CN112150831A - Big data-based smart city street lamp information control processing method - Google Patents
Big data-based smart city street lamp information control processing method Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
- G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
- G06V20/54—Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats
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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
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
Abstract
The invention provides a smart city street lamp information control processing method based on big data, which comprises the following steps: activating a plurality of shooting components carried by the light pole to shoot the road surface; the intersection signal terminal continuously transmits a signal lamp state to the street lamp integrated lamp control platform, analyzes the road condition image and calculates the road traffic flow of the intersection signal terminal when the signal lamp state is green; when the traffic flow is lower than a preset threshold value, determining all intersection signal terminals located at the same coordinate; and generating the switching signal, and driving the intersection signal terminal to switch signal lamps according to the switching signal. The invention provides a big data-based intelligent street lamp information control processing method, which takes an intelligent street lamp as a data acquisition and calculation center, performs self-adaptive traffic signal switching according to road condition information, adopts a perfect strategy to process traffic accident road conditions and track vehicles, and improves the traffic control efficiency.
Description
Technical Field
The invention relates to the Internet of things under the environment of the field of big data, in particular to a smart city street lamp information control processing method based on big data.
Background
With the development of smart cities, various related applications are emerging, including control of intersection signal terminals. In general, a conventional intersection signal terminal executes a preset control command to activate a designated signal lamp within a designated phase and make it a loop, repeating the same signal lamp control period. However, the actual traffic situation changes instantly, and there often occurs a situation that no person or no vehicle passes through the intersection signal terminal in the green light state, and a lot of people or vehicles wait to pass through the intersection signal terminal on the other side when the intersection signal terminal is in the red light state, so that the problem of low traffic control efficiency exists. There are conventional solutions that propose means for manual lamp control, which allow remote traffic control personnel to control on-line for traffic guidance. However, the traffic management personnel can only manage one intersection at most, and this method needs to set one remote-controlled traffic management personnel for each intersection, which is high in cost and low in efficiency in large-scale cities with numerous intersections. In addition, a complete monitoring network and a corresponding early warning mechanism cannot be formed for monitoring traffic accidents and vehicles.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a smart city street lamp information control processing method based on big data, which comprises the following steps:
the method comprises the steps that an identity mark is arranged at each intersection signal terminal in advance, when the intersection signal terminal is started, a plurality of shooting assemblies carried by a light pole are activated to continuously shoot the road surface, and images shot by each shooting assembly are integrated into a road condition image with a plurality of sub-pictures;
the intersection signal terminal continuously transmits a signal lamp state to the street lamp integrated lamp control platform, the street lamp integrated lamp control platform analyzes the road condition image, and the road traffic flow of the intersection signal terminal when the signal lamp state is a green lamp is calculated according to an analysis result;
when the traffic flow is lower than a preset threshold value, the street lamp integrated lamp control platform determines all the intersection signal terminals which are positioned at the same coordinate, namely the same intersection according to the identity of the corresponding intersection signal terminal;
the street lamp integrated lamp control platform generates the switching signal and transmits the switching signal to all the determined intersection signal terminals; and
and the crossing signal terminal drives the crossing signal terminal to switch signal lamps according to the received switching signal.
Preferably, the method further comprises:
according to at least one preset special vehicle characteristic, one of the sub-pictures in the road condition image is subjected to image recognition, when at least one special vehicle meeting the special vehicle characteristic exists, the moving track of the special vehicle is detected to analyze the running route of the special vehicle, and the intersection signal terminal meeting the running route is determined to be subjected to signal lamp switching,
and/or
And carrying out image recognition on one of the sub-pictures in the road condition image according to at least one preset accident characteristic, and driving the indicating board arranged at the adjacent intersection to display accident information when the accident characteristic exists.
Preferably, the integrated street lamp control platform generates corresponding adjustment information according to the traffic flow of all the determined intersection signal terminals, and embeds the adjustment information into corresponding switching signals to adjust the phases respectively, wherein the duration of the phase in which the signal lamp state is green is proportional to the congestion degree of the traffic flow.
Preferably, after the image recognition is performed on one of the sub-pictures in the road condition image, the method further includes:
if the analysis module identifies that a traffic accident exists, sending a vehicle tracking request to the street lamp integrated lamp control platform according to the identified target vehicle characteristics;
the street lamp integrated lamp control platform receives a request of vehicle tracking, and performs image recognition on all adjacent shooting components Nb (A0) of the shooting component A0 at the accident site aiming at the vehicle characteristics;
if the recognition results of all the adjacent shooting assemblies within the preset time threshold do not include the characteristics of the target vehicle, finishing the tracking;
if adjacent to the photographing component Nb (A [0]]) In which there is a camera module A [0]]iImage recognition ofThe difference corresponds to the characteristics of the target vehicle to be tracked and the time stamp t identifying the presence of the target is recorded[1]And terminating the image recognition tasks of all other adjacent shooting components;
for exclusion of shooting component A [0]]The other shooting component A [1]]Adjacent photographing component (Nb (A1)])-A[0]) Image recognition of target vehicle features is performed only for the time stamp t of the occurrence of the previously recognized target[1]Then, recognizing the shot image;
if the recognition results of all the adjacent shooting assemblies within the preset time threshold do not include the characteristics of the recognition target, finishing the tracking, and taking the shooting assembly A [1] as the travel end point of the vehicle;
if adjacent to the camera module (Nb (A1)])-A[0]) In which there is a camera module A [2 ]]Is matched with the characteristics of the vehicle to be tracked, all other neighboring camera modules (Nb (A1) are terminated])-A[0]-A[2]) And recording a time stamp t of the occurrence of the recognition target[2]；
Then, the image recognition of the target vehicle feature is carried out on the adjacent shooting component (Nb (ai) -ai-1) of the shooting component (ai) which appears the recognition target after the shooting component (ai-1) which appears the recognition target at the previous time is eliminated, the recognition is only carried out on the image shot after the timestamp which appears the recognition target at the previous time, if the recognition results of all the adjacent shooting components do not include the feature of the recognition target, the tracking is finished, and the shooting component (ai) is taken as the travel terminal of the vehicle;
within the preset time threshold, if a camera component (Nb (A [ i ]) is adjacent])-A[i-1]) In which there is a shooting component A [ i +1]If the image recognition result of (b) matches the feature of the vehicle to be tracked, all other neighboring camera modules are terminated (Nb (A [ i ])])-A[i-1]-A[i+1]) And recording a time stamp t of the occurrence of the recognition target[i+1]；
The process is repeated until a given end of travel (ae) for the vehicle occurs.
Obtaining a sequence of numbers of a group of shooting components in sequence ((A0)],t[0]), (A[i],t[i]), (A[i+1],t[i+1]),…, (A[e],t[e]) The order of the groupThe row is the travel of the target vehicle, and is recorded in the traffic accident database together with the traffic accident data.
Compared with the prior art, the invention has the following advantages:
the invention provides a big data-based intelligent city street lamp information control processing method, which takes intelligent street lamps as a data acquisition and calculation center, performs self-adaptive traffic signal switching according to road condition information, adopts a perfect strategy to process traffic accident road conditions and track vehicles, and improves the traffic control efficiency.
Drawings
Fig. 1 is a flowchart of a smart city street lamp information control processing method based on big data according to an embodiment of the present invention.
Detailed Description
A detailed description of one or more embodiments of the invention is provided below along with accompanying figures that illustrate the principles of the invention. The invention is described in connection with such embodiments, but the invention is not limited to any embodiment. The scope of the invention is limited only by the claims and the invention encompasses numerous alternatives, modifications and equivalents. Numerous specific details are set forth in the following description in order to provide a thorough understanding of the invention. These details are provided for the purpose of example and the invention may be practiced according to the claims without some or all of these specific details.
The invention provides an intelligent street lamp information control processing method based on big data. Fig. 1 is a flowchart of a smart city street lamp information control processing method based on big data according to an embodiment of the present invention.
The invention discloses a traffic signal lamp control system based on an intelligent street lamp and a method thereof. The system comprises a street lamp integrated lamp control platform and a crossing signal terminal. The street lamp integrated lamp control platform is used for issuing an edge calculation task, collecting a calculation result, storing the edge calculation result, and the intelligent routing equipment executes the edge technical task, exchanges data with the IoT terminal and transmits the calculation to the street lamp integrated lamp control platform. The IoT terminal comprises a sensor device with a sensing function and a smart lighting device. The intelligent routing equipment calculates data to obtain relevant information, issues work to the intelligent lighting equipment execution end, and simultaneously transmits information to the street lamp integrated lamp control platform. The street lamp integrated lamp control platform can also issue edge calculation tasks to the intelligent routing equipment, the routing end decomposes the tasks into different works to be executed by the corresponding IoT terminal, and based on the intelligent street lamp system connection structure, the balanced distribution and scheduling of the system tasks are realized through the design of an edge calculation algorithm.
In the application scene of the actual intelligent street lamp system, emergency situations such as network breaking, facility damage and the like can occur, and in consideration of the abnormal situation, the invention firstly sets two ways for judging the abnormality: the street lamp integrated lamp control platform finds that the edge calculation task issued to the intelligent routing equipment exceeds the maximum working time delay. And the communication between the adjacent intelligent routing equipment and the abnormal routing equipment is interrupted.
When the abnormity happens, the street lamp integrated lamp control platform does not directly intervene, and the scheduling process is as follows:
a. and the adjacent intelligent routing equipment mutually communicates and transmits the IoT terminal address table connected with the adjacent intelligent routing equipment under the normal working mode.
b. And the adjacent idle intelligent routing equipment determines that the abnormal routing equipment is abnormal and determines the physical address of the abnormal routing equipment.
c. And the adjacent idle intelligent routing equipment acquires the control authority of the equipment according to the IoT terminal address table connected with the abnormal routing equipment.
d. And the adjacent intelligent routing equipment executes the edge calculation task of the abnormal routing equipment and transmits the abnormal condition to the street lamp integrated lamp control platform.
e. After the abnormal intelligent routing equipment is recovered, the adjacent intelligent routing equipment executes a calculation task and releases the IoT terminal, and the abnormal intelligent routing equipment receives the IoT terminal again and transmits recovery information to the street lamp integrated lamp control platform.
The intelligent routing device comprises a PHY module, a switching module and a core control module. The PHY module is used for detecting whether the Ethernet communication equipment has data to be transmitted, if so, waiting, and once the network is detected to be idle, sending the data out after waiting for random time. The exchange module identifies the MAC address of the equipment connected with each port, maps the address with the corresponding port and stores the address in an MAC address table in the cache of the exchange module, processes the received service frame, and performs rate adaptation, filtering and sending control on the service frame. The core control module carries out protocol analysis on the data forwarded by the exchange module and the accessed communication equipment data, so that the street lamp pole equipment is identified, and the key data are stored. Meanwhile, the lamp post equipment data is analyzed, processed and encrypted, then protocol conversion is carried out, and finally the data is sent to a management center server through a network control interface. The core control module also realizes a local edge control function, and can analyze the data of the light pole equipment and detect and judge the input state of the switching value according to the application scene of the light pole equipment, and perform linkage control and parameter setting on the equipment. Support WIFI and 5G communication, when the lamp pole on-the-spot does not have wired network, realize the cascade between the lamp pole through WIFI, realize communicating with management center server through the 5G module at last.
Street lamp pole intelligence route has local storage function: the street lamp pole data can be stored in the street lamp pole data storage device, and real-time data of the street lamp pole can be stored in the local storage device when the network connection is abnormal. The type of the light pole equipment can be automatically identified through data protocol analysis, and the working state of the equipment is monitored. Judging the running state of the street lamp pole equipment; after the management center platform configures a linkage strategy, the intelligent route judges whether the field equipment meets a trigger condition; when the triggering condition is met, the linkage of the street lamp pole equipment is controlled.
The street lamp integrated lamp control platform comprises a shooting module, an analysis module, a determination module and a processing module. The road condition image acquisition system comprises a shooting module, a road condition image acquisition module and a road condition image acquisition module, wherein the shooting module is used for activating a plurality of shooting assemblies carried by a light pole to continuously shoot a road surface, and integrating images shot by each shooting assembly into a road condition image with a sub-picture; the analysis module is used for receiving and analyzing the signal lamp state from the intersection signal terminal, and calculating the road traffic flow when the signal lamp state is green according to the analysis result; the determining module is coupled with the analyzing module and used for determining all the intersection signal terminals which are positioned at the same coordinate, namely the same intersection according to the identity of the intersection signal terminals when the traffic flow is lower than a preset threshold value; the processing module is coupled to the determining module and is used for generating switching signals and transmitting the switching signals to all determined intersection signal terminals.
The intersection signal terminal comprises a transmission module and a driving conversion module. The transmission module is used for continuously transmitting the signal lamp state of the intersection signal terminal and receiving a switching signal from the street lamp integrated lamp control platform; the driving conversion module is coupled to the transmission module and used for driving the intersection signal terminal to switch signal lamps according to the received switching signal.
The big-data intelligent street lamp information control processing method comprises the steps of setting an identity mark at each intersection signal terminal in advance, activating a plurality of shooting components carried by a street lamp post to continuously shoot a road surface, and integrating images shot by each shooting component into a road condition image with a sub-picture; the intersection signal terminal continuously transmits the signal lamp state to the street lamp integrated lamp control platform; the street lamp integrated lamp control platform receives and analyzes the signal lamp state from the intersection signal terminal and the road condition image provided by the shooting component, and calculates the road traffic flow of the intersection signal terminal when the signal lamp state is green according to the analysis result; when the traffic flow is lower than a preset threshold value, the street lamp integrated lamp control platform determines all intersection signal terminals located at the same coordinate, namely the same intersection, according to the identity of the corresponding intersection signal terminal; the street lamp integrated lamp control platform generates a switching signal and transmits the switching signal to all determined intersection signal terminals; and the intersection signal terminal drives the intersection signal terminal to switch signal lamps according to the received switching signal.
The intelligent street lamp post can be applied to the environment of the Internet of things, the road condition images are continuously shot through the shooting component arranged on the intelligent street lamp post, and the state of the signal lamp and the road condition images are transmitted to the street lamp integrated lamp control platform. The intersection signal terminal and the street lamp integrated lamp control platform are all components of the Internet of things and are communicated with each other through a network. The Internet of things can be realized through wireless communication technologies such as WiFi, ZigBee or MQTT.
In specific implementation, the transmission module can directly sense whether the signal lamp assembly is activated or not through the voltage sensing assembly, and then the signal lamp state is acquired and transmitted to the street lamp integrated lamp control platform. Each intersection signal terminal is provided with a unique identity, the identity is embedded into the signal lamp state for identification when the transmission module transmits the signal lamp state, and in addition, the identity is also the basis for screening the specific intersection signal terminals by the street lamp integrated lamp control platform. In specific implementation, the identification may include an intersection number, an equipment number, a position code, and the like, the intersection signal terminals of the same intersection have the same intersection number in the same coordinate, for example, a level-crossing intersection is usually provided with four intersection signal terminals, the intersection number in the identification may also be "01", the equipment number respectively represents the four intersection signal terminals by a value of 1 to 4, and the position code may include information such as a direction or a longitude and latitude.
And the driving conversion module drives the intersection signal terminal to switch signal lamps according to the received switching signal. For example, assume that the default control mode of the intersection signal terminal is cycled through four phases. I.e., sequentially from a first phase, a second phase, a third phase to a fourth phase, then back to the first phase and so on, with each phase driving a respective signal lamp component, for example: the green light assembly is activated for 60 seconds in the first phase, the green light assembly is flashed 3 times in the second phase, only the yellow light assembly is activated for 3 seconds in the third phase, and only the red light assembly is activated for 45 seconds in the fourth phase. When the driving conversion module receives the switching signal to drive the intersection signal terminal to switch the signal lamp, if the intersection signal terminal is in the first phase, the driving conversion module directly ends the first phase and enters the second phase, and then signal lamp control of the third phase and the fourth phase is carried out in sequence according to a preset control mode, so that signal lamp switching from the green lamp to the red lamp is completed.
On the other hand, assuming that the traffic light status of the intersection signal terminal is red, when the driving conversion module receives the switching signal, the red light is directly switched to the green light after waiting for a period of time, which is the same as the time required for switching from the green light to the red light, that is, although the red light component needs to be activated for 45 seconds at the fourth phase by default, when the switching signal is received, even if the time is not less than 45 seconds, the corresponding traffic light component is directly returned to the first phase to drive after waiting for the period of time. In particular implementations, the phases may be generated by a timer, the drive transform module may be driven by a microcontroller at each phase by the corresponding signal lamp assembly, or the drive transform module itself may be implemented using a microcontroller to directly drive the signal lamp assembly corresponding to the next phase using the microcontroller upon receiving the switching signal.
Furthermore, the analysis module of the street lamp integrated lamp control platform can perform image recognition on the road condition image according to preset vehicle characteristics, and calculate the number of the road condition images according with the vehicle characteristics to serve as the traffic flow. In addition, in the specific implementation, the analysis module can also perform image recognition on one of the sub-images in the road condition image according to the default special vehicle characteristics and accident characteristics, and when a special vehicle meeting the special vehicle characteristics exists, the running route of the special vehicle is analyzed to determine the crossing signal terminal which the special vehicle will meet next for signal lamp switching.
In addition, the analysis module can also judge whether the current intersection has an accident according to predefined accident characteristics, and when the accident occurs, the indication boards of the adjacent intersections are driven to display accident information for guiding the vehicles at the adjacent intersections to avoid going to the accident intersections.
In a specific implementation, the processing module respectively generates corresponding adjustment information according to the traffic flow of all the determined intersection signal terminals, and embeds the adjustment information into corresponding switching signals to respectively adjust the phase of each signal lamp, wherein the signal lamp state is that the time length of the phase of the green lamp is in direct proportion to the congestion degree of the traffic flow.
The street lamp integrated lamp control platform further comprises an accident tracking module which is coupled with the analysis module, establishes relevance for two results by utilizing the image and sound recognition results of the shooting assembly and the recording assembly, further analyzes whether the traffic accident happens or not, tracks vehicles aiming at possible escape incidents, marks the vehicle track information and the traffic accident information in an electronic map, and reproduces the traffic accident information in a visual and audible mode.
The data to be collected by the accident tracking module is the relevant information of the positions of the shooting component and the recording component and the information of the adjacent shooting component and the recording component. The collection modes of the information adjacent to the shooting component and the recording component are divided into two types according to the representation modes of the positions of the shooting component and the recording component: the first mode is that when the position data of the shooting component and the recording component are expressed by GPS data, the positions of the shooting component and the recording component are calculated to be adjacent by a calculation mode, and the information of the shooting component and the recording component adjacent to each shooting component and each recording component is established according to the calculation result; the second way is to provide a user interface for the user to input data of the shooting component and the recording component adjacent to each shooting component and the recording component when the shooting component and the recording component location data correspond to the electronic map provided by the user.
The main characteristics of the traffic accident include the characteristics of clearly recognizable collision sound, instant slowing of traffic flow speed, overlapping of more than two moving objects, concentrated traffic flow changing lanes, sudden acceleration increase of objects and the like. The above features can be recognized by a voice recognition module and an image recognition module of the analysis module, and once the above conditions are judged to occur, the analysis module starts to compare the image recognition results. The present invention may use any feasible algorithm to determine the occurrence of a traffic accident event and is not limited to any one particular algorithm for detection, such as machine learning or artificial neural network algorithms using historical data.
When an accident occurs, recording a time stamp Ts of the accident and the position (an ID of a shooting component, a recording component and a GPS position) of the accident in real time, detecting the time stamp of the end of the traffic accident, and when the traffic flow direction and the speed are recovered to the traffic flow direction and the speed before the accident occurs, taking the time stamp as a time stamp Te of the end of the traffic accident, marking the section [ Ts, Te ] as the time period of the occurrence of the traffic accident by the analysis module, and storing the time stamp and the data (such as image identification characteristics, sound identification characteristics, location and the like) related to the traffic accident into a traffic accident database.
When a traffic accident event is detected, starting a tracking process of a shooting component and a recording component around the accident occurrence place for further tracking, wherein the tracking process comprises image recognition on historical images of the shooting component and the recording component at the position of the accident occurrence place, mainly recognizing images within a preset time threshold (such as minutes set by a user) before the accident occurrence, when a moving object is static and another object continuously moves and leaves the shooting range of the shooting component and the recording component of the accident occurrence place, taking the leaving object as a target escaping vehicle, recording vehicle characteristics (related information such as license plate, vehicle color, vehicle type and the like) of the object, starting a linkage recognition tracking method spanning a plurality of shooting components, and tracking the image characteristics of possible target vehicles in real time, the drive system performs a task of recognizing a plurality of images of the photographing assembly. The multiple shooting assembly linkage identification tracking method comprises the following processes:
if the analysis module identifies that a traffic accident exists, sending a vehicle tracking request to the street lamp integrated lamp control platform according to the identified target vehicle characteristics;
the street lamp integrated lamp control platform receives a request of vehicle tracking, and performs image recognition on all adjacent shooting components Nb (A0) of the shooting component A0 at the accident site aiming at the vehicle characteristics;
if the recognition results of all the adjacent shooting assemblies within the preset time threshold do not include the characteristics of the target vehicle, finishing the tracking;
if adjacent to the photographing component Nb (A [0]]) In which there is a camera module A [0]]iIs matched with the characteristics of the target vehicle to be tracked, and records the time stamp t of the occurrence of the identified target[1]And terminating the image recognition tasks of all other adjacent shooting components;
for exclusion of shooting component A [0]]The other shooting component A [1]]Adjacent photographing component (Nb (A1)])-A[0]) Image recognition of target vehicle features is performed only for the time stamp t of the occurrence of the previously recognized target[1]Then, recognizing the shot image;
if the recognition results of all the adjacent shooting assemblies within the preset time threshold do not include the characteristics of the recognition target, finishing the tracking, and taking the shooting assembly A [1] as the travel end point of the vehicle;
if adjacent to the camera module (Nb (A1)])-A[0]) In which there is a camera module A [2 ]]Is matched with the characteristics of the vehicle to be tracked, all other neighboring camera modules (Nb (A1) are terminated])-A[0]-A[2]) And recording a time stamp t of the occurrence of the recognition target[2]；
Then, the image recognition of the target vehicle feature is carried out on the adjacent shooting component (Nb (ai) -ai-1) of the shooting component (ai) which appears the recognition target after the shooting component (ai-1) which appears the recognition target at the previous time is eliminated, the recognition is only carried out on the image shot after the timestamp which appears the recognition target at the previous time, if the recognition results of all the adjacent shooting components do not include the feature of the recognition target, the tracking is finished, and the shooting component (ai) is taken as the travel terminal of the vehicle;
within the preset time threshold, if a camera component (Nb (A [ i ]) is adjacent])-A[i-1]) In which there is a shooting component A [ i +1]If the image recognition result of (b) matches the feature of the vehicle to be tracked, all other neighboring camera modules are terminated (Nb (A [ i ])])-A[i-1]-A[i+1]) And recording a time stamp t of the occurrence of the recognition target[i+1]；
The process is repeated until a given end of travel (ae) for the vehicle occurs.
Obtaining a sequence of numbers of a group of shooting components in sequence ((A0)],t[0]), (A[i],t[i]), (A[i+1],t[i+1]),…, (A[e],t[e]) The set of sequences is the journey of the target vehicle and is recorded in a traffic accident database together with the traffic accident data.
In order to determine the abnormal-driving vehicle, the analysis module is further used for integrating a plurality of identification data to generate a plurality of license plate numbers and corresponding driving trace data. Integrating all the identification data of the previous day by utilizing a big data calculation mode at fixed time such as every day, taking each license plate number as a group, summarizing all the identification data of each license plate number on the current day, forming trace data according to a time sequence, and storing all the group data in a database.
The license plate number and the corresponding tracing data can be obtained from a database, and after the car color or the car type is filtered, the error rate analysis subsystem carries out the next filtering and analysis. The error rate analysis subsystem performs track error rate analysis on the track data of at least one of the plurality of license plate numbers to obtain the track information of the license plate number to be analyzed of the at least one license plate number. Specifically, the error rate analysis subsystem can calculate the mutual error rate of the single-day traces of the single license plate number in each road section, and the error rate is smaller than a set threshold value, namely the error rate is determined to be the correct license plate number.
The abnormity analysis subsystem executes abnormity analysis on license plate number track data to be analyzed, wherein the abnormity analysis comprises loitering abnormity analysis, fake plate analysis and travel time abnormity analysis, so as to obtain loitering abnormity information, fake plate information or travel time abnormity information. Specifically, the abnormity analysis subsystem acquires the license plate number tracing information to be analyzed from a database aiming at the input monitoring license plate number, and the license plate number tracing information is acquired after the license plate number is analyzed and filtered by the error rate analysis subsystem; calculating a loitering abnormal index according to the unit time interval row trace data in the license plate number row trace data to be analyzed and the historical time interval row trace data, and further obtaining loitering abnormal information; calculating a fake plate index according to the license plate number tracking information to be analyzed, the last appearance time and place of the specific license plate number and the license plate number tracking data to be analyzed in the last appearance place, and further obtaining fake plate information; and calculating a travel time abnormity index according to the license number travel trace data to be analyzed in a specific monitoring time period in the monitoring area and the license number travel trace data to be analyzed in a historical time period in the monitoring area, and further obtaining travel time abnormity information.
In summary, the invention provides a smart city street lamp information control processing method based on big data, which takes a smart street lamp as a data acquisition and calculation center, performs adaptive traffic signal switching according to road condition information, and adopts a perfect strategy to process traffic accident road conditions and vehicle tracking, thereby improving traffic control efficiency.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented in a general purpose computing system, centralized on a single computing system, or distributed across a network of computing systems, and optionally implemented in program code that is executable by the computing system, such that the program code is stored in a storage system and executed by the computing system. Thus, the present invention is not limited to any specific combination of hardware and software.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention shall be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (4)
1. A smart city street lamp information control processing method based on big data is applied to an Internet of things environment with at least one intersection signal terminal and a street lamp integrated lamp control platform, and is characterized by comprising the following steps:
the method comprises the steps that an identity mark is arranged at each intersection signal terminal in advance, when the intersection signal terminal is started, a plurality of shooting assemblies carried by a light pole are activated to continuously shoot the road surface, and images shot by each shooting assembly are integrated into a road condition image with a plurality of sub-pictures;
the intersection signal terminal continuously transmits a signal lamp state to the street lamp integrated lamp control platform, the street lamp integrated lamp control platform analyzes the road condition image, and the road traffic flow of the intersection signal terminal when the signal lamp state is a green lamp is calculated according to an analysis result;
when the traffic flow is lower than a preset threshold value, the street lamp integrated lamp control platform determines all the intersection signal terminals which are positioned at the same coordinate, namely the same intersection according to the identity of the corresponding intersection signal terminal;
the street lamp integrated lamp control platform generates the switching signal and transmits the switching signal to all the determined intersection signal terminals; and
and the crossing signal terminal drives the crossing signal terminal to switch signal lamps according to the received switching signal.
2. The smart city street lamp information control processing method based on big data as claimed in claim 1, further comprising:
according to at least one preset special vehicle characteristic, one of the sub-pictures in the road condition image is subjected to image recognition, when at least one special vehicle meeting the special vehicle characteristic exists, the moving track of the special vehicle is detected to analyze the running route of the special vehicle, and the intersection signal terminal meeting the running route is determined to be subjected to signal lamp switching,
and/or
And carrying out image recognition on one of the sub-pictures in the road condition image according to at least one preset accident characteristic, and driving the indicating board arranged at the adjacent intersection to display accident information when the accident characteristic exists.
3. The smart city street lamp information control processing method based on big data as claimed in claim 1, wherein the street lamp integrated lamp control platform generates corresponding adjustment information according to the traffic flow of all the determined intersection signal terminals, and embeds the adjustment information into corresponding switching signals to adjust the phases, respectively, wherein the duration of the phase in which the signal lamp status is green is proportional to the congestion degree of the traffic flow.
4. The intelligent city street lamp information control processing method based on big data as claimed in claim 2, wherein after image recognition is performed on one of the sub-pictures in the road condition image, the method further comprises:
if the analysis module identifies that a traffic accident exists, sending a vehicle tracking request to the street lamp integrated lamp control platform according to the identified target vehicle characteristics;
the street lamp integrated lamp control platform receives a request of vehicle tracking, and performs image recognition on all adjacent shooting components Nb (A0) of the shooting component A0 at the accident site aiming at the vehicle characteristics;
if the recognition results of all the adjacent shooting assemblies within the preset time threshold do not include the characteristics of the target vehicle, finishing the tracking;
if adjacent to the photographing component Nb (A [0]]) In which there is a camera module A [0]]iIs matched with the characteristics of the target vehicle to be tracked, and records the time stamp t of the occurrence of the identified target[1]And terminating the image recognition tasks of all other adjacent shooting components;
for exclusion of shooting component A [0]]The other shooting component A [1]]Adjacent photographing component (Nb (A1)])-A[0]) Image recognition of target vehicle features is performed only for the time stamp t of the occurrence of the previously recognized target[1]Then, recognizing the shot image;
if the recognition results of all the adjacent shooting assemblies within the preset time threshold do not include the characteristics of the recognition target, finishing the tracking, and taking the shooting assembly A [1] as the travel end point of the vehicle;
if adjacent to the camera module (Nb (A1)])-A[0]) In which there is a camera module A [2 ]]Is matched with the characteristics of the vehicle to be tracked, all other neighboring camera modules (Nb (A1) are terminated])-A[0]-A[2]) And recording a time stamp t of the occurrence of the recognition target[2]；
Then, the image recognition of the target vehicle feature is carried out on the adjacent shooting component (Nb (ai) -ai-1) of the shooting component (ai) which appears the recognition target after the shooting component (ai-1) which appears the recognition target at the previous time is eliminated, the recognition is only carried out on the image shot after the timestamp which appears the recognition target at the previous time, if the recognition results of all the adjacent shooting components do not include the feature of the recognition target, the tracking is finished, and the shooting component (ai) is taken as the travel terminal of the vehicle;
within the preset time threshold, if a camera component (Nb (A [ i ]) is adjacent])-A[i-1]) In which there is a shooting component A [ i +1]If the image recognition result of (b) matches the feature of the vehicle to be tracked, all other neighboring camera modules are terminated (Nb (A [ i ])])-A[i-1]-A[i+1]) And recording a time stamp t of the occurrence of the recognition target[i+1]；
Repeating the above process until a given end of travel (ae) for the vehicle occurs;
obtaining a sequence of numbers of a group of shooting components in sequence ((A0)],t[0]), (A[i],t[i]), (A[i+1],t[i+1]),…, (A[e],t[e]) The set of sequences is the journey of the target vehicle and is recorded in a traffic accident database together with the traffic accident data.
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