Meteorological fusion data-based fusion media intelligent generation, production and analysis system

Abstract

The invention belongs to the technical field of fusion media generation and analysis, and particularly discloses an intelligent fusion media generation, production and analysis system based on meteorological fusion data, which constructs a quantitative evaluation model of a driving risk degree by introducing multi-source fusion data and integrating a real-time traffic operation state and meteorological monitoring information. A risk level differentiation-based convergence media content generation mechanism is realized, so that the generated convergence media content can be accurately matched with traffic control requirements in different risk scenes, and the pertinence and practicability of information release are improved; dynamic monitoring and feedback closed loop of user behavior response are realized by constructing a management and control efficiency evaluation mechanism after pushing after the convergence media content facing the actual driving scene is pushed, and the mechanism can quantitatively evaluate the actual guiding effect of the pushed content on the driver behavior; and data support can be provided for optimization of subsequent content generation strategies and pushing opportunities, so that the accuracy and adaptability of traffic information services are continuously improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated media generation and analysis, and specifically discloses an integrated media intelligent generation, production and analysis system based on meteorological fusion data. Background Art

[0002] By integrating the advantages of traditional media and new media in content production, dissemination methods and technical means, integrated media constructs a communication mechanism with unified content, multi-channel distribution and terminal adaptation. With the progress of information technology, integrated media has been widely used in fields such as news dissemination, government affairs release, education and culture, and has gradually extended to the field of traffic management to support dynamic traffic information services and driving guidance.

[0003] In the field of transportation, integrated media has been used to assist drivers in obtaining real-time traffic conditions. For example, it provides route suggestions and risk warnings to vehicle users in the forms of pictures, texts, voices, videos, etc. Specifically, when a vehicle enters a construction section, an accident-prone area, an area affected by bad weather or a congested section, the traffic management system can generate corresponding integrated media content based on the traffic data collected in real time, and push it through in-vehicle terminals, mobile devices or roadside information systems, aiming to improve the driver's recognition ability of emergencies, assist them in making timely and reasonable driving decisions, thereby alleviating local traffic pressure and reducing safety risks.

[0004] However, the existing integrated media applied to traffic uses fixed templates to generate content, and does not fully consider the differentiated control requirements faced by vehicle users in different driving environments, resulting in a low matching degree between the pushed content and the actual scenario, and affecting the guiding effect. In addition, there is generally a lack of a feedback mechanism after the content is pushed, and it is impossible to effectively collect the behavioral response data of users, making it difficult to quantitatively evaluate and dynamically optimize the push strategy, which limits the accuracy and practicality of integrated media in traffic information services. Summary of the Invention

[0005] In view of this, the present invention aims to propose an integrated media intelligent generation, production and analysis system based on meteorological fusion data, which effectively solves the problems existing in the prior art by improving the content generation of the existing integrated media applied to the traffic field and adding push feedback.

[0006] The object of the present invention can be achieved by the following technical solutions: An integrated media intelligent generation, production and analysis system based on meteorological fusion data, comprising: a meteorological and traffic fusion module: collecting meteorological data and traffic data of different road sections of expressways in real time, and generating a grid-based spatio-temporal fusion data set according to road section units.

[0007] A risk area division module: dynamically dividing risk areas based on the current data and historical trend data in the fusion data set, including high-risk areas and medium-risk areas.

[0008] Converged media generation module: preset converged media content templates corresponding to heavy risk and medium risk, among which the heavy risk template is configured with diversion converged media elements, and the medium risk is configured with speed limit converged media elements. Then, the converged media content template is matched according to the risk area type, and the corresponding converged media elements are extracted from the fusion data set of the risk area to fill the template and generate converged media content.

[0009] Integrated media push module: pushes matching integrated media content to vehicle users within the geographic fence range according to the geographic fence range and vehicle location of the risk area.

[0010] Push feedback adjustment module: evaluates the effectiveness of diversion or speed limit control after push, and adjusts the frequency of integrated media push accordingly.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present invention introduces multi-source fusion data to integrate real-time traffic operation status and meteorological monitoring information to construct a quantitative assessment model of driving risk level, and realizes a converged media content generation mechanism based on differentiated risk levels, so that the generated converged media content can accurately match the traffic control needs under different risk scenarios, improve the pertinence and practicality of information release, thereby enhancing the driver's response ability to complex road conditions and optimizing the guidance effect.

[0012] 2. After pushing the integrated media content for actual driving scenarios, the present invention realizes dynamic monitoring and feedback of user behavior responses by constructing a post-push management and control efficiency evaluation mechanism. This mechanism can not only quantitatively evaluate the actual guiding effect of the pushed content on the driver's behavior, but also provide data support for the optimization of subsequent content generation strategies and push timing, thereby continuously improving the accuracy and adaptability of traffic information services. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0014] Figure 1 It is a schematic diagram of the connection of system modules in the present invention.

[0015] Figure 2 The invention sets a time window for obtaining weather data and traffic time operation diagrams of road sections.

[0016] Figure 3 This is an operation diagram for evaluating the diversion or speed limit control efficiency after push in the present invention. DETAILED DESCRIPTION

[0017] 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 only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] Please refer to Figure 1 As shown in the figure, a media integration intelligent generation and production analysis system based on meteorological fusion data includes a meteorological and traffic integration module, a risk area division module, a media integration generation module, a media integration push module, and a push feedback adjustment module. The modules are connected end to end and communicate with each other to form a closed-loop driven information processing and optimization process, realizing the full-process intelligent management from environmental perception, risk assessment, content generation, precise push to effect feedback.

[0019] The meteorological and traffic integration module collects meteorological data and traffic data of different road sections on the highway in real time and generates a grid-based spatio-temporal fusion data set according to road section units.

[0020] It should be noted that the media integration intelligent generation method involved in the present invention mainly faces the highway traffic management scenario. Due to the characteristics of long routes, long driving distances, and high passing speeds on highways, their operating environments are easily affected by meteorological changes and traffic fluctuations, resulting in a significant increase in driving risks. By generating and pushing adaptable media integration content based on real-time data, it can effectively improve the driver's perception and response ability to dynamic road conditions, enhance the timeliness and guiding effect of traffic control, and has high practical value in actual applications.

[0021] The prerequisite operation for implementing the above modules is to divide different road sections on the highway. This is because highways have characteristics such as long routes and complex passing environments, making it difficult to achieve global traffic control through a unified strategy. At the same time, the driving risks faced in actual operation usually show obvious local characteristics and are concentrated in specific road sections. Therefore, the refined road section division of the highway can provide basic support for subsequent risk identification, media integration content generation, and precise push based on road section units.

[0022] The specific process of road section division is as follows: Extract the spatial position information of traffic monitoring devices and meteorological monitoring devices from the highway infrastructure layout map.

[0023] As an example of the above solution, the traffic monitoring device is a traffic camera, and the meteorological monitoring device is a meteorological radar.

[0024] It should be noted that traffic monitoring devices are usually deployed on highways to monitor the number of vehicles, vehicle speeds, and densities on the road in real time, helping the management department understand the current traffic conditions, detect congestion or accidents in a timely manner. At the same time, meteorological monitoring devices are also deployed to monitor the weather changes along the highway in real time, including key parameters such as rainfall, wind speed, and visibility, providing timely meteorological warning information for drivers.

[0025] Define the section between adjacent traffic monitoring devices as a candidate road unit.

[0026] Analyze the meteorological monitoring coverage status of each candidate road unit. If there is a meteorological monitoring device installed inside the current candidate road unit, determine it as an independent road section; if there is no meteorological monitoring device installed, extend and merge it with its adjacent candidate road unit, and continue to judge whether the extended section contains a meteorological monitoring device installation point.

[0027] When the extended section contains a meteorological monitoring device, take the extended section as a finally determined road section, otherwise continue to extend until the conditions are met or the preset maximum extension length is reached.

[0028] Determine the corresponding traffic monitoring devices and meteorological monitoring devices for each road section according to the above division results.

[0029] The above road section division fully considers the spatial consistency problem of meteorological data collection ability and traffic monitoring coverage rate. By taking traffic cameras as the basis for division granularity and dynamically merging in combination with the layout of meteorological radars, it is ensured that each road section has a complete meteorological and traffic data collection ability.

[0030] It should be pointed out in the above road section division scheme that when there is no meteorological radar in a candidate road unit, it is extended to adjacent units until a position containing a meteorological radar is found. This approach not only considers the actual distribution of devices but also maximizes the use of existing resources, avoiding inaccurate risk assessment caused by the lack of meteorological data.

[0031] It should be explained that since meteorological monitoring devices are usually not evenly distributed on highways, there are cases where some candidate road units and their extended areas still cannot cover meteorological monitoring points. Without restricting the extension length, it may lead to overly large road section divisions, losing the meaning of local refined management. Therefore, in the present invention, by setting a maximum extension length threshold, it is ensured that the road section division can reasonably match meteorological monitoring resources in space, and at the same time maintain the controllability of section granularity and the effectiveness of management, avoiding problems such as fuzzy environmental perception, response delay, and decreased information adaptability caused by overly long road sections.

[0032] It should be further explained that when the preset maximum extension length is reached and the meteorological monitoring equipment layout points are still not included, the meteorological data for the road section can be estimated and interpolated using data from adjacent areas to fill the information gap. Specifically, the existing meteorological monitoring data of adjacent road sections are used, and a spatial interpolation algorithm is adopted to generate the estimated meteorological parameters for this road section.

[0033] Among them, spatial interpolation is a mature technology in the existing technology and will not be elaborated here.

[0034] See Figure 2 As shown, after the road section division, the specific content of the meteorological traffic integration module is as follows: within a set time window, vehicle driving images are collected at fixed time intervals through the traffic monitoring equipment of each road section to form a continuous sequence of image frames.

[0035] It should be pointed out that the reason for setting the above time window is as follows: The data at a single time point can only reflect the traffic conditions at a certain moment. Such instantaneous data lacks representativeness of the overall traffic state and is prone to misjudgment. At the same time, road traffic is random and volatile. The data at a single time point may be affected by accidental events, generating large noise and deviations, and unable to accurately reflect the true operating state of the road section. To ensure the statistical stability of the data analysis results, it is necessary to collect a sufficient number of sample data within a time window, which can smooth short-term fluctuations, reveal a more stable traffic state, and provide a reliable basis for subsequent risk assessment and decision support.

[0036] In the further implementation of the above solution, the setting of the time window can be dynamically adjusted based on the time dimension. For example, during the traffic peak period, due to the large traffic flow and frequent changes in the operating state, to improve the timeliness of data collection and analysis, the time window can be set to 5 minutes; while during the non-peak period, the traffic flow is relatively stable, and the system's requirement for real-time response is reduced. At this time, the time window can be extended to 10 minutes to balance data stability and system processing efficiency.

[0037] Based on each frame of the collected vehicle driving images, the number of vehicles is counted, and the ratio to the length of the corresponding road section is calculated to obtain the instantaneous traffic flow density corresponding to each frame of the image.

[0038] For the instantaneous traffic flow density of the continuous sequence of image frames, the average traffic flow density is selected as the traffic flow density of the corresponding time window.

[0039] The geometric dimensions of each individual vehicle are extracted from each frame of the collected vehicle driving images, where the geometric dimensions include length, width, and height, and compared with the geometric dimension boundary values set for large vehicles and ordinary vehicles. The number of large vehicles is counted from them to calculate the proportion of large vehicles.

[0040] In the specific implementation of the above operations, the geometric dimension limit values of large vehicles and ordinary vehicles can refer to the vehicle classification standard.

[0041] Select the average value of the large vehicle proportion in the continuous image frame sequence as the large vehicle proportion in the corresponding time window.

[0042] By positioning the driving positions of each individual vehicle in the continuous image frame sequence, the displacement distance between adjacent frames of the individual vehicle can be obtained, and these displacements are accumulated to obtain the driving distance of the vehicle.

[0043] Calculate the average driving distance of all vehicles per unit time to obtain the vehicle driving speed in the corresponding time window.

[0044] Take the traffic flow density, vehicle driving speed, and large vehicle proportion as traffic data.

[0045] It should be explained that taking the traffic flow density, vehicle driving speed, and large vehicle proportion as the traffic data of the road section is because these indicators can comprehensively and accurately reflect the operating state and safety of road traffic. Specifically, the traffic flow density is used to represent the number of vehicles on a unit length of road and is the core parameter for evaluating road congestion and traffic capacity; a higher traffic flow density means an increase in the interaction frequency between vehicles, thus increasing the possibility of conflicts or collisions and having a high potential for accident risks.

[0046] The vehicle driving speed reflects the traffic efficiency of the road and the overall dynamic characteristics of the traffic flow. A significant decrease in the average speed is usually an early signal of congestion or abnormal events, while a sharp fluctuation in speed may indicate the existence of unstable driving behaviors, thus affecting driving safety.

[0047] The large vehicle proportion reflects the influence degree of large vehicles on the overall traffic flow in the road section. Due to the characteristics of large vehicles such as large volume, slow acceleration, and long braking distance, the higher their proportion in the traffic flow, the more likely it is to cause a decrease in local traffic efficiency and pose a potential threat to the safety of surrounding small vehicles.

[0048] Synchronously collect visibility, precipitation intensity, and wind speed through the meteorological monitoring equipment of each road section, and perform mean processing on the data of multiple consecutive time points as meteorological data.

[0049] It should be explained that the above-mentioned traffic data and meteorological data collected continuously within the set time window are taken as the traffic data and meteorological data of the corresponding time window because the average value can reflect the overall traffic and meteorological conditions within a time period, avoiding data deviations caused by abnormally high or low values ​​at individual moments, and helping to filter out fluctuations in a short period of time, making the data analysis results more stable and reliable. However, relying solely on the average value may not fully and accurately characterize the real change characteristics of the data. In another exemplary implementation, a differential fluctuation analysis mechanism can also be introduced to monitor the data change amplitude of adjacent time points, identify and select relatively stable time period data as representative values.

[0050] The meteorological data and traffic data are aligned according to the timestamps of the time window to eliminate the collection time difference.

[0051] The processed data are superimposed on the electronic map of the highway to generate a gridded spatiotemporal fusion dataset based on road section units.

[0052] It should be noted that highways have the characteristics of long routes, complex structures, and significant regional differences. By combining the time-aligned meteorological data and traffic data of each road section with the electronic map, the abstract data can be matched one-to-one with the specific spatial location, ensuring that the meteorological and traffic conditions of each section of road can be accurately identified and independently analyzed, thereby achieving refined management based on road section units.

[0053] The risk area division module dynamically divides risk areas based on current data and historical trend data in the fused data set, including heavy risk areas and medium risk areas.

[0054] Preferably, the risk area division module is implemented as follows: the meteorological risk is defined as the result obtained by normalizing the deviations between various meteorological data and corresponding safety thresholds and averaging them according to preset weights.

[0055] In a specific preferred implementation, the definition of meteorological risk is based on the following steps: calculating the deviation between each meteorological data and its corresponding safety threshold.

[0056] The deviation values ​​are normalized so that all data are in the same dimension for easy comparison and weighting.

[0057] Assign preset weights to each piece of weather data based on its importance to driving safety.

[0058] The normalized deviation values ​​are weighted averaged according to the preset weights to obtain the final meteorological risk value.

[0059] The above definition of meteorological risk takes into account the impacts of various key meteorological factors, avoiding the risk assessment deviation that may be caused by relying solely on a single indicator, and enhancing the comprehensiveness and accuracy of risk identification. At the same time, by introducing a weighting mechanism to assign different weights according to the actual impact degree of different meteorological factors on traffic safety, the calculated result of risk can more truly reflect the dangerous degree of the actual driving environment.

[0060] Exemplarily, the safety threshold in the above operations can be determined according to the relevant standards and specifications issued by national or local traffic management departments.

[0061] The weight setting of the above meteorological data can be obtained based on historical data analysis. Exemplarily, the accident proportion of various meteorological conditions in traffic accidents caused by meteorological factors can be statistically analyzed as the weight basis of this type of meteorological data. This method can reflect the actual impact degree of different meteorological factors on traffic safety, making the calculation of meteorological risk more objective and practical.

[0062] The traffic risk degree is defined similarly with reference to the above.

[0063] Extract the meteorological and traffic data of each road segment in the current time window from the grid-based spatio-temporal fusion dataset, and compare them with their respective safety thresholds respectively. According to the above definition method, calculate the meteorological risk degree and traffic risk degree of each road segment in this time window.

[0064] Extend the current time window forward by several historical time windows to form a continuous time series as the historical observation window.

[0065] As an example, several historical time windows can be 3.

[0066] Draw the risk evolution curves of the meteorological risk degree and traffic risk degree of each road segment in the historical observation window corresponding to the current time window, and then use the overall slope of the risk evolution curve as the meteorological risk trend and traffic risk trend.

[0067] The above-drawn risk evolution curve is drawn with the time window as the horizontal axis and the meteorological risk degree and traffic risk degree as the vertical axis.

[0068] It should be noted that the risk evolution curve reflects the change process of the risk value over time, and its local slope may fluctuate greatly at different time points. To accurately depict the overall change trend, a representative overall slope index needs to be extracted. For this purpose, usually perform linear fitting or trend smoothing processing on the original risk evolution curve, and then use the slope of this fitted straight line as the risk trend.

[0069] It should be noted that the overall slope of the risk evolution curve has a clear trend indication meaning: its sign reflects the change direction of the risk during the observation time period. When the overall slope is positive, it indicates that the risk shows an upward trend; when the overall slope is close to zero, it means that the risk level is in a maintained state; and when the overall slope is negative, it means that the risk is gradually decreasing.

[0070] The meteorological risk degree and traffic risk degree of each road section in the current time window are fused and calculated to obtain the comprehensive risk degree.

[0071] In the above, the meteorological risk degree and traffic risk degree can be fused and analyzed to obtain a comprehensive risk degree index for characterizing the overall driving risk level in the current road environment. Exemplarily, this fusion process can be achieved by using the weighted average method, that is, setting the weight values of meteorological factors and traffic factors on driving risk. Considering that meteorological conditions have an auxiliary impact on traffic safety in most cases, while the traffic flow state is the main factor determining the road driving risk, therefore, in this example, the weight of the meteorological risk degree is set to 0.4, and the weight of the traffic risk degree is set to 0.6.

[0072] According to the comprehensive risk degree and the corresponding meteorological risk trend and traffic risk trend, a multi-dimensional decision rule is adopted for risk area division: if the comprehensive risk degree is greater than the warning value, and at least one risk trend is in a maintained or upward state, then this road section is divided into a high-risk area.

[0073] If the comprehensive risk degree is greater than the warning value, but all risk trends are in a downward state, then this road section is divided into a medium-risk area.

[0074] The above risk division of road sections breaks through the traditional static risk assessment method and introduces a risk evolution trend analysis mechanism, enabling the system to not only identify the current risk level but also predict the future development trend. This dual judgment logic of trend + current value can effectively improve the scientificity of risk identification, especially applicable to scenarios where traffic situations change rapidly.

[0075] It should be pointed out that the above risk area division mechanism is only triggered when the comprehensive risk degree exceeds the set warning value. For the situation where the comprehensive risk degree is lower than the warning value:

[0076] When the risk trend is in a downward state, it indicates that the current operating environment is in a safe and controllable state and does not need to be included in the risk area management.

[0077] When the risk trend is in a maintained or upward state, although the current comprehensive risk degree has not reached the warning level, due to the dynamic calculation method of risk assessment based on the time window, there is a trend of continuous risk accumulation and it may break through the warning threshold in subsequent time windows.

[0078] Therefore, in this situation, although it is not immediately classified as a risk area, the system will continuously monitor its evolution trend. Once the risk trend further intensifies within the next time window, resulting in the comprehensive risk degree approaching or exceeding the warning value, the system will promptly identify and classify it into the medium-risk area or high-risk area within that time window, thus realizing the dynamic update and early warning of the risk area.

[0079] The media integration generation module is used to preset media integration content templates corresponding to high risks and medium risks. Among them, the high-risk template is configured with traffic diversion media elements, and the medium-risk template is configured with speed limit media elements. Then, according to the risk area type, the media integration content template is matched, and the corresponding media integration elements are extracted from the integrated dataset of the risk area to fill the template and generate media integration content.

[0080] It should be noted that in the media integration content templates corresponding to high risks and medium risks, the high-risk template is configured with traffic diversion media elements. This is because the high-risk area usually indicates that there are relatively high safety hazards on the current road section. In this case, the most effective countermeasure is to divert and guide vehicles to avoid high-risk areas through traffic diversion, so as to reduce the possibility of accidents and relieve the local traffic pressure. Therefore, configuring the high-risk template with traffic diversion media elements can effectively guide drivers to take avoidance measures in advance and reduce the accident rate.

[0081] Configuring speed limit media elements in the medium-risk area is based on the judgment that although there are certain driving risks in this type of area, the overall traffic operation state is still within a controllable range. In this case, the main control objective is to guide vehicles to reduce their driving speed through traffic guidance, so as to slow down the further evolution of potential risks and ensure the safe passage of vehicles under the current environmental conditions; in addition, if the same traffic diversion measures as those in the high-risk area are taken in the medium-risk area, it may cause additional traffic pressure on the surrounding road network, especially may exacerbate the traffic burden on the alternative paths near the high-risk area, and then affect the traffic efficiency and safety level of the overall road network.

[0082] The traffic diversion media elements include the real-time traffic data and meteorological data of the diversion point location and diversion path, and the speed limit media element is the speed limit value displayed according to the real-time comprehensive risk degree.

[0083] Preferably, the implementation of extracting the corresponding media integration elements from the integrated dataset of the risk area to fill the template and generate media integration content is as follows: load the corresponding media integration content template according to the risk area divided by each road section in the current time window, and locate the position of the media integration element placeholder to be injected in the media integration template.

[0084] For the road section classified as the high-risk area, search for the nearest entrance and exit in the driving direction on the highway electronic map with this area as the center as the diversion point.

[0085] It should be noted that due to the characteristics of one-way traffic and fully enclosed road structure on expressways, vehicles cannot turn around or cross the road at will, and path changes can only be achieved through entrances and exits set at specific locations. Therefore, to ensure the practical operability and guiding effectiveness of the diversion strategy, the system only considers the entrances and exits within the downstream range of the driving direction that are available for leaving or turning as diversion points. This design conforms to the operating characteristics of expressway traffic flow and can effectively guide vehicles to leave high-risk sections under the premise of legality and safety, improving the implementation and practicality of traffic management measures.

[0086] Based on the current road section and the location of the diversion point, plan the diversion path, and start the traffic monitoring equipment and meteorological monitoring equipment arranged along the diversion path to collect traffic data and meteorological data of relevant sections in real time.

[0087] In the above implementation, the diversion path planning uses a path planning algorithm to calculate the passing path between the current section and the diversion point. This path planning process comprehensively considers the real-time traffic state to ensure that the generated diversion path has high passing efficiency and safety.

[0088] It should be noted that the path planning algorithm is an existing and mature technology in the field of intelligent transportation. Therefore, the present invention will not elaborate on the specific implementation details of path planning here.

[0089] For the road sections classified as medium-risk areas, call the preset mapping rules based on the comprehensive risk degree of the sections to dynamically generate speed limit recommendation values adapted to the current risk level.

[0090] It should be added that the prerequisite for the implementation of the above scheme is to pre-construct the mapping rules between the comprehensive risk degree and the speed limit value. Exemplarily, the mapping rules can be constructed in the following way:

[0091] Collect the historical traffic data and corresponding meteorological data of the expressway, and perform data cleaning and time alignment processing on the data to ensure data quality and consistency.

[0092] Based on the definitions of traffic risk degree, meteorological risk degree, and comprehensive risk degree, calculate the comprehensive risk degree of the historical data to obtain comprehensive risk degree labels in different scenarios.

[0093] Statistically analyze the actual driving speed distribution of vehicles under each historical comprehensive risk degree label, and combine with safety events such as whether traffic accidents and congestion occur to identify the reasonable speed range that can still ensure driving safety at a specific risk level.

[0094] Extract the safe passing speeds corresponding to different comprehensive risk degree intervals through cluster analysis to construct the mapping rules between the comprehensive risk degree and the speed limit value.

[0095] Embed the refined converged media elements into the corresponding placeholder positions in the converged media template to generate complete converged media content.

[0096] It should be pointed out that after the refined converged media elements are embedded into the corresponding placeholder positions in the converged media template, content rendering and format packaging operations are also performed to generate a complete converged media content instance.

[0097] For example, the content rendering performed for the diversion integrated media element extracted from the high-risk area may be a dynamic diversion route animation, and the content rendering performed for the speed limit integrated media element extracted from the medium-risk area may be a dynamic speed limit icon.

[0098] The present invention realizes the automatic generation of integrated media content for different risk scenarios by constructing an intelligent linkage mechanism between risk areas, content templates and dynamic data. The system can not only quickly match content templates according to risk assessment results, but also extract key elements from the fused data set for dynamic filling, thereby improving the intelligence level of traffic warning information release and user perception effect.

[0099] The integrated media push module is used to push matching integrated media content to vehicle users within the geographic fence range according to the geographic fence range and vehicle location of the risk area.

[0100] The content of the above module is implemented as follows: the boundary of the risk area is extracted from the electronic map of the highway as the geographic fence range.

[0101] Obtain the real-time geographic location coordinates of the vehicle through the mobile terminal.

[0102] The geographic location coordinates are compared with the geographic fence range to identify the vehicle user currently located within the geographic fence range of the risk area.

[0103] For the identified target vehicle users, the integrated media content matching the risk area is pushed to their mobile terminals at the initially set frequency.

[0104] In the above example, the initial frequency is to push once every 5 minutes.

[0105] It is important to know that the on-board terminals currently equipped in vehicles and the mobile terminals held by users are all integrated with positioning modules that can collect the vehicle's geographic location information in real time. This location data can be uploaded to the traffic management system via a wireless communication network to achieve dynamic perception of the vehicle's operating status. Through this mechanism, the traffic management system can accurately grasp the spatial distribution and driving trajectory of vehicles in the road network, and combine it with geographic fencing technology to identify vehicle users in specific risk areas, and accordingly push to them integrated media content that matches the current road environment.

[0106] The push feedback adjustment module is used to evaluate the shunt or speed limit control effectiveness after pushing, and accordingly adjust the media integration push frequency.

[0107] Preferably, for the above module, refer to Figure 3 As shown, to evaluate the shunt or speed limit control effectiveness after pushing, refer to the following process: Track the driving trajectories of vehicle users in the high-risk area, and compare with the recommended shunt routes to determine whether the vehicle users have followed the shunt paths. Calculate the proportion of vehicles that choose the recommended shunt paths based on the statistical results as the control effectiveness.

[0108] In specific implementation: Tracking the driving trajectories of vehicle users in the high-risk area can obtain the real-time position information of the vehicle through the positioning module of the on-vehicle terminal or mobile device to form the driving trajectory.

[0109] Monitor the driving speed of vehicle users in the medium-risk area, and compare with the dynamically pushed speed limit value to determine whether the vehicle has complied with the speed limit. Statistically calculate the proportion of vehicles that comply with the speed limit as the control effectiveness.

[0110] Further preferably, for the above module, the media integration push frequency is adjusted as follows: Compare the shunt or speed limit control effectiveness with the configured effective threshold within the set time period after pushing. If the shunt or speed limit control effectiveness does not reach the effective threshold, increase the push frequency; otherwise, maintain the initial frequency.

[0111] Exemplarily, the effective threshold of the control effectiveness is 0.5.

[0112] Specifically, increasing the push frequency can be adjusted according to the proportion of the initial frequency. Exemplarily, push once every 2.5 minutes.

[0113] It should be emphasized that the length of the set time period after pushing is usually dynamically associated with the actual duration of the risk area. Since the division of the risk area is based on the periodic analysis of real-time traffic and meteorological data within the set time window, the length of this set time period should be less than or equal to the length of the time window to ensure effective information intervention and driving behavior guidance for target users within the validity period of the current risk state.

[0114] The core purpose of the above operations is: within the time range when the risk area has not changed, continuously evaluate the control effectiveness of the media integration push, and dynamically adjust the push strategy according to the evaluation results, so as to improve the effectiveness of information guidance. Once the status of the risk area changes, the system will immediately terminate the evaluation of the media integration push effectiveness and the adjustment operation of the push frequency for this section of the road to avoid sending redundant information to users who have left the risk environment, thus ensuring the rationality of system resource scheduling and the timeliness and pertinence of information push.

[0115] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0116] Those of ordinary skill in the art will realize that the modules of each example described in connection with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0117] As described above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0118] Finally, the above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A media intelligent generation, production and analysis system based on meteorological fusion data, characterized by ,include: Meteorological and traffic fusion module: collects meteorological and traffic data of different sections of highways in real time, and generates gridded spatiotemporal fusion data sets by section units; Risk area division module: Dynamically divide risk areas based on current data and historical trend data in the fused data set, including heavy risk areas and medium risk areas; Converged media generation module: preset converged media content templates corresponding to heavy risk and medium risk, where the heavy risk template is configured with diversion converged media elements, and the medium risk template is configured with speed limit converged media elements, and then the converged media content template is matched according to the risk area type, and the corresponding converged media elements are extracted from the fusion data set of the risk area to fill the template and generate converged media content; Integrated media push module: pushes matching integrated media content to vehicle users within the geographic fence according to the geographic fence range and vehicle location of the risk area; Push feedback adjustment module: evaluates the effectiveness of diversion or speed limit control after push, and adjusts the frequency of integrated media push accordingly.

2. The intelligent generation, production and analysis system of integrated media based on meteorological fusion data according to claim 1, characterized in that: The different road sections of the expressway refer to the following division process: Extract the spatial location information of traffic monitoring equipment and meteorological monitoring equipment from the highway infrastructure layout map; The road segments between adjacent traffic monitoring devices are defined as candidate road units; The meteorological monitoring coverage status of each candidate road unit is analyzed. If the current candidate road unit is equipped with meteorological monitoring equipment, it is determined as an independent road section. If no meteorological monitoring equipment is installed, it is extended and merged to the adjacent candidate road units, and it is further determined whether the extended road section contains a meteorological monitoring equipment installation point. When the extended road section contains meteorological monitoring equipment, the extended road section is regarded as a final road section, otherwise, the extension continues until the conditions are met or the preset maximum extension length is reached; The traffic monitoring equipment and meteorological monitoring equipment corresponding to each road section are determined based on the above division results.

3. The intelligent generation, production and analysis system of integrated media based on meteorological fusion data according to claim 2, characterized in that: The meteorological data and traffic data are collected as follows: The traffic monitoring equipment of each road section collects vehicle driving images at fixed time intervals within a set time window to form a continuous image frame sequence; The number of vehicles is counted based on each frame of vehicle driving image collected, and compared with the length of the corresponding road segment to obtain the instantaneous traffic density corresponding to each frame of image; The average traffic density of the instantaneous traffic density of the continuous image frame sequence is selected as the traffic density of the corresponding time window; Extract the geometric dimensions of each single vehicle from each frame of the collected vehicle driving image, and compare them with the set geometric dimension limit values ​​of large vehicles and ordinary vehicles, and count the number of large vehicles to calculate the proportion of large vehicles; The average of the proportion of large vehicles in the continuous image frame sequence is selected as the proportion of large vehicles in the corresponding time window; By locating the driving position of each single vehicle in the continuous image frame sequence, the displacement distance of the single vehicle between adjacent frames is obtained, and these displacements are accumulated to obtain the driving distance of the vehicle; Calculate the average distance traveled by all vehicles in unit time to obtain the vehicle speed in the corresponding time window; The traffic density, vehicle speed and the proportion of large vehicles are used as traffic data; Visibility, precipitation intensity and wind speed are collected synchronously by meteorological monitoring equipment on each road section, and the data at multiple consecutive time points are averaged as meteorological data.

4. The intelligent generation, production and analysis system of integrated media based on meteorological fusion data according to claim 1, characterized in that: The specific steps of generating a gridded spatiotemporal fusion data set according to road section units are as follows: Align meteorological data and traffic data according to timestamps to eliminate collection time differences; The processed data are superimposed on the electronic map of the highway to generate a gridded spatiotemporal fusion dataset based on road section units.

5. The intelligent generation, production and analysis system of integrated media based on meteorological fusion data according to claim 1, characterized in that: The risk area division module is implemented as follows: The meteorological risk is defined as the result of normalizing the deviation between each meteorological data and the corresponding safety threshold and averaging it according to the preset weights. The traffic risk is defined in the same way. Extract the weather and traffic data of each road segment in the current time window from the gridded spatiotemporal fusion data set, compare them with their respective safety thresholds, and calculate the weather risk and traffic risk of each road segment in the time window according to the above definition method; Extend the current time window forward by several historical time windows to form a continuous time series as a historical observation window; The risk evolution curve is drawn for the meteorological risk and traffic risk of each road section in the historical observation window corresponding to the current time window, and the overall slope of the risk evolution curve is used as the meteorological risk trend and traffic risk trend; The meteorological risk and traffic risk of each road section in the current time window are integrated and calculated to obtain a comprehensive risk; According to the comprehensive risk level and the corresponding meteorological risk trend and traffic risk trend, multi-dimensional decision rules are used to divide the risk area: If the comprehensive risk level is greater than the warning value, and at least one risk trend is maintaining or increasing, the road section is classified as a severe risk area; If the comprehensive risk level is greater than the warning value, but all risk trends are decreasing, the road section will be classified as a medium-risk area.

6. The intelligent generation, production and analysis system of integrated media based on meteorological fusion data according to claim 5, characterized in that: The diversion integrated media element includes the diversion point location and real-time traffic data and meteorological data of the diversion path, and the speed limit integrated media element displays the speed limit value based on the real-time comprehensive risk level.

7. The intelligent generation, production and analysis system of integrated media based on meteorological fusion data according to claim 6, characterized in that: The extraction of corresponding integrated media elements from the fused data set of the risk area to fill the template and generate integrated media content is implemented as follows: Load the corresponding integrated media content template according to the risk area divided by each road section in the current time window, and locate the placeholder position of the integrated media element to be injected from the integrated media template; For road sections classified as heavy risk areas, the nearest entrance and exit in the driving direction is searched in the electronic map of the expressway with the area as the center as the diversion point; Plan the diversion path based on the current road section and diversion point location, and activate the traffic monitoring equipment and meteorological monitoring equipment deployed along the diversion path to collect traffic data and meteorological data of the relevant road section in real time; For road sections classified as medium-risk areas, based on the comprehensive risk level of the road section, a preset mapping rule is called to dynamically generate a speed limit recommendation value adapted to the current risk level; Embed the refined converged media elements into the corresponding placeholder positions in the converged media template to generate complete converged media content.

8. The intelligent generation, production and analysis system of integrated media based on meteorological fusion data according to claim 1, characterized in that: The integrated media push module is implemented as follows: Extract the boundary of the risk area from the electronic map of the highway as the scope of the geo-fence; Obtain the real-time geographic location coordinates of the vehicle through a mobile terminal; Comparing the geographic location coordinates with the geographic fence range to identify vehicle users currently located within the risk area geographic fence range; For the identified target vehicle users, the integrated media content matching the risk area is pushed to their mobile terminals at an initially set frequency.

9. The intelligent generation, production and analysis system of integrated media based on meteorological fusion data according to claim 7, characterized in that: The evaluation of the traffic diversion or speed limit control efficiency after push is as follows: Track the driving trajectory of vehicle users in high-risk areas and compare it with the recommended diversion route to determine whether the vehicle users follow the diversion path. Calculate the proportion of vehicles that choose the recommended diversion path based on the statistical results as the control efficiency; The driving speed of vehicle users in medium-risk areas is monitored and compared with the pushed dynamic speed limit value to determine whether the vehicle complies with the speed limit. The proportion of vehicles complying with the speed limit is counted as the management and control effectiveness.

10. The intelligent generation, production and analysis system of integrated media based on meteorological fusion data according to claim 8, characterized in that: The adjustment of the frequency of integrated media push is implemented as follows: During the set period after push, the diversion or speed limit control efficiency is compared with the configured effective threshold. If the diversion or speed limit control efficiency does not reach the effective threshold, the push frequency is increased, otherwise the initial frequency is maintained.

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