CN114548512A - Road operation data estimation method and device for digital twins - Google Patents
Road operation data estimation method and device for digital twins Download PDFInfo
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Abstract
The invention discloses a road operation data estimation method and a device facing to digital twins, wherein the method comprises the steps of constructing a digital twins road traffic system based on actual road data, and establishing a mapping relation of vehicles on the actual road and a virtual road of the digital twins road traffic system; constructing an intelligent interpolation data set based on vehicle running state data collected by road sensors discontinuously arranged on an actual road, and calculating road running state estimation data of any position of a virtual road according to the intelligent interpolation data set; and generating a digital twin road traffic system state diagram according to the estimated data of the running state of each road, and displaying the digital twin road traffic system state diagram to the vehicle in real time. The invention realizes intelligent interpolation of vehicle running state data acquired by road sensors arranged on the roadside, and on-line verification of vehicles passing through in real time, thereby dynamically correcting the interpolation data and realizing accurate estimation of road running data.
Description
Technical Field
The application relates to the technical field of digital twin systems, in particular to a road operation data estimation method and device for digital twin.
Background
In recent years, Information and Communications Technology (ICT) has been increasingly invested in city construction control in each city, and knowledge of city informatization has also been upgraded from digital cities and smart cities to new smart cities. The intelligent city management urgently needs effective digital management means to master and analyze the city running state in real time, and a digital twin technology is developed. The digital twin is a digital mapping system which fully utilizes data such as physical models, sensor updating, operation history and the like, integrates a multidisciplinary, multi-physical quantity, multi-scale and multi-probability simulation process, and completes mapping in a virtual space so as to reflect the full life cycle process of corresponding entity equipment. The intelligent analysis system realizes that an object in the real world is projected into the digital world to form a twin body for observing and predicting the development of the object, and further can carry out intelligent analysis on the running state of an urban road.
However, the digital twin depends on a high-density and high-precision dynamic sensing system, which is mainly based on a large number of intelligent sensors for real-time data acquisition, and these sensors often have the disadvantages of high construction cost, high failure rate, and the like, so that the digital twin technology is difficult to actually deploy, the overall cost is high, and the road running state data sensed by the digital twin system is not accurate enough.
Disclosure of Invention
In order to solve the above problems, embodiments of the present application provide a digital twin-oriented road operation data estimation method and apparatus.
In a first aspect, an embodiment of the present application provides a digital twin-oriented road operation data estimation method, where the method includes:
constructing a digital twin road traffic system based on actual road data, and establishing a mapping relation of vehicles on an actual road and a virtual road of the digital twin road traffic system;
constructing an intelligent interpolation data set based on vehicle running state data collected by road sensors discontinuously arranged on the actual road, and calculating road running state estimated data of any position of the virtual road according to the intelligent interpolation data set;
and generating a digital twin road traffic system state diagram according to the road running state estimation data, and displaying the digital twin road traffic system state diagram to the vehicle in real time.
Preferably, the actual road data includes electronic map data of a road, first historical road data collected by the road sensor, and second historical road data uploaded by the vehicle.
Preferably, the establishing of the mapping relationship of the vehicle on the actual road and the virtual road of the digital twin road traffic system includes:
the method comprises the steps of obtaining self-running state data of a vehicle based on a vehicle-mounted sensor, and establishing a mapping relation of the vehicle on an actual road and a virtual road of the digital twin road traffic system according to the self-running state data, wherein the vehicle-mounted sensor comprises a GPS (global positioning system) positioning sensor, an ETC (electronic toll collection) sensor and a gyroscope.
Preferably, the constructing an intelligent interpolation data set based on the vehicle driving state data collected by each road sensor discontinuously arranged on the actual road, and calculating the estimated road running state data of any position of the virtual road according to the intelligent interpolation data set includes:
constructing an intelligent interpolation data set based on vehicle running state data historically acquired by road sensors discontinuously arranged on the actual road;
acquiring a real-time position of the vehicle on the virtual road, and inquiring the road sensor in a preset detection range corresponding to the real-time position;
when a target road sensor is inquired in the preset detection range, controlling the target road sensor to acquire real-time vehicle running state data of the vehicle, and adding the real-time vehicle running state data to the intelligent interpolation data set;
when a target road sensor is not inquired in the preset detection range, determining a first road sensor and a second road sensor which are adjacent to the vehicle, and performing road section interpolation analysis according to first vehicle running state data and second vehicle running state data in an intelligent interpolation data set to obtain an analysis result, wherein the first vehicle running state data is the vehicle running state data of the first road sensor in the intelligent interpolation data set, and the second vehicle running state data is the vehicle running state data of the second road sensor in the intelligent interpolation data set;
and calculating road running state estimated data of any position of the virtual road based on each analysis result.
Preferably, after calculating the estimated road operation state data at any position of the virtual road based on each analysis result, the method further includes:
and generating traffic running state early warning information based on the road running state estimated data in the vehicle advancing direction, and sending the traffic running state early warning information to the vehicle.
Preferably, the generating a digital twin road traffic system state diagram according to the estimated road running state data includes:
continuously comparing the current driving state data of the vehicle with the matching state of the road running state estimated data corresponding to the current vehicle position on the virtual road in the driving process of the vehicle;
for the road running state estimated data which is successfully matched, marking the road running state estimated data as available data, and generating a digital twin road traffic system state diagram according to each available data;
and for the road running state estimated data which fails to be matched, marking the road running state estimated data as unavailable data, taking the unavailable data as a negative feedback sample, and performing negative feedback optimization on the step of calculating the road running state estimated data of any position of the virtual road according to the intelligent interpolation data set.
Preferably, the generating a digital twin road traffic system state diagram from each of the available data comprises:
judging whether the available data can represent the whole road state or not by superposition;
when the overall state of the road can be represented, generating a digital twin road traffic system state diagram based on all the available data;
and when the overall state of the road cannot be represented, repeating the step of continuously comparing the current driving state data of the vehicle with the matching state of the road running state estimated data corresponding to the current vehicle position on the virtual road.
In a second aspect, an embodiment of the present application provides a digital twin-oriented road operation data estimation device, where the device includes:
the construction module is used for constructing a digital twin road traffic system based on actual road data and establishing a mapping relation of vehicles on an actual road and a virtual road of the digital twin road traffic system;
the calculation module is used for constructing an intelligent interpolation data set based on vehicle running state data collected by road sensors discontinuously arranged on the actual road and calculating road running state estimation data of any position of the virtual road according to the intelligent interpolation data set;
and the generating module is used for generating a digital twin road traffic system state diagram according to the estimated data of the road running states and displaying the digital twin road traffic system state diagram to the vehicle in real time.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method as provided in the first aspect or any one of the possible implementation manners of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method as provided in the first aspect or any one of the possible implementations of the first aspect.
The invention has the beneficial effects that: 1. the intelligent interpolation is carried out on the vehicle running state data acquired by the road sensor arranged on the roadside, and the online verification is carried out on the vehicle passing through in real time, so that the interpolation data is dynamically corrected, and the accurate estimation of the road running data is realized.
2. The problems that the traditional sensor is difficult to realize global perception of urban data, high in deployment cost and difficult in system operation and maintenance are effectively solved, novel smart city construction can be further promoted, traffic operation conditions are sensed more comprehensively, and a new urban road traffic development form of virtual-real combination and twin interaction is formed.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flowchart of a digital twin-oriented road operation data estimation method according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a digital twin-oriented road operation data estimation device according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
In the following description, the terms "first" and "second" are used for descriptive purposes only and are not intended to indicate or imply relative importance. The following description provides embodiments of the present application, where different embodiments may be substituted or combined, and thus the present application is intended to include all possible combinations of the same and/or different embodiments described. Thus, if one embodiment includes feature A, B, C and another embodiment includes feature B, D, then this application should also be construed to include embodiments that include A, B, C, D in all other possible combinations, even though such embodiments may not be explicitly recited in the text that follows.
The following description provides examples, and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made in the function and arrangement of elements described without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as appropriate. For example, the described methods may be performed in an order different than the order described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.
Referring to fig. 1, fig. 1 is a schematic flowchart of a road operation data estimation method for a digital twin according to an embodiment of the present application. In an embodiment of the present application, the method includes:
s101, constructing a digital twin road traffic system based on actual road data, and establishing a mapping relation of vehicles on an actual road and a virtual road of the digital twin road traffic system.
The execution main body of the application can be a cloud server.
In the embodiment of the application, the cloud server acquires actual road data on an actual road through modes of sensor data acquisition, traffic management platform data query and the like, and a virtual digital twin road traffic system is constructed on the basis of the actual road data. After the system is built, the cloud server also establishes a mapping relation of each vehicle on an actual road and a virtual road, so that the running state of the vehicle on the actual road can be represented correspondingly on the virtual road in the system, the cloud server can directly judge the running state of each vehicle according to the digital twin road traffic system, and further judge road states such as congestion. In the case that the actual road data required for constructing the system is determined, the construction process of the digital twin road traffic system is well known in the prior art, and the specific construction process of the digital twin road traffic system is not the focus of the present application, so that it is not described in detail herein.
In one possible implementation, the actual road data includes electronic map data of the road, first historical road data collected by the road sensor, and second historical road data uploaded by the vehicle.
In the embodiment of the application, the actual road data comprises electronic map data of each road acquired through a third-party platform, first historical road data acquired by passing vehicles through which road sensors arranged on the roadside pass, and second historical road data uploaded to a cloud server by each vehicle in the driving process.
In one possible embodiment, the establishing a mapping relationship of vehicles on an actual road and a virtual road of the digital twin road traffic system includes:
the method comprises the steps of obtaining self-running state data of a vehicle based on a vehicle-mounted sensor, and establishing a mapping relation of the vehicle on an actual road and a virtual road of the digital twin road traffic system according to the self-running state data, wherein the vehicle-mounted sensor comprises a GPS (global positioning system) positioning sensor, an ETC (electronic toll collection) sensor and a gyroscope.
In this application embodiment, the vehicle can constantly acquire the self driving state data of vehicle according to vehicle-mounted sensor equipment such as GPS miniature positioning system, ETC, gyroscope in the course of traveling to upload these data to high in the clouds server. The cloud server can determine the position, the azimuth angle, the speed and other data of the vehicle on the actual road based on the data, and further represents the vehicle state on the virtual road of the system, so that the mapping relation of the vehicle on the actual road and the virtual road is established.
S102, constructing an intelligent interpolation data set based on vehicle running state data collected by road sensors discontinuously arranged on the actual road, and calculating road running state estimation data of any position of the virtual road according to the intelligent interpolation data set.
In the embodiment of the application, a plurality of road sensors are intermittently arranged on an actual road at preset distances, each vehicle uploads running data of the vehicle to a cloud server, the road sensors can acquire the vehicle running state data of the vehicles, the cloud server can construct an intelligent interpolation data set according to the vehicle running state data, the intelligent interpolation data set is equivalent to summarize the vehicle running state data acquired by each road sensor by detecting the vehicles, so that the vehicle running speeds of different road positions and different moments can be determined based on the intelligent interpolation data set, the current congestion state of the road can be judged, and estimated road running state data can be calculated on the corresponding virtual road.
Specifically, because the cost for completely covering the road collected data by arranging a large number of road sensors is too high, a discontinuous arrangement mode is adopted, one road sensor is arranged at a certain distance, the running state of the road between two adjacent road sensors is interpolated, estimated and calculated according to two end points of a road section, namely the data collected by the two road sensors, so that a predicted value is obtained, the system construction cost is controlled, and the maintenance and the stability of the system are easier to realize due to the reduction of the number of the road sensors. The interpolation estimation calculation mode can be realized by training the neural convolution network model through a large amount of vehicle driving state sample data acquired by the road sensor at the early stage. Since the training process of the neural convolutional network model is prior art and is not the focus of the present application, it is not described in detail here.
In one possible embodiment, step S102 includes:
constructing an intelligent interpolation data set based on vehicle running state data historically acquired by road sensors discontinuously arranged on the actual road;
acquiring a real-time position of the vehicle on the virtual road, and inquiring the road sensor in a preset detection range corresponding to the real-time position;
when a target road sensor is inquired in the preset detection range, controlling the target road sensor to acquire real-time vehicle running state data of the vehicle, and adding the real-time vehicle running state data to the intelligent interpolation data set;
when a target road sensor is not inquired in the preset detection range, determining a first road sensor and a second road sensor which are adjacent to the vehicle, and performing road section interpolation analysis according to first vehicle running state data and second vehicle running state data in an intelligent interpolation data set to obtain an analysis result, wherein the first vehicle running state data is the vehicle running state data of the first road sensor in the intelligent interpolation data set, and the second vehicle running state data is the vehicle running state data of the second road sensor in the intelligent interpolation data set;
and calculating road running state estimated data of any position of the virtual road based on each analysis result.
The target road sensor may be understood as a road sensor queried within a preset detection range in the embodiment of the present application.
In the embodiment of the application, the intelligent interpolation data set constructs the driving state data of each vehicle collected by each road sensor history. The interpolation calculation process is to calculate the driving state of the vehicle in a section which can not be detected by the road sensor, so as to judge the road operation state, so that firstly, the real-time position of the vehicle needs to be obtained, whether the road sensor can be inquired in a preset detection range corresponding to the vehicle is judged according to the real-time position, and if the cloud server can inquire the road sensor in the preset detection range, the road sensor is indicated to be capable of acquiring the vehicle driving state data of the vehicle. For the condition that the target road sensor can be inquired, the target road sensor can directly acquire the real-time vehicle running state data of the vehicle, so that interpolation calculation is not needed, only the latest acquired real-time vehicle running state data is added into the intelligent interpolation data set, and the data sample in the intelligent interpolation data set is ensured to be large enough, so that the accuracy in subsequent interpolation calculation is ensured. For the situation that the target road sensor cannot be queried, the cloud server performs interpolation analysis calculation according to data collected by two road sensors adjacent to the vehicle, so as to obtain an analysis result. Different analysis results obtained by interpolation analysis of different vehicles at different road positions are summarized, so that the road running state of any position of the virtual road can be determined and estimated, and road running state estimation data is obtained. It should be noted that, the above process is performed for each vehicle in the road, that is, for a certain vehicle, data obtained by performing interpolation calculation on a road segment between the road sensors is driving data collected from the other vehicles that have passed through two sensors in the vicinity of the same time period, so that mutual dynamic auxiliary calculation between the vehicle data is realized, and accuracy of the finally calculated data is ensured.
In an implementation manner, after calculating the estimated road operation state data of any position of the virtual road based on each analysis result, the method further includes:
and generating traffic running state early warning information based on the road running state estimated data in the vehicle advancing direction, and sending the traffic running state early warning information to the vehicle.
In the embodiment of the application, after the road running state estimation data of each position of the virtual road is determined, early warning information is generated according to the road running state estimation data in the vehicle running direction to early warn a vehicle, so that a driver is reminded of the road condition ahead in advance.
S103, generating a digital twin road traffic system state diagram according to the estimated data of the road running states, and displaying the digital twin road traffic system state diagram to the vehicle in real time.
In the embodiment of the application, the cloud server can generate the state diagram of the whole digital twin road traffic system by integrating the calculated estimated data of the running states of all roads, and the driver can know the road condition state of the whole road at any time and can more comprehensively sense the traffic running state by displaying the state diagram of the digital twin road traffic system to the vehicle.
It should be noted that, compared with the traditional mobile phone APP navigation mode, the navigation APP determines the estimation of the road conditions through the acquisition of data of the traffic guidance center, data returning of the terminal device, real-time reporting of the road conditions by a special reporting person or device in a fixed place, and the like, and the data acquisition process is complex, the cost is low, and the navigation is only for achieving the purpose of navigation. The arrangement of the road sensor in the application is the basis for the construction of the digital twin system, namely the road sensor is not specially arranged for realizing the process of the application. The mode of this application can reduce the quantity that sets up of road sensor, through the calculation mode of this application to the data that road sensor gathered, has not only reduced the cost of setting up of system, can also realize the accurate prediction to whole traffic system's road running state, the realization of the other functions of supplementary twin road traffic system of digit.
In one embodiment, the generating a digital twin road traffic system state diagram according to the estimated road operation state data comprises:
continuously comparing the current driving state data of the vehicle with the matching state of the road running state estimated data corresponding to the current vehicle position on the virtual road in the driving process of the vehicle;
for the road running state estimated data which is successfully matched, marking the road running state estimated data as available data, and generating a digital twin road traffic system state diagram according to each available data;
and for the road running state estimated data which fails to be matched, marking the road running state estimated data as unavailable data, taking the unavailable data as a negative feedback sample, and performing negative feedback optimization on the step of calculating the road running state estimated data of any position of the virtual road according to the intelligent interpolation data set.
In the embodiment of the application, in order to guarantee the accuracy of the estimated data of the running state of the road, in the running process of the vehicle, the cloud server can continuously compare the current running state data collected by the sensor of the vehicle and the estimated data of the running state of the road corresponding to the current vehicle position, determine whether the current running state data and the estimated data of the running state of the road are matched, and only the available data which are successfully matched are used for generating and constructing the state diagram, so that the accuracy of the state diagram is guaranteed. And for the road running state estimated data which fails to be matched, the estimated data is used as a negative feedback sample to be sent back to the neural convolution network model for negative feedback learning, so that the subsequent calculation process is optimized and iterated, and the accuracy and the stability of the subsequent calculation result are ensured.
In one possible embodiment, the generating a digital twin road traffic system state diagram from each of the available data includes:
judging whether the available data can represent the whole road state or not by superposition;
when the overall state of the road can be represented, generating a digital twin road traffic system state diagram based on all the available data;
and when the overall state of the road cannot be represented, repeating the step of continuously comparing the current driving state data of the vehicle with the matching state of the road running state estimated data corresponding to the current vehicle position on the virtual road.
In the embodiment of the application, when the digital twin road traffic system state diagram is generated, the cloud server needs to judge that the current available data are overlapped together to represent the state of the whole road, and only when the whole road state can be represented, the cloud server can generate the digital twin road traffic system state diagram, otherwise, the available data can be continuously acquired until the available data can represent the whole road state, and the digital twin road traffic system state diagram is generated.
The digital twin-oriented road operation data estimation device provided by the embodiment of the present application will be described in detail below with reference to fig. 2. It should be noted that, the digital twin oriented road operation data estimation device shown in fig. 2 is used for executing the method of the embodiment shown in fig. 1 of the present application, and for convenience of description, only the portion related to the embodiment of the present application is shown, and details of the technology are not disclosed, please refer to the embodiment shown in fig. 1 of the present application.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a road operation data estimation device for digital twins according to an embodiment of the present disclosure. As shown in fig. 2, the apparatus includes:
the construction module 201 is used for constructing a digital twin road traffic system based on actual road data and establishing a mapping relation of vehicles on an actual road and a virtual road of the digital twin road traffic system;
the calculation module 202 is configured to construct an intelligent interpolation data set based on vehicle driving state data acquired by road sensors intermittently arranged on the actual road, and calculate road running state estimation data of any position of the virtual road according to the intelligent interpolation data set;
the generating module 203 is configured to generate a digital twin road traffic system state diagram according to the estimated road running state data, and display the digital twin road traffic system state diagram to the vehicle in real time.
In one possible implementation, the actual road data includes electronic map data of the road, first historical road data collected by the road sensor, and second historical road data uploaded by the vehicle.
In one possible implementation, the building module 201 includes:
the mapping establishing unit is used for acquiring self-running state data of a vehicle based on a vehicle-mounted sensor, and establishing a mapping relation of the vehicle on an actual road and a virtual road of the digital twin road traffic system according to the self-running state data, wherein the vehicle-mounted sensor comprises a GPS (global positioning system) positioning sensor, an ETC (electronic toll collection) sensor and a gyroscope.
In one possible implementation, the calculation module 202 includes:
the construction unit is used for constructing an intelligent interpolation data set based on vehicle running state data historically collected by road sensors discontinuously arranged on the actual road;
the query unit is used for acquiring the real-time position of the vehicle on the virtual road and querying the road sensor in a preset detection range corresponding to the real-time position;
the first judgment unit is used for controlling the target road sensor to acquire real-time vehicle running state data of the vehicle and adding the real-time vehicle running state data to the intelligent interpolation data set when the target road sensor is inquired in the preset detection range;
a second judging unit, configured to determine a first road sensor and a second road sensor adjacent to the vehicle when a target road sensor is not queried in the preset detection range, and perform road segment interpolation analysis according to first vehicle driving state data and second vehicle driving state data in an intelligent interpolation data set to obtain an analysis result, where the first vehicle driving state data is vehicle driving state data of the first road sensor in the intelligent interpolation data set, and the second vehicle driving state data is vehicle driving state data of the second road sensor in the intelligent interpolation data set;
and the first calculation unit is used for calculating road running state estimation data of any position of the virtual road based on each analysis result.
In one possible implementation, the calculation module 202 further includes:
and the sending unit is used for generating traffic running state early warning information based on the road running state estimated data in the vehicle advancing direction and sending the traffic running state early warning information to the vehicle.
In one possible implementation, the generation module 203 includes:
the comparison unit is used for continuously comparing the current driving state data of the vehicle with the matching state of the road running state estimated data corresponding to the current vehicle position on the virtual road in the driving process of the vehicle;
the third judging unit is used for marking the road running state estimated data as available data for the road running state estimated data which is successfully matched, and generating a digital twin road traffic system state diagram according to each available data;
and the fourth judging unit is used for marking the road running state estimated data which are failed to be matched as unavailable data, using the unavailable data as a negative feedback sample, and performing negative feedback optimization on the step of calculating the road running state estimated data of any position of the virtual road according to the intelligent interpolation data set.
In one possible implementation, the generating module 203 further includes:
a fifth judging unit, configured to judge whether all the available data are superimposed to represent an overall road state;
the sixth judging unit is used for generating a digital twin road traffic system state diagram based on all the available data when the overall state of the road can be represented;
and the seventh judging unit is used for repeating the step of continuously comparing the matching state of the current driving state data of the vehicle and the road running state estimated data corresponding to the current vehicle position on the virtual road when the overall road state cannot be represented.
It is clear to a person skilled in the art that the solution according to the embodiments of the present application can be implemented by means of software and/or hardware. The term "unit" and "module" in this specification refers to software and/or hardware capable of performing a specific function independently or in cooperation with other components, wherein the hardware may be, for example, a Field-Programmable Gate Array (FPGA), an Integrated Circuit (IC), or the like.
Each processing unit and/or module in the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.
Referring to fig. 3, a schematic structural diagram of an electronic device according to an embodiment of the present application is shown, where the electronic device may be used to implement the method in the embodiment shown in fig. 1. As shown in fig. 3, the electronic device 300 may include: at least one central processor 301, at least one network interface 304, a user interface 303, a memory 305, at least one communication bus 302.
Wherein a communication bus 302 is used to enable the connection communication between these components.
The user interface 303 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 303 may further include a standard wired interface and a wireless interface.
The network interface 304 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
The central processor 301 may include one or more processing cores. The central processor 301 connects various parts within the entire electronic device 300 using various interfaces and lines, and performs various functions of the terminal 300 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 305 and calling data stored in the memory 305. Alternatively, the central Processing unit 301 may be implemented in at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The CPU 301 may integrate one or a combination of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a modem, and the like. Wherein, the CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the cpu 301, but may be implemented by a single chip.
The Memory 305 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable medium. The memory 305 may be used to store instructions, programs, code sets, or instruction sets. The memory 305 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 305 may alternatively be at least one storage device located remotely from the central processor 301. As shown in fig. 3, memory 305, which is a type of computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.
In the electronic device 300 shown in fig. 3, the user interface 303 is mainly used for providing an input interface for a user to obtain data input by the user; the cpu 301 may be configured to call the digital twin-oriented road operation data prediction application stored in the memory 305, and specifically perform the following operations:
constructing a digital twin road traffic system based on actual road data, and establishing a mapping relation of vehicles on an actual road and a virtual road of the digital twin road traffic system;
constructing an intelligent interpolation data set based on vehicle running state data collected by road sensors discontinuously arranged on the actual road, and calculating road running state estimated data of any position of the virtual road according to the intelligent interpolation data set;
and generating a digital twin road traffic system state diagram according to the road running state estimation data, and displaying the digital twin road traffic system state diagram to the vehicle in real time.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk including floppy disks, optical disks, DVD, CD-ROMs, microdrive, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some service interfaces, devices or units, and may be an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
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 memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory comprises: various media capable of storing program codes, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program, which is stored in a computer-readable memory, and the memory may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The above description is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure should not be limited thereby. That is, all equivalent changes and modifications made in accordance with the teachings of the present disclosure are intended to be included within the scope of the present disclosure. Embodiments of the present disclosure will be readily apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. A road operation data prediction method facing digital twins is characterized by comprising the following steps:
constructing a digital twin road traffic system based on actual road data, and establishing a mapping relation of vehicles on an actual road and a virtual road of the digital twin road traffic system;
constructing an intelligent interpolation data set based on vehicle running state data collected by road sensors discontinuously arranged on the actual road, and calculating road running state estimated data of any position of the virtual road according to the intelligent interpolation data set;
and generating a digital twin road traffic system state diagram according to the road running state estimation data, and displaying the digital twin road traffic system state diagram to the vehicle in real time.
2. The method of claim 1, wherein the actual road data comprises electronic map data of a road, first historical road data collected by the road sensor, and second historical road data uploaded by the vehicle.
3. The method of claim 1, wherein the establishing a mapping of vehicles on an actual road and a virtual road of the digital twin road traffic system comprises:
the method comprises the steps of obtaining self-running state data of a vehicle based on a vehicle-mounted sensor, and establishing a mapping relation of the vehicle on an actual road and a virtual road of the digital twin road traffic system according to the self-running state data, wherein the vehicle-mounted sensor comprises a GPS (global positioning system) positioning sensor, an ETC (electronic toll collection) sensor and a gyroscope.
4. The method according to claim 1, wherein the constructing an intelligent interpolation data set based on vehicle driving state data collected by road sensors discontinuously arranged on the actual road and calculating road operation state estimation data of any position of the virtual road according to the intelligent interpolation data set comprises:
constructing an intelligent interpolation data set based on vehicle running state data historically acquired by road sensors discontinuously arranged on the actual road;
acquiring a real-time position of the vehicle on the virtual road, and inquiring the road sensor in a preset detection range corresponding to the real-time position;
when a target road sensor is inquired in the preset detection range, controlling the target road sensor to acquire real-time vehicle running state data of the vehicle, and adding the real-time vehicle running state data to the intelligent interpolation data set;
when a target road sensor is not inquired in the preset detection range, determining a first road sensor and a second road sensor which are adjacent to the vehicle, and performing road section interpolation analysis according to first vehicle running state data and second vehicle running state data in an intelligent interpolation data set to obtain an analysis result, wherein the first vehicle running state data is the vehicle running state data of the first road sensor in the intelligent interpolation data set, and the second vehicle running state data is the vehicle running state data of the second road sensor in the intelligent interpolation data set;
and calculating road running state estimation data of any position of the virtual road based on each analysis result.
5. The method of claim 4, wherein after calculating the estimated road operating state data at any position of the virtual road based on each analysis result, the method further comprises:
and generating traffic running state early warning information based on the road running state estimated data in the vehicle advancing direction, and sending the traffic running state early warning information to the vehicle.
6. The method of claim 1, wherein said generating a digital twin road traffic system state map from each of said predicted road operating state data comprises:
continuously comparing the current driving state data of the vehicle with the matching state of the road running state estimated data corresponding to the current vehicle position on the virtual road in the driving process of the vehicle;
for the road running state estimated data which is successfully matched, marking the road running state estimated data as available data, and generating a digital twin road traffic system state diagram according to each available data;
and for the road running state estimated data which fails to be matched, marking the road running state estimated data as unavailable data, taking the unavailable data as a negative feedback sample, and performing negative feedback optimization on the step of calculating the road running state estimated data of any position of the virtual road according to the intelligent interpolation data set.
7. The method of claim 6, wherein said generating a digital twin road traffic system state map from each of said available data comprises:
judging whether the available data can represent the whole road state or not by superposition;
when the overall state of the road can be represented, generating a digital twin road traffic system state diagram based on all the available data;
and when the overall state of the road cannot be represented, repeating the step of continuously comparing the current driving state data of the vehicle with the matching state of the road running state estimated data corresponding to the current vehicle position on the virtual road.
8. A digital twin-oriented road running data estimation device, comprising:
the construction module is used for constructing a digital twin road traffic system based on actual road data and establishing a mapping relation of vehicles on an actual road and a virtual road of the digital twin road traffic system;
the calculation module is used for constructing an intelligent interpolation data set based on vehicle running state data collected by road sensors discontinuously arranged on the actual road and calculating road running state estimation data of any position of the virtual road according to the intelligent interpolation data set;
and the generating module is used for generating a digital twin road traffic system state diagram according to the estimated data of the road running states and displaying the digital twin road traffic system state diagram to the vehicle in real time.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1-7 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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| CN202210058269.XA CN114548512A (en) | 2022-01-19 | 2022-01-19 | Road operation data estimation method and device for digital twins |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114863699A (en) * | 2022-06-14 | 2022-08-05 | 电子科技大学 | Urban vehicle-road cooperative traffic flow prediction method based on digital twins |
| CN115391477A (en) * | 2022-10-31 | 2022-11-25 | 智道网联科技(北京)有限公司 | Data processing method and device, electronic equipment and storage medium |
| CN116543586A (en) * | 2023-07-03 | 2023-08-04 | 深圳市视想科技有限公司 | Intelligent public transportation information display method and display equipment based on digital twinning |
-
2022
- 2022-01-19 CN CN202210058269.XA patent/CN114548512A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114863699A (en) * | 2022-06-14 | 2022-08-05 | 电子科技大学 | Urban vehicle-road cooperative traffic flow prediction method based on digital twins |
| CN115391477A (en) * | 2022-10-31 | 2022-11-25 | 智道网联科技(北京)有限公司 | Data processing method and device, electronic equipment and storage medium |
| CN116543586A (en) * | 2023-07-03 | 2023-08-04 | 深圳市视想科技有限公司 | Intelligent public transportation information display method and display equipment based on digital twinning |
| CN116543586B (en) * | 2023-07-03 | 2023-09-08 | 深圳市视想科技有限公司 | Intelligent public transportation information display method and display equipment based on digital twinning |
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