CN112749630A - Intelligent video monitoring method and system for road conditions - Google Patents
Intelligent video monitoring method and system for road conditions Download PDFInfo
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- CN112749630A CN112749630A CN202011492048.0A CN202011492048A CN112749630A CN 112749630 A CN112749630 A CN 112749630A CN 202011492048 A CN202011492048 A CN 202011492048A CN 112749630 A CN112749630 A CN 112749630A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
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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
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
Abstract
The invention discloses an intelligent video monitoring method and system for road conditions, wherein the method comprises the following steps: acquiring scene videos of passing vehicles on a road, identifying and analyzing the passing vehicle videos based on a deep learning neural network, and analyzing and acquiring vehicle data in real time; identifying scene videos of vehicles passing by the road, which are acquired from different places of the same road section, determining and counting the number of the vehicles ID in the area within a fixed time interval to obtain the results of traffic flow and vehicle speed; and deducing road conditions to a big data platform by using the obtained traffic flow and the vehicle speed result, and performing early warning decision. By applying the intelligent video monitoring method and system for road conditions, the real-time intelligent video monitoring for the road conditions is carried out through a deep learning neural network, the traffic passing conditions are found in time, the behaviors and personnel violating safety regulations are automatically reported to the back-end traffic platform, and the back-end traffic platform informs relevant workers of processing immediately, so that the traffic safety is ensured.
Description
Technical Field
The invention relates to the field of video security monitoring, in particular to an intelligent video monitoring method and system for road conditions.
Background
At present, roads are a main component of cities, and the urban service capacity can be improved through road intellectualization. Wisdom road construction has following meaning and effect: the defects of insufficient information acquisition coverage and difficult data integration of the current road traffic situation are overcome; the intelligent supervision and intelligent scheduling of transportation management are realized; intelligent identification and automatic early warning of one side condition of a road are realized; the method integrates real-time road conditions to realize the prejudgment and early warning of dangerous road conditions, traffic jam and emergency events. Therefore, an intelligent video monitoring method and system for road conditions are needed to realize intelligent identification and automatic early warning of real-time road conditions.
Disclosure of Invention
The embodiment of the invention provides an intelligent video monitoring method and system for road conditions, which can solve the technical problems that in the prior art, the road video security monitoring mainly depends on manpower, the efficiency is low, the early warning is not timely, the information acquisition coverage is insufficient, and the data integration is difficult.
In order to achieve the above object, a first aspect of the present invention provides an intelligent video monitoring method for road conditions, including: acquiring scene videos of passing vehicles on a road, identifying and analyzing the passing vehicle videos based on a deep learning neural network, and analyzing and acquiring vehicle data in real time;
identifying scene videos of vehicles passing by the road, which are acquired from different places of the same road section, determining and counting the number of the vehicles ID in the area within a fixed time interval to obtain the results of traffic flow and vehicle speed;
and deducing road conditions to a big data platform by using the obtained traffic flow and the speed result, and performing early warning decision.
Further, the method further comprises the step of carrying out recognition vehicle data analysis model training on the past vehicle scene based on the deep learning neural network.
Further, the recognition analysis model training of the passing vehicle scene based on the deep learning neural network further comprises road surface dangerous case recognition.
Further, the recognition analysis model training of the passing vehicle scene based on the deep learning neural network further comprises recognition of traffic violation events.
Further, the recognition analysis model training of the passing vehicle scene based on the deep learning neural network further comprises recognition of non-motor vehicle illegal events.
Further, the model training of the recognition analysis of the passing vehicle scene based on the deep learning neural network further comprises the recognition of the travel state of the pedestrian.
Further, the recognition analysis model training of the passing vehicle scene based on the deep learning neural network further comprises the recognition of cross-zone line behaviors of motor vehicles, pedestrians and non-motor vehicles.
And further, after the neural network based on deep learning identifies and analyzes the passing vehicle scene, the identified road condition result is sent to a background.
And further, deducing road conditions to a big data platform by using the obtained traffic flow and the vehicle speed result, and carrying out early warning decision including setting corresponding road condition threshold values, and carrying out alarm prompt if the road condition threshold values are exceeded.
In order to achieve the above object, an embodiment of the present invention further provides an intelligent video monitoring system for road conditions, which comprises an acquisition and identification module, a traffic flow speed result determination module, and an early warning decision module,
the acquisition and recognition module is used for acquiring scene videos of passing vehicles on the road, carrying out recognition analysis on the passing vehicle videos based on a deep learning neural network, and analyzing and collecting vehicle data in real time;
the traffic flow speed result determining module is used for identifying scene videos of vehicles passing through the road, which are acquired from different places of the same road section, determining and counting the number of the vehicles ID in the area within a fixed time interval, and obtaining a traffic flow and speed result;
and the early warning decision module is used for deducing road conditions to a big data platform by using the obtained traffic flow and the vehicle speed result to carry out early warning decision.
The embodiment of the invention provides an intelligent video monitoring method for road conditions, which comprises the following steps: acquiring scene videos of passing vehicles on a road, identifying and analyzing the passing vehicle videos based on a deep learning neural network, and analyzing and acquiring vehicle data in real time; identifying scene videos of vehicles passing by the road, which are acquired from different places of the same road section, determining and counting the number of the vehicles ID in the area within a fixed time interval to obtain a traffic flow and a vehicle speed result; and deducing road conditions to a big data platform by using the obtained traffic flow and the speed result, and performing early warning decision. By applying the intelligent video monitoring method for the road condition, the road condition is intelligently monitored in real time through a deep learning neural network, and vehicle data are analyzed and collected in real time; identifying scene videos of vehicles passing by roads and obtained from different places of the same road section, and determining and counting the number of the vehicles ID in the area within a fixed time interval to obtain traffic flow and vehicle speed results; the obtained traffic flow and the vehicle speed result are used for deducing road conditions to a big data platform, early warning decision is carried out, traffic passing conditions are found in time, behaviors and personnel violating safety regulations are judged, the congestion states of pedestrians and vehicles are judged at the same time, automatic alarm is given and uploaded to a rear-end traffic platform, the rear-end traffic platform informs relevant workers of processing immediately, the road passing safety is ensured, and the intelligent video monitoring method and the intelligent video monitoring system for the road conditions select a common camera to install an embedded intelligent analysis box, so that the cost of the whole system is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating steps of an intelligent video monitoring method for road conditions according to a first embodiment of the present invention;
fig. 2 is a schematic view of an intelligent video monitoring system for road conditions according to a second embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating steps of an intelligent video monitoring method for road conditions according to a first embodiment of the present invention, in which the method includes:
101, acquiring scene videos of passing vehicles on a road, identifying and analyzing the passing vehicle videos based on a neural network of deep learning, and analyzing and acquiring vehicle data in real time; specifically, all intelligent analysis algorithms are based on videos, all algorithms adopt a neural network deep learning algorithm, all the intelligent analysis algorithms are realized through an intelligent analysis box, the box is connected into an existing monitoring camera to obtain scene videos of roads and passing vehicles, real-time intelligent video monitoring is carried out on the scene of the passing vehicles on the roads through the deep learning neural network, and vehicle data such as license plate numbers, license plate colors, license plate types, vehicle body colors, vehicle types and vehicle brands are acquired through real-time analysis; identifying scene videos of vehicles passing by the road, which are acquired from different places of the same road section, determining and counting the number of the vehicles ID in the area within a fixed time interval to obtain the results of traffic flow and vehicle speed; the obtained traffic flow and the vehicle speed result are used for deducing road conditions to a big data platform, pre-warning decision is carried out, traffic passing conditions are found in time, behaviors and personnel violating safety regulations are judged, meanwhile, the congestion states of pedestrians and vehicles are judged, automatic alarm is carried out, a rear-end traffic platform is uploaded, the rear-end traffic platform informs relevant workers of processing immediately, the road passing safety is ensured, and the intelligent video monitoring method and the intelligent video monitoring system for the road conditions select a common camera to install an embedded intelligent analysis box, so that the cost of the whole system is reduced.
102, identifying scene videos of vehicles passing through the road, which are acquired from different places of the same road section, determining and counting the number of the vehicles ID in the area within a fixed time interval, and obtaining results of traffic flow and vehicle speed; specifically, vehicle ID tracking is performed for each vehicle appearing in the field angle of the camera, one area is arranged in the camera video, the number of vehicle IDs passing through the area in a fixed time interval is calculated, and the traffic flow and the vehicle speed of the same vehicle ID are calculated. The collected data are automatically uploaded to a rear-end intelligent traffic big data platform, the ID speed of the same vehicle can be obtained according to a distance-time formula, and the number of the ID vehicles, namely the traffic flow and the ID speed of the same vehicle are determined by identifying the scene videos of the vehicles passing through the road.
And 103, deducing road conditions to a big data platform by using the obtained traffic flow and the vehicle speed result, and performing early warning decision, wherein in the concrete implementation, a manager can set an alarm threshold value for the obtained traffic flow and the vehicle speed result, and when the vehicle speed result exceeds the alarm threshold value, the traffic jam condition of the road section is early warned, for example, the traffic flow is large, and when the vehicle speed is slow enough, the traffic jam early warning is extremely occurred, so that the surrounding police force is timely mobilized, the traffic is timely eased, the manual real-time monitoring is not needed, the early warning is timely, the efficiency is high, and the traffic smoothness of urban trip is.
The detailed subdivision steps of the steps are as follows:
and further, the method also comprises the step of carrying out recognition vehicle data analysis model training on the past vehicle scene based on a deep learning neural network, for example, the deep learning neural network is trained to recognize and collect vehicle data in real time through vehicle detection, vehicle tracking, vehicle optimization and vehicle attribute recognition, wherein the vehicle data comprises license plate numbers, license plate colors, license plate types, vehicle body colors, vehicle types, vehicle brands, vehicle speeds and vehicle flow, so that the number of the ID of the vehicles passing through the area is determined through the recognition of the past vehicles, and the vehicle flow and the vehicle speed result are obtained.
Further, the neural network based on deep learning is used for carrying out recognition analysis model training on the past vehicle scene and also comprises pavement dangerous case recognition, if the neural network based on deep learning is used for training the road abnormal conditions including pavement dangerous cases (collapse, cracks, accumulated water and foreign matters) and facility damage (well covers, guardrails, marked lines and signboards), the neural network based on deep learning is used for collecting sample pictures of abnormal states of various roads in various time periods, a deep learning frame is used for training to form models for recognizing various abnormalities of the roads, and the models are deployed to an intelligent analysis box. The intelligent analysis box automatically identifies the road abnormalities through the accessed camera, automatically alarms and uploads a rear-end traffic platform, and the rear-end traffic platform informs related workers of handling the road abnormalities immediately, so that accidents are prevented.
Further, the recognition analysis model training of the passing vehicle scene based on the deep learning neural network further comprises the steps of recognizing traffic violation events including congestion, vehicle violation and vehicle violation at special intersections, taking pictures in time to punish when the traffic violation events are recognized, and uploading a background report. The intelligent analysis box carries out vehicle ID tracking on each vehicle appearing in the field angle of the camera, calculates the time of each vehicle appearing in the field angle, then calculates an average value, if the average value exceeds a set threshold value, the current road is indicated to be congested, a plurality of threshold values can be set, different congestion states of the current road are indicated, and if congestion is found, the congestion is uploaded to a rear-end traffic platform, and workers are dispatched to assist processing. In order to reduce the cost, a ball machine is deployed, and different illegal parking areas are polled and monitored by setting different preset positions. And configuring an illegal parking area on the box access video, and identifying illegal parking if the vehicle is found to enter the area and leaves the area after the time exceeds a set threshold value. Meanwhile, the illegal parking area and the time period can be set, and the vehicle type can be matched for recognition, so that illegal parking recognition of specific types of vehicles can be carried out, for example, the motor vehicles occupy bus lanes and the like. And identifying the license plate number, the license plate type, the license plate color, the body color, the vehicle type and the vehicle brand of the vehicle, automatically uploading to a rear-end traffic platform, and containing vehicle attributes and illegal parking pictures.
Further, performing recognition analysis model training on the passing vehicle scene based on the deep learning neural network further comprises recognizing a non-motor vehicle illegal event,
further, performing recognition analysis model training on the passing vehicle scene based on the deep learning neural network further comprises recognizing a pedestrian traveling state.
In particular, for non-motor vehicles, such as non-motor vehicles without safety helmets, overloading, umbrella installation, running red light and running in the reverse direction. Pedestrian attribute identification can be identified, and identifying attributes comprises: the color of the upper garment, the style of the upper garment, the color of the lower garment, the style of the lower garment, whether to use a backpack or not, whether to wear a hat or not and whether to hold a child or not. And simultaneously judging the congestion states of the pedestrians and the vehicles. And identifying the pedestrian attributes and collecting the pedestrian traffic conditions in real time. The intelligent analysis box carries out pedestrian detection, pedestrian tracking, pedestrian are preferred, carry out pedestrian attribute discernment according to preferred result to the camera of inserting, and the discernment attribute includes: the color of the upper garment, the style of the upper garment, the color of the lower garment, the style of the lower garment, whether to carry a backpack or not, whether to wear a hat or not and whether to hold a child or not. The box detects pedestrians and vehicles in the area of the camera, counts the number of the pedestrians, is matched with the area calibration of the camera, counts the crowd density of the camera, indicates that the current road is congested if the number of the pedestrians and vehicles exceeds a set threshold value, can set a plurality of threshold values to indicate different congestion states of the current road, and sends the congestion states to a rear-end traffic platform to send workers for assisting processing if the congestion states are found.
Further, the model training of the recognition analysis of the passing vehicle scene based on the neural network of deep learning also comprises the recognition of the cross-zone line crossing behaviors of motor vehicles, pedestrians and non-motor vehicles, for example, the red light running detection is realized by accessing a red-green light detector and matching with regional invasion; by drawing 3 lines and setting the line direction, the retrograde detection is realized in cooperation with line crossing. And when the illegal type is found, if the face can be detected, the face detection is carried out, if the face cannot be detected, the license plate of the non-motor vehicle is identified, the illegal identity information is confirmed through the face or the license plate of the non-motor vehicle, and the illegal identity information is automatically uploaded to a rear-end traffic platform.
And further, after the neural network based on deep learning identifies and analyzes the passing vehicle scene, the identified road condition result is sent to a background.
And further, deducing road conditions to a big data platform by using the obtained traffic flow and the vehicle speed result, and carrying out early warning decision including setting corresponding road condition threshold values, and carrying out alarm prompt if the road condition threshold values are exceeded.
In this embodiment, the specific implementation steps include:
step 1: and installing a common camera at a place needing real-time intelligent monitoring, and adjusting the height and the angle of view of the camera.
Step 2: an intelligent analysis box is installed on a common camera, and each camera detection area and intelligent analysis type are configured.
And step 3: and an intelligent early warning platform is deployed at the rear end, and an intelligent analysis box is accessed.
To achieve the above object, please refer to fig. 2, an embodiment of the present invention further provides an intelligent video monitoring system for road conditions, including: an acquisition and identification module, a traffic flow speed result determination module, an early warning decision module,
the acquisition and recognition module is used for acquiring scene videos of passing vehicles on the road, carrying out recognition analysis on the passing vehicle videos based on a deep learning neural network, and analyzing and collecting vehicle data in real time;
the traffic flow speed result determining module is used for identifying scene videos of vehicles passing through the road, which are acquired from different places of the same road section, determining and counting the number of the vehicles ID in the area within a fixed time interval, and obtaining a traffic flow and speed result;
and the early warning decision module is used for deducing road conditions to a big data platform by using the obtained traffic flow and the vehicle speed result to carry out early warning decision.
The system modules are all used for realizing the formulation of the method examples, the description is not repeated, and the deep learning neural network is trained by adopting the existing open source model training framework without special limitation.
The embodiment of the invention provides an intelligent video monitoring method for road conditions, which comprises the following steps: acquiring scene videos of passing vehicles on a road, identifying and analyzing the passing vehicle videos based on a deep learning neural network, and analyzing and acquiring vehicle data in real time; identifying scene videos of vehicles passing by the road, which are acquired from different places of the same road section, determining and counting the number of the vehicles ID in the area within a fixed time interval to obtain a traffic flow and a vehicle speed result; and deducing road conditions to a big data platform by using the obtained traffic flow and the speed result, and performing early warning decision. By applying the intelligent video monitoring method for the road condition, the road condition is intelligently monitored in real time through a deep learning neural network, and vehicle data are analyzed and collected in real time; identifying scene videos of vehicles passing by roads and obtained from different places of the same road section, and determining and counting the number of the vehicles ID in the area within a fixed time interval to obtain traffic flow and vehicle speed results; the obtained traffic flow and the vehicle speed result are used for deducing road conditions to a big data platform, early warning decision is carried out, traffic passing conditions are found in time, behaviors and personnel violating safety regulations are judged, the congestion states of pedestrians and vehicles are judged at the same time, automatic alarm is given and uploaded to a rear-end traffic platform, the rear-end traffic platform informs relevant workers of processing immediately, the road passing safety is ensured, and the intelligent video monitoring method and the intelligent video monitoring system for the road conditions select a common camera to install an embedded intelligent analysis box, so that the cost of the whole system is reduced.
It should be noted that, for the sake of simplicity, the above-mentioned method embodiments are described as a series of acts or combinations, but those skilled in the art should understand that the present invention is not limited by the described order of acts, because some steps can be performed in other orders or simultaneously according to the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no acts or modules are necessarily required of the invention.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The foregoing is provided to illustrate and explain the present invention by way of example and not by way of limitation, and it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. An intelligent video monitoring method for road conditions is characterized by comprising the following steps: acquiring scene videos of passing vehicles on a road, identifying and analyzing the passing vehicle videos based on a deep learning neural network, and analyzing and acquiring vehicle data in real time;
identifying scene videos of vehicles passing by the road, which are acquired from different places of the same road section, determining and counting the number of the vehicles ID in the area within a fixed time interval to obtain the results of traffic flow and vehicle speed;
and deducing road conditions to a big data platform by using the obtained traffic flow and the vehicle speed result, and performing early warning decision.
2. The video monitoring method of claim 1, further comprising performing recognition vehicle data analysis model training on the past vehicle scene based on a deep learning neural network.
3. The video monitoring method of claim 2, wherein the deep learning based neural network recognition analysis model training of the past vehicle scene further comprises road surface danger recognition.
4. The video monitoring method of claim 2, wherein performing recognition analysis model training on the past vehicle scene based on a deep learning neural network further comprises recognizing a traffic violation event.
5. The video surveillance method of claim 2, wherein performing recognition analysis model training on the past vehicle scene based on a deep learning neural network further comprises recognizing a non-motor vehicle violation event.
6. The video monitoring method according to claim 2, wherein performing recognition analysis model training on the past vehicle scene based on the deep learning neural network further comprises recognizing a pedestrian traveling state.
7. The video monitoring method of claim 2, wherein the model training of the recognition analysis of the past vehicle scene based on the deep learning neural network further comprises recognizing the cross-over line behavior of a motor vehicle, a pedestrian, and a non-motor vehicle.
8. The video monitoring method according to claim 1, wherein after the deep learning-based neural network performs recognition analysis on the past vehicle scene, the method further comprises sending a recognized road condition result to a background.
9. The video monitoring method according to claim 1, wherein the traffic flow and the vehicle speed are used to derive road conditions to a big data platform, and the early warning decision making comprises setting corresponding road condition threshold values, and if the road condition threshold values are exceeded, an alarm prompt is given.
10. An intelligent video monitoring system for road conditions, which is characterized by comprising an acquisition and identification module, a traffic flow speed result determination module and an early warning decision module,
the acquisition and recognition module is used for acquiring scene videos of passing vehicles on the road, carrying out recognition analysis on the passing vehicle videos based on a deep learning neural network, and analyzing and collecting vehicle data in real time;
the traffic flow speed result determining module is used for identifying scene videos of vehicles passing through the road, which are acquired from different places of the same road section, determining and counting the number of the vehicles ID in the area within a fixed time interval, and obtaining a traffic flow and speed result;
and the early warning decision module is used for deducing road conditions to a big data platform by using the obtained traffic flow and the vehicle speed result to carry out early warning decision.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113762255A (en) * | 2021-09-14 | 2021-12-07 | 陕西科云信息技术有限公司 | Off-site overload monitoring method based on multi-source data fusion deep learning |
| CN114446062A (en) * | 2022-01-28 | 2022-05-06 | 刘银环 | Dynamic allocation system and method based on big data service |
| CN115311849A (en) * | 2022-07-07 | 2022-11-08 | 开研(宁波)信息科技有限公司 | Traffic organization problem diagnosis device and method based on big data system |
| CN115331440A (en) * | 2022-08-09 | 2022-11-11 | 山东旗帜信息有限公司 | High-adaptation early warning method and system based on monitoring threshold information |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113762255A (en) * | 2021-09-14 | 2021-12-07 | 陕西科云信息技术有限公司 | Off-site overload monitoring method based on multi-source data fusion deep learning |
| CN114446062A (en) * | 2022-01-28 | 2022-05-06 | 刘银环 | Dynamic allocation system and method based on big data service |
| CN114446062B (en) * | 2022-01-28 | 2022-09-27 | 深圳市八度云计算信息技术有限公司 | Dynamic allocation system and method based on big data service |
| CN115311849A (en) * | 2022-07-07 | 2022-11-08 | 开研(宁波)信息科技有限公司 | Traffic organization problem diagnosis device and method based on big data system |
| CN115331440A (en) * | 2022-08-09 | 2022-11-11 | 山东旗帜信息有限公司 | High-adaptation early warning method and system based on monitoring threshold information |
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