Foggy weather highway safety monitoring system and method based on meteorological cloud big data

Abstract

The invention relates to the technical field of traffic monitoring, in particular to a foggy weather highway safety monitoring system and method based on meteorological cloud big data. The foggy day high-speed safety monitoring system based on meteorological cloud big data comprises a data acquisition unit, a data processing unit and a monitoring response unit. According to the invention, the data of the meteorological cloud platform and the data collected by the expressway field sensor are integrated and fused, so that a more accurate and reliable fused visibility optimal estimation value is obtained, the data quality of the whole monitoring system is improved, and meanwhile, a multi-level comparison mode is adopted, so that the real-time performance of the monitoring system is improved. According to the method, more accurate and effective safety guarantee measures are provided for highway driving in foggy days, in addition, secondary comparison is carried out based on the first result, the comparison amount is reduced, and the early warning response speed of the system is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic monitoring, and particularly to a foggy-day highway safety monitoring system and method based on meteorological cloud big data. Background Art

[0002] With the booming development of the transportation industry, the highway network has become increasingly dense and the traffic flow has been continuously increasing. However, fog, as a common and extremely dangerous meteorological condition, has posed a severe challenge to highway traffic safety. Fog can significantly reduce visibility, making it difficult for drivers to clearly observe road conditions and judge vehicle distances, thus greatly increasing the probability of traffic accidents such as rear-end collisions, seriously threatening people's lives and property safety as well as the normal operation order of highways. Therefore, it is urgent to effectively monitor foggy-day highways for safety.

[0003] Based on this, the present invention provides a foggy-day highway safety monitoring system and method based on meteorological cloud big data to solve the above-mentioned technical problems. Summary of the Invention

[0004] The purpose of the present invention is to provide a foggy-day highway safety monitoring system and method based on meteorological cloud big data, which integrates the data of the meteorological cloud platform and the data collected by on-site sensors on the highway, fuses the two, so as to obtain a more accurate and reliable optimal estimated value of the fused visibility, improves the quality of the data of the entire monitoring system. At the same time, a multi-level comparison method is adopted to provide more accurate and effective safety guarantee measures for driving on foggy-day highways. In addition, based on the first result, a secondary comparison is carried out, reducing the amount of comparison and improving the early warning response speed of the system.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: The first aspect of the present invention: provides a foggy-day highway safety monitoring system based on meteorological cloud big data, including a data acquisition unit, a data processing unit and a monitoring response unit, wherein: The data acquisition unit is used to obtain the data information of the meteorological cloud platform and the data information collected by on-site sensors in the monitoring area section of the highway; The data processing unit is used to fuse the data information of the meteorological cloud platform and the data information collected by on-site sensors, and compare the obtained optimal estimated value of the fused visibility with a preset driving speed threshold to determine whether to issue a deceleration warning instruction. The data processing unit is connected to the data acquisition unit; The monitoring response unit is used to issue a deceleration warning message and upload preset information. The monitoring response unit is connected to the data processing unit.

[0006] The further setting of the present invention is that the data acquisition unit includes a meteorological cloud data module, a field acquisition module, and a first communication module, where: The meteorological cloud data module uses the meteorological cloud platform to obtain and store the meteorological data of the highway section; The field acquisition module uses the field sensors arranged on the highway section to collect the data information of the corresponding highway section; The first communication module is used to realize the information interaction between the data acquisition unit and the data processing unit, and the first communication module is connected to both the meteorological cloud data module and the field acquisition module.

[0007] The further setting of the present invention is that the data processing unit includes a second communication module, a data extraction module, and a data fusion module, where: The second communication module is used to realize the information interaction between the data processing unit and the data acquisition unit and the monitoring response unit; The data extraction module is used to extract the corresponding visibility, humidity, and wind speed information from the data information of the meteorological cloud platform and the data information collected by the field sensors, and the data extraction module is connected to the second communication module; The data fusion module is used to fuse the data information of the meteorological cloud platform and the data information collected by the field sensors. The data fusion module is connected to the data extraction module, and the fusion process is as follows: Based on the data information of the meteorological cloud platform, construct the state prediction vector of the highway monitoring area section at time k , where is the state prediction vector of the meteorological cloud platform at time k, is the state transition matrix of visibility, is the state vector of the meteorological cloud platform at time k - 1, , where , and are the visibility, humidity, and wind speed of the meteorological cloud platform corresponding to the monitoring area section at time k - 1 respectively. The state transition matrix of visibility , where is the visibility weight coefficient, is the decrease in visibility per unit increase in humidity, is the increase in visibility per 1 m / s increase in wind speed; Based on the data information collected by the field sensors, construct the observation equation of the highway monitoring area section at time k , where is the observed value of visibility collected by the field sensors at time k, is the observation conversion coefficient, is the observation noise; According to the state prediction vector , obtain the state prediction visibility value at time k , and fuse the predicted visibility value and the observed value of the visibility collected by the on-site sensor . In the formula, , where is the gain coefficient, is the optimal estimated value of the fused visibility.

[0008] A further setting of the present invention is that the data processing unit further includes a database module and a comparison and warning module, where: The database module is used to store the received data, and the database module is connected to both the second communication module and the data fusion module; The comparison and warning module is used to obtain the optimal estimated value of the fused visibility at time k in the highway monitoring area section as the first visibility value, and obtain the average value of the optimal estimated values of the fused visibility at consecutive k + n times in the corresponding section as the second visibility value; compare the first driving speed threshold corresponding to the first visibility value with the vehicle speed at time k to obtain the first comparison result; based on the first comparison result, compare the second driving speed threshold corresponding to the second visibility value with the vehicle speed at time k to obtain the second comparison result; issue a warning to the high-speed driving vehicle according to the first comparison result and the second comparison result. The comparison and warning module is connected to the second communication module, the data fusion module, and the database module. Among them, the process of obtaining the second comparison result is as follows: If the vehicle speed at time k is less than the first driving speed threshold, then compare the second driving speed threshold corresponding to the second visibility value with the vehicle speed at time k to obtain the second comparison result; Otherwise, do not compare the second driving speed threshold corresponding to the second visibility value with the vehicle speed at time k; The process of issuing a warning to the high-speed driving vehicle according to the first comparison result and the second comparison result is as follows: When the first comparison result is that the vehicle speed at time k is less than the first driving speed threshold, compare the second driving speed threshold corresponding to the second visibility value with the vehicle speed at time k. If the second driving speed threshold is less than the vehicle speed at time k, no deceleration warning instruction is issued; When the first comparison result is that the vehicle speed at time k is less than the first driving speed threshold, compare the second driving speed threshold corresponding to the second visibility value with the vehicle speed at time k. If the second driving speed threshold is equal to or greater than the vehicle speed at time k, an early deceleration warning instruction is issued; When the vehicle speed at the k-th moment of the first comparison result is equal to or greater than the first driving speed threshold, an early deceleration warning instruction is issued.

[0009] The present invention is further configured such that: the monitoring response unit includes a third communication module, an information display module, a warning release module, and an information import module, wherein: The third communication module is used to realize information interaction between the monitoring response unit and the data acquisition unit and the data processing unit; The information display module is used to display warning information and the uploaded and imported information, and the information display module is connected to the third communication module; The warning release module issues an early deceleration warning information according to the received early deceleration warning instruction, and the warning release module is connected to both the third communication module and the information display module; The information import module is used to upload preset information, and the information import module is connected to both the third communication module and the information display module.

[0010] The second aspect of the present invention: There is also provided the above-mentioned foggy-day highway safety monitoring method based on meteorological cloud big data, including the following steps: Obtain the data information of the meteorological cloud platform and the data information collected by the on-site sensors in the highway monitoring area section; Fuse the data information of the meteorological cloud platform and the data information collected by the on-site sensors; Obtain the optimal estimated value of the fused visibility at the k-th moment in the highway monitoring area section as the first visibility value, and obtain the average value of the optimal estimated values of the fused visibility at the continuous k + n moments in the corresponding section as the second visibility value; Compare the first driving speed threshold corresponding to the first visibility value with the vehicle speed at the k-th moment to obtain the first comparison result; Based on the first comparison result, compare the second driving speed threshold corresponding to the second visibility value with the vehicle speed at the k-th moment to obtain the second comparison result; Issue a warning to the high-speed driving vehicle according to the first comparison result and the second comparison result.

[0011] The present invention is further configured such that: the fusion process is as follows: Based on the data information of the meteorological cloud platform, construct the state prediction vector of the highway monitoring area section at the k-th moment , where is the state prediction vector of the meteorological cloud platform at the k-th moment, is the state transition matrix of visibility, is the state vector of the meteorological cloud platform at the k - 1 moment, , where 、 and are the visibility, humidity, and wind speed of the road section in the monitoring area corresponding to the meteorological cloud platform at time k-1, respectively; Based on the data information collected by on-site sensors, an observation equation for the road section in the monitoring area of the highway at time k is constructed , where, is the observed value of the visibility collected by on-site sensors at time k, is the observation conversion coefficient, is the observation noise; According to the state prediction vector , the state prediction visibility value at time k is obtained. The predicted visibility value and the observed value of the visibility collected by on-site sensors are fused, , where, is the gain coefficient, is the optimal estimated value of the fused visibility.

[0012] The further setting of the present invention is that: the state transition matrix of the visibility , where, is the visibility weight coefficient, is the decrease in visibility when the humidity increases by 1 unit, is the increase in visibility when the wind speed increases by 1 m / s.

[0013] The further setting of the present invention is that: the process of obtaining the second comparison result is as follows: If the vehicle speed at time k is less than the first driving speed threshold, then the second driving speed threshold corresponding to the second visibility value is compared with the vehicle speed at time k to obtain the second comparison result; Otherwise, the second driving speed threshold corresponding to the second visibility value is not compared with the vehicle speed at time k.

[0014] The further setting of the present invention is that: the process of issuing a warning to the vehicle traveling at high speed according to the first comparison result and the second comparison result is as follows: When the first comparison result is that the vehicle speed at time k is less than the first driving speed threshold, the second driving speed threshold corresponding to the second visibility value is compared with the vehicle speed at time k. If the second driving speed threshold is less than the vehicle speed at time k, no deceleration warning is issued; When the first comparison result is that the vehicle speed at time k is less than the first driving speed threshold, the second driving speed threshold corresponding to the second visibility value is compared with the vehicle speed at time k. If the second driving speed threshold is equal to or greater than the vehicle speed at time k, an early deceleration warning is issued; When the vehicle speed at the k moment of the first comparison result is equal to or greater than the first driving speed threshold, an early deceleration warning is issued.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention integrates the data of the meteorological cloud platform and the data collected by the highway on-site sensors, and fuses the two to obtain a more accurate and reliable optimal estimated value of the fused visibility, effectively reducing the error that may occur due to solely relying on a certain type of data, improving the quality of the data of the entire monitoring system. At the same time, a multi-level comparison method is adopted, which first compares the optimal estimated value of the fused visibility at the current moment with the corresponding driving speed threshold, and then further compares the speed threshold corresponding to the average visibility in the subsequent time period with the vehicle speed based on this result. Furthermore, the judgment of the dynamic change trend of the fog situation can be considered, avoiding the problem of missing warnings for future time periods by only relying on the data at a single moment, providing a more accurate and effective safety guarantee measure for driving on the highway in foggy weather. In addition, based on the first result, a secondary comparison is carried out, reducing the comparison amount and improving the warning response speed of the system. Description of the Drawings

[0016] Figure 1 It is a system diagram of the foggy weather highway safety monitoring system based on meteorological cloud big data of the present invention.

[0017] Figure 2 It is a system diagram of the data acquisition unit in the foggy weather highway safety monitoring system based on meteorological cloud big data of the present invention.

[0018] Figure 3 It is a system diagram of the data processing unit in the foggy weather highway safety monitoring system based on meteorological cloud big data of the present invention.

[0019] Figure 4 It is a system diagram of the monitoring response unit in the foggy weather highway safety monitoring system based on meteorological cloud big data of the present invention.

[0020] Explanation of the Reference Numerals in the Drawings 100, data acquisition unit; 110, meteorological cloud data module; 120, on-site acquisition module; 130, first communication module; 200, data processing unit; 210, second communication module; 220, data extraction module; 230, data fusion module; 240, database module; 250, comparison and warning module; 300, monitoring response unit; 310, third communication module; 320, information display module; 330, warning release module; 340, information import module. Detailed Embodiments

[0021] Next, in combination with the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. Embodiment:

[0022] As Figures 1-4 shown, this embodiment provides a foggy day highway safety monitoring system based on meteorological cloud big data, including a data acquisition unit 100, a data processing unit 200, and a monitoring response unit 300, where: the data acquisition unit 100 is used to obtain the data information of the meteorological cloud platform in the monitoring area section of the highway and the data information collected by on-site sensors; the data processing unit 200 is used to fuse the data information of the meteorological cloud platform and the data information collected by on-site sensors, and compare the optimal estimated value of the fused visibility with a preset driving speed threshold to determine whether to issue a deceleration warning instruction. The data processing unit 200 is connected to the data acquisition unit 100; the monitoring response unit 300 is used to issue a deceleration warning message and upload preset information. The monitoring response unit 300 is connected to the data processing unit 200.

[0023] In this embodiment, it should be noted that the data acquisition unit 100 obtains the data information of the meteorological cloud platform and the data information collected by on-site sensors, and uploads the above data to the data processing unit 200. The data processing unit 200 will first extract the corresponding visibility, humidity, and wind speed information from it, and then fuse the data information of the meteorological cloud platform and the data information collected by on-site sensors to obtain fused data. The meteorological cloud platform data can grasp the large-scale fog trend, and the on-site sensor data can accurately reflect the actual situation of the section. By fusing these two different levels of data, the limitations of a single data source in describing the foggy day highway traffic conditions are overcome, making the evaluation of key factors such as visibility more accurate and comprehensive. Then, the fused data is compared with the driving speed threshold, that is, the first driving speed threshold corresponding to the first visibility value is compared with the vehicle speed at time k to determine whether the driving speed under the current visibility exceeds the safety threshold. If not, the second driving speed threshold corresponding to the second visibility value will also be compared with the vehicle speed at time k, and then it can be obtained whether the current driving speed exceeds the safety threshold under the predicted visibility situation, thus achieving a dual monitoring and warning effect, making the warning more in line with the complexity of the fog situation and vehicle speed changes in the actual foggy day driving scenario. When a warning instruction is generated, it is fed back to the monitoring response unit 300, and the monitoring response unit 300 can send a warning message to the corresponding vehicle, which can help the driver decelerate in advance and improve the safety of driving on the highway in foggy days.

[0024] In the present invention, the data acquisition unit 100 includes a meteorological cloud data module 110, a field acquisition module 120, and a first communication module 130, where: the meteorological cloud data module 110 obtains and stores meteorological data of the highway section by using a meteorological cloud platform; the field acquisition module 120 acquires data information of the corresponding highway section by using field sensors arranged on the highway section; the first communication module 130 is used to realize information interaction between the data acquisition unit 100 and the data processing unit 200, and the first communication module 130 is connected to both the meteorological cloud data module 110 and the field acquisition module 120.

[0025] In this embodiment, it should be noted that the meteorological cloud data module 110 obtains and stores meteorological data of the highway section by using the meteorological cloud platform as a data source, which can provide large-scale background information for subsequent analysis of foggy weather. The meteorological data includes, but is not limited to, data such as visibility, humidity, and wind speed. The data information of the meteorological cloud data module 110 and the field acquisition module 120 is uploaded to the data processing unit 200 through the first communication module 130.

[0026] In the present invention, the data processing unit 200 includes a second communication module 210, a data extraction module 220, and a data fusion module 230, where: the second communication module 210 is used to realize information interaction between the data processing unit 200 and the data acquisition unit 100 and the monitoring response unit 300; the data extraction module 220 is used to extract corresponding visibility, humidity, and wind speed information from the data information of the meteorological cloud platform and the data information collected by the field sensors, and the data extraction module 220 is connected to the second communication module 210; the data fusion module 230 is used to fuse the data information of the meteorological cloud platform and the data information collected by the field sensors. The data fusion module 230 is connected to the data extraction module 220. The fusion process is as follows: Based on the data information of the meteorological cloud platform, construct the state prediction vector of the highway monitoring area section at time k , where is the state prediction vector of the meteorological cloud platform at time k, is the state transition matrix of visibility, is the state vector of the meteorological cloud platform at time k - 1, , where , and are the visibility, humidity, and wind speed of the meteorological cloud platform corresponding to the monitoring area section at time k - 1 respectively. The state transition matrix of visibility , where is the visibility weight coefficient, is the decrease in visibility per unit increase in humidity, is the increase in visibility for every 1 m / s increase in wind speed; Based on the data information collected by on-site sensors, an observation equation for the highway monitoring area section at time k is constructed , where is the observed value of visibility collected by on-site sensors at time k, is the observation conversion coefficient, is the observation noise; According to the state prediction vector , the state prediction visibility value at time k is obtained. The predicted visibility value and the observed value of visibility collected by on-site sensors are fused, , where is the gain coefficient, is the optimal estimated value of the fused visibility.

[0027] In addition, the data processing unit 200 further includes a database module 240 and a comparison and warning module 250, where: the database module 240 is used to store the received data, and the database module 240 is connected to both the second communication module 210 and the data fusion module 230; the comparison and warning module 250 is used to obtain the optimal estimated value of the fused visibility for the highway monitoring area section at time k as the first visibility value, and obtain the average value of the optimal estimated values of the fused visibility for the corresponding section at consecutive times k + n as the second visibility value; compare the first driving speed threshold corresponding to the first visibility value with the vehicle speed at time k to obtain a first comparison result; based on the first comparison result, compare the second driving speed threshold corresponding to the second visibility value with the vehicle speed at time k to obtain a second comparison result; issue a warning to the high-speed driving vehicle according to the first comparison result and the second comparison result. The comparison and warning module 250 is connected to the second communication module 210, the data fusion module 230, and the database module 240. Among them, the process of obtaining the second comparison result is as follows: If the vehicle speed at time k is less than the first driving speed threshold, then compare the second driving speed threshold corresponding to the second visibility value with the vehicle speed at time k to obtain a second comparison result; Otherwise, do not compare the second driving speed threshold corresponding to the second visibility value with the vehicle speed at time k; The process of issuing a warning to the high-speed driving vehicle according to the first comparison result and the second comparison result is as follows: When the first comparison result is that the vehicle speed at time k is less than the first driving speed threshold, compare the second driving speed threshold corresponding to the second visibility value with the vehicle speed at time k. If the second driving speed threshold is less than the vehicle speed at time k, then do not issue a deceleration warning instruction; When the vehicle speed at time k in the first comparison result is less than the first driving speed threshold, compare the second driving speed threshold corresponding to the second visibility value with the vehicle speed at time k. If the second driving speed threshold is equal to or greater than the vehicle speed at time k, issue an early deceleration warning instruction. When the vehicle speed at time k in the first comparison result is equal to or greater than the first driving speed threshold, issue an early deceleration warning instruction.

[0028] In this embodiment, it should be noted that the data information of the data acquisition unit 100 is received through the second communication module 210 and uploaded to the data extraction module 220. The data extraction module 220 accurately extracts the visibility, humidity, and wind speed information closely related to fog monitoring from the massive meteorological cloud platform data information and the data information collected by the on-site sensors, and then transmits the above information to the data fusion module 230, that is, fuses the data information of the meteorological cloud platform and the data information collected by the on-site sensors, combines the advantages of both, and finally obtains the optimal estimated value of the fused visibility, making the judgment of visibility more accurate and reliable. Then, the comparison and warning module 250 determines whether to issue a warning to the high-speed vehicle by obtaining the optimal estimated value of the fused visibility at different times and comparing it with the corresponding driving speed threshold. This multi-level comparison mechanism comprehensively considers the fog situation change trend in the current and next time periods and the current vehicle speed situation, making the warning more scientific and reasonable.

[0029] In the present invention, the monitoring response unit 300 includes a third communication module 310, an information display module 320, a warning release module 330, and an information import module 340, where: the third communication module 310 is used to realize the information interaction between the monitoring response unit 300 and the data acquisition unit 100 and the data processing unit 200; the information display module 320 is used to display warning information and the uploaded and imported information, and the information display module 320 is connected to the third communication module 310; the warning release module 330 issues an early deceleration warning information according to the received early deceleration warning instruction, and the warning release module 330 is connected to both the third communication module 310 and the information display module 320; the information import module 340 is used to upload preset information, and the information import module 340 is connected to both the third communication module 310 and the information display module 320.

[0030] In this embodiment, it should be noted that the deceleration warning instruction is received through the set third communication module 310 and transmitted to the warning release module 330, and the warning release module 330 issues the corresponding warning information to remind the vehicle to decelerate. At the same time, the set information display module 320 will also display the warning information. In addition, the management personnel can also upload preset information through the information import module 340, including but not limited to preset threshold information.

[0031] In addition, this embodiment also provides the above-mentioned foggy-day highway safety monitoring method based on meteorological cloud big data, including the following steps: Step 1: Obtain the data information of the meteorological cloud platform and the data information collected by on-site sensors for the highway monitoring area section.

[0032] Step 2: Integrate the data information of the meteorological cloud platform and the data information collected by on-site sensors.

[0033] Among them, the integration process is as follows: Based on the data information of the meteorological cloud platform, construct the state prediction vector of the highway monitoring area section at time k , where is the state prediction vector of the meteorological cloud platform at time k, is the state transition matrix of visibility, is the state vector of the meteorological cloud platform at time k-1, , where , and are the visibility, humidity, and wind speed of the meteorological cloud platform corresponding to the highway monitoring area section at time k-1, respectively; Based on the data information collected by on-site sensors, construct the observation equation of the highway monitoring area section at time k , where is the observed value of visibility collected by on-site sensors at time k, is the observation conversion coefficient, is the observation noise; According to the state prediction vector , obtain the state prediction visibility value at time k, and fuse the predicted visibility value and the observed value of visibility collected by on-site sensors, , where is the gain coefficient, is the optimal estimated value of the fused visibility.

[0034] Furthermore, the state transition matrix of visibility , where is the visibility weight coefficient, is the decrease in visibility when the humidity increases by 1 unit, is the increase in visibility when the wind speed increases by 1 m / s.

[0035] In this embodiment, as an example, assume that the visibility weight coefficient is 0.8, and when the humidity increases by 1 unit, the visibility will decrease by 5 meters. is -5, and the increase in visibility for every 1 m / s increase in wind speed is 10 m, that is = 10, then the state transition matrix of visibility , if the state vector of the meteorological cloud platform at time k - 1 , then the state prediction vector at time k ; Assume that the observed value is the same as the actual displayed value, that is, the observation conversion coefficient , the observation noise is -5 m, then according to the observation equation , assume the gain coefficient is 0.5, and the optimal estimated value of the fused visibility is 150 + 0.5(145 - 1×150) = 147.5.

[0036] Step three: Obtain the optimal estimated value of the fused visibility at time k for the highway monitoring area section as the first visibility value, and obtain the average value of the optimal estimated values of the fused visibility for the corresponding section at consecutive k + n times as the second visibility value.

[0037] In this embodiment, it should be noted that by obtaining the average value of the optimal estimated values of the fused visibility for the corresponding section at consecutive k + n times, the predicted visibility for the next stage can be obtained, that is, the second visibility, which can be used for subsequent monitoring of the predicted visibility.

[0038] Step four: Compare the first driving speed threshold corresponding to the first visibility value with the vehicle speed at time k to obtain the first comparison result.

[0039] Step five: Based on the first comparison result, compare the second driving speed threshold corresponding to the second visibility value with the vehicle speed at time k to obtain the second comparison result.

[0040] Among them, the process of obtaining the second comparison result is as follows: If the vehicle speed at time k is less than the first driving speed threshold, then compare the second driving speed threshold corresponding to the second visibility value with the vehicle speed at time k to obtain the second comparison result; Otherwise, do not compare the second driving speed threshold corresponding to the second visibility value with the vehicle speed at time k.

[0041] Step six: Issue a warning to the high - speed driving vehicle according to the first comparison result and the second comparison result.

[0042] Among them, the process of issuing a warning to the high - speed driving vehicle according to the first comparison result and the second comparison result is as follows: When the vehicle speed at the k-th moment of the first comparison result is less than the first driving speed threshold, compare the second driving speed threshold corresponding to the second visibility value with the vehicle speed at the k-th moment. If the second driving speed threshold is less than the vehicle speed at the k-th moment, no deceleration warning is issued; When the vehicle speed at the k-th moment of the first comparison result is less than the first driving speed threshold, compare the second driving speed threshold corresponding to the second visibility value with the vehicle speed at the k-th moment. If the second driving speed threshold is equal to or greater than the vehicle speed at the k-th moment, an early deceleration warning is issued; When the vehicle speed at the k-th moment of the first comparison result is equal to or greater than the first driving speed threshold, an early deceleration warning is issued.

[0043] In the description of this specification, the descriptions with reference to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0044] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. The foggy highway safety monitoring system based on meteorological cloud big data is characterized by: The system comprises a data collection unit (100), a data processing unit (200) and a monitoring response unit (300), wherein: The data collection unit (100) is used to obtain data information from a meteorological cloud platform and data information collected by on-site sensors in a monitored area section of a highway; The data processing unit (200) is used to fuse the data information of the meteorological cloud platform and the data information collected by the on-site sensors, and compare the fused visibility optimal estimation value obtained by fusion with a preset driving speed threshold to determine whether to issue a deceleration warning instruction. The data processing unit (200) is connected to the data collection unit (100); The monitoring response unit (300) is used to issue deceleration warning information and upload preset information, and the monitoring response unit (300) is connected to the data processing unit (200).

2. The foggy highway safety monitoring system based on meteorological cloud big data according to claim 1 is characterized in that: The data collection unit (100) comprises a meteorological cloud data module (110), a field collection module (120) and a first communication module (130), wherein: The meteorological cloud data module (110) uses the meteorological cloud platform to acquire and store meteorological data of the expressway section; The on-site collection module (120) uses on-site sensors arranged on the expressway section to collect data information corresponding to the expressway section; The first communication module (130) is used to realize information interaction between the data collection unit (100) and the data processing unit (200), and the first communication module (130) is connected to both the meteorological cloud data module (110) and the on-site collection module (120).

3. The foggy highway safety monitoring system based on meteorological cloud big data according to claim 1 is characterized in that: The data processing unit (200) comprises a second communication module (210), a data extraction module (220) and a data fusion module (230), wherein: The second communication module (210) is used to realize information interaction between the data processing unit (200) and the data acquisition unit (100) and the monitoring response unit (300); The data extraction module (220) is used to extract corresponding visibility, humidity and wind speed information from the data information of the meteorological cloud platform and the data information collected by the on-site sensor, and the data extraction module (220) is connected to the second communication module (210); The data fusion module (230) is used to fuse the data information of the meteorological cloud platform and the data information collected by the on-site sensors. The data fusion module (230) is connected to the data extraction module (220), wherein the fusion process is as follows: Based on the data information of the meteorological cloud platform, the state prediction vector of the highway monitoring area section at time k is constructed , where is the state prediction vector of the meteorological cloud platform at time k, is the state transition matrix of visibility, is the state vector of the meteorological cloud platform at time k-1, , where , and They are the visibility, humidity and wind speed of the road section in the monitoring area corresponding to the meteorological cloud platform at time k-1. The state transition matrix of the visibility is , where is the visibility weight coefficient, is the decrease in visibility for every 1 unit increase in humidity, The increase in visibility for every 1 m / s increase in wind speed; Based on the data information collected by the on-site sensors, the observation equation of the highway monitoring area section at time k is constructed , where is the visibility observation value collected by the on-site sensor at time k, is the observation conversion coefficient, is the observation noise; According to the state prediction vector , get the state prediction visibility value at time k , the visibility value will be predicted and on-site sensors to collect visibility observations To integrate, , where is the gain coefficient, is the optimal estimate of visibility after fusion.

4. The foggy highway safety monitoring system based on meteorological cloud big data according to claim 3 is characterized in that: The data processing unit (200) further comprises a database module (240) and a comparison and early warning module (250), wherein: The database module (240) is used to store the received data, and the database module (240) is connected to both the second communication module (210) and the data fusion module (230); The comparison warning module (250) is used to obtain the optimal estimated value of the fused visibility at time k of the highway monitoring area section as the first visibility value, and to obtain the average value of the optimal estimated value of the fused visibility at consecutive k+n times of the corresponding section as the second visibility value; compare the first driving speed threshold corresponding to the first visibility value with the vehicle speed at time k to obtain a first comparison result; based on the first comparison result, compare the second driving speed threshold corresponding to the second visibility value with the vehicle speed at time k to obtain a second comparison result; and issue a warning to the high-speed vehicle based on the first comparison result and the second comparison result. The comparison warning module (250) is connected to the second communication module (210), the data fusion module (230) and the database module (240), wherein the process of obtaining the second comparison result is as follows: If the vehicle speed at time k is less than the first driving speed threshold, the second driving speed threshold corresponding to the second visibility value is compared with the vehicle speed at time k to obtain a second comparison result; Otherwise, the second driving speed threshold corresponding to the second visibility value is not compared with the vehicle speed at time k; The process of issuing a warning to a high-speed vehicle according to the first comparison result and the second comparison result is as follows: When the first comparison result is that the vehicle speed at time k is less than the first driving speed threshold, the second driving speed threshold corresponding to the second visibility value is compared with the vehicle speed at time k, and if the second driving speed threshold is less than the vehicle speed at time k, no deceleration warning instruction is issued; When the first comparison result is that the vehicle speed at time k is less than the first driving speed threshold, the second driving speed threshold corresponding to the second visibility value is compared with the vehicle speed at time k, and if the second driving speed threshold is equal to or greater than the vehicle speed at time k, an early deceleration warning instruction is issued; When the first comparison result is that the vehicle speed at time k is equal to or greater than the first driving speed threshold, an early deceleration warning instruction is issued.

5. The foggy highway safety monitoring system based on meteorological cloud big data according to claim 1 is characterized in that: The monitoring response unit (300) comprises a third communication module (310), an information display module (320), an early warning release module (330) and an information import module (340), wherein: The third communication module (310) is used to realize information interaction between the monitoring response unit (300) and the data acquisition unit (100) and the data processing unit (200); The information display module (320) is used to display warning information and uploaded or imported information, and the information display module (320) is connected to the third communication module (310); The warning issuing module (330) issues early deceleration warning information according to the received early deceleration warning instruction, and the warning issuing module (330) is connected to both the third communication module (310) and the information display module (320); The information import module (340) is used to upload preset information, and the information import module (340) is connected to both the third communication module (310) and the information display module (320).

6. A method for monitoring highway safety in foggy weather based on meteorological cloud big data, characterized in that: The following steps are involved: Obtain data information from the meteorological cloud platform and data information collected by on-site sensors in the monitored area of ​​the expressway; Integrate the data information of the meteorological cloud platform with the data information collected by on-site sensors; Obtaining the optimal estimated value of the fused visibility at time k of the road section in the monitoring area of ​​the expressway as the first visibility value, and obtaining the average value of the optimal estimated value of the fused visibility at consecutive k+n times of the corresponding road section as the second visibility value; Compare the first driving speed threshold corresponding to the first visibility value with the vehicle speed at time k to obtain a first comparison result; Based on the first comparison result, comparing the second driving speed threshold corresponding to the second visibility value with the vehicle speed at time k to obtain a second comparison result; Based on the first comparison result and the second comparison result, a warning is issued to the high-speed vehicle.

7. The method for monitoring highway safety in foggy weather based on meteorological cloud big data according to claim 6 is characterized in that: The fusion process is as follows: Based on the data information of the meteorological cloud platform, the state prediction vector of the highway monitoring area section at time k is constructed , where is the state prediction vector of the meteorological cloud platform at time k, is the state transition matrix of visibility, is the state vector of the meteorological cloud platform at time k-1, , where , and They are visibility, humidity and wind speed of the road section in the corresponding monitoring area of ​​the meteorological cloud platform at time k-1 respectively; Based on the data information collected by the on-site sensors, the observation equation of the highway monitoring area section at time k is constructed , where is the visibility observation value collected by the on-site sensor at time k, is the observation conversion coefficient, is the observation noise; According to the state prediction vector , get the state prediction visibility value at time k , the visibility value will be predicted and on-site sensors to collect visibility observations To integrate, , where is the gain coefficient, is the optimal estimate of visibility after fusion.

8. The method for monitoring high-speed safety in foggy weather based on meteorological cloud big data according to claim 7 is characterized in that: The visibility state transition matrix , where is the visibility weight coefficient, is the decrease in visibility for every 1 unit increase in humidity, It is the increase in visibility for every increase in wind speed of 1 m / s.

9. The method for high-speed safety monitoring in foggy weather based on meteorological cloud big data according to claim 6 is characterized in that: The process of obtaining the second comparison result is as follows: If the vehicle speed at time k is less than the first driving speed threshold, the second driving speed threshold corresponding to the second visibility value is compared with the vehicle speed at time k to obtain a second comparison result; Otherwise, the second driving speed threshold corresponding to the second visibility value is not compared with the vehicle speed at time k.

10. The method for monitoring highway safety in foggy weather based on meteorological cloud big data according to claim 6, characterized in that: The process of issuing a warning to a high-speed vehicle according to the first comparison result and the second comparison result is as follows: When the first comparison result is that the vehicle speed at time k is less than the first driving speed threshold, the second driving speed threshold corresponding to the second visibility value is compared with the vehicle speed at time k, and if the second driving speed threshold is less than the vehicle speed at time k, no deceleration warning is issued; When the first comparison result is that the vehicle speed at time k is less than the first driving speed threshold, the second driving speed threshold corresponding to the second visibility value is compared with the vehicle speed at time k, and if the second driving speed threshold is equal to or greater than the vehicle speed at time k, an early deceleration warning is issued; When the first comparison result is that the vehicle speed at time k is equal to or greater than the first driving speed threshold, an early deceleration warning is issued.

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