Method and apparatus for determining road class

Abstract

The present disclosure discloses a road grade determination method, relates to the field of artificial intelligence and big data, and particularly relates to the field of intelligent transportation, and can be used in a road grade judgment scene in a road network connection graph. The specific implementation scheme is as follows: determining road betweenness of each road in a road network; and performing clustering processing based on the road betweenness of each road to obtain at least one clustering result about the road betweenness of each road; and preferentially determining a trunk road in the road network based on the at least one clustering result.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of artificial intelligence and big data, in particular to the field of intelligent driving, and can be used in the road level judgment scene in the road network connection graph. BACKGROUND

[0002] When performing map analysis and processing, it is often necessary to know the levels of roads in the road network. For example, when determining a navigation path, the roads in the regional road network should be avoided as the navigation path according to the road level. The regional road network refers to the road network in a place. Specifically, the place can be a community, a school, a park, a hospital, and the like. SUMMARY

[0003] The present disclosure provides a road level determination method, device, equipment, storage medium, and computer program product.

[0004] According to an aspect of the present disclosure, a road level determination method is provided, including: determining road betweenness of each road in a road network; performing clustering processing based on the road betweenness of the each road to obtain at least one clustering result about the road betweenness of the each road; and preferentially determining a trunk road in the road network based on the at least one clustering result.

[0005] According to another aspect of the present disclosure, a road level determination device is provided, including: a determination module configured to determine road betweenness of each road in a road network; a clustering module configured to perform clustering processing based on the road betweenness of the each road to obtain at least one clustering result about the road betweenness of the each road; and a first determination module configured to preferentially determine a trunk road in the road network based on the at least one clustering result.

[0006] According to another aspect of the present disclosure, an electronic device is provided, including: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to the embodiments of the present disclosure.

[0007] According to another aspect of the present disclosure, a non-transitory computer readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to perform the method according to the embodiments of the present disclosure.

[0008] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, which, when executed by a processor, implements the method according to the embodiments of the present disclosure.

[0009] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0010] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0011] Figure 1 An exemplary system architecture suitable for embodiments of this disclosure is shown;

[0012] Figure 2 A flowchart of a method for determining road grades according to an embodiment of the present disclosure is illustrated;

[0013] Figure 3 An exemplary schematic diagram illustrating the process of traveling from one community to another according to an embodiment of this disclosure is shown;

[0014] Figure 4 An exemplary schematic diagram illustrating the calculation of road betweenness according to an embodiment of the present disclosure is shown;

[0015] Figure 5 An exemplary diagram illustrating the segmentation of a road network into sections according to an embodiment of the present disclosure is shown.

[0016] Figure 6A and Figure 6B An exemplary schematic diagram of a road network area according to an embodiment of the present disclosure is shown;

[0017] Figure 7A and Figure 7B An exemplary schematic diagram of the distribution of betweenness numbers of roads in a full road network and a partial road network according to embodiments of the present disclosure is shown;

[0018] Figure 8 A block diagram of a road classification determination apparatus according to an embodiment of the present disclosure is shown as an example;

[0019] Figure 9 A block diagram of an electronic device used to implement embodiments of the present disclosure is shown as an example. Detailed Implementation

[0020] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0021] It should be understood that in actual application, the road grades of roads in the road network can be determined in the following ways. For example, images are collected by a collection vehicle and are manually determined; or images are collected by crowd sourcing from social vehicles and are manually determined; or remote sensing images are manually interpreted; or historical trajectory data such as driving paths, average speeds, traffic volumes, road widths, etc. are machine determined.

[0022] Among them, through manual judgment, there are problems such as large operation difficulty, long time consumption, and unstable judgment quality. Through historical trajectory data judgment, the grades of urban branch roads or scenes with large traffic volume can be determined, but it is difficult to determine the scenes of roads in a district or small traffic volume.

[0023] To this end, the disclosure embodiments provide a road grade determination method based on road betweenness, which can overcome the above problems and greatly improve the recognition efficiency and accuracy of roads of different grades. In addition, it can also reduce the labor cost.

[0024] The disclosure will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] The system architecture suitable for the road grade determination method and device of the embodiments of the disclosure is introduced as follows.

[0026] Figure 1 An exemplary system architecture suitable for the embodiments of the disclosure is shown. It should be noted that, Figure 1 The system architecture shown is only an example of a system architecture to which the embodiments of the disclosure can be applied, to help those skilled in the art understand the technical content of the disclosure, but it does not mean that the embodiments of the disclosure cannot be used in other environments or scenarios.

[0027] As Figure 1 shown, the system architecture 100 in the embodiments of the disclosure can include a server 101 and a terminal device 102. The server 101 can be used to execute the road grade determination method in the embodiments of the disclosure. The terminal device 102 can be used to display the data output by the server 101, including but not limited to the road betweenness calculation result, the distribution of road betweenness in the road network, and the road network connection graph.

[0028] It should be understood that Figure 1 the number of terminal devices in the system architecture 100 is only illustrative. According to the needs of implementation, there can be any number of terminal devices.

[0029] According to the embodiments of the disclosure, the disclosure provides a road grade determination method.

[0030] Figure 2 An exemplary flowchart of the road grade determination method according to the embodiments of the disclosure is shown.

[0031] AsFigure 2 As shown, the road grade determination method 200 can include operations S210-S230.

[0032] In operation S210, the road betweenness of each road in the road network is determined.

[0033] In operation S220, clustering processing is performed based on the road betweenness of each road to obtain at least one clustering result of the road betweenness of each road.

[0034] In operation S230, the main road in the road network is determined preferentially based on the at least one clustering result.

[0035] In an embodiment of the present disclosure, the road betweenness of each road in the road network can be pre-calculated according to the betweenness calculation formula and the road network data. Therefore, in operation S210, the road betweenness of each road in the road network can be directly read based on the pre-calculation result. By using this embodiment, the processing efficiency of the entire road grade determination method can be improved.

[0036] Alternatively, in another embodiment of the present disclosure, in operation S210, the road betweenness of each road in the road network can also be calculated in real time according to the betweenness calculation formula and the road network data. By using this embodiment, the latest road betweenness of each road in the road network can be accurately obtained in real time.

[0037] In addition, in the embodiment of the present disclosure, each road in the road network can be divided into a main road, a distribution road and a community road based on the road betweenness of each road in the road network. In some cases, the distribution road can be further divided into a main distribution road and a secondary distribution road.

[0038] It should be noted that the main distribution road or the distribution road is mainly responsible for converging the traffic flow in the road network area to the main road. The secondary distribution road is mainly responsible for converging the traffic flow in the road network area to the main distribution road. The community road mainly includes the roads in the closed or semi-closed areas such as communities, parks, supermarket parking lots, etc., and is responsible for converging the traffic flow in the road network area to the secondary distribution road.

[0039] In addition, in an embodiment of the present disclosure, the road betweenness described above can include at least one of a distribution value, a connectivity value and a penetration value. For any road, the distribution value, the connectivity value and the penetration value in its road betweenness satisfy the relationship of distribution value = connectivity value + penetration value.

[0040] It should be noted that for any road, its distribution value is equal to the sum of the weighted weights of each path contained in the road network; its connectivity value is equal to the sum of the weighted weights contributed by the road network area in its distribution value; and its penetration value is equal to the sum of the weighted weights contributed by the road network area outside in its distribution value.

[0041] It should be understood that in the real world, whether it is life, work, or entertainment, people always move from one community road in a district to another community road in another district. For example, as shown in Figure 3 assuming that it is needed to move from district A to district C, the following path needs to be taken: A (community road) -> L1 / L2 (sub-distributor road) -> L3 (main distributor road) -> L5 (trunk road) -> L4 (distributor road) -> C (community road).

[0042] Therefore, it can be understood that the trunk road can divide the road network into independent road network districts, which also leads to the fact that the distribution value of the trunk road is much larger than the distribution value of each road in the corresponding road network district.

[0043] Therefore, based on the above characteristics of the distribution value of the trunk road, in operations S220 and S230, clustering processing can be performed based on the distribution value of each road in the road network to obtain at least one clustering result about the distribution value of the road, such as clustering the distribution values of the roads by order of magnitude to obtain a plurality of distribution value clusters of different orders of magnitude, and then finding a distribution value cluster with the largest order of magnitude from these clustering results. The roads corresponding to each distribution value in the cluster are the trunk roads in the road network.

[0044] In addition, in other embodiments of the present disclosure, in operation S220, when performing clustering processing, clustering processing can also be performed based on the connectivity value of each road in the road network to obtain at least one clustering result about the connectivity value of the road, and / or clustering processing can also be performed based on the penetration value of each road in the road network to obtain at least one clustering result about the penetration value of the road. Then, in operation S230, these clustering results can also be used as an auxiliary to determine the trunk road in the road network in priority in combination with at least one clustering result about the distribution value of the road.

[0045] In addition, for example, as shown in Figure 4 the calculation steps of the road betweenness (which can be the distribution value / connectivity value / penetration value) of road T in the road network structure are as follows:

[0046] Step 1, determine the road set L1 (L1, L2) and R1 (L3, L4) connected to the left and right ends of T;

[0047] Step 2, further determine the set L2 (p1, p2) and R2 (p3, p4) connected to the farthest points (i.e., far from T) on the left and right sides of T;

[0048] Step 3, combine each set element in the remote point set L2(p1, p2) with each set element in R2(p3, p4) respectively to obtain a point pair set P(<p1, p3>, <p1, p4>, <p2, p3>, <p2, p4>), wherein it is assumed that the length of the point pair set is m;

[0049] Step 4, calculate the topological shortest path values v1-v4 of each group of point pairs in the set P without passing through T, and substitute the values into Formula 1 to calculate corresponding r1-r4, and calculate the sum s of r1-r4;

[0050] Formula 1:

[0051] Wherein, L and R in the denominator represent the road set on the left and right of T; the fourth power (weight parameter 1) and 700 (weight parameter 2) in the numerator are used to control the convergence speed of the shortest path calculation process.

[0052] It should be noted that by setting the weight parameter 1 and the weight parameter 2 in the numerator of the above Formula 1, the weight of the road network area in the entire road network can be restored, and each road network area is comparable. It should be understood that the weight parameter 1 and the weight parameter 2 in the embodiment of the present disclosure can be set or adjusted according to actual needs.

[0053] Step 5, according to the above, the road betweenness value c of the road T is s / m.

[0054] According to the embodiment of the present disclosure, different characteristics of different levels of roads in the road network on different road betweenness can be automatically determined, such as the distribution value of the main road in the road network is much larger than the distribution value of other level roads in the road network area. The main road in the road network can be automatically determined. Therefore, the identification efficiency and accuracy of each level road can be greatly improved. In addition, the road level of the road network can be determined based on the road network topology structure only, without relying on other factors outside the road network topology structure, and without the help of artificial judgment, so as to further reduce the labor cost.

[0055] As an optional embodiment, the method further comprises: in response to successfully determining the main road in the road network, cutting the road network into a plurality of road network areas adjacent to each other and not overlapping with each other, taking the main road in the road network as a framework; and determining the level of each road in each road network area in the plurality of road network areas.

[0056] As shown in Figure 5 The main road in the road network connection diagram can divide the road network into a plurality of closed or semi-closed road network areas adjacent to each other and not overlapping with each other.

[0057] Therefore, in the embodiment, in response to successfully determining the trunk roads in the road network, the road network can be divided into a plurality of road network regions adjacent to and not overlapping with each other by taking the trunk roads in the road network as a framework, and then the ranks of the roads in each of the road network regions can be further determined based on the characteristics of the corresponding road betweenness.

[0058] As shown in Figure 6A , the road network regions in the figure are obtained by using the above method, in which the outermost road network region is the trunk road, and the other roads are the roads in the road network region. As shown in Figure 6B , the darkly marked roads in the figure are the trunk roads, and the lightly marked roads are the roads in the road network region.

[0059] It should be understood that the roads in each road network region can be divided into two or three based on the corresponding road betweenness. In the case of two, it usually includes the distribution road and the community road. In the case of three, it usually includes the main distribution road, the secondary distribution road and the community road.

[0060] For example, in the case of three, the main distribution road in the road network region is mainly responsible for converging the traffic flow in the region to the trunk road, and the connectivity value is at least one order of magnitude higher than that of other roads, but one order of magnitude lower than that of the trunk road; the secondary distribution road is mainly responsible for converging the traffic flow in the region to the main distribution road, and the connectivity value is lower than that of the main distribution road, but higher than that of the community road, and the penetration value is basically the same as that of the community road; the distribution value and the penetration value of the community road are close to 0 or equal to 0.

[0061] The above conclusion can be verified based on the data as shown in Figure 7A and Figure 7B . Specifically, as shown in Figure 7B , after being divided according to the distribution of the distribution value, the connectivity value and the penetration value, it can be observed that the community road (such as the road numbered 01) passes through the secondary distribution road (such as the road numbered 02) and the distribution road (such as the road numbered 03) to the trunk road (such as the road numbered 04), and the closer to the trunk road, the closer the distribution value (i.e. belonging to the same order of magnitude), and the smaller the connectivity value and the larger the penetration value, indicating that the influence from the outside of the region is greater. As can be seen from the figure, the penetration value of the community road is almost 0 or equal to 0; the penetration value of the secondary distribution road and the main distribution road is one order of magnitude higher than that of the community road; the connectivity value of the main distribution road is one order of magnitude lower than that of the trunk road; thus, the observation value is consistent with the calculated value. In addition, the verification work can also be completed manually, but the cost of manual verification is high and prone to errors.

[0062] Therefore, the identification of the road levels inside the road network blocks can be completed based on the characteristics of the distribution values, the connectivity values and the penetration values of the main and secondary distribution roads and the community roads inside the road network blocks.

[0063] According to the embodiments of the present disclosure, the road levels of the road network can be confirmed level by level based on the characteristics of the road betweenness and the characteristics of the main roads, so that the identification efficiency and the identification accuracy of the roads of different levels can be greatly improved.

[0064] As an optional embodiment, the method for determining the levels of the roads inside each road network block in the plurality of road network blocks comprises: for each road network block, performing clustering processing based on the road betweenness of the roads inside the current road network block to obtain at least one clustering result of the road betweenness of the roads inside the current road network block; and based on the at least one clustering result, preferentially determining the community roads inside the current road network block.

[0065] Since the distribution values and the penetration values of the community roads inside the road network blocks tend to 0 or equal to 0, the community roads inside the road network blocks can be preferentially determined based on this.

[0066] In an embodiment of the present disclosure, for each road inside a road network block, the distribution value, the connectivity value and the penetration value in the road betweenness thereof can be calculated respectively. Then, for all the roads inside the road network block, the distribution value clustering, the connectivity value clustering and the penetration value clustering are performed respectively. Finally, the community roads inside the road network block can be preferentially determined based on the connectivity value clustering and the penetration value clustering. For example, the roads with the connectivity value and the penetration value tending to 0 or equal to 0 are selected as the community roads.

[0067] According to the embodiments of the present disclosure, the community roads inside the road network blocks can be distinguished first according to the distribution of the distribution values and the penetration values, and then the remaining roads can be determined whether to be distinguished as the main distribution roads and the secondary distribution roads according to the order of magnitude distribution of the distribution values. Therefore, the identification efficiency and the identification accuracy of the roads of different levels can be greatly improved.

[0068] As an optional embodiment, the method for determining the levels of the roads inside each road network block in the plurality of road network blocks further comprises: for each road network block, in response to successfully determining the community roads inside the current road network block, determining the levels of the other remaining roads inside the current road network block based on at least one clustering result of the road betweenness of the other remaining roads.

[0069] Since the roads inside the road network blocks have the following characteristics: (1) the distribution value: the main distribution road > the secondary distribution road > the community road, and the distribution values of the roads of different levels belong to different orders of magnitude; (2) the connectivity value: the secondary distribution road ≈ the community road; (3) the community road: the distribution value and the penetration value tend to 0 or equal to 0; (4) the main distribution road: the connectivity value tends to 0.

[0070] Therefore, according to the distribution of the collection and distribution values and the through value, the community roads in the road network area are distinguished first, and then for the remaining roads, the orders of magnitude of the collection and distribution values are distributed, that is, the collection and distribution values of the remaining roads are clustered, and whether the main collection and distribution road and the secondary collection and distribution road need to be distinguished is determined based on the clustering result. Thus, the identification efficiency and accuracy of the roads of different levels can be greatly improved, and fine identification can be achieved.

[0071] As an optional embodiment, the level of the other remaining roads is determined based on at least one clustering result of the road betweenness of the other remaining roads in the current road network area, including: determining a collection and distribution value clustering result in the at least one clustering result of the road betweenness of the other remaining roads; and in response to the collection and distribution value clustering result including two different clusters of different orders of magnitude, determining a main collection and distribution road and a secondary collection and distribution road in the other remaining roads, wherein the order of magnitude of the collection and distribution value of the main collection and distribution road is higher than that of the secondary collection and distribution road.

[0072] Further, the method further includes: in response to the collection and distribution value clustering result including one cluster, determining that the other remaining roads are collection and distribution roads.

[0073] In an embodiment of the present disclosure, after the community roads in the road network area are determined, if the collection and distribution values of the remaining roads in the road network area belong to the same order of magnitude, it is considered that the capacities of the remaining roads in terms of traffic flow convergence are not much different, and thus the levels of the roads do not need to be further distinguished. If the collection and distribution values of the remaining roads in the road network area belong to two different orders of magnitude, it is considered that the capacities of the remaining roads in terms of traffic flow convergence have a certain gap, and thus the levels of the roads can be further distinguished, for example, the road with a large order of magnitude of the collection and distribution value is taken as the main collection and distribution road, and the road with a small order of magnitude of the collection and distribution value is taken as the secondary collection and distribution road.

[0074] According to the embodiments of the present disclosure, after the community roads in the road network area are distinguished first, for the remaining roads, whether the main collection and distribution road and the secondary collection and distribution road need to be distinguished can be further determined according to the orders of magnitude of the collection and distribution values. Thus, the fineness of road identification can be greatly improved.

[0075] As an optional embodiment, the road betweenness includes at least one of the convergence value, the connectivity value and the through value.

[0076] The present disclosure will be described in detail below with reference to a specific embodiment.

[0077] In the embodiment, the levels of the roads in the road network can be identified according to the following process:

[0078] Step 1: The road betweenness of all the roads in the network is calculated, and the main roads are distinguished first. The road network is cut into a plurality of road network areas which are adjacent to each other and do not overlap, taking the main roads as the framework;

[0079] Step 2, secondary betweenness is calculated for each road in each road network section, and normalization is performed, then community roads in each road network section are distinguished based on the distribution of the gathering value and the through value, and then the remaining roads in each road network section are determined whether to distinguish the main gathering road and the secondary gathering road according to the order of magnitude distribution of the gathering value.

[0080] According to the embodiments of the present disclosure, the data characteristics of the road betweenness of each road in the road network are used to automatically identify the road grades, so that the identification efficiency can be greatly improved, and the accuracy is also guaranteed.

[0081] According to the embodiments of the present disclosure, the present disclosure also provides a road grade determination device.

[0082] Figure 8 An exemplary block diagram of a road grade determination device according to an embodiment of the present disclosure is shown.

[0083] As shown in Figure 8 The road grade determination device 800 includes a determination module 810, a clustering module 820, and a first determination module 830.

[0084] The determination module 810 is configured to determine the road betweenness of each road in the road network.

[0085] The clustering module 820 is configured to perform clustering processing based on the road betweenness of each road to obtain at least one clustering result of the road betweenness of each road.

[0086] The first determination module 830 is configured to preferentially determine the main road in the road network based on the at least one clustering result.

[0087] As an optional embodiment, the device further includes a cutting module configured to, in response to successfully determining the main road in the road network, cut the road network into a plurality of road network sections adjacent to each other and not overlapping with each other, taking the main road in the road network as a framework; and a second determination module configured to determine the grades of each road in each road network section.

[0088] As an optional embodiment, the second determination module includes a clustering unit configured to, for each road network section, perform clustering processing based on the road betweenness of each road in the current road network section to obtain at least one clustering result of the road betweenness of each road in the current road network section; and a first determination unit configured to preferentially determine the community road in the current road network section based on the at least one clustering result.

[0089] As an optional embodiment, the second determining module further includes a second determining unit, configured to, for each road network region, in response to successfully determining the community road within the current road network region, determine the level of the other remaining roads based on at least one clustering result of the road betweenness of the other remaining roads within the current road network region.

[0090] As an optional embodiment, the second determining unit includes a first determining sub-unit, configured to determine the clustering result of the betweenness value in the at least one clustering result of the road betweenness of the other remaining roads; and a second determining sub-unit, configured to, in response to the clustering result of the betweenness value including two clusters of different orders of magnitude, determine the primary betweenness road and the secondary betweenness road in the other remaining roads, wherein the order of magnitude of the betweenness value of the primary betweenness road is higher than the order of magnitude of the betweenness value of the secondary betweenness road.

[0091] As an optional embodiment, the second determining unit further includes a third determining sub-unit, configured to, in response to the clustering result of the betweenness value including one cluster, determine the other remaining roads as the betweenness road.

[0092] As an optional embodiment, the road betweenness includes at least one of the betweenness value, the connectivity value and the through value.

[0093] It should be understood that the embodiments of the device part of the present disclosure correspond to the same or similar embodiments of the method part of the present disclosure, and the technical problems solved and the technical effects achieved also correspond to the same or similar. Therefore, the present disclosure will not be repeated here.

[0094] According to embodiments of the present disclosure, the present disclosure further provides an electronic device, a readable storage medium and a computer program product.

[0095] Figure 9 A schematic block diagram of an example electronic device 900 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present disclosure described and / or claimed in this document.

[0096] As Figure 9As shown, the electronic device 900 includes a computing unit 901 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 902 or a computer program loaded from a storage unit 908 into a random access memory (RAM) 903. Various programs and data required for the operation of the electronic device 900 can also be stored in the RAM 903. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other through a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0097] A plurality of components in the electronic device 900 are connected to the I / O interface 905, including an input unit 906 such as a keyboard, a mouse, and the like; an output unit 907 such as various types of displays, a speaker, and the like; a storage unit 908 such as a magnetic disk, an optical disk, and the like; and a communication unit 909 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 909 allows the device 900 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0098] The computing unit 901 can be various general and / or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, and the like. The computing unit 901 performs various methods and processes described above, such as the road grade determination method. For example, in some embodiments, the road grade determination method can be implemented as a computer software program that is tangibly embodied in a machine-readable medium, such as the storage unit 908. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 900 via the ROM 902 and / or the communication unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of the road grade determination method described above can be performed. Alternatively, in other embodiments, the computing unit 901 can be configured to perform the road grade determination method by any other appropriate means, such as by means of firmware.

[0099] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a load programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0100] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or the block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, or entirely on a remote machine or server.

[0101] In the context of the present disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can 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 the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0102] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0103] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0104] The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS (Virtual Private Server, or VPS for short) services. The server can also be a server of a distributed system, or a server combined with a blockchain.

[0105] In the technical solutions of the present disclosure, the road network data records, storage and application involved all comply with relevant legal regulations and do not violate public order and good customs.

[0106] It should be understood that the various forms of flow shown above can be used to reorder, add, or remove steps. For example, the steps described in the present disclosure can be executed in parallel, in sequence, or in a different order, as long as the desired results of the technical solutions of the present disclosure can be achieved, and the present disclosure is not limited herein.

[0107] The foregoing detailed description has set forth various embodiments of the devices and / or processes via the use of specific terminology. However, embodiments thereof can be practiced with the exact description not being set forth but with the same essence; the principles set forth herein can be practiced with plasticity in a manner appropriate to specific implementations. The scope of the disclosure is therefore intended to be on the true spirit and principles of the technology disclosed herein, and further scope can be provided by the appended claims.

Claims

1. A method for determining road levels, comprising: determining road betweenness of each road in a road network; the road betweenness comprises at least one of a dispersion value, a connectivity value and a penetration value; for any road, the dispersion value thereof is equal to a sum of weighted weights of each path containing the road in the road network, the connectivity value thereof is equal to a sum of weighted weights contributed by an inner region of a road network district in the dispersion value thereof, and the penetration value thereof is equal to a sum of weighted weights contributed by an outer region of the road network district in the dispersion value thereof; performing clustering processing based on the road betweenness of each road to obtain at least one clustering result about the road betweenness of each road; and determining a trunk road in the road network preferentially based on the at least one clustering result; the method further comprises, in response to successfully determining the trunk road in the road network, cutting the road network into a plurality of road network districts adjacent to and non-overlapping with each other with the trunk road in the road network as a framework, and determining levels of each road in each road network district of the plurality of road network districts.

2. The method of claim 1, wherein, determining the levels of each road in each road network district of the plurality of road network districts comprises, for each road network district, performing clustering processing based on the road betweenness of each road in a current road network district to obtain at least one clustering result about the road betweenness of each road in the current road network district; and determining a community road in the current road network district preferentially based on the at least one clustering result.

3. The method of claim 2, wherein, determining the levels of each road in each road network district of the plurality of road network districts further comprises, for each road network district, in response to successfully determining the community road in the current road network district, determining levels of other remaining roads in the current road network district based on at least one clustering result of the road betweenness of the other remaining roads.

4. The method of claim 3, wherein, determining the levels of the other remaining roads in the current road network district based on the at least one clustering result of the road betweenness of the other remaining roads comprises: determining a dispersion value clustering result in the at least one clustering result of the road betweenness of the other remaining roads; in response to the dispersion value clustering result comprising two clusters of different orders of magnitude, determining a main dispersion road and a secondary dispersion road in the other remaining roads, wherein an order of magnitude of the dispersion value of the main dispersion road is higher than that of the secondary dispersion road. 5.The method of claim 4, further comprising: in response to the dispersion value clustering result comprising one cluster, determining the other remaining roads as dispersion roads. 6.An apparatus for determining road levels, comprising: a determining module configured to determine road betweenness of each road in a road network; the road betweenness comprises at least one of a dispersion value, a connectivity value and a penetration value; for any road, the dispersion value thereof is equal to a sum of weighted weights of each path containing the road in the road network, the connectivity value thereof is equal to a sum of weighted weights contributed by an inner region of a road network district in the dispersion value thereof, and the penetration value thereof is equal to a sum of weighted weights contributed by an outer region of the road network district in the dispersion value thereof; a clustering module configured to perform clustering processing based on the road betweenness of each road to obtain at least one clustering result about the road betweenness of each road; and The first determining module is configured to determine a main road in the road network based on the at least one clustering result. The cutting module is configured to, in response to successful determination of the main road in the road network, cut the road network into a plurality of road network regions adjacent to each other and not overlapping, with the main road in the road network as a framework; and The second determining module is configured to determine the levels of roads in each of the plurality of road network regions.

7. The apparatus of claim 6, wherein, The second determining module includes: The clustering unit is configured to, for each of the plurality of road network regions, perform clustering processing based on the road betweenness of roads in a current road network region, to obtain at least one clustering result about the road betweenness of roads in the current road network region; and The first determining unit is configured to determine community roads in the current road network region based on the at least one clustering result.

8. The apparatus of claim 7, wherein, The second determining module further includes: The second determining unit is configured to, for each of the plurality of road network regions, in response to successful determination of the community roads in the current road network region, determine the levels of other remaining roads in the current road network region based on at least one clustering result of the road betweenness of the other remaining roads.

9. The apparatus of claim 8, wherein, The second determining unit includes: The first determining sub-unit is configured to determine a cluster-dispersion value clustering result in the at least one clustering result of the road betweenness of the other remaining roads. The second determining sub-unit is configured to, in response to the cluster-dispersion value clustering result including two clusters of different orders of magnitude, determine a main cluster-dispersion road and a secondary cluster-dispersion road in the other remaining roads, wherein the order of magnitude of the cluster-dispersion value of the main cluster-dispersion road is higher than the order of magnitude of the cluster-dispersion value of the secondary cluster-dispersion road.

10. The apparatus of claim 9, wherein, The second determining unit further includes: The third determining sub-unit is configured to, in response to the cluster-dispersion value clustering result including one cluster, determine the other remaining roads as cluster-dispersion roads.

11. An electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

12. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to perform the method of any one of claims 1-5.

13. A computer program product comprising a computer program which, when executed by a processor, implements the method of any one of claims 1-5.

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