elements such as a time axis can be added to the map to show the time change of freezing rain occurrence.

[0040] Perform visualization processing on the constructed freezing rain distribution map to make it easier to understand and use. Add basic map operation functions such as zooming, panning, and rotating to facilitate users to view the meteorological conditions in different regions. Filtering, querying, and other functions can also be added to enable users to quickly locate the freezing rain information in a specific area or specific time period.

[0041] The vehicle networking data can be updated regularly to ensure the real-time and accuracy of the freezing rain distribution map. Maintain and upgrade the GIS software and map visualization tools to improve the stability of the system and the user experience.

[0042] The method for constructing a freezing rain distribution map based on vehicle networking data analysis provided by this embodiment can capture the freezing rain conditions in different regions based on vehicle networking data from numerous vehicle terminals. By using the location information of vehicles that trigger the anti-lock braking system or the electronic control suspension system, the areas affected by freezing rain can be accurately located. Combining multiple data sources such as rainfall, temperature, humidity, and vehicle behavior provides comprehensive information support for freezing rain prediction and distribution map construction. Through the fusion analysis of multi-source data, the robustness and accuracy of freezing rain identification and prediction can be improved. Through map visualization technology, the freezing rain distribution can be presented in an intuitive and understandable way, facilitating decision-makers to quickly understand and respond.

[0043] In some alternative embodiments, based on the data reporting time, the probability of freezing rain occurrence, and the number of vehicles, determining the time of freezing rain occurrence and the areas affected by freezing rain includes: determining the confidence level of the probability of freezing rain occurrence based on the number of vehicles; determining the areas affected by freezing rain based on the confidence level and location information; and determining the time of freezing rain occurrence based on the data reporting time corresponding to the triggering of the anti-lock braking system or the electronic control suspension system.

[0044] In this embodiment, the probability of freezing rain occurrence can be predicted by analyzing data such as rainfall, temperature, and humidity reported by a large number of vehicles in the same region and at the same time period, in combination with machine learning or statistical models. After determining the probability of freezing rain occurrence and its confidence level, the areas affected by freezing rain can be further determined in combination with the location information of the vehicles. This can be achieved by analyzing the vehicle location data when the anti-lock braking system or the electronic control suspension system is triggered. When the anti-lock braking system or the electronic control suspension system is frequently triggered in a specific area, in combination with the previously predicted probability of freezing rain occurrence, it can be more accurately determined that this area is affected by freezing rain.

[0045] Based on the time data reported by vehicles when the anti-lock braking system or the electronic control suspension system is triggered, the approximate time of freezing rain occurrence can be determined. These time data can reflect the specific moment when freezing rain begins to affect vehicle driving. By analyzing these time data, a time series graph of freezing rain occurrence can be drawn.

[0046] In this way, the more vehicles that trigger the anti-lock braking system or the electronic control suspension system, the denser the data points provided, which can more accurately reflect the trends and patterns of meteorological changes, thereby improving the confidence level of the prediction of the probability of freezing rain occurrence. This not only improves the accuracy and timeliness of meteorological prediction but also provides strong decision-making support for traffic management and intelligent driving.

[0047] In some alternative embodiments, determining the confidence level of the probability of freezing rain occurring based on the number of vehicles includes: if the number of vehicles is greater than or equal to a first preset threshold, the confidence level is a first confidence level, and if the number of vehicles increases, the first confidence level increases by a first confidence increment; if the number of vehicles is less than the first preset threshold and greater than or equal to a second preset threshold, the confidence level is a second confidence level, and if the number of vehicles increases, the second confidence level increases by a second confidence increment, where the first confidence increment is less than the second confidence increment.

[0048] In this embodiment, if the number of vehicles is less than the second preset threshold and greater than or equal to a third preset threshold, the confidence level is a third confidence level, and if the number of vehicles increases, the third confidence level increases by a third confidence increment; if the number of vehicles is less than the third preset threshold and greater than or equal to a fourth preset threshold, the confidence level is a fourth confidence level, and if the number of vehicles increases, the fourth confidence level increases by a fourth confidence increment. Among them, the third confidence increment may be the same as or different from the fourth confidence increment.

[0049] Specifically, the first preset threshold can be set to 50, the second preset threshold to 30, the third preset threshold to 20, the fourth preset threshold to 10, the first confidence level to 90, the second confidence level to 80 - 90, the third confidence level to 70 - 79, the fourth confidence level to 60 - 69, the first confidence increment can be set to 0.2, the second confidence increment can be set to 0.5, and the third confidence increment and the fourth confidence increment can be set to 1. When the number of vehicles triggering the anti-lock braking system or the electronic control suspension system increases by one, the first confidence level increases by 0.2, the second confidence level increases by 0.5, and the third confidence level and the fourth confidence level increase by 1.

[0050] When the event of triggering the anti-lock braking system or the electronic control suspension system persists, the confidence level is pushed and updated every 30 seconds. When calculating the confidence level, rounding can be used, and the initial parameters can be set as follows: 5 vehicles with ABS + 5 vehicles with ECS can be regarded as 10 vehicles; for a vehicle with dual signals, only +1 is counted. In the absence of an event of triggering the anti-lock braking system or the electronic control suspension system, the layer of the area affected by freezing rain can be deleted from the map layer.

[0051] In this way, according to the actual number of vehicles triggering the anti-lock braking system or the electronic control suspension system, the confidence level is dynamically adjusted, making the prediction result closer to the actual situation and improving the accuracy and reliability of the prediction. The fine division of the confidence interval helps to more accurately describe the relationship between the number of vehicles and the probability of freezing rain occurring, and can provide more detailed prediction information.

[0052] In the actual road environment, the occurrence probability of freezing rain is affected by various factors, including road surface conditions, weather conditions, vehicle types, etc. By considering the number of vehicles that trigger the anti-lock braking system or the electronic control suspension system, the influence of these factors on the occurrence probability of freezing rain is indirectly reflected. An increase in the number of vehicles may mean that the road surface conditions deteriorate or the weather conditions become more severe, thus increasing the likelihood of freezing rain. In this way, the complexity of the actual situation can be better reflected.

[0053] In some alternative embodiments, based on rainfall information, temperature information, and humidity information, the probability of freezing rain occurrence is predicted, including: extracting rainfall features based on the rainfall information; extracting temperature features based on the temperature information; extracting humidity features based on the humidity information; constructing a feature vector based on the rainfall features, temperature features, and humidity features; and inputting the feature vector into a trained freezing rain prediction model to generate the probability of freezing rain occurrence.

[0054] In this embodiment, for different weather phenomena such as freezing rain, the freezing rain prediction model can select distinguishable features for extraction. For example, for the identification of fog, features such as humidity, visibility, and fog lights are extracted; for the identification of rain, features such as rainfall scraping speed and humidity are concerned. Using the obtained data and features, the freezing rain prediction model is trained and optimized. By adjusting the parameters and structure of the freezing rain prediction model, it can accurately identify weather phenomena such as freezing rain. Through continuous learning and optimization, the prediction accuracy can be improved. The freezing rain prediction model can be used to parse the received raw data into the format required for map display: location, temperature, humidity, precipitation type, fog type, etc.

[0055] A suitable vehicle networking big data model can be selected according to specific requirements and data characteristics, such as deep learning models (such as convolutional neural network CNN, recurrent neural network RNN, etc.) or machine learning models (such as linear regression, support vector machine SVM, etc.).

[0056] In some alternative embodiments, the foregoing method for constructing the freezing rain distribution drip based on vehicle networking data analysis further includes: if the rainfall amount represented by the rainfall information is less than the first rainfall threshold, increasing the frequency of obtaining the rainfall information; if the rainfall amount represented by the rainfall information is greater than or equal to the first rainfall threshold and less than the second rainfall threshold, normalizing the rainfall information; and if the rainfall amount represented by the rainfall information is greater than the second rainfall threshold, performing data calibration and correction on the rainfall information.

[0057] In this embodiment, the rainfall characterized by rainfall information can be classified, and the rainfall can be divided into scenarios such as light rainfall, moderate rainfall, and heavy rainfall. Among them, the light rainfall can be less than 10 mm / h. In the light rainfall scenario, due to the large number and density of raindrops, the frequency of obtaining rainfall information can be increased, and data filtering processing can be performed to remove abnormal data and noise, ensuring that the windshield wiper works at an appropriate frequency.

[0058] The moderate rainfall can be 10 to 15 mm / h. In the moderate rainfall scenario, the raindrop sizes are uniform, and the rainfall information can be normalized to eliminate the influence of size differences on data processing, enabling the windshield wiper to adapt to raindrops of different sizes.

[0059] The heavy rainfall can be greater than 50 mm / h. In the heavy rainfall scenario, the raindrops are large and the time intervals between rainfalls are long. The rainfall information can be data calibrated and corrected to eliminate the influence of different raindrop sizes on data processing and adjust the working speed and mode of the windshield wiper.

[0060] In this way, the rainfall data can be processed in real time, and the working state of the windshield wiper can be adjusted immediately to ensure the driver's clear vision and improve driving safety.

[0061] In some alternative embodiments, based on the time of freezing rain occurrence and the areas affected by freezing rain, a freezing rain distribution map is constructed, including: based on the time of freezing rain occurrence, a weather layer, a temperature and humidity layer, and a layer of the areas affected by freezing rain are superimposed on the map in the form of layers, wherein the weather layer and the temperature and humidity layer are generated based on the probability of freezing rain occurrence.

[0062] In this embodiment, different vehicle networking data can be superimposed on the map in the form of layers, and the real-time update of the map can be achieved by dynamically updating the layer data. Rich interactive functions such as zooming, dragging, and click query can be provided to facilitate users to view and operate the map.

[0063] Through the display of the freezing rain distribution map, the weather information can be effectively displayed in the map. The weather information can include key indicators such as temperature, humidity, wind speed, and precipitation.

[0064] In some alternative embodiments, the method for constructing the freezing rain distribution map based on vehicle networking data analysis described above further includes: preprocessing the vehicle networking data, including: deleting duplicate data, error data, and abnormal data in the vehicle networking data to obtain the cleaned data; performing standardization processing on the cleaned data to obtain the standardized data; and determining the time of freezing rain occurrence and the areas affected by freezing rain based on the standardized data.

[0065] In this embodiment, event clustering can be performed through the Density-Based Spatial Clustering of Applications with Noise (abbreviated as DBScan) to ensure the accuracy and consistency of data. The original data is converted into a unified format and standard for subsequent data processing and model training.

[0066] For vehicle speed information, data with a vehicle speed greater than or equal to 1 km / h is valid data. The ABS signal is statistically counted, and only counted once for the same vehicle within the same time range; the ECS signal is statistically counted, and only counted once for the same vehicle within the same time range; the temperature range is -40 to 50 °C, and the temperature difference within the range is <1 °C for valid vehicles; after data cleaning, clustering statistics are performed according to the DBScan algorithm, and the statistical values are: longitude and latitude - the center point of the clustering cluster, number of times - the number of clustering clusters; the scanning radius is 30 meters; the time range is 30 seconds; the minimum number of trigger points is 10, and real-time push is performed after the event is triggered.

[0067] In this way, deleting duplicate data, error data, and abnormal data can significantly improve the accuracy and consistency of data, providing a reliable basis for subsequent data analysis. Data standardization processing can eliminate the dimensional differences between different features, making the data comparable, thereby improving the accuracy and reliability of data analysis. The preprocessed data provides more reliable information support for decision-making. Through data preprocessing, noise and redundant information can be removed, the most representative features can be retained, the learning process of the model can be optimized, and the decision-making quality can be improved.

[0068] The embodiment of the present invention also provides a method for constructing a freezing rain distribution map based on vehicle networking data analysis. Figure 2 It shows a schematic flow chart of another method for constructing a freezing rain distribution map based on vehicle networking data analysis according to the embodiment of the present invention. As Figure 2 shown, the method for constructing a freezing rain distribution map based on vehicle networking data analysis includes:

[0069] Step S201, determine whether the vehicle is powered on based on a preset time. If the vehicle is not powered on, go to step S202; if the vehicle is powered on, go to step S203;

[0070] Step S202, the terminal running the method for constructing a freezing rain distribution map based on vehicle networking data analysis enters the sleep mode;

[0071] Step S203, trigger the anti-lock braking system or the electronic control suspension system, obtain vehicle networking data, identify rain, fog, and snow events, temperature and humidity information, and the areas affected by freezing rain based on the vehicle networking data, and summarize and report the identification results;

[0072] Step S204: Determine whether there is a spatio-temporal aggregation of multiple vehicles at the same time. If so, go to step S205; if not, go to step S206;

[0073] Step S205: Based on the judgment result, generate a map layer, which includes: a weather layer, a temperature and humidity layer, and a manually affected area layer, and publish the map layer to each map provider;

[0074] Step S206: Poll to determine whether there is a spatio-temporal aggregation of multiple vehicles at the same time.

[0075] Converge weather-related signals through industry vehicle networking data, and improve the weather forecast information function by using the construction ability of vehicle networking data analysis. The freezing rain distribution map based on vehicle networking data analysis can timely collect the communication data of vehicles and the CAN bus signals, and use algorithm models to identify freezing rain events and the spatio-temporal scope of their impacts. It can provide accurate and timely weather information support for meteorological departments, airlines, agricultural departments, etc., to help them make more scientific decisions.

[0076] Through the vehicle position information that triggers the anti-lock braking system or the electronic control suspension system, the areas affected by freezing rain can be accurately located. Combining multiple data sources such as rainfall, temperature, humidity, and vehicle behavior, it can provide comprehensive information support for freezing rain prediction and distribution map construction. Through the fusion analysis of multi-source data, the robustness and accuracy of freezing rain identification and prediction can be improved. Through map visualization technology, the freezing rain distribution can be displayed in an intuitive and easy-to-understand way, facilitating decision-makers to quickly understand and respond.

[0077] In some optional implementation manners, the foregoing method for constructing a freezing rain distribution map based on vehicle networking data analysis further includes: based on the freezing rain distribution map based on vehicle networking data analysis, real-time receiving and processing new vehicle networking data, and updating the map display.

[0078] Requests can be sent to the data source at regular intervals to obtain the latest vehicle networking data. For example, poll once per minute, and the specific frequency depends on the update frequency of the data source and user requirements. If the data source supports the push function, a push mechanism can be established to receive the new data pushed by the data source in real time. This method can reduce the polling delay and achieve true real-time updates.

[0079] Vehicle networking big data technology can be used to achieve the fusion of multi-source and multi-element data, including data from different sources such as meteorological observation data, satellite remote sensing data, and radar echoes. These data cover various meteorological elements such as temperature, humidity, air pressure, wind speed, and wind direction. Ensure that the data source can provide real-time updated vehicle networking data to meet the requirements of the freezing rain distribution map based on vehicle networking data analysis.

[0080] In this way, it can be ensured that the weather information on the map is always kept up-to-date.

[0081] The performance metrics of the system, such as response time, data processing speed, etc., can be monitored in real time to ensure the stable operation of the system. The system is optimized according to the monitoring data, such as adjusting the polling frequency, optimizing the data processing algorithm, etc., to improve the real-time performance and accuracy of the system. The data transmitted can also be encrypted to ensure data security. The system data and configuration information are backed up regularly so that the system can be quickly restored in case of a failure.

[0082] By establishing the above real-time update mechanism, the freezing rain distribution map based on vehicle networking data analysis can receive and process new vehicle networking data in real time, and quickly update the map display, providing accurate and timely weather information for users. At the same time, the system also needs to have characteristics such as high performance, stability, and security to meet the growing needs of users.

[0083] By adopting the method for constructing a freezing rain distribution map based on vehicle networking data analysis provided in the embodiment of the present invention, the speed of vehicle networking data analysis in weather forecasting is increased by more than 10 times, and the vehicle networking data analysis can complete the regional identification and prediction of freezing rain faster. The vehicle networking data analysis can continuously optimize the data accuracy according to big data training, improving the weather prediction range and accuracy. In the Chinese region, the vehicle networking data forecast with a maximum time resolution of 1 minute and a spatial resolution of 1 km can be updated, providing the ability for refined forecasting.

[0084] In this embodiment, a system for constructing a freezing rain distribution map based on vehicle networking data analysis is also provided. This system is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the system described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0085] This embodiment provides a system for constructing a freezing rain distribution map based on vehicle networking data analysis. Figure 3 The structural schematic diagram of the system for constructing a freezing rain distribution map based on vehicle networking data analysis is shown, as Figure 3 shown, including:

[0086] An acquisition module 301, which is used to acquire vehicle networking data. The vehicle networking data includes the data reporting time, the rainfall information, temperature information, humidity information collected by each vehicle terminal, and the position information of the vehicle terminal that triggers the anti-lock braking system or the electronic control suspension system.

[0087] A prediction module 302, configured to predict the probability of freezing rain occurring based on rainfall information, temperature information, and humidity information; determine the number of vehicles triggering the anti-lock braking system or the electronically controlled suspension system based on location information; and determine the time of freezing rain occurrence and the area affected by freezing rain based on the data reporting time, the probability of freezing rain occurrence, and the number of vehicles.

[0088] A construction module 303, configured to construct a freezing rain distribution map based on the time of freezing rain occurrence and the area affected by freezing rain.

[0089] In some alternative embodiments, the prediction module 302 includes:

[0090] The first unit of the prediction module is configured to determine the confidence level of the probability of freezing rain occurrence based on the number of vehicles; determine the area affected by freezing rain based on the confidence level and location information; and determine the time of freezing rain occurrence based on the data reporting time corresponding to triggering the anti-lock braking system or the electronically controlled suspension system.

[0091] In some alternative embodiments, the first unit of the prediction module includes:

[0092] The first sub-unit of the prediction module is configured to, if the number of vehicles is greater than or equal to a first preset threshold, the confidence level is a first confidence level, and if the number of vehicles increases, the first confidence level increases by a first confidence increment; if the number of vehicles is less than the first preset threshold and greater than or equal to a second preset threshold, the confidence level is a second confidence level, and if the number of vehicles increases, the second confidence level increases by a second confidence increment, and the first confidence increment is less than the second confidence increment.

[0093] In some alternative embodiments, the prediction module 302 further includes:

[0094] The second unit of the prediction module is configured to extract rainfall features based on rainfall information; extract temperature features based on temperature information; extract humidity features based on humidity information; construct a feature vector based on the rainfall features, temperature features, and humidity features; and input the feature vector into a trained freezing rain prediction model to generate the probability of freezing rain occurrence.

[0095] In some alternative embodiments, the aforementioned system for constructing a freezing rain distribution map based on vehicle networking data analysis further includes:

[0096] A rainfall processing module, configured to, if the rainfall represented by the rainfall information is less than a first rainfall threshold, increase the frequency of obtaining rainfall information; if the rainfall represented by the rainfall information is greater than or equal to the first rainfall threshold and less than a second rainfall threshold, perform normalization processing on the rainfall information; and if the rainfall represented by the rainfall information is greater than the second rainfall threshold, perform data calibration and correction on the rainfall information.

[0097] In some alternative embodiments, the construction module 303 includes:

[0098] A building unit is configured to, based on the time of freezing rain occurrence, overlay a weather layer, a temperature and humidity layer, and a layer of the area affected by freezing rain in the form of layers on the basis of a map, wherein the weather layer and the temperature and humidity layer are generated based on the probability of freezing rain occurrence.

[0099] In some alternative embodiments, the system for constructing a freezing rain distribution map based on vehicle networking data analysis further includes:

[0100] A preprocessing module is configured to preprocess the vehicle networking data, including: deleting duplicate data, error data, and abnormal data in the vehicle networking data to obtain cleaned data; performing standardization processing on the cleaned data to obtain standardized data; and determining the time of freezing rain occurrence and the area affected by freezing rain based on the standardized data.

[0101] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding foregoing embodiments, and will not be elaborated herein.

[0102] The system for constructing a freezing rain distribution map based on vehicle networking data analysis in this embodiment is presented in the form of functional units. Here, the unit refers to an Application Specific Integrated Circuit (ASIC) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0103] The embodiment of the present invention further provides a computer device having the above Figure 3 system for constructing a freezing rain distribution map based on vehicle networking data analysis as shown.

[0104] Please refer to Figure 4 , Figure 4 which is a schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. As shown in Figure 4As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of a graphical user interface on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (such as an array of servers, a set of blade servers, or a multi-processor system). Figure 4 Taking one processor 10 as an example in

[0105] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above programmable logic device can be a complex programmable logic device, a field programmable gate array, a generic array logic, or any combination thereof.

[0106] Among them, the aforementioned memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiments.

[0107] The memory 20 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device. In addition, the memory 20 can include high-speed random access memory and can also include non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 can optionally include a memory remotely set relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0108] The memory 20 can include volatile memory, such as random access memory; the memory can also include non-volatile memory, such as flash memory, a hard disk, or a solid-state drive; the memory 20 can also include a combination of the above types of memory.

[0109] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30, and the output device 40 may be connected through a bus or other means. Figure 4 Take the connection through the bus as an example.

[0110] The input device 30 can receive input digital or character information and generate key signal inputs related to the user settings and function controls of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (such as a light-emitting diode), and a haptic feedback device (such as a vibration motor), etc. The above display device includes but is not limited to a liquid crystal display, a light-emitting diode, a display, and a plasma display. In some alternative embodiments, the display device may be a touch screen.

[0111] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code that is originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be processed by such software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium may further include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiment is implemented.

[0112] A part of the present invention can be applied as a computer program product, such as computer program instructions. When executed by a computer, through the operation of the computer, the methods and / or technical solutions according to the present invention can be called or provided. Those skilled in the art should be able to understand that the forms of existence of computer program instructions in a computer-readable medium include but are not limited to source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include but are not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible by the computer.

[0113] Although embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for constructing a freezing rain distribution map based on vehicle networking data analysis, characterized in that: The method comprises: Acquire Internet of Vehicles data, the Internet of Vehicles data including data reporting time, rainfall information, temperature information, humidity information collected by each vehicle terminal, and location information of the vehicle terminal that triggers the anti-lock braking system or the electronic control suspension system; Based on the rainfall information, the temperature information and the humidity information, the probability of freezing rain is predicted; based on the location information, the number of vehicles that trigger the anti-lock braking system or the electronic control suspension system is determined; based on the data reporting time, the probability of freezing rain occurrence and the number of vehicles, the time of freezing rain occurrence and the area affected by freezing rain are determined; A freezing rain distribution map is constructed based on the time when the freezing rain occurs and the area affected by the freezing rain.

2. The method according to claim 1, characterized in that The determining the time of occurrence of freezing rain and the area affected by freezing rain based on the data reporting time, the probability of occurrence of freezing rain and the number of vehicles includes: Determining a confidence level of the probability of the freezing rain occurring based on the number of vehicles; Based on the confidence level and the location information, determining the area affected by freezing rain; The time when the freezing rain occurs is determined based on the data reporting time corresponding to triggering the anti-lock braking system or the electronic control suspension system.

3. The method according to claim 2, characterized in that The step of determining the confidence level of the probability of the occurrence of freezing rain based on the number of vehicles includes: If the number of vehicles is greater than or equal to a first preset threshold, the confidence level is a first confidence level, and if the number of vehicles increases, the first confidence level increases by a first confidence increment; If the number of vehicles is less than the first preset threshold and the number of vehicles is greater than or equal to the second preset threshold, the confidence level is the second confidence level. If the number of vehicles increases, the second confidence level increases by a second confidence increment, and the first confidence increment is less than the second confidence increment.

4. The method according to claim 1, characterized in that: The predicting the probability of freezing rain occurrence based on the rainfall information, the temperature information and the humidity information includes: extracting rainfall features based on the rainfall information; extracting temperature features based on the temperature information; Extracting humidity features based on the humidity information; constructing a feature vector based on the rainfall feature, the temperature feature and the humidity feature; The feature vector is input into a trained freezing rain prediction model to generate the probability of the freezing rain occurring.

5. The method according to claim 1, characterized in that The method further comprises: If the rainfall represented by the rainfall information is less than a first rainfall threshold, increasing the frequency of obtaining the rainfall information; If the rainfall represented by the rainfall information is greater than or equal to the first rainfall threshold and less than the second rainfall threshold, normalizing the rainfall information; If the rainfall represented by the rainfall information is greater than the second rainfall threshold, data calibration and correction are performed on the rainfall information.

6. The method according to claim 1, characterized in that The constructing of a freezing rain distribution map based on the time when the freezing rain occurs and the area affected by the freezing rain comprises: Based on the time when the freezing rain occurs, a weather layer, a temperature and humidity layer, and a layer of the area affected by the freezing rain are superimposed in the form of layers on the basis of the map, wherein the weather layer and the temperature and humidity layer are generated based on the probability of the freezing rain occurring.

7. The method according to claim 1, characterized in that The method further includes preprocessing the Internet of Vehicles data, including: Deleting duplicate data, erroneous data and abnormal data in the Internet of Vehicles data to obtain cleaned data; Performing standardization processing on the cleaned data to obtain standardized data; Based on the standardized data, the time when the freezing rain occurs and the area affected by the freezing rain are determined.

8. A system for constructing a freezing rain distribution map based on vehicle networking data analysis, characterized in that: The system comprises: An acquisition module is used to acquire Internet of Vehicles data, wherein the Internet of Vehicles data includes data reporting time, rainfall information, temperature information, humidity information collected by each vehicle terminal, and location information of the vehicle terminal that triggers the anti-lock braking system or the electronic control suspension system; A prediction module is used to predict the probability of freezing rain based on the rainfall information, the temperature information and the humidity information; determine the number of vehicles that trigger the anti-lock braking system or the electronic control suspension system based on the location information; determine the time of freezing rain occurrence and the area affected by freezing rain based on the data reporting time, the probability of freezing rain occurrence and the number of vehicles; The prediction module is used to construct a freezing rain distribution map based on the time when the freezing rain occurs and the area affected by the freezing rain.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for constructing a freezing rain distribution map based on vehicle network data analysis as described in any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for constructing a freezing rain distribution map based on vehicle network data analysis as described in any one of claims 1 to 7.

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