Cloud network space topology dynamic surveying and mapping updating method, device, equipment, medium and program product

Abstract

The invention relates to the technical field of network space surveying and mapping, in particular to a cloud network space topology dynamic surveying and mapping updating method, device and equipment, a medium and a program product. The method comprises the following steps: acquiring operation parameters of local equipment through a terminal node, and calculating real-time confidence of an associated node based on the operation parameters; acquiring data reported by each terminal node, generating a node early warning parameter based on the real-time confidence coefficient, and identifying a to-be-updated path set by using the node early warning parameter; extracting path features of each path in the to-be-updated path set, and inputting the path features to a pre-trained neural network model to predict an updating mode of the path; and generating a control instruction according to the predicted updating mode, issuing the control instruction to the corresponding terminal node, and executing the updating of the terminal node to realize the updating of the network space topological graph. By adopting the method, the timeliness of network space surveying and mapping updating can be improved.

Description

Technical Field

[0001] This application relates to the field of cyberspace mapping technology, and in particular to a method, apparatus, equipment, medium, and program product for dynamic mapping and updating of cloud cyberspace topology. Background Technology

[0002] With the rapid development of information technology, cyberspace has become a digital environment parallel to the physical world. Cyberspace refers to the virtual space composed of the Internet, communication systems, computer systems, various terminals, and the data transmitted, stored, and processed within them. It includes not only tangible network equipment and infrastructure but also intangible logical connections, services, and applications, serving as a crucial support for the operation of modern society.

[0003] In recent years, with the widespread application of technologies such as cloud computing, containerization, and microservice architecture, the "cloud-native" concept has gradually become an important direction for the evolution of cyberspace. Cloud-native cyberspace refers to a network environment built and run on cloud-native technologies, whose core characteristics include elastic scaling, dynamic orchestration, high availability, and high automation. In cloud-native cyberspace, resources are dynamically allocated and managed in the form of services, applications are typically deployed in containers using microservice architectures, and efficient communication and governance are achieved through technologies such as service meshes. This environment greatly improves resource utilization efficiency and the flexibility of business deployment, but its dynamic, distributed, and heterogeneous characteristics also make the form and behavior of cyberspace more complex and difficult to control.

[0004] To effectively understand, manage, and defend cyberspace, especially cloud-native cyberspace, cyberspace mapping technology has emerged. Cyberspace mapping refers to the process of identifying, modeling, and visualizing entities (such as IP addresses, domain names, network devices, services, and applications), topological relationships, resource status, and security attributes in cyberspace through active or passive detection, data collection, and analysis. Its purpose is to construct an accurate, real-time, multi-dimensional, and understandable cyberspace map.

[0005] In related technologies, cyberspace mapping is typically achieved through a combination of fundamental techniques. One basic and widely used method is active probing. Its core is sending specific probe packets to a target network area and inferring the existence and attributes of network entities based on the responses. Another common method is passive traffic analysis. This method does not actively inject traffic into the network; instead, it listens to the traffic of key network nodes (such as gateways and mirror ports) and analyzes information such as source / destination IPs, ports, protocol types, DNS lookup records, and TLS certificates in the communication packets to infer active hosts, services, and their communication relationships within the network.

[0006] However, current methods for updating cyberspace mapping have the following technical problems:

[0007] The existing network-based mapping technology has a significant lag in updates and needs to be optimized. Summary of the Invention

[0008] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for dynamic mapping and updating of cloud network space topology, which can improve the timeliness of network space mapping updates, in response to the above-mentioned technical problems.

[0009] Firstly, this application provides a method for dynamic mapping and updating of cloud network spatial topology. The method includes:

[0010] The terminal node collects the operating parameters of the local device and calculates the real-time confidence level of the associated node based on the operating parameters;

[0011] The data reported by each terminal node is obtained, node warning parameters are generated based on the real-time confidence level, and the set of paths to be updated is identified using the node warning parameters.

[0012] For each path in the set of paths to be updated, extract path features and input them into a pre-trained neural network model to predict how the path will be updated.

[0013] Control commands are generated based on the predicted update method and sent to the corresponding terminal nodes to execute the updates of the terminal nodes, thereby updating the network space topology.

[0014] In one embodiment, the step of collecting the operating parameters of the local device through the terminal node and calculating the real-time confidence level of the associated node based on the operating parameters includes:

[0015] The operating parameters are compared with a preset standard parameter range;

[0016] If any of the operating parameters exceeds the range of the standard parameters, the real-time confidence level is determined to be 0; otherwise, the confidence level is determined based on the degree of deviation of all real-time operating parameters from the center value of the real-time standard parameter range, and the confidence level is between 0 and 1.

[0017] In one embodiment, the steps of acquiring the data reported by each of the terminal nodes, generating node warning parameters based on the real-time confidence level, and identifying the set of paths to be updated using the node warning parameters include:

[0018] If the real-time confidence level of the terminal node is higher than a preset threshold, then the current operating parameter value of the terminal node is used as the corresponding node warning parameter.

[0019] If the real-time confidence level of the terminal node is less than or equal to the preset threshold, then the historical reliable data of the terminal node is obtained from the cloud, and a parameter value is selected from the historical reliable data as the corresponding node warning parameter based on the matching degree between the current environment and the historical environment.

[0020] In one embodiment, the steps of acquiring the data reported by each of the terminal nodes, generating node warning parameters based on the real-time confidence level, and identifying the set of paths to be updated using the node warning parameters include:

[0021] Construct a graph structure from all the terminal nodes in the target network space and the corresponding early warning parameters;

[0022] Traverse the terminal nodes in the graph structure and calculate the difference between the current warning parameters and historical warning parameters of adjacent nodes;

[0023] If the difference exceeds a preset tolerance, the corresponding path between the terminal nodes is added to the set of paths to be updated.

[0024] In one embodiment, extracting path features for each path in the set of paths to be updated and inputting them into a pre-trained neural network model to predict the update method of the path includes:

[0025] Determine the probability value of the update method output by the neural network model;

[0026] The update method corresponding to the highest probability value is selected as the update method for the final output.

[0027] In one embodiment, after generating control commands based on the predicted update method and issuing them to the corresponding terminal nodes, and executing the updates of the terminal nodes to update the network space topology, the method further includes:

[0028] Obtain the confirmation information returned by the terminal node after executing the control command;

[0029] The edge server updates the local topology map based on the confirmation information and synchronizes the incremental update information to the cloud server.

[0030] Secondly, this application also provides a cloud network spatial topology dynamic mapping and updating device. The device includes:

[0031] The data acquisition module is used to collect the operating parameters of the local device through the terminal node, and calculate the real-time confidence of the associated node based on the operating parameters;

[0032] The early warning parameter module is used to acquire the data reported by each terminal node, generate node early warning parameters based on the real-time confidence level, and use the node early warning parameters to identify the set of paths to be updated.

[0033] The update prediction module is used to extract path features for each path in the set of paths to be updated and input them into a pre-trained neural network model to predict how the path will be updated.

[0034] The topology update module is used to generate control commands based on the predicted update method and send them to the corresponding terminal nodes to execute the updates of the terminal nodes in order to update the network space topology map.

[0035] In one embodiment, the data acquisition module includes:

[0036] The comparison module is used to compare the operating parameters with a preset standard parameter range;

[0037] The confidence module is used to determine that the real-time confidence is 0 if any of the operating parameters exceeds the range of the standard parameters; otherwise, it determines the confidence value based on the degree of deviation of all real-time operating parameters from the center value of the real-time standard parameter range, wherein the confidence value is between 0 and 1.

[0038] In one embodiment, the warning parameter module includes:

[0039] The first confidence module is used to take the current operating parameter value of the terminal node as the corresponding node warning parameter if the real-time confidence of the terminal node is higher than a preset threshold.

[0040] The second confidence module is used to obtain historical reliable data of the terminal node from the cloud if the real-time confidence of the terminal node is less than or equal to the preset threshold, and select a parameter value from the historical reliable data as the corresponding node warning parameter based on the matching degree between the current environment and the historical environment.

[0041] In one embodiment, the warning parameter module includes:

[0042] The graph structure module is used to construct a graph structure from all the terminal nodes and the corresponding early warning parameters in the target network space.

[0043] The difference comparison module is used to traverse the terminal nodes in the graph structure and calculate the difference between the current warning parameter and the historical warning parameter of the adjacent nodes.

[0044] The path monitoring module is used to add the corresponding path between the terminal nodes to the set of paths to be updated if the difference exceeds a preset tolerance.

[0045] In one embodiment, the update prediction module includes:

[0046] The probability value module is used to determine the probability value of the update method output by the neural network model;

[0047] The method selection module is used to select the update method corresponding to the maximum probability value as the update method of the final output.

[0048] In one embodiment, after the topology update module, the following is further included:

[0049] The node confirmation module is used to obtain confirmation information returned by the terminal node after executing the control command;

[0050] The update information module is used to update the local topology map on the edge server based on the confirmation information and synchronize the incremental update information to the cloud server.

[0051] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of a cloud network spatial topology dynamic mapping and updating method as described in any embodiment of the first aspect.

[0052] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of a cloud network spatial topology dynamic mapping and updating method as described in any embodiment of the first aspect.

[0053] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of a cloud network spatial topology dynamic mapping and updating method as described in any embodiment of the first aspect.

[0054] The aforementioned cloud network spatial topology dynamic mapping and updating method, apparatus, computer equipment, storage medium, and computer program product, derived from the technical features in the embodiments, can achieve the following beneficial effects to address the technical problems in the background art:

[0055] This application provides a method for dynamic mapping and updating of cloud network spatial topology, comprising: collecting operating parameters of local devices through terminal nodes and calculating the real-time confidence of associated nodes based on the operating parameters; acquiring data reported by each terminal node, generating node warning parameters based on the real-time confidence, and identifying a set of paths to be updated using the node warning parameters; extracting path features for each path in the set of paths to be updated and inputting them into a pre-trained neural network model to predict the update method of the path; generating control commands according to the predicted update method and issuing them to the corresponding terminal nodes to execute the update of the terminal nodes to realize the update of the network spatial topology map. In implementation, by constructing an automated closed-loop process from data perception to intelligent decision-making, a fundamental transformation of cloud network topology from static and lagging to dynamic and real-time is achieved. This method first introduces real-time confidence calculation on the terminal side, providing accurate data quality basis for subsequent judgment and improving data reliability from the source. Subsequently, by dynamically generating node warning parameters based on confidence levels and intelligently identifying the set of paths to be updated, the traditional "periodic full scan" is transformed into "event-driven incremental update," significantly reducing the scope of data processing. Then, a pre-trained neural network model is used to predict the update method for critical paths, achieving a leap from simple threshold judgment to intelligent decision-making, ensuring the accuracy and efficiency of the update strategy. Finally, a complete control loop is formed through command issuance and execution. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a schematic diagram of the first process of a cloud network spatial topology dynamic mapping and updating method in one embodiment;

[0058] Figure 2 This is a schematic diagram of the second process of a cloud network spatial topology dynamic mapping and updating method in another embodiment;

[0059] Figure 3 This is a schematic diagram of the third process of a cloud network spatial topology dynamic mapping and updating method in another embodiment;

[0060] Figure 4 This is a schematic diagram of the fourth process of a cloud network spatial topology dynamic mapping and updating method in another embodiment;

[0061] Figure 5 This is a schematic diagram of the fifth process of a cloud network spatial topology dynamic mapping and updating method in another embodiment;

[0062] Figure 6 This is a schematic diagram of the sixth process of a cloud network spatial topology dynamic mapping and updating method in another embodiment;

[0063] Figure 7 This is a structural block diagram of a cloud network spatial topology dynamic mapping and updating device in one embodiment;

[0064] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0066] In one embodiment, such as Figure 1 As shown, a method for dynamic mapping and updating of cloud network spatial topology is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and can also be applied to a system including both a terminal and a server, and implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0067] Step 102: Collect the operating parameters of the local device through the terminal node, and calculate the real-time confidence of the associated node based on the operating parameters.

[0068] Step 104: Obtain the data reported by each terminal node, generate node warning parameters based on the real-time confidence level, and use the node warning parameters to identify the set of paths to be updated.

[0069] Step 106: For each path in the set of paths to be updated, extract path features and input them into a pre-trained neural network model to predict how the path will be updated.

[0070] The path characteristics may include harmonic distortion rate, power direction change, temperature rise rate per unit time, and event time weight.

[0071] Step 108: Generate control instructions based on the predicted update method and send them to the corresponding terminal nodes to execute the update of the terminal nodes in order to update the network space topology map.

[0072] In the above-mentioned method for dynamic mapping and updating of cloud network spatial topology, reasonable derivation based on the technical features in the embodiments achieves the beneficial effect of solving the technical problems raised in the background art:

[0073] This application provides a method for dynamic mapping and updating of cloud network spatial topology, comprising: collecting operating parameters of local devices through terminal nodes and calculating the real-time confidence of associated nodes based on the operating parameters; acquiring data reported by each terminal node, generating node warning parameters based on the real-time confidence, and identifying a set of paths to be updated using the node warning parameters; extracting path features for each path in the set of paths to be updated and inputting them into a pre-trained neural network model to predict the update method of the path; generating control commands according to the predicted update method and issuing them to the corresponding terminal nodes to execute the update of the terminal nodes to realize the update of the network spatial topology map. In implementation, by constructing an automated closed-loop process from data perception to intelligent decision-making, a fundamental transformation of cloud network topology from static and lagging to dynamic and real-time is achieved. This method first introduces real-time confidence calculation on the terminal side, providing accurate data quality basis for subsequent judgment and improving data reliability from the source. Subsequently, by dynamically generating node warning parameters based on confidence levels and intelligently identifying the set of paths to be updated, the traditional "periodic full scan" is transformed into "event-driven incremental update," significantly reducing the scope of data processing. Then, a pre-trained neural network model is used to predict the update method for critical paths, achieving a leap from simple threshold judgment to intelligent decision-making, ensuring the accuracy and efficiency of the update strategy. Finally, a complete control loop is formed through command issuance and execution.

[0074] In one embodiment, it can be as follows Figure 2 As shown, step 102 includes:

[0075] Step 202: Compare the operating parameters with the preset standard parameter range.

[0076] Step 204: If any of the operating parameters exceeds the range of the standard parameters, the real-time confidence level is determined to be 0; otherwise, the confidence level is determined based on the degree of deviation of all real-time operating parameters from the center value of the real-time standard parameter range, and the confidence level is between 0 and 1.

[0077] For example, the terminal can collect the current, voltage, temperature, and power (CT / PT / temperature sensor) of local power grid equipment every 200ms. In the terminal's built-in lightweight inference engine, it compares these four types of raw values ​​with a standard parameter table sent from the cloud: if any value exceeds the limit, the confidence level W=0; if all values ​​are within the limit, the confidence level is calculated using the formula: W=1 / n·Σexp(-Dz / Dall) (Dz: distance from the current value to the center of the interval; Dall: interval length). Subsequently, the terminal can package the "current node parameter + W + timestamp" into an MQTT message and upload it to its corresponding edge server via LoRaWAN.

[0078] In this embodiment, comparing the operating parameters with standard intervals and calculating the confidence level helps provide key data quality criteria for subsequent processing. Transforming vague "data anomalies" into quantifiable "confidence level" indicators not only filters out unreliable data at the source and avoids error propagation, but also helps ensure that subsequent early warning parameter generation and graph calculations are based on reliable data. This significantly improves the accuracy and reliability of the entire topology update process, laying a solid data foundation for second-level real-time updates.

[0079] In one embodiment, it can be as follows Figure 3 As shown, step 104 includes:

[0080] Step 302: If the real-time confidence level of the terminal node is higher than the preset threshold, then the current operating parameter value of the terminal node is used as the corresponding node warning parameter.

[0081] Step 304: If the real-time confidence level of the terminal node is less than or equal to the preset threshold, then obtain the historical reliable data of the terminal node from the cloud, and select a parameter value from the historical reliable data as the corresponding node warning parameter based on the matching degree between the current environment and the historical environment.

[0082] For example, the preset threshold can be 0.85. After the edge server receives all terminal messages:

[0083] If W>0.85, the current node voltage can be directly used as the node early warning parameter.

[0084] If W ≤ 0.85, it is marked as an abnormal node, and the historical trusted database of the node is immediately requested from the cloud. The cloud returns the trusted voltage curves of the same season and load segment over the past 30 days; the deviation distance between the current environment and the environment at each historical moment is calculated using L = Σr·|XY| at the edge, and the historical voltage corresponding to the smallest L is selected as the warning parameter for this period.

[0085] In this embodiment, the strategy for generating early warning parameters is dynamically selected based on confidence levels, achieving both real-time performance and fault tolerance. At high confidence levels, real-time data is used directly to ensure rapid response; at low confidence levels, historical reliable data is used for intelligent repair, effectively combating data fluctuations and transient anomalies. This ensures that the node states upon which subsequent path identification is based are always reliable, fundamentally reducing the risk of misjudgment and improving the robustness and accuracy of topology update decisions.

[0086] In one embodiment, it can be as follows Figure 4 As shown, step 104 includes:

[0087] Step 402: Construct a graph structure of all the terminal nodes in the target network space and the corresponding early warning parameters.

[0088] Step 404: Traverse the terminal nodes in the graph structure and calculate the difference between the current warning parameters and historical warning parameters of adjacent nodes.

[0089] Step 406: If the difference exceeds the preset tolerance, the corresponding path between the terminal nodes is added to the set of paths to be updated.

[0090] For example, the terminal can load the entire network's node warning parameter table into an undirected weighted graph through the graph computing engine. The first-to-last node stripping algorithm is used: when the graph is not empty, take the node with the smallest current number, v0, and for each of its neighbors vn, calculate the topology update value ΔR = |Rnow - Rhist|. If ΔR > 0.05, add the edge (v0, vn) to the queue to be updated and delete v0 from the graph.

[0091] In this embodiment, constructing all network nodes as a graph structure and performing difference comparisons helps to achieve a precise transition from "node-level monitoring" to "path-level updating." By only selecting paths with significant state changes for updating, the enormous load caused by a full topology scan is avoided. This incremental identification mechanism greatly narrows the scope of computation and updates, helping to reduce system resource consumption and improve the efficiency of real-time topology updates, thereby significantly improving efficiency while ensuring accuracy.

[0092] In one embodiment, it can be as follows Figure 5 As shown, step 106 includes:

[0093] Step 502: Determine the probability value of the update method output by the neural network model.

[0094] Step 504: Select the update method corresponding to the maximum probability value as the update method of the final output.

[0095] In this embodiment, by determining the probability value of the neural network output and selecting the update method corresponding to the highest probability value, the complex path state evaluation is transformed into a clear and optimal operational decision. This helps overcome the rigid limitations of traditional rule engines, endowing the system with the ability to handle complex and nonlinear path states, thereby significantly improving the intelligence level of update decisions and the accuracy of final execution, ensuring the precision and efficiency of each topology adjustment.

[0096] In one embodiment, it can be as follows Figure 6 As shown, after step 108, the method further includes:

[0097] Step 602: Obtain the confirmation information returned by the terminal node after executing the control command.

[0098] Step 604: The edge server updates the local topology map based on the confirmation information and synchronizes the incremental update information to the cloud server.

[0099] In this embodiment, by introducing an execution confirmation and cloud synchronization mechanism, a complete "decision-execution-feedback" closed loop is formed, which helps to ensure that each topology update is reliably executed and recorded in real time at the edge, while synchronizing incremental information to the cloud. This maintains the instantaneous accuracy of the local topology map and ensures the eventual consistency of the global view in the cloud, thereby realizing the controllability, traceability and unified management of the topology state in the distributed system.

[0100] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0101] Based on the same inventive concept, this application also provides a cloud network spatial topology dynamic mapping and updating device for implementing the aforementioned cloud network spatial topology dynamic mapping and updating method. The solution provided by this device is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more embodiments of the cloud network spatial topology dynamic mapping and updating device provided below can be found in the limitations of the cloud network spatial topology dynamic mapping and updating method described above, and will not be repeated here.

[0102] In one embodiment, such as Figure 7 As shown, a cloud network spatial topology dynamic mapping and updating device is provided, including: a data acquisition module, an early warning parameter module, an update prediction module, and a topology update module, wherein:

[0103] The data acquisition module is used to collect the operating parameters of the local device through the terminal node, and calculate the real-time confidence of the associated node based on the operating parameters;

[0104] The early warning parameter module is used to acquire the data reported by each terminal node, generate node early warning parameters based on the real-time confidence level, and use the node early warning parameters to identify the set of paths to be updated.

[0105] The update prediction module is used to extract path features for each path in the set of paths to be updated and input them into a pre-trained neural network model to predict how the path will be updated.

[0106] The topology update module is used to generate control commands based on the predicted update method and send them to the corresponding terminal nodes to execute the updates of the terminal nodes in order to update the network space topology map.

[0107] In one embodiment, the data acquisition module includes:

[0108] The comparison module is used to compare the operating parameters with a preset standard parameter range;

[0109] The confidence module is used to determine that the real-time confidence is 0 if any of the operating parameters exceeds the range of the standard parameters; otherwise, it determines the confidence value based on the degree of deviation of all real-time operating parameters from the center value of the real-time standard parameter range, wherein the confidence value is between 0 and 1.

[0110] In one embodiment, the warning parameter module includes:

[0111] The first confidence module is used to take the current operating parameter value of the terminal node as the corresponding node warning parameter if the real-time confidence of the terminal node is higher than a preset threshold.

[0112] The second confidence module is used to obtain historical reliable data of the terminal node from the cloud if the real-time confidence of the terminal node is less than or equal to the preset threshold, and select a parameter value from the historical reliable data as the corresponding node warning parameter based on the matching degree between the current environment and the historical environment.

[0113] In one embodiment, the warning parameter module includes:

[0114] The graph structure module is used to construct a graph structure from all the terminal nodes and the corresponding early warning parameters in the target network space.

[0115] The difference comparison module is used to traverse the terminal nodes in the graph structure and calculate the difference between the current warning parameter and the historical warning parameter of the adjacent nodes.

[0116] The path monitoring module is used to add the corresponding path between the terminal nodes to the set of paths to be updated if the difference exceeds a preset tolerance.

[0117] In one embodiment, the update prediction module includes:

[0118] The probability value module is used to determine the probability value of the update method output by the neural network model;

[0119] The method selection module is used to select the update method corresponding to the maximum probability value as the update method of the final output.

[0120] In one embodiment, after the topology update module, the following is further included:

[0121] The node confirmation module is used to obtain confirmation information returned by the terminal node after executing the control command;

[0122] The update information module is used to update the local topology map on the edge server based on the confirmation information and synchronize the incremental update information to the cloud server.

[0123] The modules in the aforementioned cloud network spatial topology dynamic mapping and updating device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0124] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for dynamically mapping and updating the spatial topology of a cloud network.

[0125] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0126] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0127] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0128] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0129] 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, data stored, data displayed, 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 related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0130] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this 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 can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0131] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0132] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for dynamic mapping and updating of cloud network spatial topology, characterized in that, The method includes: The terminal node collects the operating parameters of the local device and calculates the real-time confidence level of the associated node based on the operating parameters; The data reported by each terminal node is obtained, node warning parameters are generated based on the real-time confidence level, and the set of paths to be updated is identified using the node warning parameters. For each path in the set of paths to be updated, extract path features and input them into a pre-trained neural network model to predict how the path will be updated. Control commands are generated based on the predicted update method and sent to the corresponding terminal nodes to execute the updates of the terminal nodes, thereby updating the network space topology.

2. The method according to claim 1, characterized in that, The step of collecting local device operating parameters through terminal nodes and calculating the real-time confidence level of associated nodes based on the operating parameters includes: The operating parameters are compared with a preset standard parameter range; If any of the operating parameters exceeds the range of the standard parameters, the real-time confidence level is determined to be 0; otherwise, the confidence level is determined based on the degree of deviation of all real-time operating parameters from the center value of the real-time standard parameter range, and the confidence level is between 0 and 1.

3. The method according to claim 1, characterized in that, The steps of acquiring data reported by each terminal node, generating node warning parameters based on the real-time confidence level, and identifying the set of paths to be updated using the node warning parameters include: If the real-time confidence level of the terminal node is higher than a preset threshold, then the current operating parameter value of the terminal node is used as the corresponding node warning parameter. If the real-time confidence level of the terminal node is less than or equal to the preset threshold, then the historical reliable data of the terminal node is obtained from the cloud, and a parameter value is selected from the historical reliable data as the corresponding node warning parameter based on the matching degree between the current environment and the historical environment.

4. The method according to claim 1, characterized in that, The steps of acquiring data reported by each terminal node, generating node warning parameters based on the real-time confidence level, and identifying the set of paths to be updated using the node warning parameters include: Construct a graph structure from all the terminal nodes in the target network space and the corresponding early warning parameters; Traverse the terminal nodes in the graph structure and calculate the difference between the current warning parameters and historical warning parameters of adjacent nodes; If the difference exceeds a preset tolerance, the corresponding path between the terminal nodes is added to the set of paths to be updated.

5. The method according to any one of claims 1 to 4, characterized in that, The step of extracting path features for each path in the set of paths to be updated and inputting them into a pre-trained neural network model to predict the update method for that path includes: Determine the probability value of the update method output by the neural network model; The update method corresponding to the highest probability value is selected as the update method for the final output.

6. The method according to claim 1, characterized in that, After generating control commands based on the predicted update method and issuing them to the corresponding terminal nodes, and executing the updates of the terminal nodes to update the network space topology, the method further includes: Obtain the confirmation information returned by the terminal node after executing the control command; The edge server updates the local topology map based on the confirmation information and synchronizes the incremental update information to the cloud server.

7. A cloud network spatial topology dynamic mapping and updating device, characterized in that, The device includes: The data acquisition module is used to collect the operating parameters of the local device through the terminal node, and calculate the real-time confidence of the associated node based on the operating parameters; The early warning parameter module is used to acquire the data reported by each terminal node, generate node early warning parameters based on the real-time confidence level, and use the node early warning parameters to identify the set of paths to be updated. The update prediction module is used to extract path features for each path in the set of paths to be updated and input them into a pre-trained neural network model to predict how the path will be updated. The topology update module is used to generate control commands based on the predicted update method and send them to the corresponding terminal nodes to execute the updates of the terminal nodes in order to update the network space topology map.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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