CN115359663A - Disaster-resistant toughness calculation method and device for mountain road disaster section and electronic equipment - Google Patents
Disaster-resistant toughness calculation method and device for mountain road disaster section and electronic equipment Download PDFInfo
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- CN115359663A CN115359663A CN202211290040.5A CN202211290040A CN115359663A CN 115359663 A CN115359663 A CN 115359663A CN 202211290040 A CN202211290040 A CN 202211290040A CN 115359663 A CN115359663 A CN 115359663A
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Abstract
The embodiment of the invention discloses a method and a device for calculating disaster-resistant toughness of a highway disaster section in a mountainous area and electronic equipment, and belongs to the technical field of highways. The method can quantitatively analyze the toughness of the road network, firstly, a core road section influencing the operation of the road network is determined from each road section of the road network as a target road section, the index toughness of the target road section is mainly considered, and the toughness value of the road network is indirectly determined through the index toughness of each target road section.
Description
Technical Field
The disclosure relates to the technical field of roads, in particular to a method and a device for calculating disaster-resistant toughness of a disaster section of a mountain road.
Background
The urgency of tough traffic construction is increasingly prominent as the population rapidly grows and global warming increases the frequency and intensity of natural disasters. In order to adapt to and deal with the influence of environmental factors such as climate change, natural disasters and the like, various countries actively build tough traffic.
At present, geological disasters of roads in mountainous areas are frequent, and once important road sections are buried by landslides or broken by debris flows, traffic paralysis is even caused, and road rescue is difficult to perform. The road network system is complex, the evaluation indexes of the toughness of the road network are many, and the indexes are mutually influenced, so that the toughness of the road network cannot be evaluated.
Disclosure of Invention
This disclosure is provided to introduce concepts in a simplified form that are further described below in the detailed description. This disclosure is not intended to identify target or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
The embodiment of the disclosure provides a method and a device for calculating disaster-resistant toughness of a highway disaster section in a mountain area, which can quantitatively analyze the toughness of a highway network, determine the toughness value of the highway network, facilitate reconstruction and upgrading of existing facilities, and enhance planning and management of expanding new roads.
In a first aspect, an embodiment of the present disclosure provides a method for calculating disaster-resistant toughness of a disaster-resistant road section in a mountain area, where the method includes:
determining each road junction of a target road network, forming a road section between each adjacent road junctions, weighting by taking the road junctions as points and the road sections as edges and taking section traffic flow to determine a road network model;
determining a road network service quality model based on the average speed of vehicles on road sections and the traffic flow of the road sections, setting a road network service quality threshold value, dynamically adjusting the threshold value to a critical threshold value with huge node clusters, and determining a target road section influencing the smooth traffic of a traffic network;
determining the index toughness of each target road section based on the interference resistance index, the adaptability index and the recovery index of each target road section which influence the smoothness of the traffic network;
and determining the toughness value of the road network based on the index toughness of each target road section.
With reference to the embodiments of the first aspect, in some embodiments, the road network topology network model specifically includes:
Wherein, V represents a point set, E represents an edge set, and W represents a weight set.
With reference to the embodiments of the first aspect, in some embodiments, determining the road network service quality model based on the average vehicle speed of the road section and the traffic flow of the road section specifically includes:
determining a road network service quality model based on the average speed of vehicles on road sections and the traffic flow of the road sections, wherein the road network service quality model is as follows:
In the formula (I), the compound is shown in the specification,
the method comprises the steps of collecting service quality indexes of a road network in a specified time period d;
service quality indexes of the road network in the road section mn under the time interval d;
is the average speed of the vehicles for the road section mn,
is the traffic flow of the road section mn in the time period d.
In combination with embodiments of the first aspect, in some embodiments,
the running speed of the vehicle between the highway junction points m and n,
the traffic flow between the highway junction points m and n in the time period d specifically comprises the following steps:
road sectionmnThe vehicle running speed of
In the formula (I), the compound is shown in the specification,lmn is the distance of the highway road mn,t_mn is the passing time of the road section mn;
the traffic flow between the intersection points m and n of the highway in the time period d is
In the formula (I), the compound is shown in the specification,Q (mn,d,1) is time of dayThe traffic flow of the vehicles on the mn highway section in the section d;Q (mn,d,2) the traffic flow of getting off the highway section mn in the time period d is obtained;
to obtain:
formula 6
Substituting the road section data into the road section data to obtain each road section
To correspond to the set
The network model is given the edge weight.
With reference to the embodiments of the first aspect, in some embodiments, a threshold of the service quality of the road network is set and dynamically adjusted to a critical threshold where a huge node cluster appears, and a target road segment affecting smooth traffic of the traffic network is determined, specifically:
setting a road network service quality threshold value and obtaining the traffic state of each road section;
and dynamically adjusting the threshold value to the critical threshold value of the huge node cluster, enabling the whole network of the road network to be smooth, enabling the service quality of the road network to be the highest, when the critical threshold value is slightly increased, enabling the whole network to be smooth and converted into local smooth, and enabling the road section converted from the smooth section into the crowded section to be the target section.
With reference to the embodiments of the first aspect, in some embodiments, the index toughness of each target road segment is determined based on the noise immunity index, the adaptability index and the recovery index of each target road segment influencing the traffic network smoothness, including
Determining the immunity index of each target road section based on the scale of the potential disaster body, the influence range of the disaster and the probability of the potential disaster body, wherein the immunity index is calculated according to the formula:
In the formula (I), the compound is shown in the specification,
as an index of noise immunity for the target link mn,
the scale characterization coefficient of the potential disaster body of the target road section mn is 0.2, wherein the scale of the disaster body is giant; the scale of the disaster body is large, and 0.4 is taken; the scale of the disaster body is small, and is 0.6; taking 1.0 without a disaster body;
for the length of the disturbed road section in the road section mn,
probability of occurrence of a potential disaster;
determining an adaptability index of each target road section based on the average speed of each target road section, wherein the calculation formula of the adaptability index is as follows:
In the formula (I), the compound is shown in the specification,
an immunity index of the target road section mn;
based on the damage of each target road section, determining a restorability index according to the time for recovering the normal vehicle speed from the lower vehicle speed, wherein the restorability index is calculated according to the formula:
In the formula (I), the compound is shown in the specification,
the recovery index of the target road section mn is obtained;
determining an initial weight of an immunity index, an adaptability index and a restorability index based on an analytic hierarchy process;
determining influence indexes influencing the occurrence of traffic congestion states of all target road sections and the importance of the indexes based on the operation information of the target road network, acquiring the percentage of each index, and acquiring the experience weight of the index after normalization processing;
based on the initial weight and the empirical weight of the index, determining a dynamic weight according to an entropy weight method, wherein the dynamic weight is calculated according to a formula:
In the formula:
is as follows
Is a index of
The dynamic weight after the second update;
is a first
Is a index of
Entropy weights of the secondary updates;
in order to update the number of times of the dynamic weight,
is as follows
An index initial weight;
is a first
The empirical weight of each index;
based on the noise immunity index, the adaptability index, the recovery index and the corresponding dynamic weight, the toughness of each index, namely the noise immunity toughness, the adaptability toughness and the recovery toughness, is determined, wherein the toughness is the product of the index value and the corresponding dynamic weight.
With reference to the embodiments of the first aspect, in some embodiments, a road network toughness value is determined based on the index toughness of each target road segment, the immunity toughness, the adaptability toughness and the recovery toughness of each target road segment are subjected to forward processing, a three-dimensional coordinate system with an origin O as an origin is established, the immunity index toughness, the adaptability index toughness and the recovery index toughness are respectively projected to the three-dimensional coordinate system, and an obtained vector mode length of each target road segment is the toughness value of the road network.
In a second aspect, an embodiment of the present disclosure provides an apparatus including:
constructing a model unit, wherein the model unit is used for determining each road junction of a target road network, a road section is formed between each two adjacent road junctions, the road junctions are taken as points, the road sections are taken as edges, and the cross section traffic flow is weighted to determine a road network model;
the method comprises the steps of determining a target unit, wherein the target unit is used for determining a road network service quality model based on the average speed of vehicles on road sections and the traffic flow of the road sections, setting a road network service quality threshold value, dynamically adjusting the threshold value to a critical threshold value with huge node clusters, and determining a target road section influencing the smooth traffic of a traffic network;
the solving unit is used for determining the index toughness of each target road section based on the immunity index, the adaptability index and the recovery index of each target road section which influence the smoothness of the traffic network;
a determination unit for determining a road network toughness value based on the index toughness of each target road segment.
With reference to the embodiments of the second aspect, in some embodiments, the determining the road network service quality model based on the average vehicle speed of the road section and the traffic flow of the road section is specifically:
determining a road network service quality model based on the average speed of vehicles on road sections and the traffic flow of the road sections, wherein the road network service quality model is as follows:
In the formula (I), the compound is shown in the specification,
a service quality index set of the road network in a specified period d is obtained;
service quality indexes of the road network in the road section mn under the time interval d;
is the average speed of the vehicles for the road section mn,
is the traffic flow of the road section mn in the time period d.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a storage device, configured to store one or more programs, where when the one or more programs are executed by the one or more processors, the one or more processors implement the method for calculating disaster-resistant toughness for the disaster-resistant road section on the mountain road according to the first aspect.
The method for calculating disaster-resistant toughness of the road disaster section in the mountainous area can quantitatively analyze the toughness of the road network, firstly, a core road section influencing the operation of the road network is determined from each road section of the road network as a target road section, the index toughness of the target road section is considered, and the toughness value of the road network is indirectly determined through the index toughness of each target road section.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and components are not necessarily drawn to scale.
Fig. 1 is a flowchart of an embodiment of a method for calculating disaster-resistant toughness of a mountain road disaster section according to the present disclosure;
fig. 2 is a schematic diagram of a basic structure of an electronic device provided according to an embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and the embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence of the functions performed by the devices, modules or units.
It is noted that references to "a" or "an" in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will appreciate that references to "one or more" are intended to be exemplary and not limiting unless the context clearly indicates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Referring to fig. 1, a flow of an embodiment of a disaster-resistant toughness calculation method for a disaster-resistant road section in a mountain road according to the disclosure is shown. The method for calculating disaster-resistant toughness of the mountain road disaster section can be applied to toughness evaluation of a road network, but is not limited thereto. As shown in fig. 1, the method for calculating disaster-resistant toughness of the disaster-resistant road section in the mountain area includes the following steps:
Here, the road network topology network model specifically includes:
Wherein, V represents a point set, E represents an edge set, and W represents a weight set.
Here, it sets the road network service quality threshold value by means of the seepage theory, and when dynamically adjusting the threshold value, if the critical threshold value of the huge node cluster appears, the congested road section.
The method for determining the road network service quality model based on the average speed of vehicles on the road section and the traffic flow on the road section comprises the following steps:
determining a road network service quality model based on the average speed of vehicles on road sections and the traffic flow of the road sections, wherein the road network service quality model is as follows:
In the formula (I), the compound is shown in the specification,
a service quality index set of the road network in a specified period d is obtained;
service quality indexes of the road network in the road section mn under the time interval d;
is the average speed of the vehicles for the road section mn,
is the traffic flow of the road section mn in the time period d.
Wherein, the first and the second end of the pipe are connected with each other,
the driving speed of the vehicle between the highway junctions m and n,
the traffic flow between the highway junction points m and n in the time period d specifically comprises the following steps:
road sectionmnThe vehicle running speed of
In the formula (I), the compound is shown in the specification,lmn is the distance of the mn road,t_mn is the traffic time of the highway section mn;
the traffic flow between the intersection points m and n of the highway in the time period d is
In the formula (I), the compound is shown in the specification,Q (mn,d,1) the traffic flow of the vehicles on the highway section mn in the time period d;Q (mn,d,2) the traffic flow of getting off the highway section mn in the time period d is obtained;
to obtain:
formula 6
Substituting the road section data of each road to obtain each road section
To correspond to the set
The network model is given the edge weight.
Further, setting a road network service quality threshold value, dynamically adjusting the threshold value to a critical threshold value of a huge node cluster, and determining a target road section influencing traffic network smoothness, specifically:
setting a road network service quality threshold value and obtaining the traffic state of each road section;
and dynamically adjusting the threshold value to the critical threshold value of the huge node cluster, enabling the whole network of the road network to be smooth, enabling the service quality of the road network to be the highest, when the critical threshold value is slightly increased, enabling the whole network to be smooth and converted into local smooth, and enabling the road section converted from the smooth section into the crowded section to be the target section.
And 103, determining the index toughness of each target road section based on the immunity index, the adaptability index and the recovery index of each target road section which influence the smooth traffic network.
The method comprises the steps of determining an immunity index of each target road section based on the scale of a potential disaster body, the influence range of disaster occurrence and the probability of the potential disaster body occurrence; determining an adaptability index of each target road section based on the average speed of each target road section; determining a restorability index from the time when the lower vehicle speed is restored to the normal vehicle speed based on the damage of each target road section; determining initial weights of an immunity index, an adaptability index and a recovery index based on an analytic hierarchy process; determining influence indexes influencing the occurrence of traffic congestion states of all target road sections and the importance of the indexes based on the operation information of the target road network, acquiring the percentage of each index, and acquiring the experience weight of the index after normalization processing; determining a dynamic weight according to an entropy weight method based on the initial weight and the empirical weight of the index; based on the noise immunity index, the adaptability index, the recovery index and the corresponding dynamic weight, the toughness degrees of the indexes, namely the noise immunity toughness degree, the adaptability toughness degree and the recovery toughness degree, are determined.
Illustratively, the immunity indicator calculation formula:
In the formula (I), the compound is shown in the specification,
as an index of noise immunity for the target link mn,
the scale characterization coefficient of the potential disaster body of the target road section mn is 0.2, wherein the scale of the disaster body is giant; the scale of the disaster body is large, and 0.4 is taken; the scale of the disaster body is small, and is 0.6; taking 1.0 without a disaster body;
for the length of the disturbed road section mn,
probability of occurrence of a potential disaster;
the adaptability index calculation formula is as follows:
In the formula (I), the compound is shown in the specification,
taking the interference rejection index of the target road section mn
The recovery index calculation formula is as follows:
In the formula (I), the compound is shown in the specification,
is a restoration index of the target link mn.
Dynamic weight calculation formula:
In the formula:
is as follows
Is a first index
The dynamic weight after the second update;
is as follows
Is a first index
Entropy weights of the secondary updates;
in order to update the number of times of the dynamic weight,
is as follows
An index initial weight;
is as follows
Individual index empirical weights.
The toughness is the product of the index value and the corresponding dynamic weight.
And 104, determining a road network toughness value based on the index toughness of each target road section.
Here, the target link toughness, adaptive toughness, and restoration toughness are normalized, a three-dimensional coordinate system with O as an origin is established, the noise immunity index toughness, adaptive index toughness, and restoration index toughness are projected onto the three-dimensional coordinate system, and the obtained vector mode length of the target link is the toughness value of the road network.
The method for calculating disaster-resistant toughness of the road disaster section in the mountainous area can quantitatively analyze the toughness of the road network, firstly, a core road section influencing the operation of the road network is determined from each road section of the road network as a target road section, the index toughness of the target road section is considered, and the toughness value of the road network is indirectly determined through the index toughness of each target road section.
Further, as an implementation of the method shown in the above figures, the present disclosure provides a device for calculating disaster-resistant toughness of a disaster-resistant road section in a mountain area, where an embodiment of the device corresponds to the embodiment of the method shown in fig. 1, and the device may be applied to various electronic devices.
The mountain area highway disaster section disaster-resistant toughness calculation device of the embodiment includes:
constructing a model unit, wherein the model unit is used for determining each road junction of a target road network, a road section is formed between each two adjacent road junctions, the road junctions are taken as points, the road sections are taken as edges, and the cross section traffic flow is weighted to determine a road network model;
the method comprises the steps of determining a target unit, wherein the target unit is used for determining a road network service quality model based on the average speed of vehicles on road sections and the traffic flow of the road sections, setting a road network service quality threshold value, dynamically adjusting the threshold value to a critical threshold value with huge node clusters, and determining a target road section influencing the smooth traffic of a traffic network;
the solving unit is used for determining the index toughness of each target road section based on the immunity index, the adaptability index and the recovery index of each target road section which influence the smoothness of the traffic network;
a determination unit for determining a road network toughness value based on the index toughness of each target road segment.
Referring now to FIG. 2, shown is a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 2 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 2, the electronic device may include a processing apparatus (e.g., a central processing unit, a graphics processor, etc.) 201 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 202 or a program loaded from a storage apparatus 208 into a Random Access Memory (RAM) 203. In the RAM203, various programs and data necessary for the operation of the electronic apparatus 200 are also stored. The processing device 201, the ROM202, and the RAM203 are connected to each other via a bus 204. An input/output (M/O) interface 205 is also connected to bus 204.
Generally, the following devices may be connected to the M/O interface 205: input devices 206 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 207 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, or the like; storage 208 including, for example, magnetic tape, hard disk, etc.; and a communication device 202. The communication means 202 may allow the electronic device to communicate wirelessly or by wire with other devices to exchange data. While fig. 2 illustrates an electronic device having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 209, or installed from the storage means 208, or installed from the ROM 202. The computer program, when executed by the processing device 201, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Nava, smalltalk, C + +, including conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present disclosure may be implemented by software or hardware. Wherein the names of the modules do not in some cases constitute a limitation of the unit itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), application specific integrated circuits (ASMCs), application Specific Standard Products (ASSPs), systems on a chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and the technical features disclosed in the present disclosure (but not limited to) having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (10)
1. A method for calculating disaster-resistant toughness of a mountain road disaster section is characterized by comprising the following steps:
determining each road junction of a target road network, forming a road section between each adjacent road junctions, weighting by taking the road junctions as points and the road sections as edges and taking section traffic flow to determine a road network model;
determining a road network service quality model based on the average speed of vehicles on the road section and the traffic flow on the road section, setting a road network service quality threshold value, dynamically adjusting the threshold value to a critical threshold value of a huge node cluster, and determining a target road section influencing the smooth traffic of a traffic network;
determining the index toughness of each target road section based on the interference resistance index, the adaptability index and the recovery index of each target road section which influence the smoothness of the traffic network;
and determining the toughness value of the road network based on the index toughness of each target road section.
2. The method for calculating the disaster-resistant toughness of the disaster-resistant road section in the mountain area according to claim 1, wherein the road network model specifically comprises:
Wherein, V represents a point set, E represents an edge set, and W represents a weight set.
3. The method for calculating the disaster-resistant toughness of the road disaster section in the mountainous area according to claim 2, wherein the step of determining the road network service quality model based on the average vehicle speed and the vehicle flow of the road section specifically comprises the following steps:
determining a road network service quality model based on the average speed of vehicles on road sections and the traffic flow of the road sections, wherein the road network service quality model is as follows:
In the formula (I), the compound is shown in the specification,
the method comprises the steps of collecting service quality indexes of a road network in a specified time period d;
service quality indexes of the road network in the road section mn under the time interval d;
is the average speed of the vehicles for the road section mn,
is the traffic flow of the road section mn in the time period d.
4. The method according to claim 3, wherein the method comprises the steps of,
the driving speed of the vehicle between the highway junctions m and n,
the traffic flow between the highway intersection points m and n in the time period d is as follows:
road sectionmnThe vehicle running speed of
In the formula (I), the compound is shown in the specification,lmn is the distance of the highway road mn,t_mn is the traffic time of the highway section mn;
the traffic flow between the intersection points m and n of the highway in the time period d is
In the formula (I), the compound is shown in the specification,Q (mn,d,1) the traffic flow of the vehicles on the highway section mn in the time period d;Q (mn,d,2) the traffic flow of getting off the highway section mn in the time period d is obtained;
to obtain:
formula 6
Substituting the road section data of each road to obtain each road section
To correspond to the set
The model is given the side weight.
5. The method for calculating disaster-resistant toughness of the road disaster section in the mountainous area as claimed in claim 4, wherein the method comprises the steps of setting a threshold value of the service quality of the road network, dynamically adjusting the threshold value to a critical threshold value at which huge node clusters appear, and determining a target section affecting smooth traffic network, specifically:
setting a road network service quality threshold value and obtaining the traffic state of each road section;
and dynamically adjusting the threshold value to the critical threshold value of the huge node cluster, enabling the whole network of the road network to be smooth, enabling the service quality of the road network to be the highest, when the critical threshold value is slightly increased, enabling the whole network to be smooth and converted into local smooth, and enabling the road section converted from the smooth section into the crowded section to be the target section.
6. The method according to claim 5, wherein determining the target toughness of each target road segment based on the noise immunity index, the adaptability index and the recovery index of each target road segment affecting the smooth traffic network comprises
Determining the immunity index of each target road section based on the scale of the potential disaster body, the influence range of the disaster and the probability of the potential disaster body, wherein the immunity index is calculated by the formula:
In the formula (I), the compound is shown in the specification,
as an index of noise immunity for the target link mn,
the scale representation coefficient of the potential disaster body of the target road section mn is 0.2, wherein the scale of the disaster body is giant; the scale of the disaster body is large, and 0.4 is taken; the scale of the disaster body is small, and is 0.6; taking 1.0 without a disaster body;
for the length of the disturbed road section mn,
probability of occurrence of a potential disaster;
determining an adaptability index of each target road section based on the average speed of each target road section, wherein the calculation formula of the adaptability index is as follows:
In the formula (I), the compound is shown in the specification,
the noise immunity index of the target road section mn is obtained;
determining a restorability index according to the time for recovering the normal speed from the lower speed based on the damage of each target road section, wherein the restorability index is calculated according to the formula:
In the formula (I), the compound is shown in the specification,
a restorability index of the target road section mn is obtained;
determining an initial weight of an immunity index, an adaptability index and a restorability index based on an analytic hierarchy process;
determining influence indexes and index importance influencing the occurrence of traffic congestion states of all target road sections based on operation information of a target road network, acquiring percentage of each index, and acquiring experience weights of the indexes after normalization processing;
based on the initial weight and the empirical weight of the index, determining a dynamic weight according to an entropy weight method, wherein the dynamic weight is calculated according to a formula:
In the formula:
is as follows
Is a first index
The dynamic weight after the second update;
is as follows
Is a index of
Entropy weights of the secondary updates;
in order to update the number of times of the dynamic weight,
is as follows
An index initial weight;
is a first
The empirical weight of each index; based on the noise immunity index, the adaptability index, the recovery index and the corresponding dynamic weight, determining the toughness of each index, namely the noise immunity toughness, the adaptability toughness and the recovery toughness, wherein the toughness is the product of the index value and the corresponding dynamic weight.
7. The method according to claim 6, wherein determining the road network toughness value based on the index toughness of each target road segment comprises
And performing forward processing on the immunity toughness, the adaptability toughness and the recovery toughness of each target road section, establishing a three-dimensional coordinate system with O as an origin, respectively projecting the immunity index toughness, the adaptability index toughness and the recovery index toughness to the three-dimensional coordinate system, and obtaining the vector mode length of the target road section, namely the toughness value of the road network.
8. Mountain area highway disaster resistant highway section disaster resistant toughness computing device, its characterized in that, the device includes:
constructing a model unit, wherein the model unit is used for determining each road junction of a target road network, a road section is formed between each two adjacent road junctions, the road junctions are taken as points, the road sections are taken as edges, and the cross section traffic flow is weighted to determine a road network model;
determining a target unit, wherein the target unit is used for determining a road network service quality model based on the average speed of vehicles on road sections and the traffic flow of the road sections, setting a road network service quality threshold value, dynamically adjusting the threshold value to a critical threshold value with huge node clusters, and determining a target road section influencing the smooth traffic of a traffic network;
the solving unit is used for determining the index toughness of each target road section based on the immunity index, the adaptability index and the recovery index of each target road section which influence the smoothness of the traffic network;
a determination unit for determining a road network toughness value based on the index toughness of each target road segment.
9. The mountain road disaster resistant section disaster resistant toughness calculation apparatus according to claim 8, wherein the determining of the road network service quality model based on the road section vehicle average speed and the road section vehicle flow is specifically:
determining a road network service quality model based on the average speed of vehicles on road sections and the traffic flow of the road sections, wherein the road network service quality model is as follows:
In the formula (I), the compound is shown in the specification,
for the service quality of the road network in the specified period dA set of labels;
service quality indexes of the road network in the road section mn under the time interval d;
is the average speed of the vehicles for the road section mn,
is the traffic flow of the road section mn in the time period d.
10. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method for disaster-tolerant toughness calculation for a mountain highway disaster section as recited in any of claims 1-7.
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