CN111311034B - Road waterlogging risk prediction method, device, equipment and storage medium - Google Patents
Road waterlogging risk prediction method, device, equipment and storage medium Download PDFInfo
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Abstract
The application discloses a method, a device and equipment for predicting urban road waterlogging risk and a computer readable storage medium. The method comprises the steps that floating vehicle track data and first rainfall of a target road in a historical time period are matched with road network data of the target road, and a traffic flow sequence and a rainfall sequence of the target road in the historical time period are obtained; and then determining an inland inundation rainfall threshold of the target road according to the traffic flow sequence and the rainfall sequence, and predicting the inland inundation risk of the target road in the current time period according to the inland inundation rainfall threshold and the second rainfall of the current time period. The difficulty of urban road waterlogging risk prediction is reduced, and the urban road waterlogging risk prediction method can meet the requirement of predicting the urban road waterlogging risk in short-term heavy rainfall weather.
Description
Technical Field
The application belongs to the technical field of urban safety, and particularly relates to a road waterlogging risk prediction method, device, equipment and storage medium.
Background
At present, the urban road waterlogging analysis mainly simulates the waterlogging process of an urban road easy to waterlog by means of a basic hydrological model, such as a rainfall model, a convergence model or a drainage model, so as to predict the urban road waterlogging probability. Due to the fact that the modeling process of the model is complex, an accurate water accumulation process is difficult to simulate on a medium-small space scale, the model is only suitable for predicting the waterlogging risk of roads with large rainfall, and the waterlogging risk assessment of small-scale rainstorm cannot be met. Especially, in the short-time heavy rainfall weather, the urban road waterlogging risk analysis is difficult to be carried out by using the model, and the early warning of the urban road waterlogging risk cannot be accurately carried out.
Disclosure of Invention
The application provides a road waterlogging risk prediction method, a device, equipment and a storage medium, which can realize the prediction of urban road waterlogging probability by combining the track data of floating vehicles, rainfall and road network data on urban roads, reduce the difficulty of urban road waterlogging risk prediction, and can meet the requirement of predicting the waterlogging risk of urban roads in short-term heavy rainfall weather.
In a first aspect, the present application provides a method for predicting risk of road waterlogging, including:
acquiring floating vehicle track data and first rainfall of a target road in a historical time period;
matching the track data of the floating vehicle and the first rainfall into road network data of the target road to obtain a traffic flow sequence and a rainfall sequence of the target road in the historical time period;
determining an inland inundation and rainfall capacity threshold value of the target road according to the traffic flow sequence and the rainfall capacity sequence;
and predicting the risk of the waterlogging of the target road in the current time period according to the waterlogging rainfall threshold and the second rainfall of the current time period.
In an optional implementation manner, the matching the floating vehicle trajectory data and the first rainfall into the road network data of the target road to obtain a traffic flow sequence and a rainfall sequence of the target road in the historical time period includes:
storing the floating vehicle trajectory data and the first amount of rainfall to a predetermined relational database;
and matching the track data of the floating vehicle and the first rainfall into road network data of the target road according to a spatial analysis function of the relational database to obtain a traffic flow sequence and a rainfall sequence of the target road in the historical time period.
In an optional implementation manner, determining a threshold of waterlogging and rainfall for the target road according to the vehicle flow sequence and the rainfall sequence includes:
determining an inland inundation probability sequence of the target road in the historical time period according to the traffic flow sequence and the rainfall sequence;
and determining the waterlogging and rainfall threshold of the target road according to the waterlogging probability sequence and the rainfall sequence.
In an optional implementation manner, determining a waterlogging probability sequence of the target road in the historical time period according to the vehicle flow sequence and the rainfall sequence includes:
dividing the traffic flow sequence into a first traffic flow sequence and a second traffic flow sequence according to the rainfall at each moment in the rainfall sequence, wherein the rainfall at each moment contained in the first traffic flow sequence is greater than a preset rainfall threshold value, and the rainfall at each moment contained in the second traffic flow sequence is less than or equal to the preset rainfall threshold value;
respectively calculating the first vehicle traffic rate at each moment in the first vehicle traffic flow sequence to obtain a first vehicle traffic rate sequence;
respectively calculating a second vehicle traffic rate at each moment in the second vehicle traffic sequence to obtain a second vehicle traffic rate sequence;
and determining a waterlogging probability sequence on the target road within the preset time period according to the first vehicle traffic rate sequence and the second vehicle traffic rate sequence.
In an optional implementation manner, determining a waterlogging and rainfall threshold of the target road according to the waterlogging probability sequence and the rainfall sequence includes:
analyzing the waterlogging probability sequence and the rainfall sequence through a polynomial interpolation algorithm to obtain a preset waterlogging probability threshold value of the target road;
determining the waterlogging state of the target road at each moment of the historical time period according to the preset waterlogging probability threshold;
establishing an inland inundation probability model of the target road according to the rainfall sequence and the inland inundation state;
inputting the rainfall sequence into the waterlogging probability model for analysis to obtain a target waterlogging probability threshold of the target road;
and determining the waterlogging rainfall threshold of the target road according to the target waterlogging probability threshold.
In an optional implementation manner, inputting the rainfall sequence into the waterlogging probability model for analysis, so as to obtain a target waterlogging probability threshold of the target road, including:
inputting the rainfall sequence into the waterlogging probability model so that the waterlogging probability model determines the probability value of waterlogging of the target road at each moment of the historical time period according to the rainfall sequence;
if the probability value of waterlogging occurrence at any moment is predicted to be larger than or equal to the preset waterlogging probability threshold value by the waterlogging probability model, and the probability value of waterlogging occurrence at the moment is larger than the probability values of waterlogging occurrence at other moments in the historical time period, updating the preset waterlogging probability threshold value to be the probability value of waterlogging occurrence at the moment, and the updated waterlogging probability value to be the target waterlogging probability threshold value of the target road.
In an optional implementation manner, determining a threshold of waterlogging and rainfall capacity of the target road according to the threshold of the waterlogging probability includes:
and acquiring the waterlogging rainfall at the moment corresponding to the waterlogging probability threshold, and taking the waterlogging rainfall at the moment corresponding to the waterlogging probability threshold as the waterlogging rainfall threshold of the target road.
In an optional implementation manner, predicting the risk of the target road suffering from the waterlogging in the current time period according to the threshold of the waterlogging rainfall and the second rainfall in the current time period includes:
if the second rainfall at any moment in the current time period is greater than or equal to the waterlogging rainfall threshold, determining that the target road has a waterlogging risk in the current time period;
and if the second rainfall at any moment in the current time period is smaller than the waterlogging rainfall threshold, determining that the target road has no waterlogging risk in the current time period.
As can be seen from the above, in the scheme of the method for predicting a risk of waterlogging on a road provided in the first aspect of the present application, a traffic flow sequence and a rainfall sequence of a target road in a historical time period are obtained by matching floating vehicle trajectory data and a first rainfall of the target road in the historical time period to road network data of the target road; and then determining an inland inundation rainfall threshold of the target road according to the traffic flow sequence and the rainfall sequence, and predicting the inland inundation risk of the target road in the current time period according to the inland inundation rainfall threshold and the second rainfall of the current time period. The difficulty of urban road waterlogging risk prediction is reduced, and the urban road waterlogging risk prediction method can meet the requirement of predicting the urban road waterlogging risk in short-term heavy rainfall weather.
In a second aspect, the present application provides a road waterlogging risk prediction device, including:
the acquisition module is used for acquiring the track data of the floating vehicle and the first rainfall of the target road in the historical time period;
the obtaining module is used for matching the floating vehicle track data and the first rainfall into road network data of the target road to obtain a traffic flow sequence and a rainfall sequence of the target road in the historical time period;
the determining module is used for determining an inland inundation and rainfall capacity threshold value of the target road according to the traffic flow sequence and the rainfall capacity sequence;
and the prediction module is used for predicting the risk of the waterlogging of the target road in the current time period according to the waterlogging rainfall threshold and the second rainfall in the current time period.
In a third aspect, the present application provides a road waterlogging risk prediction device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to the first aspect when executing the computer program.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of the first aspect as described above.
In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by one or more processors, performs the steps of the method of the first aspect as described above.
It should be noted that, for the beneficial effects of the second aspect to the fifth aspect, reference may be made to the relevant description in the first aspect, and details are not described herein again.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic structural diagram of a road waterlogging risk prediction system provided by an embodiment of the present application;
FIG. 2 is a flowchart of an implementation of a road waterlogging risk prediction method provided by an embodiment of the present application;
FIG. 3 is a flowchart illustrating an implementation of S201 in FIG. 2;
fig. 4 is a schematic diagram of a road waterlogging risk prediction device provided in an embodiment of the present application;
fig. 5 is a schematic structural diagram of a road waterlogging risk prediction device in an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
At present, the urban road waterlogging analysis is mainly based on a classical hydrological model, such as a rainfall model, a confluence model and a drainage model, to simulate the waterlogging process of the urban waterlogging-prone road and perform waterlogging depth prediction. However, the modeling process of the models is complex, and the simulation of the water accumulation process is difficult to perform in small and medium road spaces, so that the risk assessment requirement of the rainstorm waterlogging on a small scale is difficult to meet.
In order to solve the problems, the functional relation between the rainfall and the road vehicle flow is established by fusing the trajectory data, the rainfall data and the road network data of the floating vehicle, so that the probability of waterlogging on the road under different rainfall is predicted. Compare in hydrology model, the orbit data, rainfall data and the road network data of floating vehicle all obtain more easily, consequently, can reduce the degree of difficulty of urban road waterlogging risk prediction through the scheme of this application, and can satisfy the weather of strong rainfall for a short time, predict urban road's waterlogging risk.
In order to implement the technical solution proposed in the present application, first, referring to fig. 1, an exemplary road waterlogging risk prediction system related to the present application is described. As shown in fig. 1, the road waterlogging risk prediction system provided by the embodiment of the present application includes a road waterlogging risk prediction device 100, a weather monitoring device 101, and a floating vehicle data management device 102, where the road waterlogging risk prediction device 100 is in communication connection with the weather monitoring device 101 and the floating vehicle data management device 102, when it is required to predict whether a current time period of a target road has a waterlogging risk, for example, when a strong rainstorm occurs in the current time period or water in the current time period exceeds a certain value, the road waterlogging risk prediction device 100 sends a request for acquiring a historical time period floating vehicle track data to the floating vehicle data management device 102, and sends a request for acquiring a historical time period first rainfall to the weather monitoring device 101, and according to the acquired first rainfall, floating vehicle track data, and road network data of the target road, and predicting the waterlogging risk of the target road section in the current time period.
The road waterlogging risk prediction device 100 is a hardware device for predicting the waterlogging risk of the target road in the current time period according to the floating vehicle track data of the target road in the historical time period, the first rainfall and the road network data of the target road, and the hardware device may be a handheld device, a mobile terminal, a tablet computer and other mobile devices, or a server, a robot and other devices in some application scenarios.
The weather monitoring device 101 is a hardware device used by a weather station to monitor weather data and store the weather data.
The floating vehicle data management device 102 is a hardware device used by a floating company to manage and store vehicle data, including vehicle trajectory data.
In order to explain the technical solution proposed in the present application, the following description will be given by way of specific examples.
Referring to fig. 2, fig. 2 is a flowchart illustrating an implementation of a road waterlogging risk prediction method according to an embodiment of the present application, which is detailed as follows:
s201, acquiring the track data of the floating vehicle and the first rainfall of the target road in the historical time period.
In the embodiment of the present application, the historical time period may be any time period in the past relative to the current time period, for example, the historical time period may be a past time period adjacent to the current time period, or may be a past time period not adjacent to the current time period. The specific requirement is determined according to different application scenes, for example, in an application scene, if a strong rainstorm occurs in a current time period, the corresponding historical time period is a time period discontinuous from the current time period, and the rainstorm occurs in the historical time period; in another application scenario, if the current time period has a certain amount of accumulated water and the rainfall continues, the corresponding historical time period may be a time period continuous with the current time period. It is understood that the time length of the historical time period may be set in advance according to the rainfall amount, and exemplarily, the time length of the historical time period is in units of minutes, such as 5 minutes or 10 minutes.
The target road is a road needing road waterlogging risk prediction, and is usually a road with strong rainstorm or continuous rainfall. In the embodiment of the application, under the condition that the target road is subjected to strong rainstorm or continuous rainfall, the track data of the floating vehicle and the first rainfall of the target road corresponding to the historical time period are acquired.
S202, matching the floating vehicle track data and the first rainfall into road network data of the target road to obtain a traffic flow sequence and a rainfall sequence of the target road in the historical time period.
In the embodiment of the application, the traffic flow sequence of the historical time period is an array formed by traffic flows of vehicles corresponding to all times of the historical time period, and the rainfall sequence is an array formed by rainfall corresponding to all times of the historical time period; in an optional implementation manner, the track data of the floating vehicle and the first rainfall capacity may be matched to the road network data of the target road through a predetermined relational database, so as to obtain a traffic flow sequence and a rainfall capacity sequence of the target road in the historical time period.
Specifically, as shown in fig. 3, fig. 3 is a flowchart of a specific implementation of S201 in fig. 2. As can be seen from fig. 3, in this alternative implementation, S202 includes the following steps:
s2021, storing the floating vehicle trajectory data and the first amount of rainfall to a predetermined relational database.
In this embodiment, the predetermined relational database is a database system having a spatial object, a spatial index, a spatial analysis function, and a spatial operator, such as a PostgreSQL database, and in this embodiment, the floating vehicle trajectory data and the first rainfall are stored in the relational database as the spatial object of the relational database.
S2022, according to a spatial analysis function of the relational database, matching the floating vehicle track data and the first rainfall into road network data of the target road to obtain a traffic flow sequence and a rainfall sequence of the target road in the historical time period.
In this embodiment, time and space matching is performed on the floating vehicle trajectory data, the first rainfall capacity and the road network data of the target road according to a spatial analysis function of the relational database, so that the floating vehicle trajectory data and the first rainfall capacity are matched with the road network data of the target road, and a traffic flow sequence of the target road in the historical time period and the first rainfall capacity are acquired from the road network data according to a link ID of the target road.
It can be understood that, at any time of the historical time period, a phenomenon that the traffic flow is too large, too small, or too small may exist, and at this time, the data needs to be subjected to noise reduction processing by a preset filtering method, so as to obtain a traffic flow sequence that can be used for theoretical analysis. Illustratively, the preset filtering method includes a domain filtering method or a median filtering method.
S203, determining an inland inundation and rainfall capacity threshold value of the target road according to the traffic flow sequence and the rainfall capacity sequence.
In the embodiment of the application, the waterlogging probability sequence of the target road in the historical time period is determined through the traffic flow sequence and the rainfall sequence, and the waterlogging rainfall threshold is further determined according to the waterlogging probability sequence and the rainfall sequence.
Illustratively, the process of determining the waterlogging probability sequence of the target road for the historical period of time according to the vehicle flow sequence and the rainfall sequence includes steps a1 to a 4:
a1, dividing the traffic flow sequence into a first traffic flow sequence and a second traffic flow sequence according to the rainfall at each moment in the rainfall sequence, wherein the rainfall at each moment contained in the first traffic flow sequence is larger than a preset rainfall threshold, and the rainfall at each moment contained in the second traffic flow sequence is smaller than or equal to the preset rainfall threshold.
In this embodiment of the application, the preset rainfall threshold may be preset according to an influence of rainfall on the vehicle flow, for example, in an optional implementation manner, the influence on the vehicle flow is mainly an influence under a rainfall condition and an influence under a no-rainfall condition, and then the preset rainfall threshold may be set to 0, and the vehicle flow sequence is divided into a first vehicle flow sequence under a rainfall condition and a second vehicle flow sequence under a no-rainfall condition corresponding to the preset rainfall threshold.
And A2, respectively calculating the first vehicle traffic rate at each moment in the first vehicle traffic rate sequence to obtain a first vehicle traffic rate sequence.
In this embodiment, the vehicle passing rate is the probability of at least one vehicle passing at any time, and it is assumed that the probability of at least one vehicle passing through the target road segment at any time in the first traffic flow sequence is
Respectively calculating the time of each historical time period
Corresponding from time to time
Forming the first vehicle traffic rate sequence.
Illustratively, each traffic flow in the first traffic flow sequence is subjected to normalization transformation (0-1 transformation), and the 0-1 sequence obtained after the normalization transformation is recorded as a normalized traffic flow sequence Z, wherein when the traffic flow value is greater than 0, the corresponding Z sequence has a value greater than that of 0
The value is 1, when the traffic flow value is less than or equal to 0, the corresponding Z sequence
The value is 0. The method is used for indicating whether vehicles pass through the target road at any time t. Respectively calculating the traffic rate P on the target road at each moment t according to the normalized traffic flow sequence ZtCommunication rates P corresponding to respective times ttForming the first vehicle traffic rate sequence.
It is understood that, in order to remove the influence of random noise, the traffic rate at any time t may be represented by an average traffic rate of a preset time period before and after any time t, for example, 1 hour.
And A3, respectively calculating a second vehicle traffic rate at each moment in the second traffic flow sequence to obtain a second vehicle traffic rate sequence.
Assuming that the probability that at least one vehicle passes through the target road section at any moment in the second traffic sequence is P2Respectively calculating P corresponding to each time of the historical time period2P corresponding to each time2Forming the second vehicle traffic rate sequence. It is understood that the process of obtaining the second vehicle traffic rate sequence is the same as the process of obtaining the first vehicle traffic rate sequence, and the description thereof is omitted.
A4, determining a waterlogging probability sequence on the target road in the preset time period according to the first vehicle traffic rate sequence and the second vehicle traffic rate sequence.
In the embodiment of the application, the waterlogging probability at each moment is calculated by respectively substituting the first vehicle traffic rate and the second vehicle traffic rate at each moment in the first vehicle traffic rate sequence and the second vehicle traffic rate sequence into a preset calculation formula, and the waterlogging probability at each moment constitutes the waterlogging probability sequence. Illustratively, the preset calculation formula is as follows:
P=1-P1t/P2twherein P is1tFirst vehicle passage rate, P, at any time t2tIs the second vehicle traffic rate at any time t.
In an optional implementation manner, the process of determining the waterlogging and rainfall threshold of the target road according to the waterlogging probability sequence and the rainfall sequence includes steps B1 to B5:
b1, analyzing the waterlogging probability sequence and the rainfall sequence through a polynomial interpolation algorithm to obtain a preset waterlogging probability threshold value of the target road.
In this embodiment, an interpolation function between the waterlogging probability and the first rainfall is established through a polynomial interpolation algorithm, then the change rate of the interpolation function curve is solved, and when the change rate is the maximum, the corresponding waterlogging probability value is the preset waterlogging probability threshold.
B2, determining the waterlogging state of the target road at each moment of the historical time period according to the preset waterlogging probability threshold.
In this embodiment, the waterlogging state of the target road at each time of the historical time period includes the occurrence of waterlogging or the absence of waterlogging.
The waterlogging state of the target road at each moment of the historical time period is determined by the waterlogging probability value and the waterlogging probability threshold of the target road at each moment of the historical time period, exemplarily, the waterlogging probability value corresponding to the target road at each moment of the historical time period is calculated, the waterlogging probability value corresponding to the target road at each moment of the historical time period is respectively compared with the preset waterlogging probability threshold, and if the waterlogging probability value at any moment is greater than the waterlogging probability threshold, the moment is determined as the state of waterlogging occurrence, for example, the state of waterlogging occurrence is recorded as 1; and if the waterlogging probability value at any moment is less than or equal to the waterlogging probability threshold, determining that the moment is a state without waterlogging, for example, recording the state without waterlogging as 0. It should be noted that, in this embodiment of the application, if the waterlogging probability value at any time is equal to the waterlogging probability threshold, it may be determined that the time is a state in which waterlogging occurs, or it may be determined that the time is a state in which waterlogging does not occur, and the setting may be selected according to actual requirements.
B3, establishing a waterlogging probability model of the target road according to the rainfall sequence and the waterlogging state.
In this embodiment, the waterlogging probability model is a logistic regression model, and the logistic regression model is established according to the rainfall sequence and the waterlogging state.
B4, inputting the rainfall sequence into the waterlogging probability model for analysis to obtain a target waterlogging probability threshold of the target road.
Inputting the rainfall sequence into the waterlogging probability model so that the waterlogging probability model determines the probability value of waterlogging of the target road at each moment of the historical time period according to the rainfall sequence;
if the probability value of waterlogging occurrence at any moment is predicted to be larger than or equal to the preset waterlogging probability threshold value by the waterlogging probability model, and the probability value of waterlogging occurrence at the moment is larger than the probability values of waterlogging occurrence at other moments in the historical time period, updating the preset waterlogging probability threshold value to be the probability value of waterlogging occurrence at the moment, and the updated waterlogging probability value to be the target waterlogging probability threshold value of the target road.
And B5, determining the waterlogging rainfall threshold of the target road according to the target waterlogging probability threshold.
And acquiring the waterlogging rainfall at the moment corresponding to the waterlogging probability threshold, and taking the waterlogging rainfall at the moment corresponding to the waterlogging probability threshold as the waterlogging rainfall threshold of the target road.
And S204, predicting the risk of the waterlogging of the target road in the current time period according to the threshold value of the waterlogging rainfall and the second rainfall in the current time period.
If the second rainfall at any moment in the current time period is greater than or equal to the waterlogging rainfall threshold, determining that the target road has a waterlogging risk in the current time period;
and if the second rainfall at any moment in the current time period is smaller than the waterlogging rainfall threshold, determining that the target road has no waterlogging risk in the current time period.
As can be appreciated, after it is determined that the target road has the waterlogging risk in the current time period, the waterlogging early warning information for the target road may be sent to a predetermined early warning terminal, so that the early warning terminal dynamically performs the waterlogging risk early warning in real time.
According to the analysis, the road waterlogging risk prediction method provided by the application obtains the traffic flow sequence and the rainfall sequence of the target road in the historical time period by matching the floating vehicle track data and the first rainfall of the target road in the historical time period to the road network data of the target road; and then determining an inland inundation rainfall threshold of the target road according to the traffic flow sequence and the rainfall sequence, and predicting the inland inundation risk of the target road in the current time period according to the inland inundation rainfall threshold and the second rainfall of the current time period. The difficulty of urban road waterlogging risk prediction is reduced, and the urban road waterlogging risk prediction method can meet the requirement of predicting the urban road waterlogging risk in short-term heavy rainfall weather.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Based on the road waterlogging risk prediction method provided by the embodiment, the embodiment of the invention further provides an embodiment of a device for implementing the embodiment of the method.
Referring to fig. 4, fig. 4 is a schematic diagram of a road waterlogging risk prediction device according to an embodiment of the present application. The modules included are used to perform the steps in the corresponding embodiment of fig. 2. Please refer to fig. 2 for a related description of the embodiment. For convenience of explanation, only the portions related to the present embodiment are shown. Referring to fig. 4, the road waterlogging risk prediction apparatus 400 includes:
the acquiring module 401 is configured to acquire floating vehicle trajectory data and a first rainfall of a target road in a historical time period;
an obtaining module 402, configured to match the floating vehicle trajectory data and the first rainfall to road network data of the target road, so as to obtain a traffic flow sequence and a rainfall sequence of the target road in the historical time period;
a determining module 403, configured to determine an inland inundation and rainfall capacity threshold of the target road according to the traffic flow sequence and the rainfall capacity sequence;
and the predicting module 404 is configured to predict the risk of the target road suffering from the waterlogging in the current time period according to the threshold of the waterlogging rainfall and the second rainfall in the current time period.
In an optional implementation manner, the obtaining module 402 includes:
a storage unit for storing the floating vehicle trajectory data and the first rainfall into a predetermined relational database;
and the matching unit is used for matching the track data of the floating vehicle and the first rainfall into the road network data of the target road according to the spatial analysis function of the relational database to obtain a traffic flow sequence and a rainfall sequence of the target road in the historical time period.
In an optional implementation manner, the determining module 403 includes:
the first determining unit is used for determining an inland inundation probability sequence of the target road in the historical time period according to the traffic flow sequence and the rainfall sequence;
and the second determining unit is used for determining the waterlogging and rainfall threshold of the target road according to the waterlogging probability sequence and the rainfall sequence.
In an optional implementation manner, the first determining unit includes:
the dividing subunit is configured to divide the vehicle flow sequence into a first vehicle flow sequence and a second vehicle flow sequence according to the rainfall at each time in the rainfall sequence, where the rainfall at each time included in the first vehicle flow sequence is greater than a preset rainfall threshold, and the rainfall at each time included in the second vehicle flow sequence is less than or equal to the preset rainfall threshold;
the first calculating subunit is used for respectively calculating the first vehicle traffic rate at each moment in the first vehicle traffic flow sequence to obtain a first vehicle traffic rate sequence;
the second calculating subunit is configured to calculate a second vehicle traffic rate at each time in the second vehicle flow sequence, so as to obtain a second vehicle traffic rate sequence;
and the first determining subunit is used for determining the waterlogging probability sequence on the target road in the preset time period according to the first vehicle traffic rate sequence and the second vehicle traffic rate sequence.
In an optional implementation manner, the second determining unit includes:
the first analysis subunit is used for analyzing the waterlogging probability sequence and the rainfall sequence through a polynomial interpolation algorithm to obtain a preset waterlogging probability threshold value of the target road;
the second determining subunit is used for determining the waterlogging state of the target road at each moment of the historical time period according to the preset waterlogging probability threshold;
the building subunit is used for building an inland inundation probability model of the target road according to the rainfall sequence and the inland inundation state;
the second analysis subunit is used for inputting the rainfall sequence into the waterlogging probability model for analysis to obtain a target waterlogging probability threshold of the target road;
and the third determining subunit is used for determining the waterlogging rainfall threshold of the target road according to the target waterlogging probability threshold.
In an optional implementation manner, the second analysis subunit includes:
the input subunit is used for inputting the rainfall sequence into the waterlogging probability model so that the waterlogging probability model determines the probability value of waterlogging of the target road at each moment of the historical time period according to the rainfall sequence;
and the updating subunit is used for updating the preset waterlogging probability threshold value to be the probability value of the waterlogging occurring at the moment if the probability value of the waterlogging occurring at any moment is predicted to be greater than or equal to the preset waterlogging probability threshold value and is greater than the probability values of the waterlogging occurring at other moments in the historical time period, and the updated waterlogging probability value is the target waterlogging probability threshold value of the target road.
In an optional implementation manner, the third determining subunit is specifically configured to:
and acquiring the waterlogging rainfall at the moment corresponding to the waterlogging probability threshold, and taking the waterlogging rainfall at the moment corresponding to the waterlogging probability threshold as the waterlogging rainfall threshold of the target road.
In an alternative implementation, the prediction module 404 includes:
a third determining unit, configured to determine that the target road has a risk of waterlogging in the current time period if the second rainfall at any time in the current time period is greater than or equal to the threshold of waterlogging rainfall;
and the fourth determining unit is used for determining that the target road has no risk of waterlogging in the current time period if the second rainfall at any moment in the current time period is less than the threshold value of the waterlogging rainfall.
As can be seen from the above, in the scheme of the road waterlogging risk prediction device provided by this embodiment, the vehicle flow sequence and the rainfall sequence of the target road in the historical time period are obtained by matching the floating vehicle trajectory data and the first rainfall of the target road in the historical time period to the road network data of the target road; and then determining an inland inundation rainfall threshold of the target road according to the traffic flow sequence and the rainfall sequence, and predicting the inland inundation risk of the target road in the current time period according to the inland inundation rainfall threshold and the second rainfall of the current time period. The difficulty of urban road waterlogging risk prediction is reduced, and the urban road waterlogging risk prediction method can meet the requirement of predicting the urban road waterlogging risk in short-term heavy rainfall weather.
The embodiment of the present application further provides a device for predicting risk of road waterlogging, please refer to fig. 5, where the device 5 for predicting risk of road waterlogging in the embodiment of the present application includes: a memory 501, one or more processors 502 (only one shown in fig. 5), and a computer program stored on the memory 501 and executable on the processors. Wherein: the memory 501 is used for storing software programs and units, the processor 502 executes various functional applications and data processing by running a computer program stored on the memory 501, such as a fire rescue level processing program, for example, the processor 502 implements the steps in the above-described various fire rescue level determination method embodiments, such as the steps S201 to S204 shown in fig. 2, when executing the fire rescue level determination program stored on the memory 501. Alternatively, the processor 502 implements the functions of the modules/units in the above-described device embodiments, for example, the functions of the modules 401 to 404 shown in fig. 4, when executing the computer program.
It should be understood that in the embodiments of the present Application, the Processor 502 may be a Central Processing Unit (CPU), and the Processor may be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The embodiment of the application also provides a computer readable storage medium, which stores a computer program, and the computer program can realize the above-mentioned road waterlogging risk prediction method when being executed by a processor.
The embodiment of the application provides a computer program product, and when the computer program product runs on a video processing device, the road waterlogging risk prediction device can realize the road waterlogging risk prediction method when executed.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned functions may be distributed as different functional units and modules according to needs, that is, the internal structure of the apparatus may be divided into different functional units or modules to implement all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art would appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of external device software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the above-described modules or units is only one logical functional division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The integrated unit may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. The computer program includes computer program code, and the computer program code may be in a source code form, an object code form, an executable file or some intermediate form. The computer-readable storage medium may include: any entity or device capable of carrying the above-described computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer readable Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signal, telecommunication signal, software distribution medium, etc. It should be noted that the computer readable storage medium may contain content that is subject to appropriate increase or decrease according to the requirements of legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable storage media does not include electrical carrier signals and telecommunication signals according to legislation and patent practice.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (8)
1. A road waterlogging risk prediction method is characterized by comprising the following steps:
acquiring floating vehicle track data and first rainfall of a target road in a historical time period;
matching the track data of the floating vehicle and the first rainfall into road network data of the target road to obtain a traffic flow sequence and a rainfall sequence of the target road in the historical time period;
dividing the traffic flow sequence into a first traffic flow sequence and a second traffic flow sequence according to the rainfall at each moment in the rainfall sequence, wherein the rainfall at each moment contained in the first traffic flow sequence is greater than a preset rainfall threshold value, and the rainfall at each moment contained in the second traffic flow sequence is less than or equal to the preset rainfall threshold value;
respectively calculating the first vehicle traffic rate at each moment in the first vehicle traffic flow sequence to obtain a first vehicle traffic rate sequence;
respectively calculating a second vehicle traffic rate at each moment in the second vehicle traffic sequence to obtain a second vehicle traffic rate sequence;
determining a waterlogging probability sequence on the target road within a preset time period according to the first vehicle traffic rate sequence and the second vehicle traffic rate sequence;
determining a waterlogging and rainfall threshold value of the target road in the historical time period according to the waterlogging probability sequence and the rainfall sequence;
and predicting the risk of the waterlogging of the target road in the current time period according to the waterlogging rainfall threshold and the second rainfall of the current time period.
2. The method of claim 1, wherein said matching said floating vehicle trajectory data and said first amount of rainfall into said road network data for said target road, resulting in a sequence of traffic flow and a sequence of rainfall for said target road over said historical period of time, comprises:
storing the floating vehicle trajectory data and the first amount of rainfall to a predetermined relational database;
and matching the track data of the floating vehicle and the first rainfall into road network data of the target road according to a spatial analysis function of the relational database to obtain a traffic flow sequence and a rainfall sequence of the target road in the historical time period.
3. The method of claim 2, wherein determining a threshold for waterlogging and rainfall for the target road over the historical period of time based on the sequence of waterlogging probabilities and the sequence of rainfall comprises:
analyzing the waterlogging probability sequence and the rainfall sequence through a polynomial interpolation algorithm to obtain a preset waterlogging probability threshold value of the target road;
determining the waterlogging state of the target road at each moment of the historical time period according to the preset waterlogging probability threshold;
establishing an inland inundation probability model of the target road according to the rainfall sequence and the inland inundation state;
inputting the rainfall sequence into the waterlogging probability model for analysis to obtain a target waterlogging probability threshold of the target road;
and determining the waterlogging rainfall threshold of the target road in the historical time period according to the target waterlogging probability threshold.
4. The method of claim 3, wherein inputting the rainfall sequence into the waterlogging probability model for analysis to obtain a target waterlogging probability threshold for the target road comprises:
inputting the rainfall sequence into the waterlogging probability model so that the waterlogging probability model determines the probability value of waterlogging of the target road at each moment of the historical time period according to the rainfall sequence;
if the probability value of waterlogging occurrence at any moment is predicted to be larger than or equal to the preset waterlogging probability threshold value by the waterlogging probability model, and the probability value of waterlogging occurrence at the moment is larger than the probability values of waterlogging occurrence at other moments in the historical time period, updating the preset waterlogging probability threshold value to be the probability value of waterlogging occurrence at the moment, and the updated waterlogging probability value to be the target waterlogging probability threshold value of the target road.
5. The method of claim 3 or 4, wherein determining the threshold amount of waterlogging precipitation for the target road over the historical period of time based on the target threshold probability of waterlogging comprises:
and acquiring the waterlogging rainfall at the moment corresponding to the target waterlogging probability threshold, and taking the waterlogging rainfall at the moment corresponding to the target waterlogging probability threshold as the waterlogging rainfall threshold of the target road in the historical time period.
6. The method of claim 5, wherein predicting the risk of the target road for waterlogging during the current time period based on the waterlogging rainfall threshold and a second rainfall for the current time period comprises:
if the second rainfall at any moment in the current time period is greater than or equal to the waterlogging rainfall threshold, determining that the target road has a waterlogging risk in the current time period;
and if the second rainfall at any moment in the current time period is smaller than the waterlogging rainfall threshold, determining that the target road has no waterlogging risk in the current time period.
7. A road waterlogging risk prediction device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method of any one of claims 1 to 6.
8. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 6.
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| CN114511148A (en) * | 2022-02-16 | 2022-05-17 | 长沙祥云瑞风信息技术有限公司 | Artificial intelligence-based inland inundation early warning method, system, medium and equipment |
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| CN104898183B (en) * | 2015-05-29 | 2017-10-17 | 杭州辰青和业科技有限公司 | Heavy rain urban waterlogging modelling evaluation method |
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