CN112581764A - Centralized control method, system, terminal and storage medium based on big data - Google Patents
Centralized control method, system, terminal and storage medium based on big data Download PDFInfo
- Publication number
- CN112581764A CN112581764A CN202011462824.2A CN202011462824A CN112581764A CN 112581764 A CN112581764 A CN 112581764A CN 202011462824 A CN202011462824 A CN 202011462824A CN 112581764 A CN112581764 A CN 112581764A
- Authority
- CN
- China
- Prior art keywords
- intersection
- starting
- vehicles
- time
- preset
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- 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
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- 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
- G08G1/0133—Traffic data processing for classifying traffic situation
-
- 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
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Remote Sensing (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Traffic Control Systems (AREA)
Abstract
The application relates to a centralized control method, a system, a terminal and a storage medium based on big data, which belong to the field of data processing, wherein the method comprises the steps of obtaining the traffic flow of a starting intersection and a destination intersection; calculating the difference value of the vehicle flow of the starting intersection and the terminal intersection; judging the traffic degree grade of a road section between the starting intersection and the end intersection according to the difference value of the traffic flow of the starting intersection and the end intersection and a preset road traffic degree grade table; acquiring the traffic degree grade of a road section between a starting intersection and a terminal intersection; and generating a traffic degree grade of a road section between the starting intersection and the terminal intersection on a display device preset by the starting intersection. The method and the device can analyze data of the vehicles on the road section between monitoring and controlling, obtain the traffic flow of the road section through data analysis, judge a traffic degree grade and have the effect of reminding a driver of the traffic condition of the road section in front.
Description
Technical Field
The present application relates to the field of data processing, and in particular, to a centralized control method, system, terminal, and storage medium based on big data.
Background
The data processing is the collection, storage, retrieval, processing, transformation and transmission of data. The basic purpose of data processing is to extract and deduce data that is valuable and meaningful to certain persons from a large, possibly chaotic, unintelligible amount of data, which is an essential part of system engineering and automation. Data processing is throughout various fields of social production and social life. The development of data processing technology and the breadth and depth of its application have greatly influenced the progress of human society development.
At present, with the improvement of living standard, vehicles on roads are more and more, and under general conditions, a driver cannot know the traffic condition of a road section to be entered in advance, so that when a road section is congested, the vehicle behind cannot know the condition, the vehicle behind is caused to enter the congested road section continuously, and the congested road section is more and more blocked.
Disclosure of Invention
In order to remind a driver of the traffic condition of a road section in front of the driver, the application provides a centralized control method, a system, a terminal and a storage medium based on big data.
In a first aspect, the present application provides a centralized control method based on big data, which adopts the following technical solution:
a centralized control method based on big data comprises
Acquiring the traffic flow of the starting intersection and the end intersection according to preset monitoring equipment of the starting intersection and the end intersection;
calculating the difference value of the vehicle flow of the starting intersection and the end intersection according to the vehicle flow of the starting intersection and the end intersection;
judging the traffic degree grade of a road section between the starting intersection and the end intersection according to the difference value of the traffic flow of the starting intersection and the end intersection and a preset road traffic degree grade table;
acquiring the traffic degree grade of a road section between the starting intersection and the terminal intersection;
and generating the traffic degree grade of the road section between the starting intersection and the terminal intersection on a display device preset by the starting intersection.
By adopting the technical scheme, the traffic flow of the road junction of the complaint and the terminal road junction is obtained, then the traffic flow is calculated, the calculated traffic flow is compared with the preset road traffic degree grade table, the traffic degree grade of the road section is judged, and finally the traffic degree grade of the road section is generated on the preset display equipment of the road junction, so that the driver is reminded of the traffic condition of the road section in front.
Optionally, the traffic level includes clear road section, slow road section traffic and road section congestion.
By adopting the technical scheme, the road condition in front of the driver is more clearly reminded by setting three clear levels.
Optionally, the generating, on the display device preset at the starting intersection, the traffic level grade of the road segment between the starting intersection and the ending intersection specifically includes:
when the traffic degree grade is smooth, the traffic degree grade of the road section between the starting intersection and the terminal intersection, which is generated on a display device preset at the starting intersection, is green;
when the traffic degree grade is that the road section is slow to pass, the traffic degree grade of the road section between the starting intersection and the terminal intersection, which is generated on a display device preset at the starting intersection, is orange;
when the traffic degree grade is road section congestion, the traffic degree grade of the road section between the starting intersection and the terminal intersection, which is generated on a display device preset by the starting intersection, is red, and information suggesting changing of the path is generated on the display device preset by the starting intersection;
by adopting the technical scheme, the generated information of the smooth road section is marked as green, the generated information of the slowly passing road section is marked as orange, and the generated information of the congested road section is marked as red, so that the clear contrast is formed, the road condition in front of a driver is further conveniently reminded, and the traffic jam is favorably alleviated.
Optionally, after the traffic level of the road segment between the starting intersection and the ending intersection generated on the display device preset at the starting intersection is red and the information suggesting changing the route is generated on the display device preset at the starting intersection, the method further includes:
generating a statistical table; the statistical table comprises time information when the traffic degree grade is judged to be road section congestion;
judging whether time information meeting preset rules exists in the statistical table or not;
if the time information meeting the preset rule exists, acquiring the time information meeting the preset rule;
generating time information with the next traffic degree grade as road congestion according to the time information meeting the preset rule, and defining the time information as next predicted congestion time;
generating the next predicted congestion time on the display device at a point in time before the time arrives, and stopping the display when the predicted congestion time arrives.
Through the technical scheme, the time information meeting the preset rule is inquired, the next predicted congestion time is generated and displayed on the display device, and the driver can plan the route in advance.
Optionally, after obtaining the vehicle flows at the starting intersection and the ending intersection, the method further includes:
acquiring all vehicle information entering an initial intersection according to monitoring equipment preset at the initial intersection; the vehicle information comprises time information and license plate information of the vehicle entering the starting intersection;
acquiring all vehicle information reaching the terminal crossing according to monitoring equipment preset at the terminal crossing; the vehicle information comprises time information of the vehicle reaching the terminal intersection and license plate information;
inquiring all vehicles of which the information of the vehicles entering the initial intersection is matched with the information of the vehicles arriving at the terminal intersection, and defining all vehicles of which the information of the vehicles entering the initial intersection is matched with the information of the vehicles arriving at the terminal intersection as target vehicles;
acquiring the time from all the target vehicles to enter the starting intersection to the end intersection according to all the target vehicles;
calculating the average time from all the target vehicles to enter the starting intersection to the final intersection, and defining the average time as the predicted passing time;
and generating the predicted passing time on a display device preset at the starting intersection.
By adopting the technical scheme, all vehicle information entering the starting intersection and all vehicles arriving at the end intersection are obtained through the monitoring equipment of the starting intersection and the end intersection, then all vehicles are matched with the vehicle information entering the starting intersection and the vehicle information arriving at the end intersection in the flood, and finally the average time of all vehicles from the starting intersection to the end intersection is calculated, so that the time probably needed for the driver to pass through the road section is provided for reference.
Optionally, after querying all vehicles in the vehicle information entering the starting intersection and matching with the vehicle information arriving at the ending intersection, and defining all vehicles in the vehicle information entering the starting intersection and matching with the vehicle information arriving at the ending intersection as target vehicles, the method further includes:
judging whether large vehicles exist in all the target vehicles according to the vehicle information of the target vehicles;
if the large vehicles exist in all the target vehicles, acquiring the large vehicles in all the target vehicles;
acquiring the time from all the large vehicles to enter the starting intersection to the terminal intersection;
calculating the average time from all the large vehicles entering the starting intersection to the terminal intersection, and defining the average time as the predicted passing time of the large vehicles;
and generating the predicted passing time of the large-sized vehicle on the display equipment preset at the starting intersection.
By adopting the technical scheme, the estimated passing time of the large-sized vehicle is generated, and the driver of the large-sized vehicle can conveniently refer to the estimated passing time passing through the road section.
Optionally, after querying all vehicles in the vehicle information entering the starting intersection and matching with the vehicle information arriving at the ending intersection, and defining all vehicles in the vehicle information entering the starting intersection and matching with the vehicle information arriving at the ending intersection as target vehicles, the method further includes:
judging whether small vehicles exist in all the target vehicles according to the vehicle information of the target vehicles;
if the small vehicles exist in all the target vehicles, acquiring the small vehicles in all the target vehicles;
acquiring the time from all the small cars entering the starting intersection to the end intersection;
and calculating the average time from all the small vehicles to the starting intersection to the final intersection, and defining the average time as the predicted passing time of the small vehicles.
By adopting the technical scheme, the estimated passing time of the small-sized vehicle is generated, and the driver of the small-sized vehicle can conveniently refer to the estimated passing time passing through the road section.
In a second aspect, the present application provides a centralized control system based on big data, which adopts the following technical solutions:
a centralized control system based on big data comprises
The first acquisition module is used for acquiring the traffic flow of the starting intersection and the end intersection according to preset monitoring equipment of the starting intersection and the end intersection;
a calculation module for calculating the traffic flow of the starting point road junction and the end point road junction
The difference value of the vehicle flow of the starting intersection and the terminal intersection;
a judging module for judging the difference value of the vehicle flow of the starting intersection and the end intersection and the traffic flow of the destination intersection
A road traffic degree grade table is arranged to judge the traffic degree grade of the road section between the starting intersection and the terminal intersection;
a second acquisition module for acquiring the passage of the road section between the starting intersection and the end intersection
Degree grade;
a generating module for generating the initial intersection on a display device preset at the initial intersection
And a traffic level for the road segment between the end intersections.
By adopting the technical scheme, the traffic flow of the road junction of the complaint and the terminal road junction is obtained, then the traffic flow is calculated, the calculated traffic flow is compared with the preset road traffic degree grade table, the traffic degree grade of the road section is judged, and finally the traffic degree grade of the road section is generated on the preset display equipment of the road junction, so that the driver is reminded of the traffic condition of the road section in front.
In a third aspect, the present application provides an intelligent terminal that adopts the following technical solution:
an intelligent terminal comprising a memory and a processor, the memory having stored thereon a computer program that can be loaded by the processor and that executes the method as described in the first aspect.
By adopting the technical scheme, the traffic flow of the initial intersection and the terminal intersection can be accurately obtained, the traffic flow difference value can be calculated, and then the traffic degree grade table of the road can be accurately judged according to the preset road traffic degree grade table.
In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solutions:
a computer readable storage medium comprising a computer program stored thereon which is loadable by a processor and adapted to carry out the method of the first aspect.
By adopting the technical scheme, the information processing program can be stored, and the function of reminding the driver of the traffic condition of the road section in front of the driver is realized.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the traffic flow of the complaint starting intersection and the terminal intersection is obtained, then the traffic flow is calculated and compared with a preset road traffic degree grade table after calculation, the traffic degree grade of the road section is judged, and finally the traffic degree grade of the road section is generated on a display device preset at the intersection, so that a driver is reminded of the traffic condition of the road section in front;
2. the method comprises the steps of acquiring all vehicle information entering a starting intersection and all vehicles arriving at an end intersection through monitoring equipment of the starting intersection and the end intersection, calculating the average time of all vehicles from the starting intersection to the end intersection after the vehicles arrive at the end intersection in the flood, and providing the driver with the time probably needed for passing the road section.
Drawings
Fig. 1 is a schematic flowchart of a big data-based centralized control method according to an embodiment of the present application.
Fig. 2 is a schematic flow chart showing generation of a next predicted congestion time in the centralized control method based on big data according to the embodiment of the present application.
Fig. 3 is a schematic flow chart showing the generation of the predicted transit time in the big data-based centralized control method according to the embodiment of the present application.
Fig. 4 is a schematic flow chart showing the predicted transit time of a large-sized vehicle in the large-data-based centralized control method according to the embodiment of the application.
Fig. 5 is a schematic flow chart showing the predicted transit time of a large-sized vehicle in the large-data-based centralized control method according to the embodiment of the application.
Fig. 6 is a block diagram of a big data based centralized control system according to an embodiment of the present application.
Description of reference numerals: 1. a first acquisition module; 2. a calculation module; 3. a judgment module; 4. a second acquisition module; 5. and generating a module.
Detailed Description
The present application is described in further detail below with reference to figures 1-6.
The embodiment of the application discloses a centralized control method based on big data.
Referring to fig. 1, the big data based centralized control method includes:
and S100, acquiring the traffic flow of the starting intersection and the end intersection according to preset monitoring equipment of the starting intersection and the end intersection.
The preset monitoring equipment is a camera of the starting intersection and the terminal intersection. Specifically, a period of time is specified, the number of all vehicles passing through the starting intersection and the ending intersection in the period of time is obtained according to monitoring equipment preset at the starting intersection and the ending intersection, and the number of all vehicles passing through the intersection is divided by the period of time to obtain the traffic flow of the intersection.
And S200, calculating the difference value of the vehicle flow of the starting intersection and the end intersection according to the vehicle flow of the starting intersection and the end intersection.
And S300, judging the traffic degree grade of the road section between the starting intersection and the end intersection according to the difference value of the traffic flow of the starting intersection and the end intersection and a preset road traffic degree grade table. The difference value of the vehicle flow is the vehicle flow at the starting intersection minus the vehicle flow at the terminal intersection. Each traffic flow corresponds to a traffic level. Wherein, the traffic degree grade is divided into three categories: smooth road, slow road traffic and road congestion. For example, if the difference value of the traffic flow is a, a may be a negative number, 0, or a positive number, the range of the road clear is less than a, the range of the road slow-moving is from a to B, and the range of the road congestion is greater than B; when a is less than A, the traffic degree grade is smooth, when A is less than or equal to B, the traffic degree grade is slow, and when a is greater than B, the traffic degree grade is road congestion.
S400, acquiring the traffic degree grade of the road section between the starting intersection and the terminal intersection.
And S500, generating a traffic degree grade of a road section between the starting intersection and the terminal intersection on a preset display device of the starting intersection, and returning to S100. Specifically, when the traffic degree grade of a road section between the starting intersection and the end intersection is smooth, the generated smooth road information is green; when the traffic degree grade of the road section between the starting intersection and the terminal intersection is slow road traffic, the generated slow road traffic information is orange; when the traffic level of the section between the start intersection and the end intersection is road congestion, the generated information of the road congestion is red and also information suggesting a change of the route is generated on the display device. The preset display device may be a liquid crystal display.
Further, referring to fig. 2, after the information of the road congestion generated in S500 is red and the information of the suggested changed route is also generated on the display device, the following steps may be further performed:
s501, generating a statistical table; the statistical table comprises time information when the traffic degree grade is judged to be the road section congestion. Wherein, the time information in the statistical table is arranged from morning to evening according to the date.
S502, judging whether time information meeting a preset rule exists in the statistical table or not; if yes, directly entering S503; if not, S502 is performed again. Specifically, the preset rule may be time information of a time point at which the road congestion occurs at the same time point on the same day for N consecutive weeks, or may be time information of a time point at which the road congestion occurs at the same time point on the same day for N consecutive days. For example, if there are 6 pm on 11/6/2020 (friday), 6 pm on 11/13/2020 (friday), and 6 pm on 11/20/2020 (friday) in the statistical table, the three pieces of time information mean that traffic congestion occurs in the road segment at 6 pm on friday on three consecutive weeks, and therefore 6 pm on friday is time information that satisfies the preset rule. If the statistical table has 10 am points for four consecutive days, it means that the road section is traffic jam at 10 am points for four consecutive days, and therefore 10 am points are time information meeting the preset rule.
S503, acquiring time information satisfying a preset rule.
And S504, generating time information of which the next traffic degree grade is road congestion, and defining the time information as next predicted congestion time. For example, if 6 pm on friday is time information satisfying the preset rule, the next predicted congestion time is 6 pm on friday on the next week; if the time information at 10 am satisfies the preset rule, the next predicted congestion time is 10 am on the next day.
S50, generating the next predicted congestion time on the display device at a time point before the time arrives, and stopping the display when the predicted congestion time arrives. Wherein, a time node that can be set according to actual needs at a certain time point before the time arrives, for example, the previous day or the previous N hours before the time arrives
Referring to fig. 3, the following steps are further performed after S100:
s201, acquiring all vehicle information entering the initial intersection according to monitoring equipment preset at the initial intersection.
It is to be noted that the vehicle information of all the vehicles entering the initial intersection for a certain period of time specified in S100 is acquired. The monitoring equipment preset at the initial intersection can identify the license plate information of the vehicle, wherein the license plate information comprises license plate numbers and license plate colors. The acquired vehicle information comprises time information and license plate information of the vehicle entering the initial intersection.
S202, acquiring all vehicle information reaching the terminal crossing according to monitoring equipment preset at the terminal crossing.
S203, all vehicles whose vehicle information of the vehicle entering the starting intersection matches the vehicle information of the vehicle arriving at the ending intersection are queried and defined as target vehicles. All the matched vehicles refer to vehicles with the same license plate information as the vehicles entering the initial intersection and the vehicles arriving at the final intersection.
S204, acquiring the time from all the target vehicles to the initial intersection to the final intersection.
S205, calculating the average time from the entrance of all the target vehicles to the end intersection, and defining the average time as the predicted passing time. Wherein the predicted transit time is obtained by dividing the sum of the time taken for all the target vehicles to enter the starting intersection to the ending intersection by the number of all the target vehicles.
And S206, generating expected passing time on a display device preset at the initial intersection, and returning to S201.
Referring to fig. 4, the following steps are further included after S203:
s301, judging whether large vehicles exist in all target vehicles according to the vehicle information of all target vehicles. If yes, directly entering S302; if not, S301 is performed again. According to the license plate information of the vehicles identified by the monitoring equipment, the yellow license plate indicates that the corresponding vehicle is a large-sized vehicle, and the blue license plate indicates that the corresponding vehicle is a small-sized vehicle.
S302, vehicle information of large vehicles in all target vehicles is obtained.
S303, acquiring the time from the entrance of all the large vehicles to the end intersection;
s304, calculating the average time of all the large vehicles from the entrance to the starting intersection to the end intersection, and defining the average time as the predicted passing time of the large vehicles. Wherein the predicted passing time of the large vehicles is obtained by summing the time from the entrance of all the large vehicles to the end intersection with the number of all the large vehicles.
And S305, generating predicted passing time of the large-sized vehicle on a display device preset at the starting intersection, and returning to S301.
Referring to fig. 5, the following steps are further included after S203:
s401, judging whether the small-sized vehicles exist in all the target vehicles according to the vehicle information of all the target vehicles. If yes, directly entering S402; if not, S401 is resumed. According to the license plate information of the vehicles identified by the monitoring equipment, the yellow license plate indicates that the corresponding vehicle is a large-sized vehicle, and the blue license plate indicates that the corresponding vehicle is a small-sized vehicle.
S402, vehicle information of the small vehicles in all the target vehicles is obtained.
S403, acquiring the time from the entrance of all the small vehicles to the end intersection;
s404, calculating the average time of all the small vehicles from the entrance to the starting intersection to the end intersection, and defining the average time as the predicted passing time of the small vehicles. Wherein the predicted transit time of the small cars is obtained by summing the time taken for all the small cars to enter the starting intersection to the ending intersection for all the large cars.
And S405, generating the predicted passing time of the small-sized vehicle on a display device preset at the starting intersection, and returning to S401.
Based on the method, the embodiment of the application also discloses a centralized control system based on big data.
Referring to fig. 6, the big data based centralized control system includes a first obtaining module 1, a calculating module 2, a judging module 3, a second obtaining module 4, and a generating module 5.
The first acquisition module 1 is used for acquiring the traffic flow of a starting intersection and a terminal intersection;
the calculation module 2 is used for calculating the difference value of the vehicle flow of the starting intersection and the end intersection according to the vehicle flow of the starting intersection and the end intersection;
the judging module 3 is used for judging the traffic degree grade of a road section between the starting intersection and the end intersection according to the difference value of the vehicle flow of the starting intersection and the end intersection and a preset road traffic degree grade table;
the second acquisition module 4 is used for acquiring the traffic degree grade of a road section between the starting intersection and the terminal intersection;
and the generating module 5 is used for generating the traffic degree grade of the road section between the starting intersection and the end intersection on a preset display device of the starting intersection.
The embodiment of the application also discloses an intelligent terminal, which comprises a memory and a processor, wherein the memory is stored with a computer program which can be loaded by the processor and can execute the centralized control method based on the big data.
The embodiment of the present application further discloses a computer-readable storage medium, which stores a computer program that can be loaded by a processor and execute the big data based centralized control method as described above, and the computer-readable storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above examples are only used to illustrate the technical solutions of the present application, and do not limit the scope of protection of the application. It is to be understood that the embodiments described are only some of the embodiments of the present application and not all of them. All other embodiments, which can be derived by a person skilled in the art from these embodiments without making any inventive step, are within the scope of the present application.
Claims (10)
1. A centralized control method based on big data is characterized by comprising the following steps:
acquiring the traffic flow of the starting intersection and the end intersection according to preset monitoring equipment of the starting intersection and the end intersection;
calculating the difference value of the vehicle flow of the starting intersection and the end intersection according to the vehicle flow of the starting intersection and the end intersection;
judging the traffic degree grade of a road section between the starting intersection and the end intersection according to the difference value of the traffic flow of the starting intersection and the end intersection and a preset road traffic degree grade table;
acquiring the traffic degree grade of a road section between the starting intersection and the terminal intersection;
and generating the traffic degree grade of the road section between the starting intersection and the terminal intersection on a display device preset by the starting intersection.
2. The big-data based centralized control method according to claim 1,
the traffic degree grade comprises smooth road sections, slow road sections and road section congestion.
3. The big-data based centralized control method according to claim 2,
the generating of the traffic degree grade of the road section between the starting intersection and the end intersection on the display device preset at the starting intersection specifically comprises:
when the traffic degree grade is smooth, the traffic degree grade of the road section between the starting intersection and the terminal intersection, which is generated on a display device preset at the starting intersection, is green;
when the traffic degree grade is that the road section is slow to pass, the traffic degree grade of the road section between the starting intersection and the terminal intersection, which is generated on a display device preset at the starting intersection, is orange;
and when the traffic degree grade is road section congestion, the traffic degree grade of the road section between the starting intersection and the terminal intersection, which is generated on the display equipment preset at the starting intersection, is red, and information suggesting changing of the path is generated on the display equipment preset at the starting intersection.
4. The big-data based centralized control method according to claim 3,
after the grade of the traffic degree of the road section between the starting intersection and the end intersection generated on the display device preset at the starting intersection is red and the information suggesting changing the path is generated on the display device preset at the starting intersection, the method further comprises the following steps:
generating a statistical table; the statistical table comprises time information when the traffic degree grade is judged to be road section congestion;
judging whether time information meeting preset rules exists in the statistical table or not;
if the time information meeting the preset rule exists, acquiring the time information meeting the preset rule;
generating time information with the next traffic degree grade as road congestion according to the time information meeting the preset rule, and defining the time information as next predicted congestion time;
generating the next predicted congestion time on the display device at a point in time before the time arrives, and stopping the display when the predicted congestion time arrives.
5. The big-data based centralized control method according to claim 1,
after the vehicle flow of the starting intersection and the ending intersection is obtained, the method further comprises the following steps:
acquiring vehicle information of all vehicles entering the initial intersection according to monitoring equipment preset at the initial intersection; the vehicle information comprises time information and license plate information of the vehicle entering the starting intersection;
acquiring vehicle information of all vehicles arriving at a terminal intersection according to monitoring equipment preset at the terminal intersection; the vehicle information comprises time information of the vehicle reaching the terminal intersection and license plate information;
inquiring all vehicles of which the vehicle information of all the vehicles entering the initial intersection is matched with the vehicle information of all the vehicles arriving at the terminal intersection, and defining all the vehicles of which the vehicle information of the initial intersection is matched with the vehicle information arriving at the terminal intersection as target vehicles;
acquiring the time from all the target vehicles to enter the starting intersection to the end intersection according to all the target vehicles;
calculating the average time from all the target vehicles to enter the starting intersection to the final intersection, and defining the average time as the predicted passing time;
and generating the predicted passing time on a display device preset at the starting intersection.
6. The big-data based centralized control method according to claim 5,
after querying all vehicles in the vehicle information entering the initial intersection and matched with the vehicle information reaching the final intersection and defining all vehicles in the vehicle information entering the initial intersection and matched with the vehicle information reaching the final intersection as target vehicles, the method further comprises the following steps:
judging whether large vehicles exist in all the target vehicles according to the vehicle information of the target vehicles;
if the large vehicles exist in all the target vehicles, acquiring the large vehicles in all the target vehicles;
acquiring the time from all the large vehicles to enter the starting intersection to the terminal intersection;
calculating the average time from all the large vehicles entering the starting intersection to the terminal intersection, and defining the average time as the predicted passing time of the large vehicles;
and generating the predicted passing time of the large-sized vehicle on the display equipment preset at the starting intersection.
7. The big-data based centralized control method according to claim 5,
after querying all vehicles in the vehicle information entering the initial intersection and matched with the vehicle information reaching the final intersection and defining all vehicles in the vehicle information entering the initial intersection and matched with the vehicle information reaching the final intersection as target vehicles, the method further comprises the following steps:
judging whether small vehicles exist in all the target vehicles according to the vehicle information of the target vehicles;
if the small vehicles exist in all the target vehicles, acquiring the small vehicles in all the target vehicles;
acquiring the time from all the small cars entering the starting intersection to the end intersection;
and calculating the average time from all the small vehicles to the starting intersection to the final intersection, and defining the average time as the predicted passing time of the small vehicles.
8. A big data based centralized control system, the system comprising,
the first acquisition module (1) is used for acquiring the traffic flow of the starting intersection and the end intersection according to preset monitoring equipment of the starting intersection and the end intersection;
the calculation module (2) is used for calculating the difference value of the vehicle flow of the starting intersection and the end intersection according to the vehicle flow of the starting intersection and the end intersection;
the judging module (3) is used for judging the traffic degree grade of a road section between the starting intersection and the end intersection according to the difference value of the traffic flow of the starting intersection and the end intersection and a preset road traffic degree grade table;
the second acquisition module (4) is used for acquiring the traffic degree grade of a road section between the starting intersection and the terminal intersection;
and the generating module (5) is used for generating the traffic degree grade of the road section between the starting intersection and the terminal intersection on a preset display device of the starting intersection.
9. An intelligent terminal, comprising a memory and a processor, the memory having stored thereon a computer program that can be loaded by the processor and that executes the method according to any one of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which can be loaded by a processor and which executes the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011462824.2A CN112581764A (en) | 2020-12-11 | 2020-12-11 | Centralized control method, system, terminal and storage medium based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011462824.2A CN112581764A (en) | 2020-12-11 | 2020-12-11 | Centralized control method, system, terminal and storage medium based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112581764A true CN112581764A (en) | 2021-03-30 |
Family
ID=75132288
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011462824.2A Pending CN112581764A (en) | 2020-12-11 | 2020-12-11 | Centralized control method, system, terminal and storage medium based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112581764A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114241777A (en) * | 2022-01-06 | 2022-03-25 | 交通运输部路网监测与应急处置中心 | Multi-source heterogeneous networking road condition monitoring early warning system and method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103258430A (en) * | 2013-04-26 | 2013-08-21 | 青岛海信网络科技股份有限公司 | Road traveling time calculating and traffic road condition judging method and road traveling time calculating and traffic road condition judging device |
| CN104715604A (en) * | 2014-01-13 | 2015-06-17 | 杭州海康威视数字技术股份有限公司 | Method and system for acquiring real-time traffic status information |
| CN205680280U (en) * | 2016-06-16 | 2016-11-09 | 南阳格瑞光电科技股份有限公司 | A kind of intelligent transportation electronic display inducible system |
| CN107038861A (en) * | 2017-04-07 | 2017-08-11 | 北京易华录信息技术股份有限公司 | A kind of traffic congestion detecting system, method and device based on vehicle electron identifying |
| CN108320506A (en) * | 2018-02-05 | 2018-07-24 | 青岛大学 | A kind of discovery method of the congestion period based on composite network |
| CN112053558A (en) * | 2020-08-25 | 2020-12-08 | 青岛海信网络科技股份有限公司 | Traffic jam state identification method, device and equipment |
-
2020
- 2020-12-11 CN CN202011462824.2A patent/CN112581764A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103258430A (en) * | 2013-04-26 | 2013-08-21 | 青岛海信网络科技股份有限公司 | Road traveling time calculating and traffic road condition judging method and road traveling time calculating and traffic road condition judging device |
| CN104715604A (en) * | 2014-01-13 | 2015-06-17 | 杭州海康威视数字技术股份有限公司 | Method and system for acquiring real-time traffic status information |
| CN205680280U (en) * | 2016-06-16 | 2016-11-09 | 南阳格瑞光电科技股份有限公司 | A kind of intelligent transportation electronic display inducible system |
| CN107038861A (en) * | 2017-04-07 | 2017-08-11 | 北京易华录信息技术股份有限公司 | A kind of traffic congestion detecting system, method and device based on vehicle electron identifying |
| CN108320506A (en) * | 2018-02-05 | 2018-07-24 | 青岛大学 | A kind of discovery method of the congestion period based on composite network |
| CN112053558A (en) * | 2020-08-25 | 2020-12-08 | 青岛海信网络科技股份有限公司 | Traffic jam state identification method, device and equipment |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114241777A (en) * | 2022-01-06 | 2022-03-25 | 交通运输部路网监测与应急处置中心 | Multi-source heterogeneous networking road condition monitoring early warning system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107798876B (en) | Road traffic abnormal jam judging method based on event | |
| US10354147B2 (en) | Method of data visualization and data sorting | |
| CN105225515A (en) | A kind of method for supervising based on fence and system | |
| CN106448167B (en) | A kind of congestion point judging method based on real-time road | |
| US10431078B2 (en) | Device and method for automatically updating a database of driving speed limits | |
| US20190266902A1 (en) | Method and system for generating a grid map that shows air traffic intensity | |
| CN108573600B (en) | Driver behavior induction and local traffic flow optimization method | |
| EP3358541B1 (en) | A method of predicting a traffic behaviour in a road system | |
| CN113052405B (en) | Traffic jam prediction and optimization method based on Internet of things and artificial intelligence | |
| CN112581764A (en) | Centralized control method, system, terminal and storage medium based on big data | |
| CN112434260A (en) | Road traffic state detection method and device, storage medium and terminal | |
| CN110969190A (en) | Illegal operation vehicle detection method, medium, equipment and device | |
| JP2020115306A (en) | Assessment method and determination system of pedestrian accident risk by location | |
| CN112418081A (en) | Method and system for air-ground joint rapid investigation of traffic accidents | |
| CN116434523A (en) | Vehicle active safety control method and device based on constraint degree in information perception scene | |
| CN116189436A (en) | Multi-source data fusion algorithm based on big data | |
| CN111047880B (en) | Traffic control method and device for road network, storage medium and management equipment | |
| WO2024067135A1 (en) | Unmanned aircraft management method and system based on big data identification, and medium | |
| US20230349717A1 (en) | Electronic map correction method, navigation information setting method, navigation method, and apparatus | |
| CN111695389B (en) | Lane line clustering method and device | |
| CN115440063A (en) | Traffic signal lamp control method and device, computer equipment and storage medium | |
| CN113220743B (en) | Traffic emergency plan matching method, system, terminal and medium | |
| CN103714701A (en) | Method for quickly judging overspeed of complex route | |
| CN106846794B (en) | Method and system for extracting service index and operation index of traffic cell | |
| TWI719640B (en) | Method and system for detecting traffic events |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210330 |
|
| RJ01 | Rejection of invention patent application after publication |