CN116311938B - Road hidden danger processing method and equipment based on big data - Google Patents
Road hidden danger processing method and equipment based on big data Download PDFInfo
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- CN116311938B CN116311938B CN202310294158.3A CN202310294158A CN116311938B CN 116311938 B CN116311938 B CN 116311938B CN 202310294158 A CN202310294158 A CN 202310294158A CN 116311938 B CN116311938 B CN 116311938B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
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Abstract
The application provides a road hidden danger processing method and equipment based on big data, and belongs to the technical field of traffic control systems. The method obtains road segment monitoring information from a multi-source road monitoring device. And dividing the road section monitoring information into road side analysis information and cloud analysis information through a preset road side information classification algorithm. And carrying out big data analysis on the road side analysis information, and determining the road condition hidden danger data of the current road section. And generating hidden danger processing information according to the road condition hidden danger data and the analysis result of cloud analysis information from the cloud. And based on the position information of the current road section, the hidden danger processing information is sent to corresponding traffic guidance display equipment and/or corresponding networking passing vehicles. By the method, the problems that the equipment for processing the hidden danger of the current road is not intelligent enough and timely, and labor cost and time cost are wasted in road traffic management are solved.
Description
Technical Field
The application relates to the technical field of traffic control systems, in particular to a road hidden danger processing method and equipment based on big data.
Background
With the rapid development of road construction, more and more places are available on the road. Meanwhile, the number of roads is increased, and the management capability requirement is also continuously improved. In the past, only a few cameras or traffic police can finish the road management, and more monitoring devices and traffic police are required to manage the road.
Although the monitoring equipment can monitor the hidden trouble situations of road accidents, unexpected situations, construction and the like which are easy to cause accidents or secondary accidents, the traffic police can conveniently manage or dredge road traffic. But at present, the monitoring equipment sends monitoring information to a remote background, and a background traffic police provides a treatment scheme, so that the intelligent and labor cost is wasted, and the treatment scheme is too lag and cannot be suitable for changeable road traffic.
Disclosure of Invention
The embodiment of the application provides a road hidden danger processing method and equipment based on big data, which are used for solving the problems that the equipment for processing the current road hidden danger is not intelligent enough and timely, and the labor cost and the time cost are wasted in road traffic management.
In one aspect, an embodiment of the present application provides a method for processing a road hidden trouble based on big data, where the method includes:
acquiring road section monitoring information from a multi-source road monitoring device;
dividing the road section monitoring information into road side analysis information and cloud analysis information through a preset road side information classification algorithm;
carrying out big data analysis on the road side analysis information to determine road condition hidden danger data of the current road section;
generating hidden danger processing information according to the road condition hidden danger data and an analysis result of cloud analysis information from a cloud;
and based on the position information of the current road section, the hidden danger processing information is sent to corresponding traffic guidance display equipment and/or corresponding networking passing vehicles.
In one implementation of the present application, the multi-source road monitoring device at least includes: road image snapshot equipment, a microwave sensor, video monitoring equipment, a weather monitor and a rain gauge;
the road image special shooting device, the microwave sensor and the video monitoring device are arranged on a road monitoring rod outside the preset distance of the traffic guidance display device.
In one implementation manner of the application, the road section monitoring information is divided into road side analysis information and cloud analysis information by a preset road side information classification algorithm, and the method specifically comprises the following steps:
determining hidden danger judgment types corresponding to each piece of monitoring sub-information in the road section monitoring information; wherein, the hidden danger judging type at least comprises one or more of the following: accident type, road congestion type, illegal type, weather type and attention type of weak crowd;
generating a plurality of hidden danger judgment sets corresponding to the monitoring sub-information according to the hidden danger judgment type; the hidden danger judging set at least comprises one piece of monitoring sub-information, and one hidden danger judging set corresponds to at least one hidden danger judging type;
and dividing the road section monitoring information into the road side analysis information and the cloud analysis information based on the hidden danger judgment calculation resource quantity corresponding to each hidden danger judgment set obtained by the road side information classification algorithm.
In one implementation manner of the present application, according to the hidden danger determination type, a plurality of hidden danger determination sets corresponding to each piece of monitoring sub-information are generated, and specifically include:
matching the hidden danger judgment type with a preset hidden danger judgment comparison table to determine a corresponding first hidden danger judgment type sequence; wherein the hidden danger determination type in the first hidden danger determination type sequence is not repeated;
generating a plurality of hidden danger judgment sets corresponding to the monitoring sub-information according to the first hidden danger judgment type sequence; the intersection between the hidden danger judging sets comprises an empty set and a non-empty set.
In one implementation manner of the application, big data analysis is performed on the road side analysis information to determine the road condition hidden danger data of the current road section, and the method specifically comprises the following steps:
inputting the road side analysis information into a plurality of corresponding preset analysis models of the big data analysis to determine hidden danger risk scores of a plurality of hidden danger judgment types corresponding to the road side analysis information;
determining corresponding road condition hidden danger data according to the hidden danger risk score of each hidden danger judging type and a preset hidden danger score comparison table; the road condition hidden danger data are hidden danger data affecting road traffic.
In one implementation of the present application, the method further includes:
and correcting each hidden danger comparison value interval in the hidden danger score comparison table under the condition that any hidden danger score in each hidden danger score is larger than the index score threshold value in the hidden danger score comparison table so as to update the generation standard of the road condition hidden danger data.
In one implementation manner of the present application, based on the location information of the current road section, the hidden danger processing information is sent to a corresponding traffic guidance display device and/or a corresponding networked passing vehicle, which specifically includes:
the method comprises the steps of taking the position information of the current road section as a center, and determining a plurality of to-be-determined traffic guidance display devices in a preset range on a road of the current road section or a road extension line of the current road section;
and eliminating the undetermined traffic guidance display equipment of which the road is not on the hidden danger wave road in the undetermined traffic guidance display equipment according to the hidden danger wave road corresponding to the hidden danger processing information so as to determine the traffic guidance display equipment.
In one implementation manner of the present application, the hidden danger processing information is sent to a corresponding networked passing vehicle based on the position information of the current road section, and specifically includes:
the hidden danger wave road is sent to the cloud end, so that the cloud end can obtain a plurality of networking path vehicles running on the hidden danger wave road through base station equipment;
and sending the hidden danger processing information to the corresponding networking passing vehicle through the base station equipment.
In one implementation of the present application, the method further includes:
and under the condition that the hidden danger processing information is road closure, closure indication information is generated and sent to intelligent safety cone machine equipment matched with the position information of the current road section, so that the intelligent safety cone machine equipment moves to a corresponding lane to close the lane.
On the other hand, the embodiment of the application also provides a road hidden trouble processing device based on big data, which comprises:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring road section monitoring information from a multi-source road monitoring device;
dividing the road section monitoring information into road side analysis information and cloud analysis information through a preset road side information classification algorithm;
carrying out big data analysis on the road side analysis information to determine road condition hidden danger data of the current road section;
generating hidden danger processing information according to the road condition hidden danger data and an analysis result of cloud analysis information from a cloud;
and based on the position information of the current road section, the hidden danger processing information is sent to corresponding traffic guidance display equipment and/or corresponding networking passing vehicles.
The application can analyze the hidden danger of road conditions by utilizing the data collected by the multi-source road monitoring equipment and transmit corresponding hidden danger processing information of road conditions to a vehicle driver. The vehicle and road dynamic real-time information interaction can be realized in an omnibearing manner, and the active safety control and the road collaborative management of the vehicle can be developed on the basis of the full-time empty dynamic traffic information acquisition and fusion. The effective coordination of vehicles, roads and people is realized, traffic safety is ensured, and traffic efficiency is improved, so that a safe, efficient and environment-friendly road traffic system is formed. And the intelligent ground road traffic hidden danger treatment reduces the labor input cost of the road traffic hidden danger treatment and improves the treatment efficiency of the road traffic hidden danger.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a schematic flow chart of a road hidden danger processing method based on big data in an embodiment of the application;
fig. 2 is a schematic diagram of a system structure corresponding to a road hidden danger processing method based on big data in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a road hidden danger processing device based on big data in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The occurrence of road traffic accidents is fundamentally reduced, the road and the state are required to be monitored in full time and space, the information is accurately transmitted to the road vehicles in real time, and the road is subjected to cooperative intelligent upgrading and reconstruction, so that the road becomes intelligent.
Based on the above, the embodiment of the application provides a road hidden danger processing method and equipment based on big data, which are used for intelligently processing road traffic hidden danger, reducing the labor input cost of the road hidden danger processing and improving the processing efficiency of the road traffic hidden danger.
Various embodiments of the present application are described in detail below with reference to the attached drawing figures.
The embodiment of the application provides a road hidden danger processing method based on big data, as shown in fig. 1, the method can comprise the steps of S101-S105:
s101, the road side terminal acquires road section monitoring information from the multi-source road monitoring equipment.
The Road Side terminal may be an intelligent Road Side Unit (RSU), a Road Side Unit of a Road Side system, or other edge computing terminals, which is not particularly limited in the present application.
The multi-source road monitoring device comprises at least: road image snapshot equipment, microwave sensor, video monitoring equipment, weather monitor, rain gauge. The road image special shooting device, the microwave sensor and the video monitoring device are arranged on a road monitoring rod outside the preset distance of the traffic guidance display device.
As shown in FIG. 2, a traffic system corresponding to the road hidden danger processing method based on big data is provided with a snapshot device 201, a microwave sensor 202 and a video monitoring device 203 which are arranged on a road monitoring rod 204, and a weather monitor 205 and a rain gauge 206 which are arranged on a road comprehensive rod 207 which is at a certain distance from the road monitoring rod 204. The road integrated pole 207 further comprises a traffic guidance display device 208, which may be an LED display screen, a storage battery 209, a solar charging panel 2010 and an audible and visual alarm 2011, and in addition, an intelligent safety cone robot 2012 may be included at the position of the road integrated pole 207. Each of the above devices is electrically connected to a roadside terminal 210, and the roadside terminal 210 is connected to a server cluster 220 (cloud). The server cluster 220 may be in wireless communication with other devices, such as an audible and visual alarm 2011 network, through the base station 230, and may also be electrically connected to other devices, such as a control device of the intelligent safety cone robot 2012.
According to the application, the microwave sensor equipment is arranged on the road monitoring rod, equipment debugging work is completed, equipment azimuth is adjusted, equipment parameter information is set, and data returned by the sensor equipment is acquired and transmitted to the road side terminal. The lane monitoring cameras are arranged on the same road monitoring rod to perform functions of vehicle data acquisition, snapshot, AI algorithm identification and the like, and data interaction between equipment and an intelligent service system is achieved.
The road section monitoring information may be acquired by the above-mentioned device, for example, a vehicle running image captured by the capturing device 201, vehicle information, vehicle speed, vehicle distance or road obstacle object information of the vehicle acquired by the microwave sensor, and further, for example, weather data acquired by the weather monitor.
S102, the road side terminal divides the road section monitoring information into road side analysis information and cloud analysis information through a preset road side information classification algorithm.
In the embodiment of the application, road section monitoring information is divided into road side analysis information and cloud analysis information by a road side information classification algorithm, and the method specifically comprises the following steps:
and the road side terminal determines hidden danger judgment types corresponding to each piece of monitoring sub-information in the road section monitoring information. Wherein, hidden danger judging type includes one or more of following at least: accident type, road congestion type, illegal type, weather type, and type of concern for the vulnerable group.
Each monitoring sub-information in the road section monitoring information comprises a plurality of hidden danger judging types, for example, a certain snapshot image comprises two vehicles which collide with each other and road traffic jam, and the hidden danger judging types corresponding to the monitoring sub-information (the snapshot image) comprise accident types and road congestion types. As another example, the microwave sensor may transmit a vehicle speed of the vehicle, which may correspond to the type of violation.
And then, generating a plurality of hidden danger judgment sets corresponding to each piece of monitoring sub-information according to the hidden danger judgment type. The hidden danger determination set at least comprises one monitoring sub-information, and one hidden danger determination set corresponds to at least one hidden danger determination type.
According to the hidden danger judging type, generating a plurality of hidden danger judging sets corresponding to each monitoring sub-information, specifically including:
and matching the hidden danger judgment type with a preset hidden danger judgment comparison table to determine a corresponding first hidden danger judgment type sequence. Wherein the hidden danger determination type in the first hidden danger determination type sequence is not repeated. And generating a plurality of hidden danger judgment sets corresponding to each piece of monitoring sub-information according to the first hidden danger judgment type sequence. The intersection between the hidden danger judgment sets comprises an empty set and a non-empty set.
The preset hidden danger judging comparison table may be pre-stored in the road side terminal, where the hidden danger judging comparison table is pre-set by a user and is used for matching with a hidden danger judging type, and a specific matching rule may be to compare the hidden danger judging type with a type identifier in the hidden danger judging comparison table, for example, the hidden danger judging type includes 2 types 1, 3 types 2, 1 type 3, and 1 type 1 and 1 type 3 correspond to the same monitoring sub-information. The first hidden danger judgment type sequence finally generated by the road side terminal is [ type 1, type 2, type 3] instead of [ type 1, type 2]. According to the application, a plurality of hidden danger judging sets, namely 2 hidden danger judging sets of type 1, 3 hidden danger judging sets of type 2 and 1 hidden danger judging set of type 3 can be generated according to the first hidden danger judging type sequence.
And dividing the road section monitoring information into road side analysis information and cloud analysis information based on the hidden danger judgment calculation resource quantity corresponding to each hidden danger judgment set obtained by the road side information classification algorithm.
The road side information classification algorithm is used for calculating the calculation resource quantity of the information, and the road side information classification algorithm can obtain the calculation resource quantity according to the hidden danger judgment type corresponding to the hidden danger judgment set and the data quantity in the hidden danger judgment set. For example, the amount of computing resources corresponding to the hidden danger determination type a is x1, and the amount of data in the corresponding hidden danger determination set is x2, so that the amount of computing resources is a×1+b×2, and a and b are preset computing weights for users. And the road side terminal takes the calculated resource amount as cloud analysis information under the condition that the calculated resource amount is larger than a preset threshold value B, and takes the calculated resource amount as road side analysis information under the condition that the calculated resource amount is smaller than or equal to the preset threshold value B. The preset threshold B is set by the user and is determined according to the computing power or the hardware model of the road side terminal, which is not particularly limited in the present application.
And the road side terminal processes the road side analysis information and sends the cloud analysis information to the server cluster for processing. Therefore, the calculated amount of the road side terminal is reduced, and the information processing efficiency is ensured.
And S103, the road side terminal analyzes the big data of the road side analysis information and determines the hidden danger data of the road condition of the current road section.
In the embodiment of the application, big data analysis is performed on road side analysis information to determine road condition hidden danger data of a current road section, and the method specifically comprises the following steps:
firstly, inputting road side analysis information into a plurality of preset analysis models corresponding to big data analysis to determine hidden danger risk scores of a plurality of hidden danger judgment types corresponding to the road side analysis information. And determining corresponding road condition hidden danger data according to the hidden danger risk scores of the hidden danger judgment types and the preset hidden danger scores. The road condition hidden danger data are hidden danger data affecting road traffic.
The big data analysis comprises a plurality of preset analysis models for processing road side analysis information, and an AI video recognition algorithm of the preset analysis models at least comprises: the method comprises the following steps of loading and unloading car identification, car type identification, road dust detection, smoke identification, protection tool detection, muck car cleaning identification, smoking identification, sleep post identification, welding operation identification, retrograde detection, lane management, gathering detection, fight detection, trailing detection, rapid movement, detention detection, video shielding, ground fall alarm, abnormal people in a region, X-ray detector alarm, temperature measurement point abnormal alarm, ignition alarm, fire alarm, car line alarm, smoking identification, safety helmet-free crowd density detection, upper limit of parked cars alarm, region invasion, smoke detection alarm, abnormal car in a region, vehicle direction identification alarm in a region, vehicle ignition alarm in a region, vehicle congestion alarm, vehicle queuing alarm, thermal imaging hot spot abnormal alarm, thermal imaging rule temperature difference abnormal alarm, radar warning region detection, temperature abnormality, upper limit of parked cars alarm, flame detection, smoke detection, road obstacle detection, group fog detection, fire fighting lane, non-motor car detection, personnel fall detection, yellow line detection, traffic lane construction detection, license plate detection, obstacle detection, vehicle overspeed detection, no-front safety maintenance detection, no-speed limit detection, no-stop car detection, no-speed limit detection, no-stop car overload detection, no-speed limit detection, no-stop detection, and the like. The preset analysis model can calculate hidden danger risk scores of hidden danger judgment types contained in the road side analysis information, for example, overspeed detection is carried out, and corresponding hidden danger risk scores are obtained according to the overspeed proportion, wherein the hidden danger risk scores are larger when the overspeed proportion is higher.
The hidden danger score comparison table can be set by a user according to experience, or can be generated by training a neural network model based on a plurality of historical road side analysis information to obtain hidden danger comparison score intervals corresponding to different hidden danger judgment types. The hidden danger score comparison table can be understood as that the hidden danger risk score is lower than the minimum value of the score interval corresponding to the hidden danger score, the hidden danger risk score is not used as the hidden danger, and if the hidden danger risk score is located in the hidden danger score interval, the hidden danger score is used as road condition hidden danger data.
In addition, under the condition that any hidden danger risk score in the hidden danger risk scores is larger than the index score threshold value in the hidden danger score comparison table, correcting each hidden danger comparison score interval in the hidden danger score comparison table so as to update the generation standard of the road condition hidden danger data.
That is, if there is a risk score greater than the index score threshold, such as the maximum value of the risk score interval, or another set threshold (set by the user), the road side terminal will modify the risk comparison score interval, such as reducing the minimum value of the risk comparison score interval.
For example, when the risk score of a hidden danger is 50, the hidden danger determination type is an accident type, the event is an accident, and the index score threshold is 40, the minimum value of the hidden danger comparison score interval of the hidden danger determination type in the hidden danger score comparison table corresponding to the road section can be reduced according to the preset reduction step. For example, the potential hazard comparison and division value interval of a certain potential hazard judgment type is (10, 40), and is corrected to (5, 40), in other words, under the condition that a serious potential hazard occurs, the generation standard of the potential hazard data of the determined road condition can be adjusted, so that secondary accidents are avoided. For example, if the original traffic jam occurs when 10 vehicles are blocked, the traffic jam is judged as traffic hidden danger data, and after correction, the traffic jam is judged as traffic hidden danger data when 5 vehicles are blocked.
Through the correction of the potential hazard score comparison table, the probability of secondary accidents can be avoided, and the potential hazards of the road can be processed more intelligently.
S104, the road side terminal generates hidden danger processing information according to the road condition hidden danger data and an analysis result of cloud analysis information from the cloud.
In the embodiment of the application, after the road side terminal obtains the road condition hidden danger data, the road side terminal can also receive the analysis result of the cloud analysis information by the cloud, and further can generate hidden danger processing information simultaneously or sequentially. The hidden danger processing information is a hidden danger risk management scheme generated based on the road condition hidden danger data, for example, the hidden danger is a traffic accident, and the hidden danger processing information can be used for sealing a certain lane; for another example, the hidden danger is raining, and the hidden danger processing information is used for prompting a driver to control the speed of the vehicle to be within 30 km/h. For another example, the road in front of the intersection is congested, and the hidden danger processing information is used for prompting a driver to perform zipper-type driving. The analysis result is generated by a cloud server cluster, and is used for processing data with large computing resource quantity, for example, integrating a plurality of road side analysis information to obtain the predicted future traffic flow or traffic congestion state and the like, and the predicted future traffic flow or traffic congestion state is used as the analysis result.
And S105, the road side terminal sends hidden danger processing information to corresponding traffic guidance display equipment and/or corresponding networking passing vehicles based on the position information of the current road section.
In the embodiment of the application, based on the position information of the current road section, hidden danger processing information is sent to corresponding traffic guidance display equipment and/or corresponding networking passing vehicles, and the method specifically comprises the following steps:
the road side terminal can determine a plurality of pending traffic guidance display devices in a preset range on the road of the current road section or the road extension line of the current road section by taking the position information of the current road section as a center. And removing the undetermined traffic guidance display equipment which is not on the hidden danger wave and road from the undetermined traffic guidance display equipment according to the hidden danger wave and road corresponding to the hidden danger processing information so as to determine the traffic guidance display equipment.
In the embodiment of the application, the traffic guidance display device is provided with a plurality of traffic guidance display devices, and the traffic guidance display devices are used for issuing information to a driver, such as prompting weather conditions, hidden danger of a road section in front and real-time conditions of roads. As shown in fig. 2, a general traffic guidance display device is located within a preset distance along the reverse direction of a road of a device such as a microwave sensor for prompting a driver of a vehicle who is about to travel to a place where a hidden trouble occurs. However, since the roads are all around, the roads are not just one, as in fig. 2, and there are intersections and entrances. Therefore, the method and the system take the position information of the current road section as a center, determine a plurality of pending traffic guidance display devices in a preset range on the road of the current road section or the road extension line of the current road section, and determine which pending traffic guidance display devices corresponding to road sections to drive vehicles from the plurality of pending traffic guidance display devices to be influenced by hidden danger, thereby determining the traffic guidance display devices to be sent with hidden danger processing information.
In addition, based on the position information of the current road section, the hidden danger processing information is sent to the corresponding networking passing vehicle, and the method specifically comprises the following steps:
the road side terminal can send the hidden danger swept road to the cloud end, so that the cloud end can obtain a plurality of networking path vehicles running on the hidden danger swept road through the base station equipment. And the hidden danger processing information is sent to the corresponding networking passing vehicle through the base station equipment.
That is, the application can determine the network path vehicles through the cloud and the base station, and send the hidden trouble processing information to the network path vehicles. The application can send hidden danger processing information to the network path vehicle through LTE-V2X.
In one embodiment of the application, when the hidden danger processing information is road closure, closure indication information is generated and sent to the intelligent safety cone machine equipment matched with the position information of the current road section, so that the intelligent safety cone machine equipment moves to a corresponding lane to close the lane.
In other words, the intelligent safety cone robot is controlled by the road side terminal to automatically seal the lane, and the hidden danger can be more efficiently and with low delay by controlling the intelligent safety cone robot by the road side terminal.
In addition, the communication of the application can adopt 5G communication, thereby further ensuring hidden danger processing efficiency.
According to the scheme, automatic snapshot, license plate recognition and speed measurement of the vehicle can be realized, dynamic real-time information interaction of the vehicle and the vehicle road is realized in all directions, active safety control and road collaborative management of the vehicle are carried out on the basis of full-time empty dynamic traffic information acquisition and fusion, effective collaboration of the vehicle, the road and people is fully realized, traffic safety is guaranteed, traffic efficiency is improved, and therefore a safe, efficient and environment-friendly road traffic system is formed, real-time traffic statistics data such as signal control, electronic gate, bus passenger transport and the like are shared by butting with government departments such as public security traffic police, city management and traffic bureau, network road sensing and network traffic detection capabilities are perfected, road side terminals can be shared with traffic police and city management departments, and key areas are selected to build a 5G-V2X vehicle-cloud integrated infrastructure and matched services.
Furthermore, the application can analyze the hidden danger of road conditions by utilizing the data collected by the multi-source road monitoring equipment, and transmit corresponding hidden danger processing information of road conditions to the vehicle driver, thereby intelligently processing the hidden danger of road traffic, reducing the labor input cost of hidden danger processing and improving the processing efficiency of hidden danger of road traffic.
Fig. 3 is a schematic structural diagram of a road hidden danger processing device based on big data according to an embodiment of the present application, where, as shown in fig. 3, the device includes:
at least one processor; and a memory communicatively coupled to the at least one processor. Wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to:
road segment monitoring information from a multi-source road monitoring device is acquired. And dividing the road section monitoring information into road side analysis information and cloud analysis information through a preset road side information classification algorithm. And carrying out big data analysis on the road side analysis information, and determining the road condition hidden danger data of the current road section. And generating hidden danger processing information according to the road condition hidden danger data and the analysis result of cloud analysis information from the cloud. And based on the position information of the current road section, the hidden danger processing information is sent to corresponding traffic guidance display equipment and/or corresponding networking passing vehicles.
The embodiments of the present application are described in a progressive manner, and the same and similar parts of the embodiments are all referred to each other, and each embodiment is mainly described in the differences from the other embodiments. In particular, for the apparatus embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments in part.
The devices and the methods provided in the embodiments of the present application are in one-to-one correspondence, so that the devices also have similar beneficial technical effects as the corresponding methods, and since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices are not described here again.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (7)
1. The road hidden danger processing method based on big data is characterized by comprising the following steps:
acquiring road section monitoring information from a multi-source road monitoring device;
dividing the road section monitoring information into road side analysis information and cloud analysis information through a preset road side information classification algorithm;
carrying out big data analysis on the road side analysis information to determine road condition hidden danger data of the current road section;
generating hidden danger processing information according to the road condition hidden danger data and an analysis result of cloud analysis information from a cloud;
based on the position information of the current road section, the hidden danger processing information is sent to corresponding traffic guidance display equipment and/or corresponding networking passing vehicles;
the road section monitoring information is divided into road side analysis information and cloud analysis information through a preset road side information classification algorithm, and the method specifically comprises the following steps:
determining hidden danger judgment types corresponding to each piece of monitoring sub-information in the road section monitoring information; wherein, the hidden danger judging type at least comprises one or more of the following: accident type, road congestion type, illegal type, weather type and attention type of weak crowd;
generating one or more hidden danger judgment sets corresponding to the monitoring sub-information according to the hidden danger judgment types; the hidden danger judging set at least comprises one piece of monitoring sub-information, and one hidden danger judging set corresponds to one hidden danger judging type;
dividing the road section monitoring information into the road side analysis information and the cloud analysis information based on hidden danger judgment calculation resource quantity corresponding to each hidden danger judgment set obtained by the road side information classification algorithm;
the road side information classification algorithm is used for obtaining the computing resource amount according to the hidden danger judgment type corresponding to the hidden danger judgment set and the data amount in the hidden danger judgment set; taking the road section monitoring information as cloud analysis information when the calculated resource amount is larger than a preset threshold value, and taking the road section monitoring information as road side analysis information when the calculated resource amount is smaller than or equal to the preset threshold value;
according to each hidden danger judging type, generating one or more hidden danger judging sets corresponding to each monitoring sub-information, wherein the hidden danger judging sets specifically comprise:
matching each hidden danger judgment type with a preset hidden danger judgment comparison table to determine a corresponding first hidden danger judgment type sequence; wherein the hidden danger determination type in the first hidden danger determination type sequence is not repeated;
generating one or more hidden danger judgment sets corresponding to the monitoring sub-information according to the first hidden danger judgment type sequence; the intersection set between the hidden danger judging sets comprises an empty set and a non-empty set;
the method for analyzing the road side analysis information comprises the steps of analyzing the road side analysis information in big data, and determining the road condition hidden danger data of the current road section, wherein the method specifically comprises the following steps:
inputting the road side analysis information into a plurality of corresponding preset analysis models of the big data analysis to determine hidden danger risk scores of a plurality of hidden danger judgment types corresponding to the road side analysis information;
determining corresponding road condition hidden danger data according to the hidden danger risk score of each hidden danger judging type and a preset hidden danger score comparison table; the road condition hidden danger data are hidden danger data affecting road traffic.
2. The method of claim 1, wherein the multi-source road monitoring device comprises at least: road image snapshot equipment, a microwave sensor, video monitoring equipment, a weather monitor and a rain gauge;
the road image special shooting device, the microwave sensor and the video monitoring device are arranged on a road monitoring rod outside the preset distance of the traffic guidance display device.
3. The method according to claim 1, wherein the method further comprises:
and correcting each hidden danger comparison value interval in the hidden danger score comparison table under the condition that any hidden danger score in each hidden danger score is larger than the index score threshold value in the hidden danger score comparison table so as to update the generation standard of the road condition hidden danger data.
4. The method according to claim 1, wherein the hidden danger processing information is sent to a corresponding traffic guidance display device and/or a corresponding networked passing vehicle based on the position information of the current road segment, specifically comprising:
the method comprises the steps of taking the position information of the current road section as a center, and determining a plurality of to-be-determined traffic guidance display devices in a preset range on a road of the current road section or a road extension line of the current road section;
and eliminating the undetermined traffic guidance display equipment of which the road is not on the hidden danger wave road in the undetermined traffic guidance display equipment according to the hidden danger wave road corresponding to the hidden danger processing information so as to determine the traffic guidance display equipment.
5. The method of claim 4, wherein the hidden danger processing information is sent to the corresponding networked passing vehicles based on the position information of the current road segment, and specifically comprises:
the hidden danger wave road is sent to the cloud end, so that the cloud end can obtain a plurality of networking path vehicles running on the hidden danger wave road through base station equipment;
and sending the hidden danger processing information to the corresponding networking passing vehicle through the base station equipment.
6. The method according to claim 1, wherein the method further comprises:
and under the condition that the hidden danger processing information is road closure, closure indication information is generated and sent to intelligent safety cone machine equipment matched with the position information of the current road section, so that the intelligent safety cone machine equipment moves to a corresponding lane to close the lane.
7. A road hazard processing device based on big data, the device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a big data based roadway hazard processing method as claimed in any one of the preceding claims 1-6.
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