CN112241806A - Road damage probability prediction method, device terminal equipment and readable storage medium - Google Patents
Road damage probability prediction method, device terminal equipment and readable storage medium Download PDFInfo
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Abstract
The application belongs to the technical field of traffic management and provides a road damage probability prediction method, a device terminal device and a readable storage medium, wherein the method comprises the following steps: and acquiring the predicted number of the breakage events of the target road section in the target time period according to the number of the breakage events of the target road section in the first historical time period. And acquiring a first threshold value according to a lower-bound curve of the traffic flow basic graph model of the target road section in a second historical time period. And determining the breakage probability of the target road section in the target time period by a Bayesian probability formula according to the predicted breakage event number and the first threshold value. The method and the device realize the prediction of the probability of breakage of the target road section in the target time period according to the quantity of breakage events in the historical time period of the target road section and the traffic flow basic graph model. And then determining whether the target road section is damaged or not according to the probability that the target road section is damaged in the target time period.
Description
Technical Field
The application belongs to the technical field of traffic management, and particularly relates to a road damage probability prediction method, a device terminal device and a readable storage medium.
Background
The urban road is damaged and is the condition that the urban road can not be avoided, and after each road is used for a period of time, the damaged conditions of pavement cracking, sinking, bulging, mark line damage and the like in different degrees can be generated.
In the prior art, in order to determine the damage condition of a road, only a manual inspection mode can be adopted, and whether the road is damaged or not is confirmed manually.
However, the manual inspection is used to determine whether the road is damaged, and the related personnel needs to periodically perform inspection, so that the efficiency is low, and the cost is high.
Disclosure of Invention
The embodiment of the application provides a road damage probability prediction method, a device terminal device and a readable storage medium, which can solve the problems of low efficiency and high cost when determining whether a road is damaged through manual inspection.
In a first aspect, an embodiment of the present application provides a road breakage probability prediction method, including:
and acquiring the predicted number of the breakage events of the target road section in the target time period according to the number of the breakage events of the target road section in the first historical time period. And acquiring a first threshold value according to a lower-bound curve of the traffic flow basic graph model of the target road section in the second historical time period, wherein the first threshold value is determined according to the number of coordinate points which are continuously positioned on the lower-bound curve in the traffic flow basic graph model. And determining the breakage probability of the target road section in the target time period by a Bayesian probability formula according to the predicted breakage event number and the first threshold value.
In some embodiments, obtaining a predicted number of breakage events of the target road segment within the target time period according to the number of breakage events of the target road segment within the first historical time period includes: and acquiring a time sequence of the number of breakage events of the target road section in the first historical time period. And fitting the time sequence through a gray Markov model to obtain the predicted damage event number of the target road section in the target time period.
In some embodiments, the traffic flow basic map model comprises at least two of a traffic flow density index, a vehicle average speed index and a traffic flow index.
In some embodiments, obtaining the first threshold value according to the lower-bound curve of the traffic flow basic graph model of the target road segment in the second historical time period comprises: and acquiring a traffic flow basic graph model of the target road section and a lower bound curve of the traffic flow basic graph model according to the traffic flow density index and the vehicle average speed index of the target road section in the second historical time period. And determining a first threshold value according to the relative position relation between a first coordinate point indicated by the traffic flow density index and the vehicle average speed index of the target road section in the second historical time period and the lower-bound curve in the traffic flow basic graph model.
In some embodiments, the second history period includes N cycles, and a same number of cycles of a second coordinate point is obtained from the N cycles, where the second coordinate point is a first coordinate point located continuously below the lower-bound curve, and N is an integer greater than 1.
Determining a first threshold value comprising: and if the number of periods in which the same number of second coordinate points exist is reduced as the number of second coordinate points existing in the periods is increased, determining the number of second coordinate points in the period with the largest number of second coordinate points existing in the N periods as the first threshold value. Or, if the number of periods in which the same number of second coordinate points exist is reduced to a fixed value as the number of second coordinate points existing in a period increases, the number of second coordinate points in one period in which the number of periods in the N periods is the fixed value is determined to be the first threshold.
In some embodiments, determining the breakage probability of the target road segment in the target time period according to the number of predicted breakage events and the first threshold value by using a bayesian probability formula comprises: and obtaining the probability of i continuous coordinate points which are positioned on the lower-bound curve of the target road section under the condition of no damage according to the first threshold value to obtain a first parameter of a Bayesian probability formula, wherein i is an integer greater than or equal to 0. And acquiring the ratio of the logarithm of the average value of the number of the breakage events to the logarithm of the predicted number of the breakage events at intervals of the same duration as the target time period in the first historical time period, and acquiring a second parameter of the Bayesian probability formula. And inputting the first parameter and the second parameter into a Bayesian probability formula, and determining the damage probability of the target road section in the target time period.
In a second aspect, an embodiment of the present application provides a road breakage probability prediction apparatus, including: the obtaining module is used for obtaining the predicted damage event number of the target road section in the target time period according to the damage event number of the target road section in the first historical time period. And the obtaining module is further used for obtaining a first threshold according to a lower-bound curve of the traffic flow basic graph model of the target road section in a second historical time period, wherein the first threshold is determined according to the number of coordinate points continuously located on the lower-bound curve in the traffic flow basic graph model. And the prediction module is used for determining the breakage probability of the target road section in the target time period through a Bayesian probability formula according to the predicted breakage event number and the first threshold.
In some embodiments, the obtaining module is specifically configured to obtain a time series of the number of damage events of the target road segment in the first historical time period; and fitting the time sequence through a gray Markov model to obtain the predicted damage event number of the target road section in the target time period.
In some embodiments, the traffic flow basic map model comprises at least two of a traffic flow density index, a vehicle average speed index and a traffic flow index.
In some embodiments, the obtaining module is specifically configured to obtain a traffic flow basic map model of the target road segment and a lower-bound curve of the traffic flow basic map model according to the traffic flow density index and the vehicle average speed index of the target road segment in the second historical time period; and determining a first threshold value according to the relative position relation between a first coordinate point indicated by the traffic flow density index and the vehicle average speed index of the target road section in the second historical time period and the lower-bound curve in the traffic flow basic graph model.
In some embodiments, the second history period includes N cycles, and a same number of cycles of a second coordinate point is obtained from the N cycles, where the second coordinate point is a first coordinate point located continuously below the lower-bound curve, and N is an integer greater than 1.
The obtaining module is specifically configured to determine, if the number of periods in which the same number of second coordinate points exist decreases with an increase in the number of second coordinate points existing in the periods, that the number of second coordinate points existing in the most cycles among the N periods is the first threshold. Or, if the number of periods in which the same number of second coordinate points exist is reduced to a fixed value as the number of second coordinate points existing in a period increases, the number of second coordinate points in one period in which the number of periods in the N periods is the fixed value is determined to be the first threshold.
In some embodiments, the prediction module is configured to obtain, according to a first threshold, a probability that i consecutive coordinate points located on a lower-bound curve appear on a target road segment without being damaged, to obtain a first parameter of a bayesian probability formula, where i is an integer greater than or equal to 0; acquiring the ratio of the logarithm of the average value of the number of the breakage events to the logarithm of the number of the predicted breakage events at intervals of the same duration as the target time period in the first historical time period to obtain a second parameter of a Bayesian probability formula; and inputting the first parameter and the second parameter into a Bayesian probability formula, and determining the damage probability of the target road section in the target time period.
In a third aspect, an embodiment of the present application provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method provided in any one of the first aspect is implemented.
In a fourth aspect, the present application provides a computer-readable storage medium, where a computer program is stored, and when executed by a processor, the computer program implements the method provided in any one of the first aspect.
In a fifth aspect, the present application provides a computer program product, which when run on a terminal device, causes the terminal device to execute the method provided in any one of the above first aspects.
It is understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and are not described herein again.
Compared with the prior art, the embodiment of the application has the advantages that: the method comprises the steps of firstly, obtaining the predicted number of the damage events of a target road section in a target time period according to the number of the damage events of the target road section in a first historical time period. And then acquiring a first threshold according to a lower-bound curve of the traffic flow basic graph model of the target road section in a second historical time period, wherein the first threshold is determined according to the number of coordinate points continuously located on the lower-bound curve in the traffic flow basic graph model. And finally, determining the damage probability of the target road section in the target time period by a Bayesian probability formula according to the predicted damage event number and the first threshold. The method and the device realize the prediction of the probability of breakage of the target road section in the target time period according to the quantity of breakage events in the historical time period of the target road section and the traffic flow basic graph model. And then determining whether the target road section is damaged or not according to the probability that the target road section is damaged in the target time period.
Drawings
Fig. 1 is a schematic flowchart of a road damage probability prediction method according to an embodiment of the present application;
FIG. 2 is a schematic view of a basic graphical model of traffic flow;
fig. 3 is a schematic flowchart of a road damage probability prediction method according to another embodiment of the present application;
FIG. 4 is a schematic diagram of a basic graphical model of traffic flow provided by an embodiment of the present application;
fig. 5 is a schematic structural diagram of a road breakage probability prediction apparatus according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a terminal device according to 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.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when.. or" upon "or" in response to a determination "or" in response to a detection ".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise.
Fig. 1 shows a schematic flowchart of a road damage probability prediction method provided by the present application, and by way of example and not limitation, the road damage probability prediction method provided by the embodiment of the present application may be applied to a terminal device with computing capability, such as a mobile phone, a tablet computer, a desktop computer, a notebook computer, and a server, and the embodiment of the present application does not set any limitation to a specific type of the terminal device.
And S11, acquiring the predicted damage event number of the target road section in the target time period according to the damage event number of the target road section in the first historical time period.
In some embodiments, the number of breakage events occurring in the target road history within one year may be counted as the number of breakage events in the first history time period of the target road segment. Accordingly, the target time period may be one month (30 days) in length.
And S12, acquiring a first threshold value according to the lower-bound curve of the traffic flow basic graph model of the target road section in the second historical time period.
Wherein the first threshold value is determined according to the number of coordinate points continuously positioned on the lower-bound curve in the traffic flow basic graph model
The traffic flow basic graph model is a model established by the change rule of the traffic flow along with time and space and is used for describing the characteristics of the traffic flow. Traffic flow refers to the process by which the operation of a vehicle within a transportation network can be approximated as the flow of gas or liquid molecules in a medium. The traffic flow contains three parameters: traffic flow, vehicle average speed, and traffic flow density. The traffic flow rate refers to the number of vehicles passing through a specific section in unit time, the average speed of the vehicles refers to the moving distance of the vehicles in unit time, and the traffic flow density refers to the number of vehicles existing in unit road section length.
It should be noted that as the number of vehicles running on the road increases, the traffic density increases, which causes congestion and forces the driver to reduce the vehicle speed; when the traffic density is reduced, the speed of the vehicle running on the road is increased. I.e., the average vehicle speed is inversely proportional to the traffic flow density.
When a road surface is damaged, the traffic capacity of the road is affected. For example, the damaged road vehicle travels at a lower speed than the normal state at the same traffic density. That is, in the same road segment, the lower-bound curve in the damaged state is located below the lower-bound curve in the normal state, and the overall curve tends to be shifted downward as the degree of damage becomes more serious.
As an example, referring to the basic graph model of traffic flow shown in fig. 2, three lower-bound curves are shown, namely, a lower-bound curve 21 of a normal road section, a lower-bound curve 22 of a general damaged road section, and a lower-bound curve 23 of a severely damaged road section, wherein the lower-bound curve 22 of the general damaged road section is located below the lower-bound curve 21 of the normal road section, and the lower-bound curve 23 of the severely damaged road section is located below the lower-bound curve 22 of the general damaged road section as the degree of damage of the road section increases.
And S13, determining the breakage probability of the target road section in the target time period through a Bayesian probability formula according to the predicted breakage event number and the first threshold value.
It should be noted that, assuming that the probability of occurrence of two random events a and B is P (a) and P (B), respectively, P (a | B) represents the probability of occurrence of a when B occurs, and the probability of occurrence of B when a occurs can be calculated by using the bayesian probability formula:
in the present application, it may be assumed that, in the target link, i (i is any real number) consecutive data points (in the traffic flow basic map model, coordinate points representing the traffic flow density index and the vehicle average speed index) are located below the lower-bound curve of the standard basic map model as an event aiIf the broken link is defined as event B, P (B | A)i) And the probability of breakage (possible occurrence of events) of the target road section when i continuous data points of the target road section are positioned below a lower-bound curve in the through-flow basic map model (occurring events) in the target time period is shown. That is, the solution probability P (B | A)i) The probability of breakage of the target road segment within the target time period (the current month) can be determined.
Accordingly, the bayesian probability formula can be rewritten as:
due to event AiThe probability of (2) cannot be directly calculated, so that the formula two needs to be converted, and the probability can be obtained according to the total probability formula
Equation two can be converted to:
as can be seen from the traffic flow basic map model, when a target link is damaged, the road traffic capacity is inevitably reduced. When the data points are reflected in the traffic flow basic graph model, the data points are positioned below the lower bound curve of the normal basic graph. Thus A isiIf the event is a break that causes i successive points to lie below the basic graph, P (A) can be considered as an approximate inevitable eventi| B) value is set to 1. And continuously simplifying the formula III to obtain:
according to the formula IV, only calculation is needed
(first parameter of Bayesian probability formula) and
(second parameter of Bayesian probability formula), namely, the damage probability of the target road section in the target time period can be determined.
In this embodiment, the predicted number of damage events of the target road segment in the target time period is obtained according to the number of damage events of the target road segment in the first historical time period. And then acquiring a first threshold according to a lower-bound curve of the traffic flow basic graph model of the target road section in a second historical time period, wherein the first threshold is determined according to the number of coordinate points continuously located on the lower-bound curve in the traffic flow basic graph model. And finally, determining the damage probability of the target road section in the target time period by a Bayesian probability formula according to the predicted damage event number and the first threshold. The method and the device realize the prediction of the probability of breakage of the target road section in the target time period according to the quantity of breakage events in the historical time period of the target road section and the traffic flow basic graph model. And then determining whether the target road section is damaged or not according to the probability that the target road section is damaged in the target time period.
Fig. 3 is a flowchart illustrating a road damage probability prediction method according to another embodiment.
Referring to fig. 3, the method includes:
and S31, acquiring the time sequence of the number of the breakage events of the target road section in the first historical time period.
In some embodiments, the first historical time period is one year. The number of breakage events occurring per month in the history of one year and the corresponding month are combined into a time series of the number of breakage events. For example, the time series of the number of breakage events may be { x }1,x2,…,x12}。
And S32, fitting the time sequence through a gray Markov model to obtain the predicted damage event number of the target road section in the target time period.
The grey Markov model can predict a grey system according to the Markov model and a model of the grey system. A gray system refers to a system where only part of the information is observed and the rest is unknown. In the application, road breakage is related to environment, climate, pressure, traffic volume, structural material and human factors, and the life cycle of the hardware equipment also has certain regularity, so that road breakage cannot be predicted from breakage reasons. I.e. for the grey system model.
If the state of a system at the next moment only depends on the state result at the current moment, the state is not related to the state at any previous moment, and a certain transition rule exists between adjacent states, the characteristic is called Markov. Markov models are used to predict the probability of a system that conforms to Markov. The road damage events in the present application have no absolute relationship with each other and thus can be regarded as independent events. The period of processing each damage event by related personnel is known not to exceed half a month, so that the road damage event in the current month can be determined to be influenced only by the statistical result in the previous month, and a Markov model can be used.
Alternatively, when the predicted number of damage events of the target road segment in the target time period is predicted, the prediction may be implemented by other methods. For example, a fusion model of frah hurst (Verhulst) and markov, a fusion model of gray system and kalman filter, and the like.
And S33, acquiring the ratio of the logarithm of the average value of the number of the breakage events to the logarithm of the predicted number of the breakage events at intervals of the same duration as the target time period in the first historical time period, and acquiring a second parameter of the Bayesian probability formula.
It should be noted that it is difficult to effectively calculate the second parameter of the bayesian probability formula by a statistical method
And the value of P (B). However, because
The meaning of (1) is the probability ratio between the normal condition of the target road and the occurrence of the breakage, and the value of the ratio is smaller when the breakage events of the road occur more in one continuous month, and is larger otherwise. And the number of breakage events of the current month on the same road (the predicted number of breakage events of the target link in the target time period) can be obtained by the prediction result of the gray markov model. Therefore, the ratio of the logarithm of the mean value of the number of damages per month to the logarithm of the number of predicted damage events in 12 consecutive months of the target road history can be used as the second parameter of the bayesian probability formula, namely:
wherein, yjRepresents the number of breakage events of the road section at the jth month in the historical year, P (B) represents the probability of breakage of the road section,
it indicates the probability that breakage has not occurred.
S34, acquiring a traffic flow basic graph model of the target road section and a lower bound curve of the traffic flow basic graph model according to the traffic flow density index and the vehicle average speed index of the target road section in the second historical time period.
Fig. 4 shows a schematic diagram of a traffic flow basic graph model provided in an embodiment of the present application.
In some embodiments, referring to fig. 4, when a road is normal, a lower-bound curve (shown by a dotted line) of the traffic flow basic map model may be obtained by fitting according to the traffic flow density index and the vehicle average speed index obtained by the target road every 1 hour for a period of time (e.g., one month). When the target road is normal, most of the coordinate points indicated by the traffic flow density index and the vehicle average speed index of the vehicles running on the target road are located above the lower bound curve. When the target road is damaged, most of the coordinate points indicated by the traffic flow density index and the vehicle average speed index of the vehicles running on the target road are positioned below the lower bound curve.
And S35, determining a first threshold value according to the relative position relation between the first coordinate point indicated by the traffic flow density index and the vehicle average speed index of the target road section in the second historical time period and the lower-bound curve in the traffic flow basic graph model.
In order to avoid errors caused by special events such as traffic accidents, road water accumulation, legal holidays and the like, a first threshold value m can be set, if the number of the second coordinate points is smaller than m, the road can be judged to run normally, and if the number of the second coordinate points is larger than or equal to m, the reason causing the target road congestion is determined to be the occurrence of a damage event.
In some embodiments, the second history period includes N cycles, and a same number of cycles of a second coordinate point is obtained from the N cycles, where the second coordinate point is a first coordinate point located continuously below the lower-bound curve, and N is an integer greater than 1. As an example, the second historical period of time may be 1 month, one cycle may be 1 day, and the second historical period of time may include 30 or 31 cycles.
When determining the first threshold m, the following two ways may be included:
first, if the number of periods in which the same number of second coordinate points exist decreases as the number of second coordinate points existing in a period increases, it is determined that the number of second coordinate points in a period in which the number of second coordinate points existing in N periods is the largest is the first threshold.
Assuming that the number of days of the breakage event within one month is continuously reduced along with the increase of the number of the second coordinate points every day until the number is 0, the number value of the day before 0 is selected as the threshold m.
For example, in one month (the number of days of the month is 31 days), the number of days in which the number of second coordinate points is 1 is 15 days, the number of days in which the number of second coordinate points is 2 is 7 days, the number of days in which the number of second coordinate points is 3 is 4 days, the number of days in which the number of second coordinate points is 4 is 3 days, and the number of days in which the number of second coordinate points is 5 is 2 days. The number of days in which the number of second coordinate points is 5 is 0, that is, the number (5) of second coordinate points in the cycle (5) in which the second coordinate points exist most in 31 cycles may be set as the first threshold m, that is, m is 5.
Second, if the number of periods in which the same number of second coordinate points exist is reduced to a fixed value as the number of second coordinate points existing in a period increases, the number of second coordinate points in one period in which the number of periods in the N periods is the fixed value is determined to be the first threshold.
And selecting a first value with the unchanged number of days as a threshold m, wherein the number of days of occurrence is kept unchanged with the increase of the number of second coordinate points every day after the number of days of occurrence of events in one month exceeds a certain value.
For example, in one month (the number of days of the month is 31 days), the number of days in which the number of second coordinate points is 1 is 4 days, the number of days in which the number of second coordinate points is 2 days, the number of days in which the number of second coordinate points is 3 is 1 day, the number of days in which the number of second coordinate points is 4 is 1 day, and the number of days in which the number of second coordinate points is 5 is 1 day.
Then starting from the number of second coordinate points of 3, the number of days is 1 day as the number of second coordinate points increases. The number (3) of the second coordinate points in the first period in which the number of the second coordinate points is the fixed value, that is, the number (3) of the second coordinate points in the period in which the number of the periods is the smallest, may be set to the first threshold, that is, m is 3.
And S36, obtaining the probability that i continuous coordinate points located on the lower-bound curve appear on the target road section under the condition of no damage according to the first threshold value, and obtaining the first parameter of the Bayesian probability formula.
Wherein the first parameter of the Bayesian probability formula
Meaning the probability of i consecutive second coordinate points occurring in the road segment without breakage. When the first parameter of the Bayesian probability formula is determined, the report record of the target road damage event in the day of 3 days can be called first. An average of 3 days is required from the occurrence of the breakage event to the completion of the breakage repair. It can be set that if there is a report record within 3 days, it indicates that the target road is damaged. Can be directly connected with
Is set to 0.01 to represent
The probability of (a) is approximately 0.
If no report record exists within 3 days, the calculation can be carried out according to the formula six
The value of (c):
wherein n is the number of days in one month in which the second coordinate points exist, and d is the number of days in one month in which the number of the second coordinate points existing is greater than the first threshold.
It should be noted that in the calculation
In this case, statistics may be performed in a longer historical time period, for example, within two months or a year, and as long as the statistical data is complete, the longer the statistical time period is, the more accurate the predicted result is.
And S37, inputting the first parameter and the second parameter into a Bayesian probability formula, and determining the breakage probability of the target road section in the target time period.
In some embodiments, referring to formula four in S13, the obtained first parameter and the second parameter are input into formula four, so that the breakage probability of the target road segment in the target time period can be obtained.
And if the breakage probability is greater than a preset breakage threshold value, such as 0.7, 0.8 or 0.9, determining that the target road is broken.
Alternatively, after determining that the target road is damaged, the damage information (including information about the damaged road, information about the time when the damaged road occurs, and the like) may be sent to the road management department and the enterprise responsible for road maintenance to prompt the enterprise to perform the repair work on the damaged road in time.
Meanwhile, the damage information can be sent to a traffic management department, a map software operation department and the like, and the damaged road is marked on the electronic map to prompt a driver to drive around the road as required.
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.
Fig. 5 shows a block diagram of a road damage probability prediction device according to an embodiment of the present application, which corresponds to the road damage probability prediction method according to the above embodiment, and only the relevant portions according to the embodiment of the present application are shown for convenience of explanation.
Referring to fig. 5, the apparatus includes:
the obtaining module 41 is configured to obtain a predicted number of damage events of the target road segment in the target time period according to the number of damage events of the target road segment in the first historical time period. And the obtaining module is further used for obtaining a first threshold according to a lower-bound curve of the traffic flow basic graph model of the target road section in a second historical time period, wherein the first threshold is determined according to the number of coordinate points continuously located on the lower-bound curve in the traffic flow basic graph model. And the prediction module is used for determining the breakage probability of the target road section in the target time period through a Bayesian probability formula according to the predicted breakage event number and the first threshold.
In some embodiments, the obtaining module 41 is specifically configured to obtain a time series of the number of damage events of the target road segment in the first historical time period; and fitting the time sequence through a gray Markov model to obtain the predicted damage event number of the target road section in the target time period.
In some embodiments, the traffic flow basic map model comprises at least two of a traffic flow density index, a vehicle average speed index and a traffic flow index.
In some embodiments, the obtaining module 41 is specifically configured to obtain a traffic flow basic map model of the target road segment and a lower-bound curve of the traffic flow basic map model according to the traffic flow density indicator and the vehicle average speed indicator of the target road segment in the second historical time period; and determining a first threshold value according to the relative position relation between a first coordinate point indicated by the traffic flow density index and the vehicle average speed index of the target road section in the second historical time period and the lower-bound curve in the traffic flow basic graph model.
In some embodiments, the second history period includes N cycles, and a same number of cycles of a second coordinate point is obtained from the N cycles, where the second coordinate point is a first coordinate point located continuously below the lower-bound curve, and N is an integer greater than 1.
The obtaining module 41 is specifically configured to determine, if the number of periods in which the same number of second coordinate points exist decreases as the number of second coordinate points existing in a period increases, that the number of second coordinate points in a period in which the number of second coordinate points existing in N periods is the largest is the first threshold. Or, if the number of periods in which the same number of second coordinate points exist is reduced to a fixed value as the number of second coordinate points existing in a period increases, the number of second coordinate points in one period in which the number of periods in the N periods is the fixed value is determined to be the first threshold.
In some embodiments, the predicting module 42 is configured to obtain, according to a first threshold, a probability that i consecutive coordinate points located in a lower-bound curve appear on the target road segment under the condition that the target road segment is not damaged, and obtain a first parameter of a bayesian probability formula, where i is an integer greater than or equal to 0; acquiring the ratio of the logarithm of the average value of the number of the breakage events to the logarithm of the number of the predicted breakage events at intervals of the same duration as the target time period in the first historical time period to obtain a second parameter of a Bayesian probability formula; and inputting the first parameter and the second parameter into a Bayesian probability formula, and determining the damage probability of the target road section in the target time period.
It should be noted that, because the contents of information interaction, execution process, and the like between the modules are based on the same concept as that of the embodiment of the method of the present application, specific functions and technical effects thereof may be specifically referred to a part of the embodiment of the method, and details are not described here.
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 function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform 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.
Fig. 6 is a schematic structural diagram of a terminal device according to an embodiment of the present application. As shown in fig. 6, the terminal device 5 of this embodiment includes: at least one processor 51 (only one shown in fig. 6), a memory 52, and a computer program 53 stored in the memory 52 and operable on the at least one processor 51, the processor 51 implementing the steps in any of the various road damage probability prediction method embodiments described above when executing the computer program 53.
The terminal device 6 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 51, a memory 52. Those skilled in the art will appreciate that fig. 6 is merely an example of the terminal device 5, and does not constitute a limitation of the terminal device 5, and may include more or less components than those shown, or combine some components, or different components, such as an input-output device, a network access device, and the like.
The Processor 51 may be a Central Processing Unit (CPU), and the Processor 51 may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 52 may in some embodiments be an internal storage unit of the terminal device 5, such as a hard disk or a memory of the terminal device 5. The memory 52 may also be an external storage device of the terminal device 5 in other embodiments, such as a plug-in hard disk provided on the terminal device 5, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 52 may also include both an internal storage unit of the terminal device 5 and an external storage device. The memory 52 is used for storing an operating system, an application program, a BootLoader (BootLoader), data, and other programs, such as program codes of a computer program. The memory 52 may also be used to temporarily store data that has been output or is to be output.
The embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps that can be implemented in the above method embodiments.
The embodiments of the present application provide a computer program product, which when running on a mobile terminal, enables the mobile terminal to implement the steps in the above method embodiments when executed.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or apparatus capable of carrying computer program code to a terminal device, recording medium, computer Memory, Read-Only Memory (ROM), Random-Access Memory (RAM), electrical carrier wave signals, telecommunications signals, and software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
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 will 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 computer 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 embodiments of the apparatus are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, for example, a plurality of 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.
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 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 (10)
1. A road damage probability prediction method is characterized by comprising the following steps:
acquiring the predicted number of damage events of a target road section in a target time period according to the number of damage events of the target road section in a first historical time period;
acquiring a first threshold value according to a lower-bound curve of a traffic flow basic graph model of the target road section in a second historical time period, wherein the first threshold value is determined according to the number of coordinate points continuously located on the lower-bound curve in the traffic flow basic graph model;
and determining the breakage probability of the target road section in the target time period by a Bayesian probability formula according to the predicted breakage event number and the first threshold.
2. The method of claim 1, wherein obtaining the predicted number of breakage events for the target road segment during the target time period according to the number of breakage events for the target road segment during the first historical time period comprises:
acquiring a time sequence of the number of damage events of a target road section in a first historical time period;
and fitting the time sequence through a gray Markov model to obtain the predicted damage event number of the target road section in the target time period.
3. The method according to claim 1, wherein at least two of a traffic flow density index, a vehicle average speed index and a traffic flow index are included in the traffic flow basic map model.
4. The method according to claim 3, wherein obtaining a first threshold value from a lower bound curve of a basic graph model of traffic flow for the target road segment over a second historical time period comprises:
acquiring a traffic flow basic graph model of the target road section and a lower bound curve of the traffic flow basic graph model according to the traffic flow density index and the vehicle average speed index of the target road section in a second historical time period;
and determining a first threshold value according to the relative position relation between a first coordinate point indicated by the traffic flow density index and the vehicle average speed index of the target road section in the second historical time period and a lower-bound curve in the traffic flow basic graph model.
5. The method according to claim 4, wherein the second historical period of time comprises N cycles, and N cycles are obtained, wherein the same second number of cycles of coordinate points exist in the N cycles, the second coordinate point is a first coordinate point which is continuously positioned below the lower-bound curve, and N is an integer greater than 1;
the determining a first threshold includes:
if the number of the periods with the same number of the second coordinate points is reduced along with the increase of the number of the second coordinate points in the periods, determining the number of the second coordinate points in the period with the largest number of the second coordinate points in the N periods as the first threshold value; alternatively, the first and second electrodes may be,
if the number of the periods with the same number of the second coordinate points is reduced to a fixed value along with the increase of the number of the second coordinate points in the periods, determining that the number of the periods in the N periods is the fixed value, and the number of the second coordinate points in one period with the least number of the second coordinate points is the first threshold.
6. The method according to any one of claims 1-5, wherein determining the breakage probability of the target road segment within the target time period according to the predicted breakage event number and the first threshold value through a Bayesian probability formula comprises:
obtaining the probability that i continuous coordinate points located in the lower-bound curve appear on the target road section under the condition of no damage according to the first threshold value to obtain a first parameter of the Bayesian probability formula, wherein i is an integer greater than or equal to 0;
acquiring the ratio of the logarithm of the average value of the number of the breakage events to the logarithm of the predicted number of the breakage events at intervals of the same duration as the target time period in the first historical time period to obtain a second parameter of the Bayesian probability formula;
and inputting the first parameter and the second parameter into the Bayesian probability formula, and determining the breakage probability of the target road section in the target time period.
7. A road breakage probability prediction device, comprising:
the acquisition module is used for acquiring the predicted damage event number of the target road section in the target time period according to the damage event number of the target road section in the first historical time period;
the obtaining module is further configured to obtain a first threshold according to a lower-bound curve of the traffic flow basic graph model of the target road segment in a second historical time period, where the first threshold is determined according to the number of coordinate points continuously located in the lower-bound curve in the traffic flow basic graph model;
and the prediction module is used for determining the breakage probability of the target road section in the target time period through a Bayesian probability formula according to the predicted breakage event number and the first threshold.
8. The method according to claim 7, wherein the obtaining module is specifically configured to obtain a time series of the number of breakage events of the target road segment within the first historical time period;
and fitting the time sequence through a gray Markov model to obtain the predicted damage event number of the target road section in the target time period.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 6 when executing the computer program.
10. 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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