Method, device and equipment for measuring complexity of road network

Abstract

The invention discloses a road network complexity measurement method, device and equipment, and relates to the field of traffic road networks, and the method comprises the steps: calculating the degree structure entropy of a target road network; performing line element division on the target road network to obtain a line element set of the target road network; the line element set comprises a plurality of line elements, and each line element comprises a plurality of local folding sections; for any line element of the target road network, determining the geometrical shape information amount of the line element according to the length of each local folding section of the line element and the width and area of a bottom line; determining the geometrical shape information entropy of the target road network according to the geometrical shape information amount of each line element of the target road network; and performing weighted summation on the degree structure entropy of the target road network and the geometrical shape information entropy of the target road network to obtain the comprehensive complexity of the target road network. According to the invention, the precision of road network complexity measurement is improved.

Description

Technical Field

[0001] The present application relates to the field of traffic road networks, and in particular to a method, device and equipment for measuring the complexity of a road network taking into account the geometric morphology and topological structure characteristics of the road. Background Art

[0002] The quantitative study of network complexity is an important topic in the field of complex network research. For example, in urban planning and traffic engineering, understanding and quantifying the complexity of road networks is critical to optimizing traffic flow and improving the response speed of emergency management. More complex network structures may lead to greater risks of traffic congestion, and in emergencies, higher network complexity may affect the response time of emergency vehicles. Therefore, it is necessary to accurately measure the complexity of road networks to provide more comprehensive information support for traffic planners and managers, which will help to design and optimize urban road networks more scientifically. Summary of the invention

[0003] The purpose of this application is to provide a road network complexity measurement method, device and equipment, which can accurately measure the complexity of the road network.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a road network complexity measurement method, comprising:

[0006] Calculating the degree structural entropy of a target road network; the target road network includes a plurality of nodes and the degree of each node;

[0007] Dividing the target road network into line elements to obtain a line element set of the target road network; the line element set includes a plurality of line elements, and each line element includes a plurality of local segments;

[0008] For any line element of the target road network, the geometric shape information of the line element is determined according to the length of each local segment of the line element, the bottom line width of each local segment and the area of ​​each local segment; the bottom line width of the local segment is the Euclidean distance between the starting and ending points of the local segment; the area of ​​the local segment is the area of ​​the closed area enclosed by the local segment and its bottom line;

[0009] Determining the geometric shape information entropy of the target road network according to the geometric shape information amount of each line element of the target road network;

[0010] The degree structural entropy of the target road network and the geometric information entropy of the target road network are weightedly summed to obtain the comprehensive complexity of the target road network.

[0011] In a second aspect, the present application provides a road network complexity measurement device, comprising:

[0012] A degree structure entropy calculation module is used to calculate the degree structure entropy of a target road network; the target road network includes a plurality of nodes and the degree of each node;

[0013] An element division module is used to divide the target road network into line elements to obtain a line element set of the target road network; the line element set includes multiple line elements, and each line element includes multiple local segments;

[0014] a geometric shape information amount calculation module, connected to the element division module, for determining the geometric shape information amount of any line element of the target road network according to the length of each local segment of the line element, the bottom line width of each local segment and the area of ​​each local segment; the bottom line width of the local segment is the Euclidean distance between the starting and ending points of the local segment; the area of ​​the local segment is the area of ​​the closed area enclosed by the local segment and its bottom line;

[0015] A geometric shape information entropy determination module, connected to the geometric shape information amount calculation module, for determining the geometric shape information entropy of the target road network according to the geometric shape information amount of each line element of the target road network;

[0016] The complexity determination module is connected to the degree structure entropy calculation module and the geometric shape information entropy determination module respectively, and is used to perform weighted summation of the degree structure entropy of the target road network and the geometric shape information entropy of the target road network to obtain the comprehensive complexity of the target road network.

[0017] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned road network complexity measurement method.

[0018] According to the specific embodiments provided in this application, this application has the following technical effects:

[0019] The present application provides a method, device and equipment for measuring the complexity of a road network, which comprehensively considers the topological structure and road geometric features. In addition to using the degree structural entropy to measure the topological features of the road, it also introduces the road geometric shape information as a supplement, which can more accurately reflect the differences in the geometry and structure of the road network, thereby improving the accuracy of the road network complexity measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 This is an application environment diagram of a road network complexity measurement method in one embodiment of the present application;

[0022] Figure 2 A schematic diagram of a process flow of a road network complexity measurement method provided in one embodiment of the present application;

[0023] Figure 3 A schematic diagram of a process for measuring road network complexity in an embodiment of the present application;

[0024] Figure 4 This is a schematic diagram of the principle of local segmentation in one embodiment of the present application;

[0025] Figure 5 A schematic diagram of a road network in an embodiment of the present application;

[0026] Figure 6 A schematic diagram of functional modules of a road network complexity measurement device provided in one embodiment of the present application;

[0027] Figure 7 This is a schematic diagram of the first type of simple road;

[0028] Figure 8 This is a schematic diagram of the second simple road;

[0029] Fig. 9 This is a schematic diagram of the third simple road;

[0030] Fig.10 This is a schematic diagram of the fourth simple road;

[0031] Fig.11 This is a schematic diagram of the road network of the first morphological structure;

[0032] Fig.12 This is a schematic diagram of the road network of the second morphological structure. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0034] In order to measure the complexity of networks, many scholars have proposed a variety of methods to measure the complexity of networks, such as connectivity, graph diameter, clustering coefficient, information entropy, etc. Network density is an important indicator for measuring network complexity. The higher the density, the more connections there are between nodes in the network, the easier it is for information to flow between networks, and the higher the complexity. However, using network density alone to describe the complexity of a network has great limitations. It is not suitable for measuring the complexity of networks with large scale but low density in the real world, such as the Internet. Secondly, it cannot reflect the differences in heterogeneity between nodes, which also leads to its inability to describe the complexity differences between networks of different sizes.

[0035] Entropy is usually understood as a measure of chaos, disorder or uncertainty in a system. The definitions of entropy in different fields are different, but they are all based on similar concepts, namely, measuring the possibility of system states or the uncertainty of information. The methods of information entropy mainly include degree structural entropy and betweenness structural entropy. Degree structural entropy and betweenness structural entropy respectively describe the degree distribution characteristics of nodes and the complexity and uncertainty of information flow between nodes. These two methods describe the complexity of the network by quantifying the topological structural characteristics of nodes between networks through the method of information entropy. Among them, the traditional degree structural entropy method measures the complexity of the network by taking the ratio of the degree of each node in the network to the sum of the degrees of all nodes in the network as the parameter of Shannon entropy. The betweenness measures the complexity of the network by measuring the intermediary role of nodes in the network. Therefore, scholars have improved Tsallis entropy by combining degree and betweenness to obtain a new structural entropy method that combines the advantages of degree and betweenness. Based on this, information entropy has become one of the most widely used methods to measure network complexity. However, the existing entropy methods often describe the complexity of the network from a global perspective, which makes it impossible to fully consider the local characteristics of the network. Second, different topological structures (such as small-world networks, scale-free networks, and random networks) may have the same information entropy value, but their structures and properties are completely different.

[0036] This application believes that the measurement of complexity, especially when applied to the field of real road networks, needs to consider not only the topological properties of the nodes themselves but also the impact of the unique morphological characteristics of the road on the complexity of the network. In order to more accurately describe the complexity characteristics of the road network, it is necessary to describe it from two perspectives: geometry and topology. Therefore, this application proposes a road network complexity measurement method that takes into account the geometric morphology of the road and the topological structure characteristics of the intersection nodes.

[0037] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0038] The road network complexity measurement method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, or it can be integrated on the server 104, or it can be placed on the cloud or other servers. The terminal 102 can send the target road network to the server 104. After receiving the target road network, the server 104 calculates its degree structure entropy and geometric shape information entropy, and performs weighted summation of the degree structure entropy and geometric shape information entropy to obtain the comprehensive complexity of the target road network. The server 104 can feedback the comprehensive complexity of the target road network to the terminal 102. In addition, in some embodiments, the road network complexity measurement method can also be implemented separately by the server 104 or the terminal 102.

[0039] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0040] In an exemplary embodiment, Figure 2 and Figure 3 As shown, a road network complexity measurement method is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 201 to 205. Among them:

[0041] Step 201, calculate the degree structural entropy of the target road network. The target road network includes multiple nodes and the degree of each node. Specifically, firstly import the target road network data, then extract the nodes in the target road network, then calculate the degree of the nodes in the network according to the road network node data, and then calculate the degree structural entropy of the target road network.

[0042] In a road network, a node usually refers to an intersection or end point between roads. The principle of node acquisition is to break the line elements in the road network according to the intersection points, then extract the endpoints of all line elements and delete the same endpoints to obtain the node data of the road network.

[0043] Degree structural entropy is a method of measuring network complexity derived from the traditional Shannon entropy, which is used to calculate the orderliness and randomness of the network based on the probability distribution of the node degree. It can also be used to measure the complexity of the network.

[0044]

[0045] Among them, E Shannon is the Shannon entropy, N is the number of nodes in the target road network, and p i is the degree structural entropy of the i-th node.

[0046] The traditional degree structure entropy method uses the degree of the node to measure the complexity of the network. The higher the entropy value, the more complex the network. On the contrary, the lower the degree structure entropy, the simpler and more orderly the network. When it is applied to Shannon entropy, Among them, degree(i) is the degree of the i-th node, degree(j) is the degree of the j-th node, It is used to traverse the degree of each node in the network and sum them up in a loop calculation. Therefore, this application uses the following formula to calculate the degree structure entropy of the target road network:

[0047]

[0048] Among them, E D is the degree structural entropy of the target road network.

[0049] The degree structural entropy can capture the global characteristics of the network more comprehensively, reflecting the complexity, heterogeneity and dispersion of the network. At the same time, as a topological attribute of network nodes, the degree can well reflect the complexity of the network from a topological perspective.

[0050] Step 202: divide the target road network into line elements to obtain a line element set of the target road network. The line element set includes multiple line elements, and each line element includes multiple local segments.

[0051] The measurement of line elements in terms of geometric information entropy in this application is based on the geometric information volume, which focuses on the local properties of the network, that is, a single line element. Therefore, to calculate the geometric volume, the line elements need to be divided.

[0052] In an exemplary embodiment, step 202 includes the following steps 301 to 303 .

[0053] Step 301: divide the target road network into roads to obtain a plurality of line elements, each of which represents a road.

[0054] Step 302, determine all the bending points in each road.

[0055] Step 303: determine the local segments in each line element according to the bending points in each road, wherein a local segment is between two adjacent bending points.

[0056] This application adopts the method of dividing the bending point as a single line element to obtain the local line element sequence obtained by dividing the single line element. Figure 4 As shown in the figure, point A and point D are the starting and ending nodes of the arc segment, respectively, and point B and point C are the two bending vertices, respectively. The bending point is defined as the point where the twisting direction of the line element changes, and the midpoint of the local line segment in the line element where the point is located is used to represent it, that is, Figure 4 The point a1 in the figure is the bending point. The angle between the two vectors is calculated by cross product to determine whether the direction between the two vectors is twisted.

[0057] In a specific example, if Figure 5 As shown, there are 7 roads in the road network, represented by different colors. To segment the roads in the road network into local segments, each road in the road network needs to be segmented according to the above segmentation algorithm. First, all the bending points in the road are determined, and then the processed roads are segmented according to the bending points. The set of local segments segmented by each road can be obtained. By calculating the local segments in the set, the geometric shape information of each road can be obtained, and then the geometric shape information entropy of the entire road network can be obtained.

[0058] Step 203: for any line element of the target road network, determine the geometric shape information of the line element according to the length of each local segment of the line element, the bottom line width of each local segment and the area of ​​each local segment.

[0059] The width of the bottom line of the local fold segment is the Euclidean distance between the starting and ending points of the local fold segment. The area of ​​the local fold segment is the area of ​​the closed area enclosed by the local fold segment and its bottom line.

[0060] It is worth pointing out that all the above parameters can be obtained by extracting each independent road from the road network, then segmenting the local road sections, and calculating them using the existing common related distance calculation algorithm.

[0061] The basic idea of ​​geometric shape information is to use information theory methods to locally quantify the complexity of the geometric shape of a single link in the network. The complexity of the shape can be measured by the concept of information entropy. The larger the entropy, the higher the complexity of the shape and the more information required; the smaller the entropy, the simpler the shape and the less information required. In this embodiment, the method of information theory is used to describe the curvature of the line element, and then the geometric information of the line element is calculated.

[0062] In an exemplary embodiment, step 203 includes the following steps 401 to 403 .

[0063] Step 401: for any local fold segment in the line element, determine the curvature index of the local fold segment according to the length of the local fold segment and the bottom line width of the local fold segment.

[0064] Specifically, the curvature index of the mth local fold segment is determined using the following formula:

[0065]

[0066] in, is the curvature index of the mth local fold segment, is the length of the mth local segment, is the bottom line width of the mth local fold segment, 1≤m≤M.

[0067] Step 402: Determine a bending area ratio index of each local fold segment according to the area of ​​each local fold segment in the line element.

[0068] Specifically, the curvature index of the mth local fold segment is determined using the following formula:

[0069]

[0070] in, is the curvature index of the mth local fold segment, is the area of ​​the mth local segment, It is the average area of ​​all local segments in the line element to which the mth local segment belongs, 1≤m≤M.

[0071] Step 403: Determine the geometric shape information of the line element according to the curvature index and the curvature area ratio index of each local fold segment in the line element.

[0072] Specifically, the following formula is used to determine the geometric shape information of the lth line element:

[0073]

[0074] Among them, I GS (l) is the geometric shape information of the lth line element, M is the number of local segments in the lth line element, is the curvature index of the mth local fold segment, It is the curvature index of the mth local fold segment. When the logarithm base is 2, the unit of information is bit.

[0075] Step 204: Determine the geometric shape information entropy of the target road network according to the geometric shape information amount of each line element of the target road network.

[0076] Geometric information entropy is a holistic measure of the road network based on the geometric information of line elements. It combines the analysis method of information theory to quantify and evaluate the unique geometric properties of the road network from a global perspective. Specifically, geometric information entropy introduces the idea of ​​entropy on the basis of geometric information by examining the geometric complexity and spatial distribution pattern of line elements (such as roads, rivers, etc.), combining the geometric information considered for a single line element, thereby revealing the unique information redundancy and complexity characteristics of the road network structure under the influence of road morphological characteristics. When applied to the traditional Shannon entropy, That is, the geometric shape information entropy of the target road network is determined by the following formula:

[0077]

[0078] Among them, E G is the geometric shape information entropy of the target road network, L is the number of line elements in the target road network, g l is the geometric shape information entropy of the lth line element, I GS (l) is the geometric shape information of the lth line element.

[0079] Step 205 , performing weighted summation on the degree structure entropy of the target road network and the geometric shape information entropy of the target road network to obtain the comprehensive complexity of the target road network.

[0080] This application adopts a weighted method to combine two different measures, namely, degree structure entropy and geometric shape information entropy, to more comprehensively reflect the complexity of the urban road network. By weighting the two measures, the topological structure and geometric shape information can be combined to obtain a more comprehensive complexity measure.

[0081] Specifically, the comprehensive complexity of the target road network is:

[0082]

[0083] Among them, E C is the comprehensive complexity of the target road network, ω is the weight coefficient. Since the dimensions of the two measures in this application are the same, the weight coefficient is set to 0.5, that is, 50% weight is given to the degree structure entropy and the geometric shape information entropy respectively. This weighted method ensures that the two measures have equal importance in the comprehensive results.

[0084] Furthermore, the comprehensive complexity of the target road grid can be applied to the comprehensive processing of the road network map, and the road network can be graded according to the road complexity to achieve thinning and simplification operations.

[0085] Traditional road network complexity measurement methods often ignore the structural differences brought about by changes in road geometry, resulting in the difficulty in fully reflecting the differences between networks when faced with road networks of similar scale but different structures. The road network complexity measurement method provided in this application comprehensively considers the topological structure and road geometry characteristics. In addition to using the traditional node degree structure entropy to measure the topological characteristics of the road, it also introduces the road geometry information as a supplement, including but not limited to the changes in the road's morphological curvature, the curvature area ratio, etc. These characteristics can more carefully reflect the complexity of the road in geometry, and help quantify the complexity of the road network under different network structure layouts. This comprehensive method can more accurately reflect the differences in geometry and structure of the road network, providing more detailed and accurate support for complexity measurement. Therefore, considering geometric features as part of the complexity measurement can provide more comprehensive information support for traffic planners and managers, and help to design and optimize urban road networks more scientifically.

[0086] Based on the same inventive concept, the embodiment of the present application also provides a road network complexity measurement device for implementing the road network complexity measurement method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the one or more embodiments of the road network complexity measurement device provided below can refer to the limitations of the road network complexity measurement method above, and will not be repeated here.

[0087] In an exemplary embodiment, Figure 6 As shown, a road network complexity measurement device is provided, including: a degree structure entropy calculation module 601, an element division module 602, a geometric shape information amount calculation module 603, a geometric shape information entropy determination module 604 and a complexity determination module 605.

[0088] The degree structure entropy calculation module 601 is used to calculate the degree structure entropy of the target road network. The target road network includes a plurality of nodes and the degree of each node.

[0089] The element division module 602 is used to divide the target road network into line elements to obtain a line element set of the target road network. The line element set includes multiple line elements, and each line element includes multiple local segments.

[0090] The geometric shape information volume calculation module 603 is connected to the element division module 602. The geometric shape information volume calculation module 603 is used to determine the geometric shape information volume of any line element of the target road network according to the length of each local segment of the line element, the bottom line width of each local segment and the area of ​​each local segment. The bottom line width of the local segment is the Euclidean distance between the starting and ending points of the local segment. The area of ​​the local segment is the area of ​​the closed area enclosed by the local segment and its bottom line.

[0091] The geometric shape information entropy determination module 604 is connected to the geometric shape information amount calculation module 603, and the geometric shape information entropy determination module 604 is used to determine the geometric shape information entropy of the target road network according to the geometric shape information amount of each line element of the target road network.

[0092] The complexity determination module 605 is connected to the degree structure entropy calculation module 601 and the geometric shape information entropy determination module 604 respectively. The complexity determination module 605 is used to perform weighted summation of the degree structure entropy of the target road network and the geometric shape information entropy of the target road network to obtain the comprehensive complexity of the target road network.

[0093] This application uses the traditional degree structure entropy method to consider the topological structure characteristics of the nodes in the road network, and considers the geometric morphological characteristics of the road, that is, by using the concept of curvature to reflect the comprehensive consideration of the complexity of road morphology, and finally combines the traditional degree structure entropy method for the global measurement of road network complexity. Compared with the traditional degree structure entropy complexity measurement method, this application has higher sensitivity to road networks with different morphological structures, and can more accurately measure the complexity of road networks with different morphological structures, and realize the refined measurement of road network complexity. The effectiveness of the method of this application is tested using comparative experiments on multiple road networks. The results are shown in Tables 1, 2 and Figures 7 to 12 shown.

[0094] It can be concluded from Table 1 that when the topological structure characteristics of the road network nodes are considered alone to measure the complexity of a single road with fewer nodes, the results reflected are the same and single, and for a ring road with only one node, the complexity is 0. However, the use of geometric shape information can measure the complexity of the local characteristics of the road network (i.e., a single road), making the application more sensitive to the complexity of the road network.

[0095] Table 1 Complexity of four common road forms

[0096]

[0097] From the data in Table 2, it can be seen that compared with the traditional method, the geometric shape information entropy makes the difference in the results of the two networks more significant by considering the geometric structure of the road network. The result of the geometric shape information entropy is 0.0124 higher than the traditional degree structure entropy, and the final comprehensive information entropy difference is increased by 0.0109. When evaluating road networks of similar scales, this application can more keenly reflect the differences between road network structures than traditional methods, and more accurately reflect the differences between road networks of similar scales under the influence of different structures. The introduction of geometric structure makes the evaluation results more scientific and accurate, and improves the reliability of the results.

[0098] Table 2 Complexity of different road network structures

[0099]

[0100] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0101] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0102] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0103] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0104] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0105] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0106] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0107] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A road network complexity measurement method, characterized in that: The road network complexity measurement method comprises: Calculating the degree structural entropy of a target road network; the target road network includes a plurality of nodes and the degree of each node; Dividing the target road network into line elements to obtain a line element set of the target road network; the line element set includes a plurality of line elements, and each line element includes a plurality of local segments; For any line element of the target road network, the geometric shape information of the line element is determined according to the length of each local segment of the line element, the bottom line width of each local segment and the area of ​​each local segment; the bottom line width of the local segment is the Euclidean distance between the starting and ending points of the local segment; the area of ​​the local segment is the area of ​​the closed area enclosed by the local segment and its bottom line; Determining the geometric shape information entropy of the target road network according to the geometric shape information amount of each line element of the target road network; The degree structural entropy of the target road network and the geometric information entropy of the target road network are weightedly summed to obtain the comprehensive complexity of the target road network.

2. The road network complexity measurement method according to claim 1, characterized in that: The degree structural entropy of the target road network is calculated using the following formula: Among them, E D is the degree structural entropy of the target road network, N is the number of nodes in the target road network, and p i is the degree structural entropy of the i-th node, degree(i) is the degree of the i-th node, and degree(j) is the degree of the j-th node.

3. The road network complexity measurement method according to claim 1, characterized in that: Dividing the target road network into line elements to obtain a set of line elements of the target road network specifically includes: Dividing the target road network into multiple line elements, each of which represents a road; Identify all bends in each road; According to the bending points in each road, the local segments in each line feature are determined; a local segment is between two adjacent bending points.

4. The road network complexity measurement method according to claim 1, characterized in that: Determining the geometric shape information of the line element according to the length of each local segment of the line element, the bottom line width of each local segment and the area of ​​each local segment, specifically includes: For any local fold segment in the line element, determine the curvature index of the local fold segment according to the length of the local fold segment and the bottom line width of the local fold segment; Determining a bending area ratio index of each local fold segment according to the area of ​​each local fold segment in the line element; The geometric shape information amount of the line element is determined according to the curvature index and the curvature area ratio index of each local fold segment in the line element.

5. The road network complexity measurement method according to claim 4, characterized in that: The curvature index of the mth local fold segment is determined using the following formula: in, is the curvature index of the mth local fold segment, is the length of the mth local segment, is the bottom line width of the mth local segment.

6. The road network complexity measurement method according to claim 4, characterized in that: The curvature index of the mth local fold segment is determined using the following formula: in, is the curvature index of the mth local fold segment, is the area of ​​the mth local segment, It is the average area of ​​all local segments in the line feature to which the mth local segment belongs.

7. The road network complexity measurement method according to claim 4, characterized in that: The following formula is used to determine the geometric shape information of the lth line element: Among them, I GS (l) is the geometric shape information of the lth line element, M is the number of local segments in the lth line element, is the curvature index of the mth local fold segment, is the curvature index of the mth local fold segment.

8. The road network complexity measurement method according to claim 1, characterized in that: The geometric information entropy of the target road network is determined using the following formula: Among them, E G is the geometric shape information entropy of the target road network, L is the number of line elements in the target road network, g l is the geometric shape information entropy of the lth line element, I GS (l) is the geometric shape information of the lth line element.

9. A road network complexity measurement device, applied to the road network complexity measurement method according to any one of claims 1 to 8, characterized in that: The road network complexity measurement device comprises: A degree structure entropy calculation module is used to calculate the degree structure entropy of a target road network; the target road network includes a plurality of nodes and the degree of each node; An element division module is used to divide the target road network into line elements to obtain a line element set of the target road network; the line element set includes multiple line elements, and each line element includes multiple local segments; a geometric shape information amount calculation module, connected to the element division module, for determining the geometric shape information amount of any line element of the target road network according to the length of each local segment of the line element, the bottom line width of each local segment and the area of ​​each local segment; the bottom line width of the local segment is the Euclidean distance between the starting and ending points of the local segment; the area of ​​the local segment is the area of ​​the closed area enclosed by the local segment and its bottom line; A geometric shape information entropy determination module, connected to the geometric shape information amount calculation module, for determining the geometric shape information entropy of the target road network according to the geometric shape information amount of each line element of the target road network; The complexity determination module is connected to the degree structure entropy calculation module and the geometric shape information entropy determination module respectively, and is used to perform weighted summation of the degree structure entropy of the target road network and the geometric shape information entropy of the target road network to obtain the comprehensive complexity of the target road network.

10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the road network complexity measurement method according to any one of claims 1 to 8.

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