CN117351708A - Expressway safety operation management early warning method, system and storage medium - Google Patents
Expressway safety operation management early warning method, system and storage medium Download PDFInfo
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
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Abstract
The invention discloses a highway safety operation management early warning method, a system and a storage medium, and relates to the field of highway safety operation. The invention comprises the following steps: constructing an expressway integral online database and an expressway fault list; carrying out risk factor analysis on data in the whole on-line database of the expressway through a video AI algorithm to obtain an expressway abnormal event; screening expressway risk sections based on risk factors and the occurrence probability of expressway abnormal events, and establishing a risk section sub-database; performing fault troubleshooting on the data in the risk road section sub-database based on the expressway fault list; and generating early warning information based on the checking result, and updating the whole on-line database of the expressway and the expressway fault list in real time. The invention aims to solve the problem that the early warning method in the prior art cannot timely make the safety early warning, and the safety early warning is completed in an on-line monitoring mode, so that timely and accurate early warning is realized.
Description
Technical Field
The invention relates to the field of expressway safety operation, in particular to an expressway safety operation management early warning method, an expressway safety operation management early warning system and a storage medium.
Background
In recent years, with rapid development of national economy, expressways have achieved remarkable results. Expressways are one of the most important infrastructures of traffic systems, and safety production accidents and emergency events frequently occur due to the influence of various factors in operation management work. In order to effectively prevent various operation risks, safety risk evaluation work is required to be done based on the current state of expressway operation.
At present, the evaluation of the overall safety state of the expressway is limited to evaluating various indexes affecting the operation safety of the expressway in different periods of a tunnel, and the safety condition of the expressway in different operation states cannot be timely and effectively given in a mode similar to the form of irregular safety check. How to accurately identify the operation safety risk of the expressway, how to strengthen the safety risk management capability, prevent various safety accidents to the maximum extent, and has great significance for the development of expressway enterprises.
Disclosure of Invention
In view of the above, the invention provides a highway safety operation management early warning method, a system and a storage medium, which are used for solving the problem that the early warning method in the prior art cannot timely make safety early warning, and realizing timely and accurate early warning by completing the safety early warning in an on-line monitoring mode.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a highway safety operation management early warning method comprises the following steps:
constructing an expressway integral online database and an expressway fault list;
carrying out risk factor analysis on data in the whole on-line database of the expressway through a video AI algorithm to obtain an expressway abnormal event;
screening expressway risk sections based on risk factors and the occurrence probability of expressway abnormal events, and establishing a risk section sub-database;
performing fault troubleshooting on the data in the risk road section sub-database based on the expressway fault list;
and generating early warning information based on the checking result, and updating the whole on-line database of the expressway and the expressway fault list in real time.
Optionally, the data in the expressway whole online database is collected in real time through the expressway event detection device, the collected data is preprocessed, the preprocessing comprises data enhancement pixel values, and the data are classified according to data types and road sections.
Optionally, the video AI algorithm includes a data acquisition layer, a network transmission layer and a data analysis layer; the data acquisition layer acquires data in the whole on-line database of the expressway in real time and transmits the data to the data analysis layer through the network transmission layer, the data analysis layer adopts a deep learning model to extract risk factor characteristics, filters characteristics irrelevant to the risk factors, judges based on the risk factor characteristics, and obtains an expressway abnormal event according to a judging result.
Optionally, the training process of the deep learning model is as follows:
dividing historical data in the expressway into a training set and a testing set;
inputting the training set into a convolutional neural network, and training the internal weight of the convolutional neural network;
performing iterative training for multiple times until the internal weight accords with a preset first threshold value;
testing the convolutional neural network after the pre-training is completed by using a test set, and calculating a loss function;
if the loss function calculation result meets a preset second threshold, training is completed to obtain a deep learning model, otherwise, training is iterated again until the preset second threshold is met.
Optionally, the screening of highway risk road sections specifically includes: and carrying out correlation analysis on the risk factors and the occurrence probability of the expressway abnormal event, wherein if the correlation between the risk factors and the occurrence probability of the expressway abnormal event is higher than 60%, the expressway abnormal event is considered as a high-risk factor road section, and otherwise, the expressway abnormal event is considered as a low-risk road section.
Optionally, the method further comprises the step of transmitting the early warning information to a third party map service platform.
An expressway safety operation management and early warning system, comprising:
the highway data acquisition module: the system is used for constructing an expressway integral online database and an expressway fault list;
an abnormal event acquisition module: the method comprises the steps of carrying out risk factor analysis on data in an overall on-line database of the expressway through a video AI algorithm to obtain an expressway abnormal event;
database partitioning module: the method is used for screening expressway risk sections based on risk factors and the occurrence probability of expressway abnormal events and establishing a risk section sub-database;
and a fault checking module: the system is used for conducting fault troubleshooting on data in the risk road section sub-database based on the expressway fault list;
the early warning information generation module: and the system is used for generating early warning information based on the checking result and updating the whole on-line database of the expressway and the expressway fault list in real time.
A computer storage medium having a computer program stored thereon, the computer program when executed by a processor implementing the steps of any one of the highway safety operation management early warning methods.
Compared with the prior art, the invention provides the expressway safety operation management early warning method, the expressway safety operation management early warning system and the storage medium, which have the following beneficial effects:
1. according to the invention, the response speed to the fault of the expressway can be improved by constructing the database and the expressway fault list; the risk factors are classified according to the grades, so that targeted monitoring can be realized, and the monitoring capability is improved; and recording the checking result into the database and the expressway list again, realizing the real-time update of the database and the expressway list, improving the data volume of the database and enhancing the response capability to faults.
2. The invention utilizes the data visualization and big data aggregation technology to accurately analyze the road segment risk indexes and main causes, supports road segments, areas and road managers, carries out quantization, accurate, visual and scientific decisions, and improves the decision level.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of the method of the present invention;
fig. 2 is a schematic diagram of a system structure according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention discloses a highway safety operation management early warning method, as shown in fig. 1, comprising the following steps:
s1: constructing an expressway integral online database and an expressway fault list;
s2: carrying out risk factor analysis on data in the whole on-line database of the expressway through a video AI algorithm to obtain an expressway abnormal event;
s3: screening expressway risk sections based on risk factors and the occurrence probability of expressway abnormal events, and establishing a risk section sub-database;
s4: performing fault troubleshooting on the data in the risk road section sub-database based on the expressway fault list;
s5: and generating early warning information based on the checking result, and updating the whole on-line database of the expressway and the expressway fault list in real time. The early warning information establishes threshold warning triggering rules for various focus events based on multiple dimensions such as time, space and data, automatically monitors the development states of the various focus events, performs visual risk research and judgment by combining an early warning model, and performs hierarchical early warning response.
Further, in S1, the data in the highway whole online database is collected in real time through the highway event detection device, and the collected data is preprocessed, where the preprocessing includes data enhancement pixel values, and classification is performed according to the data types and road segments.
Further, in S2, the video AI algorithm includes a data acquisition layer, a network transmission layer, and a data analysis layer; the data acquisition layer acquires data in the whole on-line database of the expressway in real time and transmits the data to the data analysis layer through the network transmission layer, the data analysis layer adopts a deep learning model to extract risk factor characteristics, filters characteristics irrelevant to the risk factors, judges based on the risk factor characteristics, and obtains an expressway abnormal event according to a judging result.
Further, the data acquisition layer: the full-high-definition digital video monitoring equipment is deployed at the positions along the expressway section and the road junction, and dead angle monitoring is avoided for 24 hours.
Network transport layer: and accessing each camera to a system backbone network nearby through an access switch, and converging the cameras to a core switch to uniformly access a video management system.
Data analysis layer: the AI video analysis server analyzes in real time through an algorithm to realize abnormal alarm, and sends alarm and alarm recovery information to the central control system through a data interface to link the locomotive. The algorithm functions adopt a modularized design, and can run independently and are not interfered with each other.
Further, the training process of the deep learning model is as follows:
s21: dividing historical data in the expressway into a training set and a testing set; in the training process, a large number of labeling pictures are required to be manually input for learning, core features related to engineering are learned from the pictures, and the feature recognition mode is solidified into a final model;
s22: inputting the training set into a convolutional neural network, and training the internal weight of the convolutional neural network;
s23: performing iterative training for multiple times until the internal weight accords with a preset first threshold value;
s24: testing the convolutional neural network after the pre-training is completed by using a test set, and calculating a loss function;
s25: if the loss function calculation result meets a preset second threshold, training is completed to obtain a deep learning model, otherwise, training is iterated again until the preset second threshold is met.
In addition, the embodiment can also judge the abnormal event by establishing an accident risk assessment and prediction model, and the specific steps are as follows: collecting related data affecting traffic accidents and extracting characteristics of various data; taking the characteristics of the collected related data as independent variables, taking a traffic safety index formed by the historical traffic accident number and the grade as the dependent variables, establishing an accident risk assessment and prediction model, and solving model parameters; calculating the expectations of the number of traffic accidents in different areas according to the obtained accident risk assessment and prediction model and combining real-time road conditions, road structure attributes and driving safety coefficients, and calculating real-time traffic safety indexes in different areas according to a pre-established safety index model; and establishing a visual display platform for displaying the real-time traffic safety indexes of different areas.
Further, in S3, the screening of the highway risk road section specifically includes: and carrying out correlation analysis on the risk factors and the occurrence probability of the expressway abnormal event, wherein if the correlation between the risk factors and the occurrence probability of the expressway abnormal event is higher than 60%, the expressway abnormal event is considered as a high-risk factor road section, and otherwise, the expressway abnormal event is considered as a low-risk road section.
In this embodiment, the early warning information may also be transmitted to the third party map service platform.
Corresponding to the method shown in fig. 1, the invention also discloses a highway safety operation management early warning system for realizing the method of fig. 1, the specific structure is shown in fig. 2, and the system comprises:
the highway data acquisition module: the system is used for constructing an expressway integral online database and an expressway fault list;
an abnormal event acquisition module: carrying out risk factor analysis on data in the whole on-line database of the expressway through a video AI algorithm to obtain an expressway abnormal event;
database partitioning module: the method is used for screening expressway risk sections based on risk factors and the occurrence probability of expressway abnormal events and establishing a risk section sub-database;
and a fault checking module: the system is used for conducting fault troubleshooting on data in the risk road section sub-database based on the expressway fault list;
the early warning information generation module: and the system is used for generating early warning information based on the checking result and updating the whole on-line database of the expressway and the expressway fault list in real time.
Finally, the embodiment discloses a computer storage medium, on which a computer program is stored, and the computer program when executed by a processor implements the steps of the highway safety operation management early warning method according to any one of the above.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. The highway safety operation management early warning method is characterized by comprising the following steps:
constructing an expressway integral online database and an expressway fault list;
carrying out risk factor analysis on data in the whole on-line database of the expressway through a video AI algorithm to obtain an expressway abnormal event;
screening expressway risk sections based on risk factors and the occurrence probability of expressway abnormal events, and establishing a risk section sub-database;
performing fault troubleshooting on the data in the risk road section sub-database based on the expressway fault list;
and generating early warning information based on the checking result, and updating the whole on-line database of the expressway and the expressway fault list in real time.
2. The method for managing and early warning of highway safety operation according to claim 1, wherein the data in the whole on-line database of the highway is collected in real time by the highway event detection device, and the collected data is preprocessed, wherein the preprocessing comprises data enhancement pixel values and classification according to data types and road sections.
3. The highway safety operation management early warning method according to claim 1, wherein the video AI algorithm comprises a data acquisition layer, a network transmission layer and a data analysis layer; the data acquisition layer acquires data in the whole on-line database of the expressway in real time and transmits the data to the data analysis layer through the network transmission layer, the data analysis layer adopts a deep learning model to extract risk factor characteristics, filters characteristics irrelevant to the risk factors, judges based on the risk factor characteristics, and obtains an expressway abnormal event according to a judging result.
4. The highway safety operation management early warning method according to claim 3, wherein the training process of the deep learning model is as follows:
dividing historical data in the expressway into a training set and a testing set;
inputting the training set into a convolutional neural network, and training the internal weight of the convolutional neural network;
performing iterative training for multiple times until the internal weight accords with a preset first threshold value;
testing the convolutional neural network after the pre-training is completed by using a test set, and calculating a loss function;
if the loss function calculation result meets a preset second threshold, training is completed to obtain a deep learning model, otherwise, training is iterated again until the preset second threshold is met.
5. The method for highway safety operation management and early warning according to claim 1, wherein the screening of highway risk road sections is specifically as follows: and carrying out correlation analysis on the risk factors and the occurrence probability of the expressway abnormal event, wherein if the correlation between the risk factors and the occurrence probability of the expressway abnormal event is higher than 60%, the expressway abnormal event is considered as a high-risk factor road section, and otherwise, the expressway abnormal event is considered as a low-risk road section.
6. The highway safety operation management pre-warning method according to claim 1, further comprising transmitting pre-warning information to a third party map service platform.
7. The utility model provides a highway safety operation management early warning system which characterized in that includes:
the highway data acquisition module: the system is used for constructing an expressway integral online database and an expressway fault list;
an abnormal event acquisition module: the method comprises the steps of carrying out risk factor analysis on data in an overall on-line database of the expressway through a video AI algorithm to obtain an expressway abnormal event;
database partitioning module: the method is used for screening expressway risk sections based on risk factors and the occurrence probability of expressway abnormal events and establishing a risk section sub-database;
and a fault checking module: the system is used for conducting fault troubleshooting on data in the risk road section sub-database based on the expressway fault list;
the early warning information generation module: and the system is used for generating early warning information based on the checking result and updating the whole on-line database of the expressway and the expressway fault list in real time.
8. A computer storage medium, wherein a computer program is stored on the computer storage medium, and when executed by a processor, the computer program implements the steps of a highway safety operation management early warning method according to any one of claims 1 to 6.
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| CN111504251A (en) * | 2020-04-21 | 2020-08-07 | 北京中资国源科技有限公司 | Novel method for monitoring safety of expressway side slope |
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