Methods, apparatuses, devices, and media for determining local connectivity of a network

Abstract

This specification discloses a method, apparatus, device, and medium for determining the local connectivity of a network. It relates to the field of network communication technology. The method includes: acquiring connection information of network devices in an actual network system; constructing a network connectivity graph model based on the connection information of the network devices in the actual network system; constructing a first set of all nodes of interest that are connected based on the network connectivity graph model; calculating a first probability that the nodes of interest are connected through the first set; and summing the first probabilities corresponding to each of the first sets to obtain the local connectivity of the network. This invention can accurately determine the local connectivity of a network of interest, thereby providing a more accurate assessment of the availability of the actual network system.

Description

Technical Field

[0001] This application relates to the field of network communication technology, and in particular to a method, apparatus, device and medium for determining the local connectivity of a network. Background Technology

[0002] In the context of intelligent driving, communication is required between connected vehicles, between connected vehicles and roadside sensing devices (such as traffic cameras, traffic lights, roadside units, etc.), between connected vehicles and the cloud control platform, and between roadside sensing devices and the cloud control platform. Specifically, communication connections can be established through carrier networks, switches, wireless controllers, wireless access points, etc. These network devices and the network links connecting them constitute the actual network system.

[0003] For the monitoring and control of connected vehicles, the focus is often not on the overall connectivity of the actual network system, but rather on the connectivity of one or more local components. Certain network devices under observation constitute a local part of the actual network system. In other words, the probability of these network devices connecting to each other is called the local connectivity of the network. In practice, the connectivity of certain network devices must exceed a first preset value to meet communication requirements, thereby ensuring timely control of connected vehicles and thus recognizing the availability of the actual network system. When the availability of the actual network system is deemed insufficient, improvements are made, such as adding network links, until the actual network system meets the communication requirements.

[0004] In existing technologies, the overall connectivity of a real-world network system can be determined. By ensuring that the overall connectivity is not less than a first preset value, the connectivity of certain network devices can be guaranteed to meet communication requirements. However, in many cases, even if the local connectivity of a real-world network system is not less than the first preset value, its overall connectivity may be less than the first preset value. In other words, it is not necessary to ensure that the overall connectivity is not less than the first preset value to meet communication requirements. Therefore, overall connectivity cannot accurately reflect local connectivity. This can easily lead to incorrect assessments of the availability of the real-world network system, thereby increasing unnecessary resource consumption due to adding network links.

[0005] Therefore, determining the local connectivity of a network has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] The embodiments of this specification provide a method, apparatus, device, and medium for determining the local connectivity of a network, which can accurately and quickly determine the local connectivity of a network.

[0007] To solve the above-mentioned technical problems, the embodiments in this specification are implemented as follows:

[0008] This specification provides an embodiment of a method for determining the local connectivity of a network, comprising:

[0009] Obtain connection information of network devices in a real network system;

[0010] Based on the connection information of network devices in the actual network system, a network connectivity graph model is constructed. The network connectivity graph model includes nodes, edges, and edge weights. The nodes represent the network devices, and the edges represent the network links that connect the network devices. The edge weights represent the probability that the network links enable the network devices to connect.

[0011] Based on the network connectivity graph model, a first set of all nodes of interest that are connected is constructed; the nodes of interest are the nodes corresponding to local network devices in the actual network system; each of the first sets is obtained by selecting at least one edge from the network connectivity graph model and combining them.

[0012] Calculate the first probability that the node of interest is connected through the first set; the value of the first probability is the product of the probabilities of all edges in the network connectivity graph model; if the edge exists in the first set, the probability of the edge is the weight of the edge, and if the edge does not exist in the first set, the probability of the edge is 1 - the weight of the edge.

[0013] The local connectivity of the network is obtained by summing the first probabilities corresponding to each of the first sets.

[0014] This specification provides an embodiment of a computer device, comprising:

[0015] The first acquisition module is used to acquire connection information of network devices in the actual network system;

[0016] The first construction module is used to construct a network connectivity graph model based on the connection information of network devices in the actual network system. The network connectivity graph model includes nodes, edges, and edge weights. The nodes represent the network devices, and the edges represent the network links that connect the network devices. The edge weights represent the probability that the network links enable the network devices to connect.

[0017] The second construction module is used to construct a first set of all nodes of interest that are connected based on the network connectivity graph model; the nodes of interest are the nodes corresponding to local network devices in the actual network system; each first set is obtained by selecting at least one edge from the network connectivity graph model and combining them.

[0018] The first calculation module calculates the first probability that the node of interest is connected through the first set; the value of the first probability is the product of the probabilities of all edges in the network connectivity graph model; if the edge exists in the first set, the probability of the edge is the weight of the edge, and if the edge does not exist in the first set, the probability of the edge is 1 - the weight of the edge.

[0019] The second calculation module is used to add the first probabilities corresponding to each of the first sets to obtain the local connectivity of the network.

[0020] An embodiment of this specification provides a computer device, comprising: a processor, and a memory communicatively connected to the processor, wherein the memory stores computer-executed instructions;

[0021] The processor executes computer execution instructions stored in the memory to implement the steps of any of the methods described above.

[0022] This specification provides an embodiment of a computer-readable storage medium storing computer-executable instructions that, when executed, cause a computer to perform the steps of any of the methods described above.

[0023] At least one embodiment provided in this specification can achieve the following beneficial effects: obtaining connection information of network connection devices in a real network system; establishing a network connectivity graph model through the connection information of network devices; the connectivity probabilities of each communication link are independent events; firstly, finding all first sets that make the nodes of interest connected, calculating the first probability corresponding to each first set, and then adding the first probabilities together to obtain the probability that any two nodes of interest are connected, which is the local connectivity of the network. In this way, the local connectivity of the network of interest can be accurately determined, thereby making a more accurate assessment of the availability of the actual network system. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating a method for determining the local connectivity of a network, provided as an embodiment of this specification;

[0026] Figure 2This is a schematic diagram of a network connectivity graph model provided as an embodiment of this specification.

[0027] Figure 3 This is a schematic diagram of the first network connectivity graph model corresponding to the first alternative addition scheme provided in the embodiments of this specification.

[0028] Figure 4 This is a schematic diagram of the first network connectivity graph model corresponding to the second alternative addition scheme provided in the embodiments of this specification.

[0029] Figure 5 This is a schematic diagram of the first network connectivity graph model corresponding to the third alternative addition scheme provided in the embodiments of this specification.

[0030] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this specification;

[0031] Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this specification. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of one or more embodiments of this specification clearer, the technical solutions of one or more embodiments of this specification will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of them. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of one or more embodiments of this specification.

[0033] The technical solutions provided in the various embodiments of this specification are described in detail below with reference to the accompanying drawings.

[0034] Figure 1 This is a flowchart illustrating a method for determining the local connectivity of a network, provided in an embodiment of this specification. From a programming perspective, the entity executing this process can be a server or a client, or an application running on the server or client. Figure 1 As shown, the process includes the following steps:

[0035] Step 101: Obtain the connection information of network devices in the actual network system.

[0036] In intelligent driving, network devices refer to the following: devices used for communication in the regional cloud (data center), devices in the operator's network, switches, wireless controllers, wireless access points, connected vehicles, and devices deployed in the physical edge cloud on the roadside (such as cameras, radar, traffic lights, etc.). These network devices constitute the actual network system. In this step, the connection information of the network devices refers to how the network devices in the actual network system are connected through network links. For example, which two network devices communicate directly through the network link, which network devices require relay from other network devices to communicate with the network devices they need to connect to, and the probability of two network devices connecting through a network link.

[0037] Step 103: Based on the connection information of network devices in the actual network system, construct a network connectivity graph model; the network connectivity graph model includes nodes, edges, and edge weights, the nodes are used to represent the network devices, the edges are used to represent the network links connecting the network devices, and the edge weights are used to represent the probability that the network links enable the network devices to connect.

[0038] After obtaining the connection information of network devices in a real-world network system, a network connectivity graph model is constructed based on this information. In this model, network devices are represented by nodes, with one node corresponding to one network device. If two network devices are directly connected via a network link, their corresponding nodes are connected by an edge; the edge weight represents the probability that the two network devices are connected via the network link. In a real-world network system, network links can be either wireless or wired. In some cases, certain network devices and links can be simplified to edges. For example, two network devices may need to use certain network links and devices to communicate, but these are not within the scope of this study. To simplify the network connectivity graph model, they can be represented by an edge, with the edge weight representing the probability that the two network devices are connected via these network links and devices.

[0039] See Figure 2 , Figure 2 This is a schematic diagram of a network connectivity graph model. Nodes A, B, C, and D represent the corresponding network devices. Edges a, b, and c represent the connections between network devices A and B via network link a, B and C via network link b, and C and D via network link c, respectively. P(a), P(b), and P(c) are the weights of edge a, b, and c, representing the probabilities that network links a, b, and c are connected.

[0040] Step 105: Based on the network connectivity graph model, construct a first set of all nodes of interest that have connectivity; the nodes of interest are the nodes corresponding to the local network devices of the actual network system; each first set is obtained by selecting at least one edge from the network connectivity graph model and combining them.

[0041] After constructing the network connectivity graph model, a first set is built based on this model. This first set consists of combinations of edges, formed by selecting one or more edges from the network connectivity graph model. Each edge in the first set enables connectivity to the nodes of interest. In practical applications, due to certain needs, the overall connectivity of the actual network system is not the primary concern; rather, the focus may be on the local connectivity of the actual network system, specifically the connectivity of certain devices. These devices constitute a part of the network, and the nodes corresponding to these devices are the nodes of interest.

[0042] See Figure 2 In this context, BCD nodes are nodes of interest, and the network devices corresponding to BCD nodes constitute the local parts of the network. When constructing the first set, there are two first sets that enable connectivity between BCD nodes: {ab} and {ab c}.

[0043] Step 107: Calculate the first probability that the node of interest is connected through the first set; the value of the first probability is the product of the probabilities of all edges in the network connectivity graph model; if the edge exists in the first set, the probability of the edge is the weight of the edge, and if the edge does not exist in the first set, the probability of the edge is 1 - the weight of the edge.

[0044] After constructing all the first sets, calculate a first probability for each first set, which is the product of the probabilities of all edges; Figure 2 In the network connectivity model shown, all edges are represented as a, b, and c. The probability of each edge (a, b, c) needs to be calculated. For the first set {bc}, edge a is not in this set, so its probability is 1-P(a); edge b is in this set, so its probability is P(b); edge c is in this set, so its probability is P(c). The first probability corresponding to this first set is: P(S1) = (1-P(a))*P(b)*P(c).

[0045] Similarly, the first probability corresponding to the first set {abc} can be calculated as: P(S2) = P(a) * P(b) * P(c).

[0046] Step 109: Add the first probabilities corresponding to each of the first sets to obtain the local connectivity of the network.

[0047] After calculating the first probability corresponding to each first set, they are added together to obtain the local connectivity of the network: P = P(S1) + P(S2) = (1 - P(a)) * P(b) * P(c) + P(a) * P(b) * P(c).

[0048] Figure 1 The method described above obtains the connection information of network connection devices in the actual network system; a network connectivity graph model is established using the connection information of network devices; the connectivity probabilities of each communication link are independent events; first, find all the first sets that make the nodes of interest connected, calculate the first probability corresponding to each first set, and then add up the first probabilities to obtain the probability that any two nodes of interest are connected, which is the local connectivity of the network.

[0049] In this way, the connectivity of the local network of interest can be accurately determined, leading to a more accurate assessment of the availability of the actual network system. For a real-world network system, different localities require different levels of connectivity; for example, the connectivity requirement for the first locality might be a first threshold, while for the second locality it might be a second threshold, in order to meet the actual communication needs between network devices. In existing technologies, the overall connectivity of the actual network system needs to be calculated during the evaluation process. The overall connectivity must be no less than both the first and second thresholds to be considered sufficient to meet the communication requirements. However… Figure 1 The method described above can accurately determine the connectivity of the first and second local areas, and then compare them with the first and second thresholds respectively to make a more accurate assessment. This allows for a more accurate judgment of the availability of real-world network systems.

[0050] Optionally, the method further includes:

[0051] If the local connectivity of the network is less than a first preset value, then information on multiple candidate schemes for adding network links is obtained;

[0052] Based on the candidate solution information, edges corresponding to the added network links are added to the network connectivity model to generate a first network connectivity model;

[0053] Based on the first network connectivity model, the local connectivity of the network of the candidate scheme is determined so as to obtain a first candidate scheme whose local connectivity is not less than the first preset value.

[0054] Select the option with the lowest cost from the first candidate options as the added option;

[0055] According to the aforementioned addition scheme, network links are added to the actual network system.

[0056] When the local connectivity of the network is less than a first preset value, it indicates that the actual network system does not meet the communication requirements. At this point, multiple candidate solutions can be created. These solutions include adding network links between network devices, such as adding wired or wireless connections. Alternatively, the connectivity probability of existing network links can be improved, for example, by replacing network cables with those offering higher reliability and stronger communication capabilities. Before actually adding network links, multiple candidate solutions are created. After obtaining information on these candidate solutions, edges are added to the network connectivity model based on this information to generate the first network connectivity model.

[0057] See Figure 3 , Figure 4 and Figure 5 , Figure 3 Add the first network connectivity graph model corresponding to the first candidate scheme. Figure 4 Add the first network connectivity graph model corresponding to the second candidate scheme. Figure 5 The first network connectivity graph model corresponding to the third candidate addition scheme is used. Based on each first network connectivity model, the local connectivity of the network for each candidate scheme is determined. The scheme with local connectivity not less than a first preset value is selected as the first candidate scheme. Assuming the connectivity of the first and third schemes is not less than the first preset value, while the connectivity of the second scheme is less than the first preset value, then the first and third schemes are the first candidate schemes. The scheme with the lowest cost among the first and third schemes is selected as the addition scheme. The cost can be determined based on indicators such as the number of network links added and the length of the added network links. Assuming other conditions are the same, the first scheme adds one network link, while the third scheme adds two network links; in this case, the first scheme has a lower cost. After determining the addition scheme, network links are added to the actual network system according to the addition scheme.

[0058] In this way, it is easier and more accurate to determine whether the candidate solutions meet the communication requirements, and to select the least costly addition solution to improve the actual network system.

[0059] Optionally, the method for determining the weight of the edge specifically includes:

[0060] Obtain the nominal information of the connected devices in the network link;

[0061] The weights of the edges corresponding to the network links are determined based on the nominal information.

[0062] The edge weights represent the probability of a network link being connected. Network links can be wired or wireless. For wired connections, the connecting device in the network link is the network cable. The connection weight can be determined by the communication capabilities indicated on the network cable. For wireless connections, the connection weight can be determined based on parameters such as the packet loss rate indicated on the wireless device.

[0063] Optionally, the method for determining the weight of the edge specifically includes:

[0064] Obtain the first number of test packets sent by the first network device to the second network device through the first network link; the first network link is the network link connecting the first network device and the second network device;

[0065] Obtain the second number of test packets received by the second network device;

[0066] Based on the first quantity and the second quantity, the weight of the edge corresponding to the first network link is determined.

[0067] Another method for determining edge weights: For a given edge connecting two nodes, assuming the two nodes correspond to a first network device and a second network device respectively, and the edge corresponds to a first network link, then the first network device sends test packets to the second network device through the first network link. The number of test packets is called the first quantity. Then, the number of test packets received by the second network device is obtained, which is called the second quantity. The weight of the edge can be calculated by dividing the second quantity by the first quantity.

[0068] Optionally, constructing a first set of all nodes of interest with connectivity based on the network connectivity model specifically includes:

[0069] The edges in the network connected graph model are fully combined, and each combination forms a second set.

[0070] Determine whether the second set can enable the nodes of interest to have connectivity, and obtain the first determination result;

[0071] If the first determination result indicates that the second set can enable the nodes of interest to have connectivity, then the second set is determined to be the first set.

[0072] One method for constructing the first set is to first perform a full combination of the edges in the network connected graph model. (Refer to...) Figure 2 , Figure 2The edges in the set can be combined to obtain the second set {a}{b}{c}{ab}{ac}{bc}{abc}. This second set can also be represented as {a 00}{0 b 0}{0 0 c}{ab 0}{a 0 c}{0 bc}{abc}. In this case, when determining the probability of an edge in a given second set, if the position corresponding to the edge is 0, the probability is 1 - the edge weight; if the position corresponding to the edge is itself, the probability is the edge weight. Taking {0 bc} as an example, edge a corresponds to position 0, so the probability of edge a is 1 - P(a), and edge b corresponds to position b, so the probability of edge b is P(b). Then, it is determined whether each second set can make the node of interest connected. {a}{b}{c}{ab}{ac} cannot make the node of interest connected and are therefore excluded. The remaining second set {bc}{abc} is determined as the first set.

[0073] Optionally, determining whether the second set can enable the nodes of interest to have connectivity specifically includes:

[0074] Starting from one of the nodes of interest, and considering other nodes connected to edges in the second set as candidate nodes and edges in the second set as candidate edges, a depth-first search is performed.

[0075] After the depth-first search is completed, it is determined whether any nodes of interest have not been visited, and a second determination result is obtained;

[0076] If the second determination result indicates that a node of interest has not been visited, then it is determined that the second set cannot enable the node of interest to have connectivity.

[0077] See Figure 2 Let's take determining whether the second set {ab} enables connectivity between nodes of interest as an example. Starting from node C, a depth-first search is performed. Node C will be visited along edge b to node B, and node B will be visited along edge b to node A. After the depth-first search, node D of interest has not been visited, indicating that the second set {ab} cannot enable connectivity between nodes of interest.

[0078] Optionally, determining whether the second set can enable the nodes of interest to have connectivity specifically includes:

[0079] The root node of each of the nodes is determined to be the node itself;

[0080] Traverse the edges in the second set; if the root nodes of the two nodes connected by the edge have not been redefined, then redefined the root nodes of the two nodes as the root node of any one of the nodes; if the root node of any one of the two nodes connected by the edge has been redefined, then redefined the root nodes of the two nodes connected by the edge as the root node of that node.

[0081] After traversing all edges in the second set, determine the first number of root nodes of the nodes of interest.

[0082] If the first quantity is not 1, then the second set cannot enable the nodes of interest to have connectivity.

[0083] See Figure 2 Let's take determining whether the second set {ab} enables connectivity between nodes of interest as an example. The root nodes of nodes ABCD are defined as themselves, ABCD and ABCD respectively. Then, the edges in the second set {ab} are traversed. First, edge a is processed. Edge a connects nodes A and B. The root nodes of A and B have not been redefined, so the root nodes of A and B are set to A (or B). Then, edge b connects nodes B and C. Since the root node of node B has been redefined, it is now A (B). Therefore, the root nodes of B and C are redefined as A (B). After traversal, the root nodes of nodes B and C are both A, and the root node of node D is itself, D. The root nodes of the nodes of interest are A and D, which is 2, not 1. Therefore, the second set cannot enable connectivity between the nodes of interest.

[0084] Based on a general inventive concept, embodiments of this specification also provide a computer device corresponding to the above-described method. Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this specification, such as... Figure 6 As shown, the device includes:

[0085] The first acquisition module 601 is used to acquire connection information of network devices in the actual network system;

[0086] The first construction module 602 is used to construct a network connectivity graph model based on the connection information of network devices in the actual network system. The network connectivity graph model includes nodes, edges, and edge weights. The nodes represent the network devices, and the edges represent the network links that connect the network devices. The edge weights represent the probability that the network links enable the network devices to connect.

[0087] The second construction module 603 is used to construct a first set of all nodes of interest that are connected based on the network connectivity graph model; the nodes of interest are the nodes corresponding to local network devices in the actual network system; each first set is obtained by selecting at least one edge from the network connectivity graph model and combining them.

[0088] The first calculation module 604 calculates the first probability that the node of interest is connected through the first set; the value of the first probability is the product of the probabilities of all edges in the network connectivity graph model; if the edge exists in the first set, the probability of the edge is the weight of the edge, and if the edge does not exist in the first set, the probability of the edge is 1 - the weight of the edge.

[0089] The second calculation module 605 is used to add the first probabilities corresponding to each of the first sets to obtain the local connectivity of the network.

[0090] Based on the same idea, this specification also provides devices corresponding to the above methods in its embodiments. Figure 7 This is a schematic diagram of the structure of a computer device provided as an embodiment of this specification. Figure 7 As shown, the device 700 may include: a processor 710, and a memory 730 communicatively connected to the processor; wherein the memory 730 stores computer execution instructions 720;

[0091] The processor executes computer execution instructions stored in the memory to implement the steps of any of the methods described above.

[0092] This specification also provides a computer-readable storage medium storing computer-executable instructions that, when executed, cause a computer to perform the steps of any of the methods described above.

[0093] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, for... Figure 7 As the computer device shown is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0094] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program a digital system themselves to "integrate" it onto a PLD, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0095] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the memory's control logic. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, ASICs, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means that can be included within it to implement various functions can also be considered as structures within the hardware component. Alternatively, the means that can be used to implement various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0096] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0097] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0098] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (which may include, but are not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0099] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce a machine that can be used to implement the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0100] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture that may include instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that can be used to implement a process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0102] In a typical configuration, a computing device may include one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0103] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0104] Computer-readable media can include both permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media can include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital character versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media cannot include transient computer-readable media, such as modulated data signals and carrier waves.

[0105] It should also be noted that the terms "may include," "comprise," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that may include a list of elements may include not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "may include a…" does not exclude the presence of other identical elements in the process, method, article, or apparatus that may include said element.

[0106] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (which may include, but are not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] This application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules can include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0108] The above description is merely an embodiment of this application and should not be construed as limiting the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for determining the local connectivity of a network, characterized in that, include: Obtain connection information of network devices in a real network system; Based on the connection information of network devices in the actual network system, a network connectivity graph model is constructed. The network connectivity graph model includes nodes, edges, and edge weights. The nodes represent the network devices, and the edges represent the network links that connect the network devices. The edge weights represent the probability that the network links enable the network devices to connect. Based on the network connectivity graph model, a first set of all nodes of interest that are connected is constructed; the nodes of interest are the nodes corresponding to local network devices in the actual network system; each of the first sets is obtained by selecting at least one edge from the network connectivity graph model and combining them. Calculate the first probability that the node of interest is connected through the first set; the value of the first probability is the product of the probabilities of all edges in the network connectivity graph model; if the edge exists in the first set, the probability of the edge is the weight of the edge, and if the edge does not exist in the first set, the probability of the edge is 1 - the weight of the edge. The local connectivity of the network is obtained by summing the first probabilities corresponding to each of the first sets.

2. The method according to claim 1, characterized in that, The method further includes: If the local connectivity of the network is less than a first preset value, then information on multiple candidate schemes for adding network links is obtained; Based on the candidate solution information, edges corresponding to the added network links are added to the network connectivity model to generate a first network connectivity model; Based on the first network connectivity model, the local connectivity of the network of the candidate scheme is determined so as to obtain a first candidate scheme whose local connectivity is not less than the first preset value. Select the option with the lowest cost from the first candidate options as the added option; According to the aforementioned addition scheme, network links are added to the actual network system.

3. The method according to claim 1, characterized in that, The method for determining the weight of the edge specifically includes: Obtain the nominal information of the connected devices in the network link; The weights of the edges corresponding to the network links are determined based on the nominal information.

4. The method according to claim 1, characterized in that, The method for determining the weight of the edge specifically includes: Obtain the first number of test packets sent by the first network device to the second network device through the first network link; the first network link is the network link connecting the first network device and the second network device; Obtain the second number of test packets received by the second network device; Based on the first quantity and the second quantity, the weight of the edge corresponding to the first network link is determined.

5. The method according to claim 1, characterized in that, Based on the network connectivity model, a first set of all nodes of interest that are connected is constructed, specifically including: The edges in the network connected graph model are fully combined, and each combination forms a second set. Determine whether the second set can enable the nodes of interest to have connectivity, and obtain the first determination result; If the first determination result indicates that the second set can enable the nodes of interest to have connectivity, then the second set is determined to be the first set.

6. The method according to claim 5, characterized in that, The determination of whether the second set can enable the nodes of interest to have connectivity specifically includes: Starting from one of the nodes of interest, and considering other nodes connected to edges in the second set as candidate nodes and edges in the second set as candidate edges, a depth-first search is performed. After the depth-first search is completed, it is determined whether any nodes of interest have not been visited, and a second determination result is obtained; If the second determination result indicates that a node of interest has not been visited, then it is determined that the second set cannot enable the node of interest to have connectivity.

7. The method according to claim 5, characterized in that, The determination of whether the second set can enable the nodes of interest to have connectivity specifically includes: The root node of each node connected by an edge in the second set is determined to be the node itself; Traverse the edges in the second set; if the root nodes of the two nodes connected by the edge have not been redefined, then redefined the root nodes of the two nodes as the root node of any one of the nodes; if the root node of any one of the two nodes connected by the edge has been redefined, then redefined the root nodes of the two nodes connected by the edge as the root node of that node. After traversing all edges in the second set, determine the first number of root nodes of the nodes of interest. If the first quantity is not 1, then the second set cannot enable the nodes of interest to have connectivity.

8. A computer device, characterized in that, include: The first acquisition module is used to acquire connection information of network devices in the actual network system; The first construction module is used to construct a network connectivity graph model based on the connection information of network devices in the actual network system. The network connectivity graph model includes nodes, edges, and edge weights. The nodes represent the network devices, and the edges represent the network links that connect the network devices. The edge weights represent the probability that the network links enable the network devices to connect. The second construction module is used to construct a first set of all nodes of interest that are connected based on the network connectivity graph model; the nodes of interest are the nodes corresponding to local network devices in the actual network system; each first set is obtained by selecting at least one edge from the network connectivity graph model and combining them. The first calculation module calculates the first probability that the node of interest is connected through the first set; the value of the first probability is the product of the probabilities of all edges in the network connectivity graph model; if the edge exists in the first set, the probability of the edge is the weight of the edge, and if the edge does not exist in the first set, the probability of the edge is 1 - the weight of the edge. The second calculation module is used to add the first probabilities corresponding to each of the first sets to obtain the local connectivity of the network.

9. A computer device, comprising: A processor, and a memory communicatively connected to the processor, characterized in that the memory stores computer-executable instructions; The processor executes computer execution instructions stored in the memory to implement the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that, When the computer execution instructions are executed, the computer performs the steps of the method as described in any one of claims 1 to 7.

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