CN117746630A - Industry safety production supervision and management system based on big data - Google Patents
Industry safety production supervision and management system based on big data Download PDFInfo
- Publication number
- CN117746630A CN117746630A CN202311754439.9A CN202311754439A CN117746630A CN 117746630 A CN117746630 A CN 117746630A CN 202311754439 A CN202311754439 A CN 202311754439A CN 117746630 A CN117746630 A CN 117746630A
- Authority
- CN
- China
- Prior art keywords
- traffic
- data
- information
- module
- service
- 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.)
- Granted
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 22
- 238000005516 engineering process Methods 0.000 claims abstract description 37
- 238000012544 monitoring process Methods 0.000 claims abstract description 24
- 230000005540 biological transmission Effects 0.000 claims abstract description 18
- 230000002159 abnormal effect Effects 0.000 claims abstract description 15
- 206010039203 Road traffic accident Diseases 0.000 claims abstract description 10
- 230000015572 biosynthetic process Effects 0.000 claims abstract description 7
- 238000003786 synthesis reaction Methods 0.000 claims abstract description 7
- 230000006855 networking Effects 0.000 claims abstract description 4
- 238000004364 calculation method Methods 0.000 claims description 79
- 238000007726 management method Methods 0.000 claims description 76
- 238000000034 method Methods 0.000 claims description 32
- 230000006378 damage Effects 0.000 claims description 27
- 230000004044 response Effects 0.000 claims description 18
- 230000000007 visual effect Effects 0.000 claims description 17
- 230000010354 integration Effects 0.000 claims description 15
- 230000003993 interaction Effects 0.000 claims description 13
- 230000008569 process Effects 0.000 claims description 13
- 238000004458 analytical method Methods 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 9
- 238000011156 evaluation Methods 0.000 claims description 8
- 238000010801 machine learning Methods 0.000 claims description 8
- 238000012502 risk assessment Methods 0.000 claims description 7
- 238000009826 distribution Methods 0.000 claims description 6
- 230000007547 defect Effects 0.000 claims description 5
- 238000001303 quality assessment method Methods 0.000 claims description 5
- 238000013500 data storage Methods 0.000 claims description 4
- 230000007246 mechanism Effects 0.000 claims description 4
- 230000008447 perception Effects 0.000 claims description 4
- 238000013441 quality evaluation Methods 0.000 claims description 4
- 238000012795 verification Methods 0.000 claims description 4
- 208000027418 Wounds and injury Diseases 0.000 claims description 3
- 238000003709 image segmentation Methods 0.000 claims description 3
- 208000014674 injury Diseases 0.000 claims description 3
- 238000012217 deletion Methods 0.000 claims description 2
- 230000037430 deletion Effects 0.000 claims description 2
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 230000010485 coping Effects 0.000 abstract description 2
- 230000006870 function Effects 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 238000012423 maintenance Methods 0.000 description 3
- 230000002265 prevention Effects 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 238000013145 classification model Methods 0.000 description 2
- 238000013527 convolutional neural network Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000002708 enhancing effect Effects 0.000 description 2
- 230000008520 organization Effects 0.000 description 2
- 230000002195 synergetic effect Effects 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000012098 association analyses Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000003032 molecular docking Methods 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000003449 preventive effect Effects 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention discloses an industry safety production supervision and management system based on big data, which relates to the technical field of supervision and management, and comprises the following components: the system comprises an information acquisition module, a terminal feedback module, a data reporting module, a platform service module, a data synthesis module and a service management module, wherein: the information acquisition module is used for acquiring traffic state information in the traffic industry in real time through monitoring equipment, a sensor technology and a vehicle networking technology, so as to realize the omnibearing sensing of the traffic environment; the terminal feedback module is used for collecting traffic scene information of traffic accidents or abnormal phenomena in the traffic environment in real time through the mobile terminal, and reporting and feeding back the traffic scene information to the traffic service platform; and the data reporting module is used for providing a data transmission network to report the acquired data. The invention realizes the omnibearing sensing of the traffic environment by collecting the traffic state information in real time, provides high-precision and real-time data support for the system, and is beneficial to timely finding and coping with the traffic safety hidden trouble.
Description
Technical Field
The invention relates to the technical field of supervision and management, in particular to an industrial safety production supervision and management system based on big data.
Background
With the rapid development of information technology, technologies such as artificial intelligence, big data, internet of things and the like are widely applied in the traffic field. For example, an intelligent traffic monitoring system is used for monitoring traffic flow and vehicle running conditions at intersections in real time, and prediction, dispersion and the like of traffic jams are realized through big data analysis. The introduction of the technical means not only improves the refinement level of supervision and management, but also provides more powerful support for accident prevention.
The current traffic industry safety production supervision and management usually adopts the technologies of intelligent traffic monitoring systems, vehicle-mounted sensors, satellite navigation and the like, so that the real-time monitoring and collection of traffic data are realized. However, challenges exist in data reporting, including problems of data inconsistency, missing, time delay, etc., and false reporting may exist in part of sources, which affects accurate judgment of the traffic industry safety condition by the regulatory agency. Meanwhile, due to instability of data quality, the monitoring system may not be able to capture accident early warning signals in time, thereby reducing the efficiency of emergency response.
Therefore, it is needed to further perfect the data reporting standard and improve the data quality monitoring system so as to ensure the true reliability of the monitoring data, and provide a more accurate monitoring and management means for the safety production of the traffic industry.
Disclosure of Invention
Based on the above, it is necessary to provide an industry safety production supervision and management system based on big data.
The invention provides an industry safety production supervision and management system based on big data, which comprises: the system comprises an information acquisition module, a terminal feedback module, a data reporting module, a platform service module, a data synthesis module and a service management module, wherein:
the information acquisition module is used for acquiring traffic state information in the traffic industry in real time through monitoring equipment, a sensor technology and a vehicle networking technology, so as to realize the omnibearing sensing of the traffic environment;
the terminal feedback module is used for collecting traffic scene information of traffic accidents or abnormal phenomena in the traffic environment in real time through the mobile terminal, and reporting and feeding back the traffic scene information to the traffic service platform;
the data reporting module is used for providing a data transmission network to report the acquired data;
the platform service module is used for integrating the GIS technology, the database and the operation software to build a traffic service platform and executing safety identification analysis by utilizing the big data technology and a distributed computing mode;
The data synthesis module is used for providing unified data storage, management, distribution and sharing services, providing different service interfaces according to different traffic data services and strictly guaranteeing data security;
the business management module is used for establishing an intercommunication and interconnection channel of traffic business data, interfacing different traffic supervision units according to different traffic businesses and executing traffic industry supervision and management business.
Further, the supervision and management system further comprises an information management module, an early warning response module, a supervision recording module and a visual display module, wherein:
the information management module is used for managing registration information of enterprises, vehicles and personnel in the traffic industry and associating security supervision records matched with the registration information;
the early warning response module is used for establishing an early warning informing and emergency response mechanism, receiving real-time calculation results of traffic information and service execution instructions thereof, and sending early warning information and emergency rescue response;
the monitoring record module is used for collecting and recording safety monitoring records in the monitoring management process;
and the visual display module is used for providing a visual traffic environment information display interface.
Further, collecting traffic scene information of traffic accidents or abnormal phenomena in the traffic environment in real time through the mobile terminal, reporting and feeding back the traffic scene information to the traffic service platform comprises:
The user registers, inputs registration information containing identity information of enterprises, vehicles and personnel, and performs real-name authentication and authenticity verification;
when a user takes place or witnessed a traffic accident, providing a registration interface of accident information, wherein the accident information comprises accident vehicles, accident personnel and accident sites;
acquiring image information of an accident scene through a terminal camera, wherein the image information comprises damaged vehicles, wounded persons and damaged facilities;
accident information and image information associated with the same registration information are summarized into traffic field information, and data reporting is carried out.
Further, the platform service module comprises a GIS service sub-module, a database management sub-module, a software integration sub-module, a task scheduling sub-module, a parallel operation sub-module, an instruction issuing sub-module and a real-time interaction sub-module, wherein:
the GIS service sub-module is used for collecting and storing geographic space data according to the geographic information system and realizing visual display of traffic environment and traffic state by using map service;
the database management sub-module is used for providing a data transmission interface, managing traffic information reported by the terminal and dividing the traffic information into basic data, historical data and real-time data;
The software integration sub-module is used for integrating basic operation software and building an operable platform;
the task scheduling sub-module is used for establishing a data calculation task according to the reported traffic information, wherein the calculation task comprises data quality assessment, accident risk assessment and map service update;
the parallel operation sub-module is used for dispersing the data calculation task established by the traffic information to a plurality of calculation nodes for execution by using a distributed technology, so as to realize concurrent processing of the calculation task;
the instruction issuing submodule is used for acquiring and outputting a calculation result of a calculation task and generating a service execution instruction for maintaining data quality and traffic safety according to the calculation result;
and the real-time interaction sub-module is used for supporting the user to interact with the traffic service platform in real time and comprises the steps of reporting data, inquiring real-time traffic information, viewing a map and receiving early warning response information.
Further, by using a distributed technology, the data calculation task established by the traffic information is distributed to a plurality of calculation nodes to be executed, and the concurrent processing of the calculation task includes:
distributing the calculation tasks to available calculation nodes, and ensuring that the tasks are reasonably distributed according to set rules;
Constructing a distributed computing framework, wherein the distributed computing framework supports parallel execution of computing tasks and coordinates and manages work of each computing node;
monitoring the execution condition of calculation tasks on each calculation node, dynamically adjusting the allocation of the tasks according to the system load condition, and adjusting the task quantity of each calculation node in real time to realize load balancing;
a part of computing nodes execute a data quality evaluation task in the computing task, and data quality is evaluated by integrating a plurality of quality indexes according to traffic state information reported in real time;
part of computing nodes execute accident risk assessment tasks in the computing tasks, and the accident risk is assessed according to the traffic scene information reported in real time and the comprehensive accident indexes;
and part of the computing nodes execute a map service updating task in the computing tasks, and the computing and updating of the visually displayed traffic environment are performed by combining traffic information.
Further, according to the traffic state information reported in real time, the evaluation of the data quality by integrating a plurality of quality indexes comprises:
counting the number of all data reports, the number of data items, the number of abnormal data and the number of missing data in the traffic state information, and calculating the data accuracy of the traffic state information by using an accuracy calculation formula;
Calculating the integrity rate of the traffic state information by using an integrity rate calculation formula;
counting reporting time, occurrence time and recording time of each data item in the traffic state information, and calculating the timeliness of the traffic state information by using a timeliness calculation formula;
counting the number of redundant data items and the number of redundant report records in the traffic state information, and calculating the redundancy rate of the traffic state information by using a redundancy rate calculation formula;
and taking the accuracy rate, the integrity rate, the time-lapse rate and the redundancy rate as quality indexes, giving respective weight values, and calculating and evaluating the data quality of the traffic state information by using a weighting method.
Further, the correct rate calculation formula is:
；
in the method, in the process of the invention,Q z representing the accuracy;D q representing the number of abnormal data items;R a representing the number of data reporting;C a representing the number of data items;C s item data representing the number of deletions;
the calculation formula of the complete rate is as follows:
；
Q W representing the integrity rate;
the time rate calculation formula is:
；
in the method, in the process of the invention,Q J representing the timeliness rate;T d representing a data item reporting time;T 0 representing a data item recording time;T i representing a data item occurrence time;R i the reporting record duration of the data item is represented;mindicating the accumulated report record times;
the redundancy rate calculation formula is:
；
in the method, in the process of the invention, Q R Representing redundancy rate;R r representing the number of redundant report records;C r representing the number of redundant data items;R fr indicating the number of non-redundant report records.
Further, based on the traffic field information reported in real time, the evaluation of the accident risk by the comprehensive accident index comprises:
extracting traffic flow data in the traffic state information, and evaluating the congestion state of the local traffic environment according to the accident site to obtain congestion parameters;
acquiring accident information reported and fed back by a user, extracting accident number and injury information of vehicles and personnel in the accident information, evaluating the event severity of the accident according to a preset evaluation standard, and dividing the event grade to serve as event parameters;
extracting damaged facilities related to the image information, analyzing the damage degree of the damaged facilities, and dividing service grades according to the damage degree and taking the service grades as service parameters;
and combining the congestion parameter, the situation parameter and the service parameter serving as accident indexes in an adding mode to serve as a risk value of the current accident.
Further, extracting traffic flow data in the traffic state information, and evaluating the congestion state of the local traffic environment according to the accident site, wherein the obtaining congestion parameters comprises:
The accident site in the accident information is extracted, positioning is carried out in map service of the traffic environment, and the traffic state information acquired by the traffic road where the accident site is located is obtained;
extracting the average speed, traffic volume and traffic flow density of the traffic road through traffic state information, giving weight values to the average speed, traffic volume and traffic flow density, and calculating the congestion amount in the current period by using a weighting method;
dividing the congestion amount of the current period by the average congestion amount in the previous 3 hours to obtain a congestion coefficient, and dividing the congestion level according to the value of the congestion coefficient to serve as a congestion parameter.
Further, extracting damaged facilities related in the image information, analyzing the damage degree of the damaged facilities, classifying the service level according to the damage degree and serving as the service parameters, wherein the service parameters comprise:
identifying and positioning public facilities in the image information by utilizing a computer vision technology, and then segmenting and extracting different public facilities by utilizing an image segmentation technology to obtain a local image;
and detecting a local image containing the public facilities by utilizing a pre-constructed machine learning model, judging whether the public facilities have damaged defects, if the public facilities have no damaged defects, marking the public facilities as non-damaged facilities, if the public facilities have damaged defects, marking the public facilities as damaged facilities, classifying service grades according to the damage degree, and taking the service grades as service parameters.
The beneficial effects of the invention are as follows:
1. by collecting traffic state information in real time, the comprehensive perception of the traffic environment is realized, high-precision and real-time data support is provided for the system, traffic safety hidden danger can be found and dealt with in time, and by combining the mobile terminal to collect accident or abnormal information in real time, the traffic service platform can respond rapidly and take effective measures, and the efficiency of accident emergency treatment is improved; the data transmission network is provided to report the collected data, so that the timely transmission and sharing of the data are ensured, and a high-efficiency management means is provided for a supervision organization; meanwhile, a visual traffic environment is built by integrating a GIS technology, and safety recognition analysis is performed by combining a big data technology and a distributed computing mode, so that comprehensive management and analysis capability of the traffic environment are improved, accident risks are predicted, traffic smoothness is improved, the level of safety production supervision and management of the traffic industry is improved in all directions, and powerful support is provided for accident prevention, emergency treatment and supervision decision.
2. Through the synergistic effect of the sub-modules of the geographic information system, the database management, the software integration, the task scheduling, the parallel operation, the instruction issuing, the real-time interaction and the like in the platform service module, the comprehensive perception, the efficient calculation and analysis of the traffic environment, the intelligent instruction generation and the user-friendly interaction experience are realized, so that the system has the advantages of improving the traffic data processing and management efficiency, optimizing the traffic service platform function and enhancing the maintenance of the data quality and the traffic safety.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a system block diagram of an industry safety production monitoring and management system based on big data in accordance with an embodiment of the present invention.
Reference numerals: 1. an information acquisition module; 2. a terminal feedback module; 3. a data reporting module; 4. a platform service module; 401. GIS service sub-module; 402. a database management sub-module; 403. a software integration sub-module; 404. a task scheduling sub-module; 405. a parallel operation sub-module; 406. an instruction issuing submodule; 407. a real-time interaction sub-module; 5. a data synthesis module; 6. a service management module; 7. an information management module; 8. an early warning response module; 9. a supervision and recording module; 10. and a visual display module.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, an industry safety production supervision and management system based on big data is provided, the supervision and management system includes: the system comprises an information acquisition module 1, a terminal feedback module 2, a data reporting module 3, a platform service module 4, a data synthesis module 5 and a service management module 6, wherein:
the information acquisition module 1 is used for acquiring traffic state information in the traffic industry in real time through monitoring equipment, a sensor technology and a vehicle networking technology, so as to realize the omnibearing sensing of the traffic environment.
Specifically, the information acquisition module 1 integrates various monitoring devices, including video monitoring cameras, traffic signal lamp monitors and the like. The video monitoring cameras provide visual information on traffic flow, vehicle driving conditions and intersection conditions by capturing images of traffic segments in real time. The traffic signal lamp monitor is used for acquiring the state of the traffic signal lamp, so that the traffic flow condition of an intersection can be mastered more accurately.
Among them, regarding the application of sensor technology: the information acquisition module 1 integrates various sensor technologies, such as a meteorological sensor, a road surface sensor and the like. The weather sensor is used for monitoring weather conditions including temperature, humidity, wind speed and the like in real time and providing weather data support for traffic management. Road surface sensors are used to sense the condition of the road, such as road surface temperature, humidity and whether ice is present, to provide safety information for road travel.
The information acquisition module 1 also communicates with the vehicle in real time by integrating the internet of vehicles technology. Through the internet of vehicles, the information of the position, the speed, the running direction and the like of the vehicles can be obtained, so that the traffic mobility is monitored in real time. This helps track vehicle operating conditions, identify traffic congestion, and provide real-time navigation and route planning when needed.
With respect to real-time acquisition and data transmission, the information acquisition module 1 acquires various data in the traffic environment in real time through these devices and techniques. The collected data is rapidly transmitted to the system through the data transmission network, so that the timeliness and the accuracy of the data are ensured.
The terminal feedback module 2 is used for collecting traffic scene information of traffic accidents or abnormal phenomena in the traffic environment in real time through the mobile terminal, and reporting and feeding back the traffic scene information to the traffic service platform.
In the description of the invention, the mobile terminal is used for collecting the traffic scene information of traffic accidents or abnormal phenomena in the traffic environment in real time and reporting and feeding back the traffic scene information to the traffic service platform comprises the following steps:
the user registers, inputs registration information including identity information of enterprises, vehicles and personnel, and performs real-name authentication and authenticity verification.
When a user takes place or witnessed a traffic accident, a registration interface for accident information is provided, the accident information including accident vehicles, accident personnel and accident sites.
The terminal camera is used for acquiring image information of an accident scene, wherein the image information comprises damaged vehicles, wounded persons and damaged facilities.
Accident information and image information associated with the same registration information are summarized into traffic field information, and data reporting is carried out.
And the data reporting module 3 is used for providing a data transmission network to report the acquired data.
Specifically, the data reporting module 3 ensures safe and timely transmission of information by setting up a stable and reliable data transmission network and adopting a proper communication protocol, and the specific selection depends on the requirements and safety considerations of the system by using a communication protocol such as HTTP, HTTPS, MQTT. Meanwhile, considering that multiple types of data may be involved in the system, the data reporting module 3 also supports flexible data formats, such as JSON, XML, and the like, so as to adapt to the format requirements of different data sources.
And the platform service module 4 is used for integrating the GIS technology, the database and the operation software to build a traffic service platform and executing safety identification analysis by utilizing the big data technology and the distributed computing mode.
In the description of the present invention, the platform service module 4 includes a GIS service sub-module 401, a database management sub-module 402, a software integration sub-module 403, a task scheduling sub-module 404, a parallel operation sub-module 405, an instruction issuing sub-module 406, and a real-time interaction sub-module 407, where:
the GIS service sub-module 401 is used for collecting and storing geospatial data according to a geographic information system, and realizing visual display of traffic environment and traffic state by using map service.
Specifically, the acquisition and storage of geospatial data are performed according to a geographic information system, including the acquisition of relevant information such as geographic locations, terrains, road networks, and the like. And the map service is utilized to carry out visual display on the collected geographic space data, so that the traffic environment and the traffic state are visually presented on the map. The GIS service sub-module 401 is used for realizing the geographic information analysis of traffic data, supporting space inquiry and space association analysis and providing geographic information intelligent service.
The database management sub-module 402 is configured to provide a data transmission interface, manage traffic information reported by the terminal, and divide the traffic information into basic data, historical data and real-time data.
Specifically, the database management sub-module 402 provides a data transmission interface, so that efficient transmission and storage of traffic information reported by the terminal are realized, traffic information can be managed, and data is divided into basic data, historical data and real-time data, so that access and utilization of different service modules are facilitated. Meanwhile, management operations such as backup, recovery and data cleaning of the database are supported, and stability and reliability of the database are ensured.
The software integration sub-module 403 is used for integrating basic operation software and building an executable platform.
Specifically, the software integration sub-module 403 integrates basic operation software, and provides an operation environment for the whole system, including an operating system, a database management system, and the like. And constructing an operable platform, ensuring that all modules work cooperatively and providing an interface for external service.
The software integration sub-module 403 provides necessary hardware and software infrastructure for the system by building an executable platform, including hardware resources such as servers, storage devices, communication devices, and software resources such as operating systems, database systems, and the like. Building an operable platform is the basis for ensuring the normal operation of the system. In order to ensure that the respective subsystems and modules can communicate and cooperate well, the software integration sub-module 403 is also responsible for integrating the interfaces inside and outside the system, so as to ensure that the functions of the system can operate in coordination and in unison.
The task scheduling sub-module 404 is configured to establish a data calculation task according to the reported traffic information, where the calculation task includes data quality assessment, accident risk assessment, and map service update.
Specifically, the task scheduling sub-module 404 establishes data calculation tasks according to the reported traffic information, including tasks such as data quality assessment, accident risk assessment, map service update and the like, and realizes scheduling and distribution of the tasks, thereby ensuring that the tasks can be efficiently completed in a distributed environment.
Flexible task priority management and resource scheduling are supported, and reasonable utilization of system resources is ensured.
And the parallel operation sub-module 405 is configured to distribute the data calculation task established by the traffic information to a plurality of calculation nodes for execution by using a distributed technology, so as to implement concurrent processing of the calculation task.
In the description of the present invention, by using a distributed technology, a data calculation task established by traffic information is distributed to a plurality of calculation nodes to be executed, and the concurrent processing of the calculation task includes:
and S101, distributing the calculation tasks to available calculation nodes, and ensuring that the tasks are reasonably distributed according to set rules.
S102, building a distributed computing framework, wherein the distributed computing framework supports parallel execution of computing tasks and coordinates and manages work of each computing node.
And S103, monitoring the execution condition of the calculation tasks on each calculation node, dynamically adjusting the allocation of the tasks according to the system load condition, and adjusting the task quantity of each calculation node in real time to realize load balancing.
And S104, part of the computing nodes execute a data quality evaluation task in the computing task, and the data quality is evaluated by integrating a plurality of quality indexes according to the traffic state information reported in real time.
In the description of the invention, according to the traffic state information reported in real time, the evaluation of the data quality by integrating a plurality of quality indexes comprises the following steps:
s10401, counting the number of all data reports, the number of data items, the number of abnormal data and the number of missing data in the traffic state information, and calculating the data accuracy of the traffic state information by using an accuracy calculation formula.
The correct rate calculation formula is as follows:
；
in the method, in the process of the invention,Q z the accuracy rate is indicated as such,D q indicating the number of items of abnormal data,R a the number of data reports is indicated,C a which represents the number of data items,C s representing missing number item data.
S10402, calculating the integrity rate of the traffic state information by using an integrity rate calculation formula.
Wherein, the calculation formula of the integrity rate is:
；
Q W representing the integrity rate.
S10403, counting the reporting time, the occurrence time and the recording time of each data item in the traffic state information, and calculating the timeliness of the traffic state information by using a timeliness calculation formula.
The time rate calculation formula is as follows:
；
in the method, in the process of the invention,Q J the time-out rate is indicated and the time-out rate,T d the time of the reporting of the data item is indicated,T 0 the time of recording of the data item is indicated,T i the time of occurrence of the data item is indicated,R i the reporting record duration of the data item is represented,mindicating the accumulated number of reported records.
S10404, counting the number of redundant data items and the number of redundant report records in the traffic state information, and calculating the redundancy rate of the traffic state information by using a redundancy rate calculation formula.
The redundancy rate calculation formula is as follows:
；
in the method, in the process of the invention,Q R the redundancy rate is indicated as such,R r indicating the number of redundant report records,C r indicating the number of redundant data items,R fr indicating the number of non-redundant report records.
S10405, taking the accuracy rate, the integrity rate, the time-lapse rate and the redundancy rate as quality indexes, giving respective weight values, and calculating and evaluating the data quality of the traffic state information by using a weighting method.
S105, part of the computing nodes execute accident risk assessment tasks in the computing tasks, and the accident risk is assessed according to the comprehensive accident indexes based on the traffic scene information reported in real time.
In the description of the invention, the evaluation of the accident risk by the comprehensive accident index based on the traffic field information reported in real time comprises the following steps:
s10501, extracting traffic flow data in traffic state information, and evaluating the congestion state of the local traffic environment according to the accident site to obtain congestion parameters.
In the description of the present invention, extracting traffic flow data in traffic state information, and evaluating the congestion state of a local traffic environment according to an accident site, the obtaining congestion parameters includes:
S1050101, an accident site in the accident information is extracted, positioning is carried out in map service of the traffic environment, and the traffic state information acquired by the traffic road where the accident site is located is acquired.
S1050102, extracting the average speed, traffic volume and traffic flow density of the traffic road through traffic state information, giving weight values to the average speed, traffic volume and traffic flow density, and calculating the congestion amount in the current period by using a weighting method.
S1050103, dividing the congestion amount of the current period by the average congestion amount in the previous 3 hours to obtain a congestion coefficient, and dividing the congestion level according to the value of the congestion coefficient to serve as a congestion parameter.
S10502, obtaining accident information reported and fed back by a user, extracting the accident number and injury information of vehicles and personnel in the accident information, evaluating the event severity of the accident according to a preset evaluation standard, and dividing the event grade to serve as event parameters.
S10503, extracting damaged facilities related to the image information, analyzing the damage degree of the damaged facilities, and dividing the service level according to the damage degree and taking the service level as a service parameter.
In the description of the present invention, damaged facilities involved in image information are extracted, and damage degree of the damaged facilities is analyzed, and service classes are classified according to the damage degree and included as service parameters:
S1050301, identifying and positioning public facilities in the image information by utilizing a computer vision technology, and then segmenting and extracting different public facilities by utilizing an image segmentation technology to obtain a local image.
S1050302, detecting a local image containing public facilities by utilizing a pre-constructed machine learning model, judging whether the public facilities have damaged defects, if not, marking the public facilities as non-damaged facilities, if so, marking the public facilities as damaged facilities, classifying the service grades according to the damage degree, and taking the service grades as service parameters.
In order to achieve damage detection and classification of local images containing public facilities, the following is a basic step:
1. data preparation: an image dataset containing the public facility, each of which is labeled as damaged and damaged-degree level, is collected and prepared, which dataset is to be used for training a machine learning model.
2. Feature extraction: the invention uses deep learning architecture such as Convolutional Neural Network (CNN) to learn the features in the image.
3. Training a machine learning model: the machine learning model is trained by using a supervised learning method by using the prepared data set, and a binary classification model is adopted for judging whether the damage is caused or not, and can be a multi-class classification model for dividing different damage grades.
4. Model verification: the performance of the model is evaluated using a validation dataset that is independent of the training data, ensuring that the model has good generalization ability.
5. Damage detection and classification: and applying a trained model to the new local image to detect damage, if the model predicts damage, further judging the damage degree of the model, and distributing corresponding damage grade.
6. Service level marking: each image is assigned a service level according to the damage level, which may represent the urgency and importance of the damaged facility as a service parameter for subsequent management and decision-making.
7. And (3) system integration: the machine learning model is embedded in the overall system, ensuring that it can work in concert with other modules, such as image recognition and segmentation modules.
8. Real-time application: the system is deployed in an actual application environment to process new image data in real time, and marking and business grade division are carried out according to the output of the machine learning model.
S10504, combining the congestion parameter, the situation parameter and the service parameter as accident indexes in an adding mode to obtain the risk value of the current accident.
And S106, part of the computing nodes execute a map service updating task in the computing tasks, and the traffic environment visually displayed is computed and updated by combining traffic information.
The comprehensive data quality evaluation enables the system to reflect traffic conditions more accurately, and improves the credibility and application value of the data. The high accuracy ensures that the acquired information accords with the actual situation, the integrity rate ensures the comprehensiveness of the information, the timeliness rate ensures the real-time performance of the data, and the control of the redundancy rate is beneficial to avoiding the repetition of the data and the waste of resources. Through such comprehensive evaluation, the supervisory authorities and decision makers can more reliably make decisions based on the traffic state information provided by the system, improving the scientificity and effectiveness of traffic management. Meanwhile, by monitoring and improving the data quality, the system can continuously optimize the data acquisition and processing flow, and further improve the overall service level and decision quality. The data quality assessment mechanism is helpful for constructing a more robust and reliable traffic information management system, and provides a solid foundation for realizing safe, efficient and intelligent traffic management targets.
The instruction issuing submodule 406 is configured to obtain and output a calculation result of the calculation task, and generate a service execution instruction for maintaining data quality and traffic safety according to the calculation result.
Specifically, the submodule is responsible for obtaining the calculation results of the data calculation task, including coordination and result collection of the parallel operation submodule 405. Based on the calculation result, the sub-module generates business execution instructions related to maintaining the data quality and traffic safety, including repairing the data quality, updating database records, triggering preventive maintenance measures, and the like. And finally, the generated service execution instruction is issued to a corresponding service module, so that the execution instruction can be timely and effectively executed in the system.
The real-time interaction sub-module 407 is configured to support the user to interact with the traffic service platform in real time, including reporting data, querying real-time traffic information, viewing a map, and receiving early warning response information.
Specifically, the real-time interaction sub-module 407 provides an interface for the user to interact with the traffic service platform in real time, including a Graphical User Interface (GUI) or an Application Program Interface (API), etc., so that the user can intuitively interact with the system. The system allows the user to report traffic information in real time, such as accidents, road sealing and other conditions, so that the system responds in time. The traffic information real-time inquiry function is provided, and the traffic information real-time inquiry function comprises traffic conditions, road conditions, accident conditions and the like.
In addition, the method also supports the user to view the map in real-time interaction so as to acquire geospatial information and display related information such as traffic state on the map; and receiving early warning information generated by the system and responding in time, which may include providing advice, sending out notification or triggering emergency response, etc.
The data synthesis module 5 is used for providing unified data storage, management, distribution and sharing services, providing different service interfaces according to different traffic data services, and strictly guaranteeing data security.
Specifically, the data integration module 5 provides a unified data storage service, so as to ensure that various traffic data can be effectively stored and managed, including historical data, real-time data, and the like. The method is also used for data distribution and sharing, and provides data distribution service to ensure that different service modules and supervision units can acquire required data. Meanwhile, sharing of data is supported, and intercommunication of information of all parties is promoted. Different service interfaces are provided for different traffic data services to meet various service requirements.
The module can strictly guarantee the security of data, and adopts proper security measures including access control, encryption technology and the like so as to prevent data leakage and abuse.
The service management module 6 is used for establishing an intercommunication and interconnection channel of traffic service data, interfacing different traffic supervision units according to different traffic services, and executing traffic industry supervision and management services.
Specifically, the service management module 6 establishes an intercommunication and interconnection channel of traffic service data, so that different traffic services can realize sharing and communication of data in the system, and the working efficiency is improved. Different supervision units are docked, and the information transmission and supervision work connection are ensured by docking with different traffic supervision units according to different traffic services.
Finally, the supervision and management service is executed, and through the service management module 6, the system can execute the traffic industry supervision and management service, including safety supervision, accident investigation, violation treatment and the like.
In the description of the present invention, the supervision and management system further comprises an information management module 7, an early warning response module 8, a supervision recording module 9 and a visual display module 10, wherein:
the information management module 7 is used for managing registration information of enterprises, vehicles and personnel in the traffic industry and associating security supervision records matched with the registration information.
The early warning response module 8 is used for establishing an early warning informing and emergency response mechanism, receiving real-time calculation results of traffic information and service execution instructions thereof, and sending out early warning information and emergency rescue response.
And the supervision recording module 9 is used for collecting and recording the safety supervision records in the supervision and management process.
The visual display module 10 is used for providing a visual traffic environment information display interface.
In summary, by means of the technical scheme, the traffic state information is collected in real time, so that the omnibearing sensing of the traffic environment is realized, high-precision and real-time data support is provided for the system, the real-time detection and coping with traffic safety hidden trouble are facilitated, and the mobile terminal is combined to collect accident or abnormal information in real time, so that the traffic service platform can rapidly respond and take effective measures, and the accident emergency treatment efficiency is improved; the data transmission network is provided to report the collected data, so that the timely transmission and sharing of the data are ensured, and a high-efficiency management means is provided for a supervision organization; meanwhile, a visual traffic environment is built by integrating a GIS technology, and safety recognition analysis is performed by combining a big data technology and a distributed computing mode, so that comprehensive management and analysis capability of the traffic environment are improved, accident risks are predicted, traffic smoothness is improved, the level of safety production supervision and management of the traffic industry is improved in all directions, and powerful support is provided for accident prevention, emergency treatment and supervision decision. Through the synergistic effect of the sub-modules of the geographic information system, the database management, the software integration, the task scheduling, the parallel operation, the instruction issuing, the real-time interaction and the like in the platform service module, the comprehensive perception, the efficient calculation and analysis of the traffic environment, the intelligent instruction generation and the user-friendly interaction experience are realized, so that the system has the advantages of improving the traffic data processing and management efficiency, optimizing the traffic service platform function and enhancing the maintenance of the data quality and the traffic safety.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
Claims (10)
1. An industry safety production supervision and management system based on big data is characterized in that the supervision and management system comprises: the system comprises an information acquisition module, a terminal feedback module, a data reporting module, a platform service module, a data synthesis module and a service management module, wherein:
the information acquisition module is used for acquiring traffic state information in the traffic industry in real time through monitoring equipment, a sensor technology and a vehicle networking technology, so as to realize the omnibearing perception of the traffic environment;
The terminal feedback module is used for collecting traffic scene information of traffic accidents or abnormal phenomena in the traffic environment in real time through the mobile terminal, and reporting and feeding back the traffic scene information to the traffic service platform;
the data reporting module is used for providing a data transmission network to report the acquired data;
the platform service module is used for integrating a GIS technology, a database and operation software to build a traffic service platform and executing safety identification analysis by utilizing a big data technology and a distributed computing mode;
the data integration module is used for providing unified data storage, management, distribution and sharing services, providing different service interfaces according to different traffic data services and strictly guaranteeing data security;
the business management module is used for establishing an intercommunication and interconnection channel of traffic business data, interfacing different traffic supervision units according to different traffic businesses, and executing traffic industry supervision and management business.
2. The industrial safety production supervision and management system based on big data according to claim 1, further comprising an information management module, an early warning response module, a supervision recording module and a visual display module, wherein:
The information management module is used for managing registration information of enterprises, vehicles and personnel in the traffic industry and associating security supervision records matched with the registration information;
the early warning response module is used for establishing an early warning informing and emergency response mechanism, receiving real-time calculation results of traffic information and service execution instructions thereof, and sending early warning information and emergency rescue response;
the supervision recording module is used for collecting and recording safety supervision records in the supervision and management process;
the visual display module is used for providing a visual traffic environment information display interface.
3. The industry safety production supervision and management system based on big data according to claim 1, wherein the step of collecting traffic scene information of traffic accidents or abnormal phenomena in the traffic environment in real time through the mobile terminal, and reporting and feeding back the traffic scene information to the traffic service platform comprises the steps of:
the user registers, inputs registration information containing identity information of enterprises, vehicles and personnel, and performs real-name authentication and authenticity verification;
providing a registration interface of accident information when a user takes place or witnessed a traffic accident, wherein the accident information comprises accident vehicles, accident personnel and accident sites;
Acquiring image information of an accident scene through a terminal camera, wherein the image information comprises damaged vehicles, wounded persons and damaged facilities;
and summarizing the accident information and the image information which are related to the same registration information into traffic field information, and reporting data.
4. The industry safety production supervision and management system based on big data according to claim 3, wherein the platform service module comprises a GIS service sub-module, a database management sub-module, a software integration sub-module, a task scheduling sub-module, a parallel operation sub-module, a command issuing sub-module and a real-time interaction sub-module, wherein:
the GIS service sub-module is used for collecting and storing geographic space data according to a geographic information system and realizing visual display of traffic environment and traffic state by using map service;
the database management sub-module is used for providing a data transmission interface, managing traffic information reported by the terminal and dividing the traffic information into basic data, historical data and real-time data;
the software integration sub-module is used for integrating basic operation software and building an operable platform;
The task scheduling sub-module is used for establishing a data calculation task according to the reported traffic information, wherein the calculation task comprises data quality assessment, accident risk assessment and map service update;
the parallel operation sub-module is used for dispersing the data calculation task established by the traffic information to a plurality of calculation nodes to execute by using a distributed technology, so as to realize concurrent processing of the calculation task;
the instruction issuing submodule is used for acquiring and outputting a calculation result of a calculation task and generating a service execution instruction for maintaining data quality and traffic safety according to the calculation result;
the real-time interaction sub-module is used for supporting the user to interact with the traffic service platform in real time and comprises the steps of reporting data, inquiring real-time traffic information, viewing a map and receiving early warning response information.
5. The industry safety production supervision and management system based on big data according to claim 4, wherein the data calculation tasks established by the traffic information are distributed to a plurality of calculation nodes to be executed by using a distributed technology, and the concurrent processing of the calculation tasks includes:
distributing the computing tasks to available computing nodes, and ensuring that the tasks are reasonably distributed according to set rules;
Constructing a distributed computing framework, wherein the distributed computing framework supports parallel execution of computing tasks and coordinates and manages work of each computing node;
monitoring the execution condition of calculation tasks on each calculation node, dynamically adjusting the allocation of the tasks according to the system load condition, and adjusting the task quantity of each calculation node in real time to realize load balancing;
a part of computing nodes execute a data quality evaluation task in the computing task, and data quality is evaluated by integrating a plurality of quality indexes according to traffic state information reported in real time;
part of computing nodes execute accident risk assessment tasks in the computing tasks, and the accident risk is assessed according to the traffic scene information reported in real time and the comprehensive accident indexes;
and part of the computing nodes execute the map service updating task in the computing tasks, and the computing and updating of the visually displayed traffic environment are performed by combining traffic information.
6. The industry safety production supervision and management system based on big data according to claim 5, wherein the evaluating the data quality according to the traffic status information reported in real time by integrating a plurality of quality indexes comprises:
Counting all data reporting quantity, data item quantity, abnormal data quantity and missing data quantity in the traffic state information, and calculating the data accuracy of the traffic state information by using an accuracy calculation formula;
calculating the integrity rate of the traffic state information by using an integrity rate calculation formula;
counting the reporting time, the occurrence time and the recording time of each data item in the traffic state information, and calculating the timeliness of the traffic state information by using a timeliness calculation formula;
counting the number of redundant data items and the number of redundant report records in the traffic state information, and calculating the redundancy rate of the traffic state information by using a redundancy rate calculation formula;
and taking the accuracy rate, the integrity rate, the time-consuming rate and the redundancy rate as quality indexes and giving respective weight values, and calculating and evaluating the data quality of the traffic state information by using a weighting method.
7. The big data based industry safety production supervision and management system according to claim 6, wherein the correct rate calculation formula is:
；
in the method, in the process of the invention,Q z representing the accuracy;
D q representing the number of abnormal data items;
R a representing the number of data reporting;
C a Representing the number of data items;
C s item data representing the number of deletions;
the calculation formula of the integrity rate is as follows:
；
Q W representing the integrity rate;
the time rate calculation formula is as follows:
；
in the method, in the process of the invention,Q J representing the timeliness rate;
T d representing a data item reporting time;
T 0 representing a data item recording time;
T i representing a data item occurrence time;
R i the reporting record duration of the data item is represented;
mindicating the accumulated report record times;
the redundancy rate calculation formula is as follows:
；
in the method, in the process of the invention,Q R representing redundancy rate;
R r representing the number of redundant report records;
C r representing the number of redundant data items;
R fr indicating the number of non-redundant report records.
8. The industrial safety production monitoring and management system based on big data according to claim 6, wherein the estimating the accident risk based on the real-time reported traffic field information by the comprehensive accident index comprises:
extracting traffic flow data in the traffic state information, and evaluating the congestion state of the local traffic environment according to the accident site to obtain congestion parameters;
acquiring the accident information reported and fed back by a user, extracting the accident number and injury information of vehicles and personnel in the accident information, evaluating the event severity of the accident according to a preset evaluation standard, and dividing the event grade to serve as event parameters;
Extracting damaged facilities related to the image information, analyzing the damage degree of the damaged facilities, and dividing service grades according to the damage degree and taking the service grades as service parameters;
and combining the congestion parameter, the situation parameter and the service parameter serving as accident indexes in an adding mode to serve as a risk value of the current accident.
9. The industry safety production supervision and management system based on big data according to claim 8, wherein the extracting traffic flow data in the traffic state information and evaluating the congestion state of the local traffic environment according to the accident site to obtain the congestion parameters comprises:
extracting accident places in the accident information, positioning in map service of traffic environment, and obtaining traffic state information acquired by the traffic road where the accident places are located;
extracting the average speed, traffic volume and traffic flow density of the traffic road by the traffic state information, giving weight values to the average speed, traffic volume and traffic flow density, and calculating the congestion amount of the current period by using a weighting method;
dividing the congestion amount of the current period by the average congestion amount in the previous 3 hours to obtain a congestion coefficient, and dividing the congestion level according to the value of the congestion coefficient to serve as a congestion parameter.
10. The industry safety production supervision system based on big data according to claim 8, wherein the extracting damaged facilities involved in the image information and analyzing the damage degree of the damaged facilities, classifying the service class according to the damage degree and as the service parameter comprises:
identifying and positioning public facilities in the image information by utilizing a computer vision technology, and then segmenting and extracting different public facilities by utilizing an image segmentation technology to obtain a local image;
and detecting a local image containing the public facilities by utilizing a pre-constructed machine learning model, judging whether the public facilities have damaged defects, if not, marking the public facilities as non-damaged facilities, if so, marking the public facilities as damaged facilities, classifying the service grades according to the damage degree, and taking the service grades as service parameters.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311754439.9A CN117746630B (en) | 2023-12-20 | 2023-12-20 | Industry safety production supervision and management system based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311754439.9A CN117746630B (en) | 2023-12-20 | 2023-12-20 | Industry safety production supervision and management system based on big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117746630A true CN117746630A (en) | 2024-03-22 |
| CN117746630B CN117746630B (en) | 2024-05-24 |
Family
ID=90252290
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311754439.9A Active CN117746630B (en) | 2023-12-20 | 2023-12-20 | Industry safety production supervision and management system based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117746630B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100030586A1 (en) * | 2008-07-31 | 2010-02-04 | Choicepoint Services, Inc | Systems & methods of calculating and presenting automobile driving risks |
| CN101908274A (en) * | 2010-07-19 | 2010-12-08 | 北京世纪高通科技有限公司 | Method and device for processing road traffic accident information |
| CN106530702A (en) * | 2016-11-24 | 2017-03-22 | 广东顺德中山大学卡内基梅隆大学国际联合研究院 | Random dynamic network traffic planning method based on traffic exponent |
| US20170372431A1 (en) * | 2016-06-24 | 2017-12-28 | Swiss Reinsurance Company Ltd. | Autonomous or partially autonomous motor vehicles with automated risk-controlled systems and corresponding method thereof |
| US20190265703A1 (en) * | 2018-02-26 | 2019-08-29 | Nvidia Corporation | Systems and methods for computer-assisted shuttles, buses, robo-taxis, ride-sharing and on-demand vehicles with situational awareness |
| CN112449709A (en) * | 2019-06-29 | 2021-03-05 | 空中客车A^3有限责任公司 | Safety micro-service system structure for unmanned air traffic management |
| CN113988476A (en) * | 2021-11-26 | 2022-01-28 | 苏交科集团股份有限公司 | Dynamic assessment prediction method for road transportation safety risk |
| US20220180384A1 (en) * | 2020-03-05 | 2022-06-09 | Guangzhou Quick Decision Information Technology Co., Ltd. | Data collection method and system |
| CN116579901A (en) * | 2023-03-23 | 2023-08-11 | 宁波图锐信息科技有限公司 | Intelligent traffic operation monitoring system and monitoring method |
| CN116611621A (en) * | 2023-07-18 | 2023-08-18 | 枣庄卡企安网络科技有限公司 | Traffic safety management system with multiple roles for real-time data interaction and supervision |
-
2023
- 2023-12-20 CN CN202311754439.9A patent/CN117746630B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100030586A1 (en) * | 2008-07-31 | 2010-02-04 | Choicepoint Services, Inc | Systems & methods of calculating and presenting automobile driving risks |
| CN101908274A (en) * | 2010-07-19 | 2010-12-08 | 北京世纪高通科技有限公司 | Method and device for processing road traffic accident information |
| US20170372431A1 (en) * | 2016-06-24 | 2017-12-28 | Swiss Reinsurance Company Ltd. | Autonomous or partially autonomous motor vehicles with automated risk-controlled systems and corresponding method thereof |
| CN106530702A (en) * | 2016-11-24 | 2017-03-22 | 广东顺德中山大学卡内基梅隆大学国际联合研究院 | Random dynamic network traffic planning method based on traffic exponent |
| US20190265703A1 (en) * | 2018-02-26 | 2019-08-29 | Nvidia Corporation | Systems and methods for computer-assisted shuttles, buses, robo-taxis, ride-sharing and on-demand vehicles with situational awareness |
| CN112449709A (en) * | 2019-06-29 | 2021-03-05 | 空中客车A^3有限责任公司 | Safety micro-service system structure for unmanned air traffic management |
| US20220180384A1 (en) * | 2020-03-05 | 2022-06-09 | Guangzhou Quick Decision Information Technology Co., Ltd. | Data collection method and system |
| CN113988476A (en) * | 2021-11-26 | 2022-01-28 | 苏交科集团股份有限公司 | Dynamic assessment prediction method for road transportation safety risk |
| CN116579901A (en) * | 2023-03-23 | 2023-08-11 | 宁波图锐信息科技有限公司 | Intelligent traffic operation monitoring system and monitoring method |
| CN116611621A (en) * | 2023-07-18 | 2023-08-18 | 枣庄卡企安网络科技有限公司 | Traffic safety management system with multiple roles for real-time data interaction and supervision |
Non-Patent Citations (2)
| Title |
|---|
| 葛雨明;韩庆文;王妙琼;曾令秋;李璐;: "汽车大数据应用模式与挑战分析", 计算机科学, no. 06, 15 June 2020 (2020-06-15) * |
| 许成涛;: "江苏交通综合运行监测系统的设计与应用", 中国交通信息化, no. 02, 15 February 2018 (2018-02-15) * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117746630B (en) | 2024-05-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111242574A (en) | Intelligent site inspection management system and method based on GPS technology | |
| CN111582016A (en) | Intelligent maintenance-free power grid monitoring method and system based on cloud edge collaborative deep learning | |
| WO2021027728A1 (en) | Rail transit operation and maintenance method, device, system and apparatus, and medium | |
| CA2974401A1 (en) | A self-customizing, multi-tenanted mobile system and method for digitally gathering and disseminating real-time visual intelligence on utility asset damage enabling automated priority analysis and enhanced utility outage response | |
| CN110866642A (en) | Security monitoring method and device, electronic equipment and computer readable storage medium | |
| CN116739245B (en) | Intelligent fire-fighting city alarm receiving and scheduling system | |
| CN109141528A (en) | A kind of urban track traffic civil engineering facility intelligent real-time monitoring system | |
| CN112770088A (en) | AI video linkage perception monitoring system | |
| CN117371952B (en) | Multi-project collaborative management system | |
| CN117319609A (en) | Internet of things big data intelligent video monitoring system and method | |
| CN115310661A (en) | Railway infrastructure on-site monitoring and prediction early warning system based on fusion technology | |
| CN113672689A (en) | Intelligent frontier defense information processing system and method | |
| CN114913447B (en) | Police intelligent command room system and method based on scene recognition | |
| Statsenko et al. | Developing software and hardware for automation of ground urban transport traffic management | |
| CN117746630B (en) | Industry safety production supervision and management system based on big data | |
| Van Hoang | Impact of integrated artificial intelligence and internet of things technologies on smart city transformation | |
| CN115240101A (en) | Medical sample forwarding and monitoring system based on scene analysis | |
| Finogeev et al. | Proactive big data analysis for traffic accident prediction | |
| CN112468696A (en) | Data processing method and device | |
| CN112258371A (en) | Fault processing method and device | |
| Rindt | Situational Awareness for Transportation Management: Automated Video Incident Detection and Other Machine Learning Technologies for the Traffic Management Center | |
| KR102642540B1 (en) | Methodo of providing smart city safety service and server performing the same | |
| Hu et al. | Intelligent Engineering Construction Management: On-Site Construction Management | |
| KR102590358B1 (en) | Method and Apparatus for Processing Image of Region of Interest in Traffic Information Center | |
| CN117768500A (en) | Cloud side-based intelligent station system and interaction method, equipment and medium thereof |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |