CN118887790B - Natural disaster risk early warning system for high-speed road section - Google Patents
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Abstract
The invention discloses a natural disaster risk early warning system for a high-speed road section, which comprises an information acquisition module, an environment analysis module, a risk recognition module, a risk early warning module and a risk control module, wherein the information acquisition module is used for acquiring cooperative information of the high-speed road section in real time through Beidou satellites, unmanned aerial vehicles and ground perception terminals, the environment analysis module is used for analyzing the cooperative information to acquire environment dynamic data and geological dynamic data of the high-speed road section, the risk recognition module is used for inputting the environment dynamic data and the geological dynamic data into a preset recognition model to train and output risk coefficients and judge risk grades according to the size of the risk coefficients, the risk early warning module is used for outputting corresponding early warning strategies to traffic control centers and geological authorities according to the risk grades, and the risk control module is used for carrying out traffic control and geological risk prevention and control measures of the high-speed road section according to the early warning strategies.
Description
Technical Field
The invention relates to the field of risk early warning systems, in particular to a natural disaster risk early warning system for a high-speed road section.
Background
The expressway is an important component of the comprehensive transportation system in China, and through strong promotion of the state for many years, the expressway has achieved great achievement in construction, and the expressway is a large artery connected with each province and has strong driving effect on economic development along the line.
However, as the operation time passes, the natural environment of the operation road section is in a continuously changing state, so that the natural disaster risk after the expressway is built is increased, the realization difficulty of the effective operation target for realizing the expressway fast and safe and smooth is increased, and correspondingly, the possibility of causing economic loss is increased, and the problems of insufficient management strength and capability of the operation risk are increasingly highlighted.
In order to improve the operation economic benefit of the expressway, the existing intelligent monitoring equipment and monitoring technology are used for realizing real-time monitoring and analysis on various influence factors possibly causing natural disasters, a high-speed road section is generally constructed for carrying out natural disaster early warning systems, however, the existing natural disaster early warning systems are imperfect, statistics and comprehensive analysis are carried out by combining data of a climate department, an earthquake detection department, an environmental department, a traffic management department, a geological bureau and the like, a great deal of time is consumed for transmitting related signals of risk information and analyzing partial influence data, and all departments are required to coordinate and cooperate, so that the actual operation is complicated, therefore, after-treatment measures are carried out after natural disasters are monitored only through qualitative analysis and on-site, the early warning control systems are relatively imperfect, and the effect of early recognition and control early warning of natural disaster risks is not obvious.
Disclosure of Invention
The invention aims to provide a natural disaster risk early warning system for a high-speed road section, which solves the following technical problems:
How to perfect the natural disaster early warning system of the high-speed road section and improve the recognition efficiency of natural disaster risk early warning.
The aim of the invention can be achieved by the following technical scheme:
a natural disaster risk early warning system for a highway section, the system comprising:
the information acquisition module is used for acquiring the cooperative information of the high-speed road section in real time through the Beidou satellite, the unmanned aerial vehicle and the ground perception terminal;
the information acquisition module comprises a cooperative unit;
The Beidou satellite is used for acquiring vector information of the high-speed road section, wherein the vector information comprises positioning information, navigation image information and time information;
The unmanned aerial vehicle is used for acquiring high-definition images of the high-speed road section in real time, and acquiring geological signal data of the surrounding environment through high-definition image monitoring;
The ground sensing terminal is used for monitoring and acquiring earthquake intensity signal data and soil temperature and humidity data according to sensors distributed along the high-speed road section;
the environment analysis module is used for analyzing the cooperative information to obtain environment dynamic data and geological dynamic data of the high-speed road section;
the risk identification module is used for inputting the environmental dynamic data and the geological dynamic data into a preset identification model to train and output risk coefficients, and judging risk levels according to the sizes of the risk coefficients;
the risk early warning module is used for outputting corresponding early warning strategies to the traffic control center and the geological bureau according to the risk grade;
And the risk control module is used for carrying out traffic control and geological risk prevention and control measures on the high-speed road section according to the early warning strategy.
Preferably, the information acquisition module acquires the cooperative information by cooperatively analyzing vector information, geological signal data, seismic intensity signal data and soil temperature and humidity data through a cooperative unit:
determining time information in the vector data, and screening a geological signal data vector set, a seismic intensity signal data vector set and a soil temperature and humidity data vector set of a high-speed road section in accordance with a target time period;
Inputting a geological signal data vector set, a seismic intensity signal data vector set and a soil temperature and humidity data vector set as basic data into an ArcGIS to obtain vector intervals of all current data vector sets;
And deriving the collaborative information and an analysis report thereof according to the spatial distribution characteristics of the data view of the vector section of each data vector set.
Preferably, the manner in which the environmental analysis module obtains the environmental dynamic data and the geological dynamic data based on the spatial analysis model includes:
Inputting the cooperative information into a space analysis model to obtain the space distribution characteristic area of each data vector set;
acquiring a set of corresponding time point area differences according to the geological signal characteristic area change and the seismic intensity signal characteristic area change in the continuous time period in the cooperative information ;
Acquiring a set of corresponding time point area differences according to the geological signal characteristic area change and the soil temperature and humidity characteristic area change in the same continuous time period in the cooperative information。
Preferably, the risk identification module comprises:
by the formula Calculating risk coefficient of the high-speed road sectionWhereinFor monitoring the total number of times for successive time periods,∈;Is a first preset weight coefficient,The second preset weight coefficient is the second preset weight coefficient; Is the first The geological dynamic data parameters of secondary monitoring; Is the first Secondary monitored environmental dynamic data parameters; Is the first The geological dynamic data parameter standard value of secondary monitoring; Is the first And (5) a secondary monitoring environment dynamic data parameter standard value.
Preferably, the risk factor is calculatedAnd a preset risk coefficient threshold intervalAnd (3) performing comparison:
If it is <Judging that the occurrence rate of the natural disaster risk of the current high-speed road section is low, and judging that the current high-speed road section is three-level risk level;
If it is ≤≤Judging that the natural disaster risk occurrence rate of the current high-speed road section is medium, and judging that the current high-speed road section is a secondary risk level;
If it is >And judging that the natural disaster risk occurrence rate of the current high-speed road section is high, and judging as a first-level risk level.
Preferably, the early warning strategy comprises:
carrying out early warning marking according to the output risk level, wherein the three-level risk level is marked as 0, the two-level risk level is marked as 1, and the one-level risk level is marked as 2;
setting a monitoring frequency and a monitoring early warning response interval duration corresponding to each grade of marking data;
The risk level is positively correlated with the monitoring frequency and the monitoring early warning response interval duration.
Preferably, the risk control module includes:
Adjusting the traffic control monitoring frequency and the duration of a monitoring early warning response interval according to an early warning strategy;
And starting and suspending risk prevention operation according to the early warning strategy, and selecting to start an emergency plan so as to reduce or avoid the occurrence of a risk event.
Preferably, the system further comprises an early warning display terminal for displaying early warning signals and chart information fed back by an early warning strategy.
The invention has the beneficial effects that:
(1) According to the method, time information in vector data is determined, the vector information acquired from Beidou satellites is used as basic data based on a geological signal data vector set, a seismic intensity signal data vector set and a soil temperature and humidity data vector set which are screened, the spatial distribution range and characteristics of various data are acquired in a mode of importing the basic data into geographic information system software ArcGIS, collaborative information and analysis reports thereof are derived according to the spatial distribution characteristics of data views of vector intervals of the data vector sets, the time information and the spatial distribution characteristics are comprehensively considered, the collaborative information is derived, and a detailed analysis report is generated based on the collaborative information, so that data support is provided for risk identification.
(2) The environment analysis module is used for analyzing the cooperative information to obtain the environment dynamic data and the geological dynamic data of the high-speed road section, the deep analysis is carried out based on the cooperative information collected by the information collection module to obtain the environment dynamic data and the geological dynamic data of the high-speed road section, basic data is provided for subsequent risk identification, the current environment dynamic data and the geological dynamic data are obtained to realize the advanced identification of the specific condition of the high-speed road section, the environment analysis module can comprehensively analyze the geological and environment dynamic data from two dimensions of space and time, more comprehensive and accurate data support is provided for subsequent risk identification, precursor signals of natural disasters can be effectively captured, and the response speed and accuracy of the early warning system are improved.
(3) According to the risk assessment method, the risk identification module is arranged and used for inputting the environmental dynamic data and the geological dynamic data into the preset identification model to train and output the risk coefficient and judging the risk level according to the size of the risk coefficient, the environmental dynamic data and the geological dynamic data are input into the preset identification model, the risk coefficient is output through training of a machine learning algorithm, and the risk level is judged according to the size of the risk coefficient, so that quantitative assessment of the potential natural disaster risk is realized.
Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a natural disaster risk early warning system for a highway section 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.
In order to improve the operation economic benefit of the expressway, the existing intelligent monitoring equipment and monitoring technology are used for realizing real-time monitoring and analysis on various influence factors possibly causing natural disasters, a high-speed road section is generally constructed for carrying out natural disaster early warning systems, however, the existing natural disaster early warning systems are imperfect, statistics and comprehensive analysis are carried out by combining data of a climate department, an earthquake detection department, an environmental department, a traffic management department, a geological bureau and the like, a great deal of time is consumed for transmitting related signals of risk information and analyzing partial influence data, and all departments are required to coordinate and cooperate, so that the actual operation is complicated, therefore, after-treatment measures are carried out after natural disasters are monitored only through qualitative analysis and on-site, the early warning control systems are relatively imperfect, and the effect of early recognition and control early warning of natural disaster risks is not obvious.
In order to solve the above technical problems, referring to fig. 1, the present invention is a natural disaster risk early warning system for a high-speed road section, the system comprising:
the information acquisition module is used for acquiring the cooperative information of the high-speed road section in real time through the Beidou satellite, the unmanned aerial vehicle and the ground perception terminal;
the information acquisition module comprises a cooperative unit;
The Beidou satellite is used for acquiring vector information of the high-speed road section, wherein the vector information comprises positioning information, navigation image information and time information;
The unmanned aerial vehicle is used for acquiring high-definition images of the high-speed road section in real time, and acquiring geological signal data of the surrounding environment through high-definition image monitoring;
The ground sensing terminal is used for monitoring and acquiring earthquake intensity signal data and soil temperature and humidity data according to sensors distributed along the high-speed road section;
the environment analysis module is used for analyzing the cooperative information to obtain environment dynamic data and geological dynamic data of the high-speed road section;
the risk identification module is used for inputting the environmental dynamic data and the geological dynamic data into a preset identification model to train and output risk coefficients, and judging risk levels according to the sizes of the risk coefficients;
the risk early warning module is used for outputting corresponding early warning strategies to the traffic control center and the geological bureau according to the risk grade;
And the risk control module is used for carrying out traffic control and geological risk prevention and control measures on the high-speed road section according to the early warning strategy.
In the technical scheme, in order to solve the adverse effects of various ambiguous natural disaster factors on road conditions of the high-speed road section directly or indirectly, the design mainly considers the influence of earthquake signals and surrounding climate environments, mainly the influence of temperature and humidity in the surrounding climate environments on smooth traffic of the road section, then detects the hidden danger possibly generated for safe traffic traveling through real-time monitoring and analysis, carries out early warning prompt to avoid possible natural disasters, guides measures such as vehicle evacuation and traffic planning adjustment to carry out emergency risk avoidance, and further ensures traffic safety and personnel safety of the high-speed road section.
The natural disaster risk early warning system for the high-speed road section mainly comprises an information acquisition module, an environment analysis module, a risk identification module, a risk early warning module and a risk control module, and early warning and timely effective control of natural disasters of the high-speed road section are achieved, so that powerful support is provided for traffic safety and geological disaster management.
Firstly, an information acquisition module is arranged and used for acquiring cooperative information of a high-speed road section in real time through a Beidou satellite, an unmanned aerial vehicle and a ground perception terminal, and realizing the effect of 'day-ground-person' cooperative operation through the Beidou satellite, the unmanned aerial vehicle and the ground perception terminal, wherein specific acquisition equipment comprises:
The Beidou satellite is used for acquiring vector information of the high-speed road section, the vector information comprises positioning information, navigation image information and time information, and accurate positioning of the position is ensured through acquiring the vector information.
The high-definition image of the high-speed road section is acquired in real time through the unmanned aerial vehicle, geological signal data of the surrounding environment are acquired through high-definition image monitoring, abnormal natural disasters are mainly represented like landslide, ground cracks and other abnormal conditions, abnormal road conditions of the high-speed road section can be acquired in real time and in a visual mode through the unmanned aerial vehicle technology, and a display can be arranged for observation in actual use.
The ground sensing terminal is used for monitoring and acquiring earthquake intensity signal data and soil temperature and humidity data according to sensors distributed along the high-speed road section so as to reflect ground state change in real time and acquire data of direct influence factors of the running high-speed road surface.
And the information acquisition module is provided with a cooperative unit, and the cooperative unit is used for carrying out integrated cooperative processing on the information acquired by the Beidou satellite, the unmanned aerial vehicle and the ground perception terminal.
Specifically, as an implementation mode of the invention, the mode of acquiring the cooperative information by the information acquisition module is to perform cooperative analysis on vector information, geological signal data, seismic intensity signal data and soil temperature and humidity data through a cooperative unit:
determining time information in the vector data, and screening a geological signal data vector set, a seismic intensity signal data vector set and a soil temperature and humidity data vector set of a high-speed road section in accordance with a target time period;
Inputting a geological signal data vector set, a seismic intensity signal data vector set and a soil temperature and humidity data vector set as basic data into an ArcGIS to obtain vector intervals of all current data vector sets;
And deriving the collaborative information and an analysis report thereof according to the spatial distribution characteristics of the data view of the vector section of each data vector set.
In the technical scheme, firstly, time information in vector data is determined, the time information is extracted from the vector information acquired from a Beidou satellite, the extracted time information is used as a reference for data screening, the screened data comprise a geological signal data vector set, a seismic intensity signal data vector set and a soil temperature and humidity data vector set in a high-speed road section conforming to a target time period, and the geological signal data, the seismic intensity signal data and the soil temperature and humidity data related to the high-speed road section are screened out in the target time period to form a vector set, so that timeliness and relativity of analysis data are ensured.
And acquiring vector intervals of each data vector set by utilizing strong space data analysis capability of the software by a mode of importing the basic data into geographic information system software ArcGIS, namely acquiring the distribution range and the characteristics of various data in space.
Finally, according to the spatial distribution characteristics of the data view of the vector section of each data vector set, the collaborative information and the analysis report thereof are derived, the spatial distribution characteristics of the data view are mainly based on the vector section of each data vector set, the characteristics of the data on spatial distribution, such as a concentrated area, abnormal points and the like, are analyzed, the collaborative information, namely the interaction and the relevance information among different data types, is derived by comprehensively considering the time information and the spatial distribution characteristics, and a detailed analysis report is generated based on the collaborative information, so that data support is provided for risk identification.
And the environment analysis module is used for analyzing the cooperative information to obtain the environment dynamic data and the geological dynamic data of the high-speed road section, and the depth analysis is carried out based on the cooperative information collected by the information collection module to obtain the environment dynamic data and the geological dynamic data of the high-speed road section, so that basic data is provided for subsequent risk identification.
Specifically, as one embodiment of the present invention, the manner in which the environmental analysis module obtains the environmental dynamic data and the geological dynamic data based on the spatial analysis model includes:
Inputting the cooperative information into a space analysis model to obtain the space distribution characteristic area of each data vector set;
acquiring a set of corresponding time point area differences according to the geological signal characteristic area change and the seismic intensity signal characteristic area change in the continuous time period in the cooperative information ;
Acquiring a set of corresponding time point area differences according to the geological signal characteristic area change and the soil temperature and humidity characteristic area change in the same continuous time period in the cooperative information。
According to the technical scheme, the current environment dynamic data and the geological dynamic data are acquired through the environment analysis module to realize the advanced recognition of the specific conditions of the high-speed road section, the environment analysis module is guaranteed to comprehensively analyze the geological and environment dynamic data from two dimensions of space and time, more comprehensive and accurate data support is provided for subsequent risk recognition, and the dynamic data acquisition mode based on the space analysis model can effectively capture precursor signals of natural disasters and improve the response speed and accuracy of an early warning system.
The method comprises the steps of inputting cooperative information into a space analysis model to obtain space distribution characteristic areas of data vector sets, inputting the cooperative information into the space analysis model through the acquisition of the space distribution characteristic areas, and analyzing the space distribution characteristic areas of the data vector sets, namely the space distribution characteristic areas including geological signals, seismic intensity and soil temperature and humidity, by using a Geographic Information System (GIS) technology to calculate the space distribution characteristic areas of the data types at specific time points.
Then, according to the geological signal characteristic area change and the seismic intensity signal characteristic area change in the continuous time period in the cooperative information, acquiring a set of corresponding time point area difference valuesThe method comprises the steps of monitoring the change of the characteristic area of the geological signal, recording the area value of each time point, and obtaining the change condition of the characteristic area of the geological signal, specifically, comparing the characteristic areas of the geological signal and the characteristic areas of the seismic intensity signal at different time points, calculating the area difference value, forming a set of geological dynamic data, and intuitively reflecting the dynamic change trend of the geological state and the seismic activity in space.
Finally, similarly, acquiring a set of area differences at corresponding time points according to the geological signal characteristic area change and the soil temperature and humidity characteristic area change in the same continuous time period in the cooperative informationBased on the comparison result, an environment dynamic data set corresponding to the area difference value of the time point is obtained, potential influence of soil temperature and humidity in environmental factors on the change of geological signals is revealed, dynamic change trend of geological state and surrounding environment in time is intuitively reflected, and influence of environmental change on geological stability is evaluated.
The corresponding comparison set is constructed, so that the change condition of the characteristic area of the recorded seismic intensity signal is ensured to be obtained and the data set is obtained in the same continuous time periodAcquiring and recording the change condition of the characteristic area of the environmental temperature and humidity signal, and acquiring a data set。
Thirdly, setting a risk identification module for inputting the environmental dynamic data and the geological dynamic data into a preset identification model to train and output risk coefficients and judging the risk level according to the size of the risk coefficients, inputting the environmental dynamic data and the geological dynamic data into the preset identification model, training through a machine learning algorithm to output the risk coefficients and judging the risk level according to the size of the risk coefficients to realize quantitative assessment of the risk of the potential natural disasters.
Specifically, as an embodiment of the present invention, the risk identification module includes:
by the formula Calculating risk coefficient of the high-speed road sectionWhereinFor monitoring the total number of times for successive time periods,∈;Is a first preset weight coefficient,The second preset weight coefficient is the second preset weight coefficient; Is the first The geological dynamic data parameters of secondary monitoring; Is the first Secondary monitored environmental dynamic data parameters; Is the first The geological dynamic data parameter standard value of secondary monitoring; Is the first And (5) a secondary monitoring environment dynamic data parameter standard value.
In the above technical solution, in this embodiment, the occurrence rate of the characteristics of the related data based on the geological dynamic information and the environmental dynamic information is calculated by a calculation formula, and the risk of occurrence of the natural disaster is determined, so as to reflect the influence on the normal traffic of the high-speed road section, specifically by the risk coefficientTo realize the comprehensive risk evaluation target of the high-speed road section by the specific analysis of the size of the road section, specifically by the formulaCalculating risk coefficient of the high-speed road sectionWherein, the first preset weight coefficientSetting the specific gravity of the obstruction influence degree of the whole highway section traffic according to the monitored geosynchronous data parameters, wherein the setting size can be obtained by performing simulation analysis through specific data software or performing qualitative comparisonThe specific gravity of the obstruction influence degree of the whole traffic on the highway section is set according to the monitored environmental dynamic data parameters, and the set size can be obtained by performing simulation analysis through specific data software or by performing qualitative comparison, and the specific details are not described herein.
It should be further explained that the standard value、Machine simulation is performed based on historical experience and is not described in detail herein.
As one embodiment of the invention, the risk factor isAnd a preset risk coefficient threshold intervalAnd (3) performing comparison:
If it is <Judging that the occurrence rate of the natural disaster risk of the current high-speed road section is low, and judging that the current high-speed road section is three-level risk level;
If it is ≤≤Judging that the natural disaster risk occurrence rate of the current high-speed road section is medium, and judging that the current high-speed road section is a secondary risk level;
If it is >And judging that the natural disaster risk occurrence rate of the current high-speed road section is high, and judging as a first-level risk level.
In the above technical solution, in this embodiment, the risk factor is obtained byAnd a preset risk coefficient threshold intervalThe natural disaster risk occurrence probability of the current high-speed road section can be further analyzed by judging the size of the risk coefficient, the natural disaster risk occurrence probability is intuitively embodied by the size range of the risk coefficient, and the natural disaster risk occurrence probability is obtained according to the preset risk coefficient threshold value intervalThe grading is carried out, and the specific judging process is as follows: if it is<Judging that the occurrence rate of natural disaster risk of the current high-speed road section is low and judging that the natural disaster risk is three-level risk grades, if so≤≤Judging that the natural disaster risk occurrence rate of the current high-speed road section is medium, judging that the current high-speed road section is the secondary risk level, if>And judging that the natural disaster risk occurrence rate of the current high-speed road section is high, and judging as a first-level risk level.
And fourthly, the risk early warning module is used for outputting corresponding early warning strategies to the traffic control center and the geological bureau according to the risk level, formulating corresponding early warning strategies according to the size of the risk level, outputting early warning information to the traffic control center and the geological bureau in time, and ensuring that related departments can respond in time and take necessary preventive measures.
Specifically, as one embodiment of the present invention, the designed early warning strategy includes:
carrying out early warning marking according to the output risk level, wherein the three-level risk level is marked as 0, the two-level risk level is marked as 1, and the one-level risk level is marked as 2;
setting a monitoring frequency and a monitoring early warning response interval duration corresponding to each grade of marking data;
The risk level is positively correlated with the monitoring frequency and the monitoring early warning response interval duration.
In the above technical solution, the specific early warning strategy in this embodiment is as follows:
And (5) performing risk level early warning marking:
the third level of risk, labeled "0", indicates a lower risk, but still requires monitoring.
The secondary risk level, labeled "1", indicates that there is a medium risk and that the monitoring frequency needs to be increased.
The first-level risk grade is marked as '2', which indicates that the risk is highest, and early warning measures need to be immediately taken;
Setting a monitoring frequency:
The setting of the monitoring frequency is positively related to the risk level, i.e. the higher the risk level, the denser the monitoring frequency, and the following is an example in this embodiment:
The monitoring frequency can be set to be once every 12 hours under the mark of '0' for maintaining basic monitoring.
The secondary risk level is marked by "1", and the monitoring frequency is raised to once every 6 hours to capture potential changes more timely.
And the first-level risk level is that under the mark of '2', the monitoring frequency is increased to be once every 1-2 hours, so that the risk dynamics can be monitored in real time.
Setting the duration of a monitoring early warning response interval:
the duration of the monitoring early warning response interval is positively correlated with the risk level, namely, the higher the risk level is, the faster the response speed is required, and the following is an example in the embodiment:
the duration of the monitoring early warning response interval under the '0' mark can be set to be within 20-24 hours.
The duration of the monitoring early warning response interval under the '1' mark is shortened to be within 10-12 hours, so that the response efficiency is improved.
The first-level risk level is that the duration of the monitoring early warning response interval under the '2' mark is controlled within 2-4 hours, so that rapid action can be ensured, and potential loss is reduced.
By implementing the early warning strategy, differentiated monitoring and response measures can be adopted according to different risk levels, so that the effective utilization of resources is ensured, the overall response speed and the overall response efficiency of the early warning system are improved, and the early warning system has important significance for guaranteeing the safe operation of a high-speed road section.
And fifthly, the risk control module is used for carrying out traffic control and geological risk prevention and control measures of the high-speed road section according to the early warning strategy, carrying out traffic control of the high-speed road section according to the early warning strategy, such as temporarily closing a road, limiting speed and driving, and taking geological risk prevention and control measures, such as reinforcing a roadbed, setting anti-slip engineering and the like, so as to minimize the influence of natural disasters on the high-speed road section.
As an embodiment of the present invention, the risk control module includes:
Adjusting the traffic control monitoring frequency and the duration of a monitoring early warning response interval according to an early warning strategy;
And starting and suspending risk prevention operation according to the early warning strategy, and selecting to start an emergency plan so as to reduce or avoid the occurrence of a risk event.
In the technical scheme, the adjustment of the traffic control monitoring frequency in the embodiment comprises adjustment of the frequency of traffic control monitoring according to the monitoring frequency set in the early warning strategy, wherein the monitoring of lower frequency is maintained when the risk level is low so as to reduce resource consumption, and the monitoring frequency is improved when the risk level is high so as to ensure that potential traffic safety hazards can be found in time. And (3) adjusting the duration of the monitoring early warning response interval, shortening the duration of the monitoring early warning response interval under the high risk level, ensuring that once an abnormal condition is found, the abnormal condition can be reacted quickly, and implementing necessary traffic control or geological risk prevention and control measures. Through converting into an actual control scheme, the highway section can take appropriate traffic control and geological risk prevention and control measures under different risk levels, so that the operation safety of the highway network is ensured, and a safer and more reliable environment is provided for public travel.
As an implementation mode of the invention, the invention further comprises an early warning display terminal which is used for displaying early warning signals and chart information fed back by an early warning strategy;
The early warning display terminal generally comprises a real-time display early warning signal, a chart information feedback terminal, an early warning strategy feedback display terminal, an interactive operation interface and a historical data query and analysis interface, and is responsible for displaying the early warning signal and the early warning strategy to decision makers and related personnel in an intuitive and easily understood mode.
The functions of the early warning display terminal mainly comprise:
The real-time display early warning signals are usually arranged at traffic intersections, gates, shunting sites and the like, the early warning signals are displayed in striking colors and icons, for example, red lights indicate primary risk levels, yellow lights indicate secondary risk levels, green lights indicate tertiary risk levels, timeliness and visual impact of information are ensured, and the current risk condition is conveniently and rapidly identified.
The chart information feedback terminal is used for providing rich chart information such as a line graph, a bar chart, a thermodynamic diagram and the like, and is used for displaying the trend of the change of the risk level along with time, the risk distribution condition of different areas, the adjustment of monitoring frequency and early warning response interval duration and the like, so that a decision maker can be helped to comprehensively know the working state and effect of the early warning system.
The early warning strategy feedback display terminal ensures that a decision maker can grasp the implementation effect of system response measures in real time by displaying the execution condition of the early warning strategy, including adjustment of traffic control monitoring frequency, change of monitoring early warning response interval duration, starting and suspending states of risk prevention operation and starting condition of an emergency plan.
The interactive operation interface allows a user to manually adjust the early warning strategy according to actual conditions by providing the interactive operation interface, such as temporarily increasing monitoring frequency, manually starting risk prevention operation and the like, so that the flexibility and adaptability of the system are enhanced.
The historical data query and analysis interface has a historical data query function, can check early warning signal records and early warning strategy execution conditions in a past period of time, supports data export and analysis, and provides data support for system optimization and strategy adjustment.
Through the early warning display terminal, timely transmission of early warning information and transparent execution of an early warning strategy can be ensured, data and analysis required by decision support can be provided for a decision maker, and the early warning display terminal is an important tool for realizing efficient and accurate risk early warning and control.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, devices, non-volatile computer storage medium embodiments, the description is relatively simple, as it is substantially similar to method embodiments, with reference to the section of the method embodiments being relevant.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing is merely illustrative and explanatory of the principles of this application, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this application or beyond the scope of this application as defined in the claims.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116295641A (en) * | 2023-03-15 | 2023-06-23 | 章柳柳 | A sky-ground integrated intelligent monitoring system for road slopes |
| CN117423225A (en) * | 2023-10-23 | 2024-01-19 | 东华理工大学 | A disaster remote sensing early warning system based on high-speed railway operation |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113178082A (en) * | 2021-04-14 | 2021-07-27 | 河北锐驰交通工程咨询有限公司 | Intelligent identification method and system for safety risks of expressway |
| CN113882441B (en) * | 2021-10-13 | 2022-05-03 | 河海大学 | An early warning method for seismic instability of high and steep slopes and its application |
| CN114119575B (en) * | 2021-11-30 | 2022-07-19 | 二十一世纪空间技术应用股份有限公司 | Spatial information change detection method and system |
| CN115544734B (en) * | 2022-09-15 | 2025-09-19 | 武汉大学 | Drought monitoring and early warning method, system and equipment based on characteristic water area change |
| CN117058845A (en) * | 2023-08-07 | 2023-11-14 | 河南交院工程技术集团有限公司 | Highway high slope landslide hazard early warning method based on evaluation function training |
| CN117133099A (en) * | 2023-08-28 | 2023-11-28 | 河南交院工程技术集团有限公司 | An automatic monitoring system for high highway slope disasters |
| CN117953665B (en) * | 2024-03-27 | 2024-06-18 | 中铁水利水电规划设计集团有限公司 | A geological disaster monitoring and early warning system and early warning method thereof |
| CN118298322B (en) * | 2024-04-01 | 2025-02-11 | 广西新发展交通集团有限公司 | A sky-ground integrated highway multi-source three-dimensional rock and soil disaster identification system |
| CN118351671B (en) * | 2024-06-14 | 2024-08-27 | 山东省地质矿产勘查开发局第七地质大队（山东省第七地质矿产勘查院） | Urban geological safety geological disaster multistage early warning method |
| CN118629217B (en) * | 2024-08-14 | 2024-10-15 | 四川智慧高速科技有限公司 | A highway sound recognition accident detection system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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