A computer network information service management method and system based on cloud technology

Abstract

The present invention relates to the field of information retrieval, and particularly to a computer network information service management method and system based on cloud technology. The method includes the following steps: setting information service nodes for a computer network to obtain information service nodes; performing access order blockchain node data mapping on the information service nodes to obtain an access order blockchain; extracting user features and constructing a feature matrix for the information service nodes and uploading them to a cloud platform to obtain a cloud feature access matrix; generating an access scheduling instruction tree for the access order blockchain based on the information service nodes and the user access feature matrix to obtain an access scheduling instruction tree; when receiving an access request, performing cloud platform service preparation scheduling based on the cloud feature access matrix and the user access request to obtain a cloud platform service preparation scheduling result. The present invention generates an access scheduling instruction tree through information retrieval and processing of information service setting nodes in a computer network, which is beneficial to rapid scheduling of information services.

Description

Technical Field

[0001] The present invention relates to the field of information retrieval, and in particular, to a computer network information service management method and system based on cloud technology. Background Art

[0002] With the rapid development and popularization of the modern Internet, computer network information services play an increasingly important role in various fields. The traditional network information service management mode faces many challenges, such as low resource utilization rate, high operation and maintenance cost, and poor scalability. The emergence of cloud computing technology provides new ideas and methods to solve these problems; cloud computing, as a service mode that provides computing resources on demand, its core technologies include virtualization, distributed storage, and resource scheduling, providing strong technical support for network information service management; however, cloud technology itself has a need for network connection, and there are problems such as delayed response, multi-user competition for information service resources, and limitations in scheduling strategies when processing computer network information services, resulting in performance and latency problems, causing slow access response speed and waste of information service resources in information services, thus affecting service performance. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a computer network information service management method and system based on cloud technology to solve at least one of the above technical problems.

[0004] To achieve the above object, a computer network information service management method based on cloud technology includes the following steps:

[0005] Step S1: Set information service nodes for the computer network to obtain information service nodes; divide the information service nodes to obtain a central service node and edge service nodes; perform access order blockchain mapping on the central service node and the edge service nodes to obtain an access order blockchain;

[0006] Step S2: Extract user access feature data from the information service nodes to obtain user access feature data; construct a feature matrix for the user access feature data to obtain a user access feature matrix, and perform cloud platform upload processing on the user access feature matrix to obtain a cloud feature access matrix;

[0007] Step S3: Analyze the connection of service nodes for the information service nodes to obtain service node connection data; extract access node block identifiers from the access order blockchain based on the user access feature matrix to obtain a priority access node identifier and an unextracted node identifier; generate an access scheduling instruction tree based on the service node connection data for the priority access node identifier and the unextracted node identifier to obtain an access scheduling instruction tree;

[0008] Step S4: Upload the user access request to the cloud platform for the computer network to obtain the user access request cloud data; construct an access user index for the cloud feature access matrix to obtain the access user index; perform cloud platform service preparatory scheduling on the access scheduling instruction tree based on the user access request cloud data and the access user index to obtain the cloud platform service preparatory scheduling result.

[0009] Through the division and blockchain mapping of information service nodes, the present invention constructs a hierarchical network architecture and a traceable access order record. This structure can effectively distinguish core services and edge services, realize the reasonable allocation of resources, and at the same time use blockchain technology to ensure the access order; the extraction and matrix construction of access user characteristics can effectively model and analyze user behaviors. By uploading this behavior matrix to the cloud platform, the powerful data processing ability of cloud computing can be utilized to realize large-scale user characteristic behavior analysis, so as to more accurately understand user service access preferences; by analyzing the connection relationship between service nodes and combining user access characteristics and blockchain records, a dynamic access scheduling instruction tree is generated. In this way, the access priority of service nodes can be dynamically adjusted according to the real-time network conditions and user behavior preferences, effectively alleviating network congestion, improving user access efficiency and service quality; based on the service preparatory scheduling mechanism of the cloud platform, according to the user access request and the pre-constructed scheduling instruction tree, the optimal service node can be quickly matched, and resource reservation and service preparation can be carried out in advance. This preprocessing mechanism can effectively reduce user access latency, improve the response speed of the service and the user experience, and optimize the call order of information service nodes, so as to solve the problems of delayed response, multi-user competition for information service resources, the limitations of scheduling strategies, and the resulting slow access response speed and waste of information service resources in information services.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Set information service nodes for the computer network to obtain information service nodes;

[0012] Step S12: Analyze the node centrality index data of the information service nodes to obtain the node centrality index data;

[0013] Step S13: Analyze the access response performance of the information service nodes to obtain high-access-response-performance nodes and low-access-response-performance nodes;

[0014] Step S14: Generate central service nodes for the high-access-response-performance nodes to obtain central service nodes;

[0015] Step S15: Generate edge service nodes for the low-access-response-performance nodes to obtain edge service nodes;

[0016] Step S16: Perform access order blockchain mapping on the central service node and the edge service node based on the node centrality index data to obtain the access order blockchain.

[0017] The present invention marks the first step in constructing an efficient network service architecture by establishing information service nodes, laying a foundation for subsequent differentiation of different functional nodes and optimization of resource allocation; by analyzing the node centrality index data, quantitatively evaluating the importance and influence of each node in the network, providing data support for subsequent differentiation of core nodes and edge nodes; by analyzing the access response performance of information service nodes, identifying nodes with high access response capabilities and nodes with relatively low performance, preparing for constructing a hierarchical network architecture; defining nodes with high access response performance as central service nodes, enabling them to undertake core business processing and key data storage functions, giving full play to their performance advantages, and improving the overall service efficiency and stability; defining nodes with low access response performance as edge service nodes, enabling them to be responsible for the distribution of user requests, data caching, and some edge computing tasks, reducing the load pressure on the central node and optimizing the resource utilization efficiency; based on the node centrality index data, performing access order blockchain mapping on the central service node and the edge service node, constructing a secure, reliable, and traceable access path, using blockchain technology to ensure the transparency and anti-tampering of the access order, enhancing the security of network services, realizing the reasonable classification and optimization configuration of information service nodes through node performance analysis and centrality evaluation, and using blockchain technology to construct a secure and reliable access mechanism, ultimately improving the performance of the entire network service.

[0018] Preferably, step S13 includes the following steps:

[0019] Step S131: Analyze the access frequency threshold of information service nodes to obtain the access frequency threshold;

[0020] Step S132: Divide the information service nodes into high-access nodes and low-access nodes based on the access frequency threshold to obtain high-access nodes and low-access nodes;

[0021] Step S133: Extract the node response delay characteristics of information service nodes to obtain node response delay characteristic data;

[0022] Step S134: Evaluate the stability of node calls for high-access nodes to obtain high-access node stability data;

[0023] Step S135: Evaluate high-access response performance nodes based on the high-access node stability data and the node response delay characteristic data to obtain high-access response performance nodes;

[0024] Step S136: Evaluate the node request throughput of low-access nodes to obtain the low-access node request throughput data;

[0025] Step S137: Evaluate the low-access response performance nodes for the low-access node request throughput data and the node response latency characteristic data to obtain the low-access response performance nodes.

[0026] By analyzing the access frequency and setting thresholds, the present invention preliminarily divides information service nodes into high-access nodes and low-access nodes, providing a basis for subsequent adoption of different evaluation metrics for different types of nodes, extracting the node response latency characteristic data, which is one of the key metrics for evaluating node performance, providing a data basis for subsequent evaluation of the response performance of high-access nodes and low-access nodes. For high-access nodes, the focus is on evaluating their call stability because stability is crucial for handling a large number of concurrent requests, which ensures that high-access nodes can continuously and reliably provide services; combining the stability data and the response latency characteristic data of high-access nodes for comprehensive evaluation, screening out nodes with high-access response performance and using them as core nodes to undertake key business processing; for low-access nodes, the focus is on evaluating their request throughput because throughput reflects the node's ability to process requests, which is crucial for efficient resource utilization; combining the request throughput data and the response latency characteristic data of low-access nodes for comprehensive evaluation, screening out nodes with low-access response performance and using them as edge nodes to undertake auxiliary tasks. By separately evaluating the key performance metrics of high-access nodes and low-access nodes, more accurate node classification is achieved, laying a foundation for building an efficient and stable network service architecture, and at the same time providing data support for subsequent resource optimization configuration and load balancing.

[0027] Preferably, step S134 includes the following steps:

[0028] Step S1341: Perform data frequency domain conversion on high-access nodes to obtain high-access node spectra;

[0029] Step S1342: Calculate the spectral amplitude of the high-access node spectra to obtain high-access node amplitude data;

[0030] Step S1343: Conduct statistical analysis on the high-access node amplitude data to obtain the high-access node amplitude threshold;

[0031] Step S1344: Perform classification calculation on high-access nodes based on the high-access node amplitude threshold to obtain high-amplitude node spectra and low-amplitude node spectra;

[0032] Step S1345: Perform time domain transformation on the high-amplitude node spectra and the low-amplitude node spectra respectively to obtain low-stability node data and high-stability node data;

[0033] Step S1346: Based on the low-stability node data and the high-stability node data, perform stability node marking on the high-access nodes to obtain high-access node stability data.

[0034] In the present invention, the data of the high-access nodes is subjected to frequency domain conversion, and the spectral amplitude is calculated, mapping the performance of the nodes to the frequency domain, providing a new perspective for subsequent analysis of node stability; through statistical analysis of the amplitude data of the high-access nodes, a reasonable amplitude threshold is determined to distinguish nodes with different stabilities, and this threshold can be used as a benchmark for distinguishing high-amplitude nodes and low-amplitude nodes; according to the amplitude threshold, the spectrum of the high-access nodes is divided into a high-amplitude node spectrum and a low-amplitude node spectrum, providing a basis for subsequent evaluation of the characteristics of nodes with different stabilities respectively; the high-amplitude node spectrum and the low-amplitude node spectrum are respectively subjected to time domain transformation to convert the frequency characteristics back to the time domain, facilitating subsequent stability evaluation and marking, and helping to further analyze the differences within the high-access nodes; based on the data after time domain transformation, the high-access nodes are marked as high-stability nodes or low-stability nodes, and finally high-access node stability data is obtained, which helps to identify nodes that are stable and highly reliable under request loads, providing an important reference for subsequent node classification and resource scheduling. Through the frequency domain analysis method, the problem of stability evaluation of high-access nodes is cleverly transformed into the analysis of signal frequency characteristics, realizing the quantitative evaluation of node stability and providing technical support for building a more stable and reliable network service.

[0035] Preferably, step S2 includes the following steps:

[0036] Step S21: Obtain access user data by acquiring access user data of the information service nodes.

[0037] Step S22: Extract user access characteristics from the access user data to obtain user access characteristic data.

[0038] Step S23: Group the user access characteristic data to obtain user access grouped characteristic data.

[0039] Step S24: Based on the user access grouped characteristic data, construct a characteristic matrix for the access user data to obtain a user access characteristic matrix.

[0040] Step S25: Perform cloud platform upload processing on the user access characteristic matrix to obtain a cloud characteristic access matrix.

[0041] The present invention provides basic data for subsequent user behavior analysis by collecting user access data on information service nodes. These data include the access time, access duration, accessed pages, and operation behaviors of users, providing the original materials for user feature extraction. By extracting user features from the accessed user data, key features of users can be identified and captured. For example, the interests and hobbies of users can be inferred based on the accessed pages, and the activity level of users can be judged based on their operation behaviors, thereby transforming the chaotic original data into structured user feature data, providing a more direct basis for subsequent analysis. Based on the grouped user access feature data, the user behavior data is transformed into a matrix form, intuitively showing the feature distribution of different user groups in each behavior dimension. This matrix-form data structure facilitates data mining and analysis, such as discovering the behavior patterns and rules of different user groups. Uploading the user access feature matrix to the cloud platform can utilize the powerful computing power of cloud computing for storage, analysis, and processing, providing support for real-time analysis and model training of large-scale user data, and also enabling data sharing and collaborative analysis through the cloud platform.

[0042] Preferably, step S24 includes the following steps:

[0043] Step S241: Obtain node access process data by acquiring the node access process for the accessed user data;

[0044] Step S242: Generate user feature attributes for the grouped user access feature data to obtain user feature attribute data;

[0045] Step S243: Analyze the user behavior path for the node access process data to obtain user behavior path data;

[0046] Step S244: Construct a user access feature matrix from the user feature attribute data and the user behavior path data to obtain a user access feature matrix.

[0047] This invention focuses on the specific behavioral trajectories of users in information service nodes through this step. By recording the order and response time information of users accessing nodes, the information acquisition path of users can be restored, providing an important basis for subsequent analysis of user behavior patterns and interest preferences; based on the user access grouping feature data, more abundant user feature attributes are further mined and generated. These attribute data can depict user portraits more comprehensively and finely, providing stronger support for deeply understanding user behavior patterns and potential needs; in-depth analysis of node access process data, such as identifying typical paths of user access, discovering key nodes in the paths, and analyzing behavioral differences of users on different paths. These analysis results help to understand the information acquisition mode of users, providing data support for optimizing the layout of information service nodes and recommendation strategies; constructing a user access feature matrix can effectively combine and organize a large amount of user data. The matrix provides a structured method for quickly identifying and comparing behavioral patterns and preferences between different user groups, enabling visualization and quantification of the connections between user behaviors, and grouping users according to features and behaviors. This can make personalized measures more targeted and contribute to making better decisions.

[0048] Preferably, step S3 includes the following steps:

[0049] Step S31: Conduct service node connection analysis on the information service nodes to obtain service node connection data;

[0050] Step S32: Extract access node data from the user access feature matrix to obtain user feature access node data;

[0051] Step S33: Extract access node block identifiers from the access order blockchain based on the user feature access node data to obtain priority access node identifiers;

[0052] Step S34: Identify unextracted node identifiers from the access order blockchain based on the priority access node identifiers to obtain unextracted node identifiers;

[0053] Step S35: Generate call instructions for the priority access node identifiers and the unextracted node identifiers to obtain a priority access node scheduling instruction and an unextracted node scheduling instruction;

[0054] Step S36: Construct an access scheduling instruction tree based on the service node connection data for the priority access node scheduling instruction and the unextracted node scheduling instruction to obtain an access scheduling instruction tree.

[0055] The present invention analyzes the connection relationship between information service nodes to obtain service node connection data, providing a basic topological structure for constructing a scheduling instruction tree in the subsequent steps. This helps identify key node connection paths and provides basic data for subsequent access scheduling and optimization. Node data that users often access is extracted from the user access feature matrix, providing a basis for personalized customization of access paths and helping to understand users' access habits and trends. By combining user feature access node data, priority access node identifiers are extracted from the access order blockchain to ensure fast access to nodes that users frequently visit, which helps optimize the access order and ensures that users can quickly and efficiently access the required services. Node identifiers that have not been extracted in the access order blockchain are identified to ensure that all nodes are incorporated into the scheduling instruction tree. Call instructions are generated for priority access nodes and unextracted nodes respectively, providing specific execution instructions for constructing the scheduling instruction tree. Considering the importance, access frequency, and node connection data of the nodes, this step ensures that the construction of the access scheduling instruction tree can make full use of network resources and optimize the access path. Based on the service node connection data, the priority access node scheduling instructions and unextracted node scheduling instructions are integrated to construct a complete access scheduling instruction tree, realizing dynamic adjustment of the access path according to user behavior and service node relationships. It can efficiently and reliably route to the corresponding service nodes, while considering the overall performance and resource utilization rate of the network, achieving personalized customization and optimization of the network service access path, thereby improving the service access efficiency and enhancing the user experience.

[0056] Preferably, step S31 includes the following steps:

[0057] Step S311: Mine service association rules for information service nodes to obtain node association rule data;

[0058] Step S312: Identify connected service nodes for information service nodes based on the node association rule data to obtain connected nodes;

[0059] Step S313: Calculate the weights of the connected nodes to obtain connected node weight data;

[0060] Step S314: Construct a node connection topology for information service nodes based on the connected node weight data and the node association rule data to obtain a node connection topology structure;

[0061] Step S315: Analyze the service node connection data of the node connection topology structure to obtain service node connection data.

[0062] Through service association rule mining, the present invention can discover the association and dependency relationships between information service nodes. By analyzing these association rules, it is possible to understand which service nodes are frequently accessed or used together, as well as their functional or data dependencies. This helps to identify potential service clusters or modules and can optimize service deployment and architecture design. By identifying bridging service nodes, nodes that have a close connection or frequently co-occur in the service association rules can be found. These bridging nodes represent the transition or interaction points between service nodes. By identifying these nodes, the service process and navigation path can be optimized, enabling users to access related services more smoothly and improving service availability. Calculate the weights of the bridging nodes to obtain the bridging node weight data. It is possible to calculate the weight of each bridging node based on the node association rules and user access data, providing data support for the subsequent construction of the node bridging topology. By calculating the weights of the bridging nodes, the association strength between different nodes can be identified, which helps to determine the key service nodes and can accordingly allocate resources and optimize the deployment strategy. Based on the bridging node weight data and the node association rule data, construct the node bridging topology for the information service nodes to obtain the node bridging topology structure. It is possible to construct a connection relationship network that includes all information service nodes, providing basic data support for the subsequent node access scheduling. By constructing the node bridging topology structure, the association relationships between nodes can be clearly displayed, the best access path can be identified, the user experience can be optimized, and the access efficiency can be improved. Analyzing the node bridging topology structure can obtain service node bridging data, including the connection strength, distance, and transmission efficiency between different nodes. By analyzing these data, the information transmission and interaction between service nodes can be optimized. This helps to improve the service response speed and reduce the latency during cross-node interactions.

[0063] Preferably, step S4 includes the following steps:

[0064] Step S41: Conduct user access preference analysis on the cloud feature access matrix to obtain user access service preference data;

[0065] Step S42: Construct an access user index for the user access service preference data to obtain the access user index;

[0066] Step S43: Obtain user access requests for the computer network;

[0067] Step S44: Upload the user access requests to the cloud platform to obtain user access request cloud data;

[0068] Step S45: Generate an information service scheduling flow for the access scheduling instruction tree based on the user access request cloud data and the access user index to obtain the information service scheduling flow;

[0069] Step S46: Perform preliminary scheduling of cloud platform services for the information service scheduling flow to obtain the preliminary scheduling result of cloud platform services.

[0070] The present invention analyzes the cloud feature access matrix, identifies the access preferences of users for different services, optimizes the allocation of cloud platform resources according to user needs, provides a data basis for personalized service recommendation and resource optimization allocation, and improves user experience and system efficiency; by constructing an access user index, it can effectively organize and index user access data, which can achieve fast and accurate search and retrieval of user information, which is crucial for the management of large cloud platforms. Such an index can also enhance the access engine, provide personalized services for users, and improve their satisfaction; by obtaining the requests of users accessing the computer network, it can monitor and analyze user needs in real time, which can help the cloud platform maintain the ability to respond to changes in user needs and ensure the timeliness and effectiveness of service responses; uploading the user access requests to the cloud platform realizes centralized management and processing, facilitates global optimization and resource scheduling, and improves the overall performance of the system; combining the user access requests and the access user index to generate an information service scheduling flow, realizing personalized service scheduling based on user preferences and real-time needs, optimizing resource utilization, improving service quality and user experience; performing preliminary scheduling of cloud platform services for the information service scheduling flow, making resource reservation and allocation in advance, shortening the service response time, improving the system throughput and concurrent processing ability, ensuring service stability and reliability, and optimizing the call order of information service nodes, thereby solving the problems of delayed response, multi-user competition for information service resources, the limitations of scheduling strategies, and the resulting slow access response speed and waste of information service resources in information services.

[0071] Preferably, the present invention also provides a computer network information service management system based on cloud technology for executing the computer network information service management method based on cloud technology as described above. The computer network information service management system based on cloud technology includes:

[0072] An access order blockchain mapping module for setting information service nodes for the computer network to obtain information service nodes; dividing the information service nodes to obtain a central service node and edge service nodes; performing access order blockchain mapping on the central service node and the edge service nodes to obtain an access order blockchain;

[0073] A user access feature matrix uploading module for extracting user access feature data from the information service nodes to obtain user access feature data; constructing a feature matrix from the user access feature data to obtain a user access feature matrix, and performing cloud platform uploading processing on the user access feature matrix to obtain a cloud feature access matrix;

[0074] An access scheduling instruction tree generation module is used to perform service node connection analysis on information service nodes to obtain service node connection data; extract access node block identifiers from the access order blockchain based on the user access feature matrix to obtain priority access node identifiers and unextracted node identifiers; generate an access scheduling instruction tree based on the service node connection data for the priority access node identifiers and unextracted node identifiers to obtain an access scheduling instruction tree;

[0075] A user access pre-scheduling module is used to upload user access requests to the cloud platform for a computer network to obtain user access request cloud data; construct an access user index for the cloud feature access matrix to obtain an access user index; perform cloud platform service pre-scheduling on the access scheduling instruction tree based on the user access request cloud data and the access user index to obtain a cloud platform service pre-scheduling result.

[0076] In summary, the present invention provides a computer network information service management system based on cloud technology. The computer network information service management system based on cloud technology is composed of an access order blockchain mapping module, a user access feature matrix upload module, an access scheduling instruction tree generation module, and a user access pre-scheduling module, and can implement any computer network information service management method based on cloud technology described in the present invention. It is used to jointly implement any computer network information service management method through the operations between computer programs running on each module. The internal structure of the system cooperates with each other, which can greatly reduce repetitive work and manpower investment, and can quickly and effectively provide a more accurate and efficient computer network information service management process based on cloud technology, thereby simplifying the operation process of the computer network information service management system based on cloud technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Other features, objects, and advantages of the present invention will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0078] Figure 1 It is a schematic flow chart of the steps of a computer network information service management method based on cloud technology of the present invention;

[0079] Figure 2 is Figure 1 a detailed schematic flow chart of step S3 in

[0080] Figure 3 is Figure 2 a detailed schematic flow chart of step S31 in DETAILED DESCRIPTION OF THE EMBODIMENTS

[0081] The technical method of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0082] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0083] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed associated items.

[0084] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a computer network information service management method based on cloud technology, including the following steps:

[0085] Step S1: Set information service nodes for the computer network to obtain information service nodes; divide the information service nodes to obtain a central service node and edge service nodes; perform access order blockchain mapping on the central service node and the edge service nodes to obtain an access order blockchain;

[0086] Step S2: Extract access user characteristics from the information service nodes to obtain user access characteristic data; construct a characteristic matrix from the user access characteristic data to obtain a user access characteristic matrix, and perform cloud platform upload processing on the user access characteristic matrix to obtain a cloud characteristic access matrix;

[0087] Step S3: Conduct service node connection analysis on the information service nodes to obtain service node connection data; extract access node block identifiers from the access order blockchain based on the user access feature matrix to obtain priority access node identifiers and unextracted node identifiers; generate an access scheduling instruction tree for the priority access node identifiers and unextracted node identifiers based on the service node connection data to obtain the access scheduling instruction tree.

[0088] Step S4: Upload the user access request to the cloud platform of the computer network to obtain user access request cloud data; construct an access user index for the cloud feature access matrix to obtain the access user index; perform cloud platform service preliminary scheduling on the access scheduling instruction tree based on the user access request cloud data and the access user index to obtain the cloud platform service preliminary scheduling result.

[0089] In the embodiment of the present invention, please refer to Figure 1 As shown, it is a schematic diagram of the step flow of the computer network information service management method based on cloud technology of the present invention. In this example, the computer network information service management method based on cloud technology includes the following steps:

[0090] Step S1: Set information service nodes for the computer network to obtain information service nodes; divide the information service nodes to obtain a central service node and an edge service node; map the central service node and the edge service node to an access order blockchain to obtain the access order blockchain.

[0091] In the embodiment of the present invention, by selecting several servers as information service nodes and configuring the corresponding network environment so that they can provide information services externally, the servers can be configured as Web servers, database servers, file servers, and the corresponding network protocols and port numbers are configured for users to access; according to factors such as the performance, connection relationship, and service type of the nodes, the information service nodes are divided into a central service node and an edge service node. The central service node usually has higher processing capabilities and resources and is responsible for providing core services and data storage, while the edge service node is responsible for processing the initial stage of user requests; the central service node and the edge service node are arranged in the access order and mapped to a blockchain, and each block represents a node. Each block in the blockchain contains the information of the node and its connection relationship with the previous and subsequent nodes, and the access order blockchain can record the access order between all nodes.

[0092] Step S2: Extract user access feature data from the information service nodes to obtain user access feature data; construct a feature matrix for the user access feature data to obtain a user access feature matrix, and perform cloud platform upload processing on the user access feature matrix to obtain a cloud feature access matrix.

[0093] In an embodiment of the present invention, by analyzing the behavior of a user accessing an information service node, after obtaining the user's consent, the characteristic data of the user is extracted. For example, the user access time, access frequency, access duration, and access content can use data mining techniques, such as clustering analysis or association rule mining, to extract the user characteristic data; the extracted access user characteristic data is constructed into a characteristic matrix, where each user corresponds to a row vector in the matrix and each characteristic corresponds to a column vector in the matrix. For example, the characteristic data of the user access time, access frequency, and access duration can be used as column vectors in the matrix respectively, and the data corresponding to each user is used as a row vector in the matrix; the constructed user characteristic behavior matrix is uploaded to the cloud platform, and the computing resources of the cloud platform are used to process the matrix. For example, dimensionality reduction or clustering analysis is performed on the matrix to obtain more effective user characteristic information, and various data processing tools and services provided by the cloud platform are used to perform matrix processing.

[0094] Step S3: Perform service node connection analysis on the information service node to obtain service node connection data; extract the access node block identifiers of the access order blockchain based on the user access characteristic matrix to obtain the priority access node identifiers and the unextracted node identifiers; generate an access scheduling instruction tree for the priority access node identifiers and the unextracted node identifiers based on the service node connection data to obtain the access scheduling instruction tree.

[0095] In an embodiment of the present invention, by analyzing the mutual dependence relationship between information service nodes, such as which nodes need to rely on the services of other nodes to work properly, and recording the connection data between service nodes, network topology analysis tools can be used to analyze the mutual dependence relationship between service nodes; based on the user access characteristic matrix, according to the specific content and characteristics of the user access, the priority access node identifiers are extracted from the access order blockchain, and at the same time, the unextracted node identifiers are also recorded; based on the service node connection data, combining the priority access node identifiers and the unextracted node identifiers, an access scheduling instruction tree is constructed. The root node of the tree is the initial node of the user access, the branches represent the connection relationship between nodes, and the leaves represent the finally accessed nodes. Each node contains the instruction and conditions for accessing the node, such as the priority of accessing the node and the conditions required for accessing the node.

[0096] Step S4: Upload the user access request to the cloud platform for the computer network to obtain the user access request cloud data; construct an access user index for the cloud characteristic access matrix to obtain the access user index; perform cloud platform service preliminary scheduling on the access scheduling instruction tree based on the user access request cloud data and the access user index to obtain the cloud platform service preliminary scheduling result.

[0097] In an embodiment of the present invention, after obtaining user permission, the access request initiated by the user is uploaded to the cloud platform, which records the specific information of the user access request, such as user information, access time, and accessed resources; based on the cloud feature access matrix, the user is indexed to facilitate quick search for the user's access feature data; based on the user access request cloud data, access user index, and access scheduling instruction tree, the cloud platform performs service preparation scheduling. According to the user access request information and the user's access characteristics, a suitable access node is selected, and according to the access scheduling instruction tree, a specific access path and access instruction are generated, and resources and services are prepared in advance before the user access request arrives.

[0098] The present invention constructs a hierarchical network architecture and a traceable access order record by partitioning information service nodes and blockchain mapping. This structure can effectively distinguish core services and edge services, achieve reasonable resource allocation, and at the same time use blockchain technology to ensure the access order; the extraction and matrix construction of access user characteristics can effectively model and analyze user behavior. By uploading this behavior matrix to the cloud platform, the powerful data processing ability of cloud computing can be utilized to realize large-scale user characteristic behavior analysis, so as to more accurately understand the user service access preference; by analyzing the connection relationship between service nodes and combining user access characteristics and blockchain records, a dynamic access scheduling instruction tree is generated. In this way, the access priority of service nodes can be dynamically adjusted according to the real-time network condition and user behavior preference, effectively alleviating network congestion and improving user access efficiency and service quality; based on the service preparation scheduling mechanism of the cloud platform, according to the user access request and the pre-constructed scheduling instruction tree, the optimal service node can be quickly matched, and resource reservation and service preparation are carried out in advance. This preprocessing mechanism can effectively reduce user access latency, improve the response speed of the service and user experience, and optimize the call order of information service nodes, thus solving the problems of delayed response, multi-user competition for information service resources, limitations of scheduling strategies, and resulting slow access response speed and waste of information service resources in information services.

[0099] Preferably, step S1 includes the following steps:

[0100] Step S11: Set information service nodes for the computer network to obtain information service nodes;

[0101] In an embodiment of the present invention, by using a network scanning tool to scan the target computer network, all accessible devices in the network are identified; for each identified device, its basic information, such as IP address and port number, is collected. Port scanning tools and network protocol analysis tools can be used to obtain relevant information, and the collected node information is stored in a database to establish an information service node database.

[0102] Step S12: Conduct data analysis on the node centrality index of the information service nodes to obtain the node centrality index data;

[0103] In the embodiment of the present invention, by constructing a network topology graph according to the connection relationship between nodes in the information service node database, the connection relationship between nodes can be visualized using a graphical tool. Based on the network topology graph, calculate the centrality index of each node, such as degree centrality, betweenness centrality, eigenvector centrality, analyze the calculated centrality index data, and identify the nodes with higher centrality in the network. These nodes are usually the key nodes in the network.

[0104] Step S13: Conduct access response performance analysis on the information service nodes to obtain high-access-response-performance nodes and low-access-response-performance nodes;

[0105] In the embodiment of the present invention, by selecting a suitable performance testing tool, conduct access response performance testing on the information service nodes, design different test scenarios, such as simulating different numbers of users, access frequencies, and access contents, and test the response time, throughput, and error rate indicators of the nodes; according to the test results, analyze the access response performance of each node, and classify the nodes into high-access-response-performance nodes and low-access-response-performance nodes.

[0106] Step S14: Generate central service nodes from the high-access-response-performance nodes to obtain central service nodes;

[0107] In the embodiment of the present invention, by screening out the nodes with higher centrality from the high-access-response-performance nodes, setting the screened nodes as central service nodes, and assigning corresponding service roles, such as database servers, configure corresponding resources according to the type and service role of the central service nodes.

[0108] Step S15: Generate edge service nodes from the low-access-response-performance nodes to obtain edge service nodes;

[0109] In the embodiment of the present invention, by screening out the nodes with lower centrality from the low-access-response-performance nodes, setting the screened nodes as edge service nodes, and assigning corresponding service roles, configure corresponding resources according to the type and service role of the edge service nodes.

[0110] Step S16: Perform access order blockchain mapping on the central service nodes and edge service nodes based on the node centrality index data to obtain an access order blockchain.

[0111] In the embodiment of the present invention, by sorting the central service nodes and the edge service nodes according to the node centrality index data, for example, sorting according to the degree centrality, betweenness centrality, and eigenvector centrality indexes, the sorted nodes are mapped to a blockchain. Each block represents a node, and each block contains the information of the node, such as the node type, IP address, port number, and its connection relationship with the front and back nodes. The order of the nodes in the blockchain represents the access order. For example, the central service nodes are located at the head of the blockchain, and the edge service nodes are located at the tail of the blockchain. When users access, they access according to the blockchain order.

[0112] The present invention marks the first step in constructing an efficient network service architecture by establishing information service nodes, laying a foundation for subsequent differentiation of different functional nodes and optimization of resource allocation; by analyzing the node centrality index data, quantitatively evaluating the importance and influence of each node in the network, providing data support for subsequent differentiation of core nodes and edge nodes; by analyzing the access response performance of information service nodes, identifying nodes with high access response capabilities and nodes with relatively low performance, preparing for building a hierarchical network architecture; defining nodes with high access response performance as central service nodes, enabling them to undertake the functions of core business processing and key data storage, giving full play to their performance advantages, and improving the overall service efficiency and stability; defining nodes with low access response performance as edge service nodes, enabling them to be responsible for the distribution of user requests, data caching, and some edge computing tasks, reducing the load pressure on the central node, and optimizing the resource utilization efficiency; based on the node centrality index data, mapping the access order of central service nodes and edge service nodes to a blockchain, constructing a secure, reliable, and traceable access path, using blockchain technology to ensure the transparency and anti-tampering of the access order, enhancing the security of network services, realizing the reasonable classification and optimization configuration of information service nodes through node performance analysis and centrality evaluation, and using blockchain technology to construct a secure and reliable access mechanism, ultimately improving the performance of the entire network service.

[0113] Preferably, step S13 includes the following steps:

[0114] Step S131: Analyze the access frequency threshold of information service nodes to obtain the access frequency threshold;

[0115] In an embodiment of the present invention, by statistically analyzing the access times of each information service node based on information service access data, calculating the access frequency of each node, and analyzing the trend of the access frequency of each node changing over time according to the time information of the access data, such as peak hours and off-peak hours, and analyzing the access frequency distribution of each node, such as normal distribution and Poisson distribution; according to the statistical analysis results, an access frequency threshold is set, and the threshold can be adjusted according to the actual situation. For example, it can be set as a multiple of the average access frequency.

[0116] Step S132: Divide the information service nodes into high-access nodes and low-access nodes based on the access frequency threshold to obtain high-access nodes and low-access nodes;

[0117] In an embodiment of the present invention, by dividing the information service nodes into high-access nodes and low-access nodes according to the access frequency threshold, high-access nodes are characterized by a relatively high user access frequency, which causes greater pressure on performance and resources, while low-access nodes have a relatively low access frequency and unused capacity. Nodes with an access frequency higher than the threshold are high-access nodes, and nodes with an access frequency lower than the threshold are low-access nodes. A high-access node list and a low-access node list are generated.

[0118] Step S133: Extract node response delay characteristics of the information service nodes to obtain node response delay characteristic data;

[0119] In an embodiment of the present invention, by recording the response time of each information service node to an access request, for example, using a network performance testing tool to record the response time of each request, or analyzing server logs to obtain response time data, and analyzing the recorded response time data to extract node response delay characteristic data, such as average response time, response time variance, and response time distribution.

[0120] Step S134: Evaluate the stability of node calls for high-access nodes to obtain high-access node stability data;

[0121] In an embodiment of the present invention, by selecting appropriate stability indicators, such as the availability of nodes and response time volatility, and calculating the stability indicators of each node according to the access records and response time data of high-access nodes. For example, spectral data can be obtained according to node spectral data, and node amplitude analysis can be performed on it to obtain the high and low stability amplitude characteristics of the nodes, and high-stability nodes are marked based on this.

[0122] Step S135: Evaluate high-access response performance nodes based on the high-access node stability data and the node response delay characteristic data to obtain high-access response performance nodes;

[0123] In the embodiments of the present invention, by weighting the high-access node stability data and the node response latency characteristic data according to actual requirements, for example, a higher weight can be assigned according to the importance of the stability index, and based on the weighted index data, a comprehensive evaluation of the high-access nodes is performed to obtain high-access response performance nodes, which can be evaluated by the weighted average and the multi-index comprehensive scoring method.

[0124] Step S136: Evaluate the node request throughput of the low-access nodes to obtain the low-access node request throughput data;

[0125] In the embodiments of the present invention, by collecting the request throughput data of the low-access nodes, for example, the number of requests processed by each node per unit time, relevant data can be obtained using network traffic monitoring tools and server performance monitoring tools. According to the collected request throughput data, the throughput metrics of each node are calculated, such as the average throughput, peak throughput, and throughput distribution, and the calculated throughput metric data is organized into the low-access node request throughput data.

[0126] Step S137: Evaluate the low-access response performance nodes based on the low-access node request throughput data and the node response latency characteristic data to obtain the low-access response performance nodes.

[0127] In the embodiments of the present invention, by weighting the low-access node request throughput data and the node response latency characteristic data according to actual requirements, for example, a higher weight can be assigned according to the importance of the throughput metric, and based on the weighted index data, a comprehensive evaluation of the low-access nodes is performed to obtain the low-access response performance nodes. For example, it can be evaluated by the weighted average and the multi-index comprehensive scoring method.

[0128] The present invention preliminarily divides information service nodes into high-access nodes and low-access nodes by analyzing access frequencies and setting thresholds, providing a basis for subsequent adoption of different evaluation metrics for different types of nodes, extracting characteristic data on node response latency, which is one of the key metrics for evaluating node performance, providing a data basis for subsequent evaluation of the response performance of high-access nodes and low-access nodes. For high-access nodes, the focus is on evaluating their call stability, as stability is crucial for handling a large number of concurrent requests, ensuring that high-access nodes can continuously and reliably provide services; combining the stability data and response latency characteristic data of high-access nodes for comprehensive evaluation, screening out nodes with high-access response performance as core nodes to undertake key business processing; for low-access nodes, the focus is on evaluating their request throughput, as throughput reflects the node's ability to process requests, which is crucial for efficient resource utilization; combining the request throughput data and response latency characteristic data of low-access nodes for comprehensive evaluation, screening out nodes with low-access response performance as edge nodes to undertake auxiliary tasks. By separately evaluating the key performance metrics of high-access nodes and low-access nodes, more accurate node classification is achieved, laying a foundation for constructing an efficient and stable network service architecture, and also providing data support for subsequent resource optimization configuration and load balancing.

[0129] Preferably, step S134 includes the following steps:

[0130] Step S1341: Perform data frequency-domain conversion on high-access nodes to obtain high-access node spectra;

[0131] In an embodiment of the present invention, by collecting access data of high-access nodes, such as access time, access content, and access frequency, relevant data can be obtained using network traffic monitoring tools and log analysis tools; perform preprocessing on the collected data, such as removing abnormal data, data cleaning, and data format conversion, and perform frequency-domain conversion on the preprocessed data, such as using the fast Fourier transform to convert time-series data into spectral data.

[0132] Step S1342: Calculate the spectral amplitude of the high-access node spectra to obtain high-access node amplitude data;

[0133] In an embodiment of the present invention, by analyzing the spectral data of high-access nodes, identifying the main frequency components in the spectrum, and calculating the amplitude of each frequency component, i.e., the intensity of the signal at that frequency, the magnitude of the amplitude can reflect the stability of the node access frequency. The larger the amplitude, the more stable the node access frequency. Organize the calculated amplitude data into high-access node amplitude data.

[0134] Step S1343: Perform statistical analysis on the high-access node amplitude data to obtain a high-access node amplitude threshold;

[0135] In an embodiment of the present invention, by statistically analyzing the amplitude data of high-access nodes, such as calculating the average value, standard deviation, and distribution of the amplitude data, and setting a high-access node amplitude threshold according to the statistical analysis results, the threshold can be adjusted according to the actual situation, for example, it can be set as a multiple of the average amplitude.

[0136] Step S1344: Classify and calculate high-access nodes based on the high-access node amplitude threshold to obtain a high-amplitude node spectrum and a low-amplitude node spectrum;

[0137] In an embodiment of the present invention, by according to the high-access node amplitude threshold, the high-access nodes are divided into high-amplitude nodes and low-amplitude nodes. Nodes with an amplitude higher than the threshold are high-amplitude nodes, and nodes with an amplitude lower than the threshold are low-amplitude nodes. The high-access node spectrum data is separated according to the amplitude threshold to obtain a high-amplitude node spectrum and a low-amplitude node spectrum.

[0138] Step S1345: Perform time-domain transformation on the high-amplitude node spectrum and the low-amplitude node spectrum respectively to obtain low-stability node data and high-stability node data;

[0139] In an embodiment of the present invention, by performing time-domain transformation on the high-amplitude node spectrum and the low-amplitude node spectrum respectively, for example, using the inverse fast Fourier transform to convert the spectrum data into time series data, analyzing the data after the time-domain transformation, and identifying the stability of the node access frequency. For example, the fluctuation situation of the time series data can be observed. Nodes with a smaller fluctuation amplitude have a more stable access frequency. The analyzed stability data is sorted into low-stability node data and high-stability node data.

[0140] Step S1346: Perform stability node marking on high-access nodes based on the low-stability node data and the high-stability node data to obtain high-access node stability data.

[0141] In an embodiment of the present invention, by matching the low-stability node data and the high-stability node data with the high-access node list, the stability category of each high-access node is determined, and the matching results are sorted into high-access node stability data, which contains the stability category information of each high-access node.

[0142] The present invention maps the performance of nodes to the frequency domain by performing frequency domain conversion on the data of high-access nodes and calculating the spectral amplitude, providing a new perspective for subsequent analysis of node stability; by statistically analyzing the amplitude data of high-access nodes, a reasonable amplitude threshold is determined to distinguish nodes with different stabilities, and this threshold can be used as a benchmark for distinguishing high-amplitude nodes and low-amplitude nodes; according to the amplitude threshold, the spectrum of high-access nodes is divided into a high-amplitude node spectrum and a low-amplitude node spectrum, providing a basis for subsequent evaluation of the characteristics of nodes with different stabilities respectively; the high-amplitude node spectrum and the low-amplitude node spectrum are respectively subjected to time domain transformation to convert the frequency characteristics back to the time domain for subsequent stability evaluation and marking, which helps to further analyze the differences within high-access nodes; based on the data after time domain transformation, high-access nodes are marked as high-stability nodes or low-stability nodes, and finally high-access node stability data is obtained, which helps to identify nodes that are stable and highly reliable under request loads, providing an important reference for subsequent node classification and resource scheduling. Through the frequency domain analysis method, the problem of high-access node stability evaluation is cleverly transformed into the analysis of signal frequency characteristics, realizing the quantitative evaluation of node stability and providing technical support for building a more stable and reliable network service.

[0143] Preferably, step S2 includes the following steps:

[0144] Step S21: Obtain access user data for the information service node to obtain access user data;

[0145] In the embodiment of the present invention, after obtaining user consent through this step, user access data is obtained through the log records in the information service node. The URL and access time information accessed by the user can be extracted from the Web server log, and user data can also be obtained from other data sources, such as user registration information and user behavior data. All the obtained user data is integrated to form a complete access user data set.

[0146] Step S22: Extract user access characteristics from the access user data to obtain user access characteristic data;

[0147] In the embodiment of the present invention, by extracting characteristics from the obtained access user data, characteristic data for describing user access behavior is obtained. The number of web pages accessed by the user, access duration, access frequency, and access path characteristics are extracted. Semantic analysis can also be performed based on the content of the web pages accessed by the user to extract user interest and demand characteristics. All the extracted characteristic data is integrated to form a user access characteristic data set.

[0148] Step S23: Group the user access characteristic data to obtain user access grouped characteristic data;

[0149] In the embodiments of the present invention, by grouping the extracted user access feature data and classifying users with similar features into the same group, grouping can be performed according to the types of web pages accessed by users, access time periods, and access frequency features. The purpose of grouping is to abstract user access behaviors, facilitate the subsequent construction of a feature matrix, and integrate all the grouped feature data to form a user access grouped feature data set.

[0150] Step S24: Based on the user access grouped feature data, construct a feature matrix for the accessed user data to obtain a user access feature matrix;

[0151] In the embodiments of the present invention, by constructing a user access feature matrix based on the user access grouped feature data, the rows of the matrix represent users, the columns represent feature groups, and the elements in the matrix represent the feature values of users under the feature groups. The constructed user access feature matrix can be used for subsequent analysis and modeling.

[0152] Step S25: Perform cloud platform upload processing on the user access feature matrix to obtain a cloud feature access matrix.

[0153] In the embodiments of the present invention, by uploading the constructed user access feature matrix to the cloud platform for processing, the cloud platform can provide large-scale data storage and computing capabilities, facilitating further analysis and modeling of the feature matrix. Machine learning algorithms on the cloud platform can be used to analyze the feature matrix, identify patterns of user access behaviors, and thus perform personalized scheduling operations. The processed feature matrix is stored on the cloud platform to form a cloud feature access matrix.

[0154] The present invention provides basic data for subsequent user behavior analysis by collecting user access data on information service nodes. These data include user access time, access duration, accessed pages, and operation behaviors, providing raw materials for user feature extraction; by performing user feature extraction on the accessed user data, key features of users can be identified and captured. For example, the user's interests and hobbies can be inferred based on the accessed pages, and the user's activity level can be judged based on the operation behaviors, thus transforming the chaotic raw data into structured user feature data and providing a more direct basis for subsequent analysis; based on the user access grouped feature data, user behavior data is transformed into a matrix form, intuitively showing the feature distribution of different user groups in each behavior dimension. This matrix-form data structure facilitates data mining and analysis, such as discovering the behavior patterns and rules of different user groups; uploading the user access feature matrix to the cloud platform can utilize the powerful computing power of cloud computing for storage, analysis, and processing, providing support for real-time analysis and model training of large-scale user data, and also enabling data sharing and collaborative analysis through the cloud platform.

[0155] Preferably, step S24 includes the following steps:

[0156] Step S241: Obtain node access process data by performing a node access process on the accessed user data.

[0157] In the embodiment of the present invention, according to service requirements and data characteristics, service nodes in the user access process are obtained, the access trajectory of each user is parsed from the accessed user data, and the order and timestamp of the nodes accessed by the user are recorded; the access trajectories are integrated to form node access process data, and nested lists are used to represent the access paths of all users, where each sub-list represents the access path of a user, and the list elements are node names and corresponding timestamps.

[0158] Step S242: Generate user characteristic attributes from the user access grouped characteristic data to obtain user characteristic attribute data.

[0159] In the embodiment of the present invention, by deeply analyzing the characteristics of each group in the user access grouped characteristic data, for example, the access time of the high-frequency user group is concentrated at night, and the average access duration of the low-frequency user group is shorter. According to the group characteristics and analysis objectives, attributes that can describe user characteristics are designed, such as user-preferred service access. According to preset rules or algorithms, the values of each user on each characteristic attribute are calculated, and the characteristic attribute values of all users are integrated to form user characteristic attribute data, which can be represented by a dictionary data structure. Each user's characteristic attributes can be stored in a dictionary, where the key is the characteristic attribute name and the value is the corresponding attribute value.

[0160] Step S243: Analyze the user behavior path from the node access process data to obtain user behavior path data.

[0161] In the embodiment of the present invention, a suitable user behavior path analysis method is selected according to the analysis objective. For example, a Markov chain model is used to analyze the transition probability between different nodes of the user, predict the behavior path, and path clustering is performed. A clustering algorithm is used to divide similar user behavior paths into the same category to discover the behavior patterns of different user groups; the selected analysis method is used to analyze the node access process data, extract user behavior path features, and the analysis results are organized into user behavior path data, which can be represented by different data structures. The Markov chain model can be represented by a state transition matrix, and the path clustering result can be represented by cluster labels.

[0162] Step S244: Construct a user access feature matrix from the user characteristic attribute data and the user behavior path data to obtain a user access feature matrix.

[0163] In an embodiment of the present invention, the rows of the feature matrix represent users, the columns represent features, and the elements in the matrix represent the values of users on these features. The user feature attribute data and the user behavior path data are integrated into the feature matrix. The user feature attributes can be directly used as the columns of the feature matrix, and the user behavior path data needs to be converted according to specific analysis results. For example, the state transition probability of the Markov chain model and the cluster labels of path clustering are used as the columns of the feature matrix. According to the integrated feature data, the values of each user on each feature are filled into the feature matrix.

[0164] Through this step, the present invention focuses on the specific behavior trajectories of users in information service nodes. By recording the order and response time information of users accessing nodes, the information acquisition path of users can be restored, providing an important basis for subsequent analysis of user behavior patterns and interest preferences; based on the user access grouped feature data, more abundant user feature attributes are further mined and generated, and these attribute data can depict user portraits more comprehensively and finely, providing stronger support for in-depth understanding of user behavior patterns and potential needs; in-depth analysis of the node access process data, such as identifying typical paths of user access, discovering key nodes in the paths, and analyzing the behavior differences of users on different paths, these analysis results help to understand the information acquisition mode of users and provide data support for optimizing the layout of information service nodes and recommendation strategies; constructing a user access feature matrix can effectively combine and organize a large amount of user data. The matrix provides a structured method for quickly identifying and comparing the behavior patterns and preferences between different user groups, enabling the visualization and quantification of the connections between user behaviors, and grouping users according to features and behaviors, which can make personalized measures more targeted and contribute to making better decisions.

[0165] Preferably, step S3 includes the following steps:

[0166] Step S31: Conduct service node connection analysis on the information service nodes to obtain service node connection data;

[0167] Step S32: Extract access node data from the user access feature matrix to obtain user feature access node data;

[0168] Step S33: Extract access node block identifiers from the access order blockchain based on the user feature access node data to obtain priority access node identifiers;

[0169] Step S34: Identify unextracted node identifiers from the access order blockchain based on the priority access node identifiers to obtain unextracted node identifiers;

[0170] Step S35: Generate call instructions for the priority access node identifier and the unextracted node identifier to obtain the priority access node scheduling instruction and the unextracted node scheduling instruction;

[0171] Step S36: Based on the service node connection data, construct an access scheduling instruction tree for the priority access node scheduling instruction and the unextracted node scheduling instruction to obtain the access scheduling instruction tree.

[0172] As an embodiment of the present invention, refer to Figure 2 shown in Figure 1 is a detailed step flow diagram of step S3 in

[0173] Step S31: Analyze the service node connection of the information service node to obtain the service node connection data;

[0174] In the embodiment of the present invention, by collecting the connection relationship data between information service nodes, such as the network bandwidth, latency, and load information between nodes, relevant data can be obtained using network monitoring tools and performance testing tools, analyze the collected connection relationship data, identify the connection paths and connection quality between nodes. For example, according to the network bandwidth and latency information, the best connection path between nodes and the connection reliability between nodes can be determined, and the analyzed connection relationship data is organized into service node connection data, which contains the connection information, connection path, and connection quality of each node.

[0175] Step S32: Extract access node data from the user access feature matrix to obtain user feature access node data;

[0176] In the embodiment of the present invention, by parsing the user access feature matrix, the access feature data of each user is obtained, such as the access time, access content, and access frequency of the user. According to the user access feature data, combined with the type and function of the information service node, the nodes accessed by the user are determined, and the access feature data and access node information of each user are organized into user feature access node data.

[0177] Step S33: Extract the access node block identifier from the access order blockchain based on the user feature access node data to obtain the priority access node identifier;

[0178] In the embodiment of the present invention, by parsing the access order blockchain, the identifier and order information of each node are obtained, and the access node block that matches the service node frequently accessed by the user is searched in the access order blockchain, and the corresponding access node block identifier is extracted from the matched access node block as the priority access node identifier.

[0179] Step S34: Identify unextracted node identifiers from the access order blockchain based on the priority access node identifiers to obtain unextracted node identifiers;

[0180] In the embodiment of the present invention, by sequentially traversing all access node blocks in the access order blockchain, comparing the identifier of each access node block with the extracted priority access node identifiers, and recording the unextracted access node block identifiers in the comparison result as unextracted node identifiers.

[0181] Step S35: Generate call instructions for the priority access node identifiers and the unextracted node identifiers to obtain a priority access node scheduling instruction and an unextracted node scheduling instruction;

[0182] In the embodiment of the present invention, by distinguishing the corresponding service node types according to the priority access node identifiers and the unextracted node identifiers, and generating corresponding call instructions according to different types of service nodes, such as interface call instructions and data reading instructions, the generated call instructions are divided into a priority access node scheduling instruction and an unextracted node scheduling instruction, and different priorities are set.

[0183] Step S36: Construct an access scheduling instruction tree for the priority access node scheduling instruction and the unextracted node scheduling instruction based on the service node connection data to obtain an access scheduling instruction tree.

[0184] In the embodiment of the present invention, by creating an empty access scheduling instruction tree, using the priority access node scheduling instruction as the root node, and adding the unextracted node scheduling instructions having a connection relationship with the priority access node as child nodes to the access scheduling instruction tree according to the service node connection data, recursively adding the remaining unextracted node scheduling instructions to the access scheduling instruction tree until all instructions are added to the tree, and finally obtaining a complete access scheduling instruction tree.

[0185] The present invention analyzes the connection relationship between information service nodes to obtain service node connection data, providing a basic topological structure for constructing a scheduling instruction tree subsequently, helping to identify key node connection paths, and providing basic data for subsequent access scheduling and optimization; extracts node data that users often access from the user access feature matrix, providing a basis for personalized customized access paths and helping to understand users' access habits and trends; combines user feature access node data, extracts priority access node identifiers from the access order blockchain, and gives priority to ensuring the fast access of nodes frequently accessed by users, helping to optimize the access order and ensuring that users can access the required services quickly and efficiently; identifies node identifiers that have not been extracted in the access order blockchain to ensure that all nodes are incorporated into the scheduling instruction tree; generates call instructions for priority access nodes and unextracted nodes respectively, providing specific execution instructions for constructing the scheduling instruction tree, considering the importance, access frequency, and node connection data of nodes. This step ensures that the construction of the access scheduling instruction tree can make full use of network resources and optimize the access path; based on the service node connection data, integrates the priority access node scheduling instruction and the unextracted node scheduling instruction to construct a complete access scheduling instruction tree, realizing dynamic adjustment of the access path according to user behavior and service node relationships, being able to route to the corresponding service nodes efficiently and reliably, while considering the overall performance and resource utilization rate of the network, achieving personalized customization and optimization of the network service access path, thereby improving the service access efficiency and enhancing the user experience.

[0186] Preferably, step S31 includes the following steps:

[0187] Step S311: Mine service association rules for information service nodes to obtain node association rule data;

[0188] Step S312: Identify connection service nodes for information service nodes based on the node association rule data to obtain connection nodes;

[0189] Step S313: Calculate the weights of connection nodes to obtain connection node weight data;

[0190] Step S314: Construct a node connection topology for information service nodes based on the connection node weight data and the node association rule data to obtain a node connection topology structure;

[0191] Step S315: Analyze the service node connection data of the node connection topology structure to obtain service node connection data.

[0192] As an embodiment of the present invention, referring to Figure 3 shown, for Figure 2 the detailed step flow schematic diagram of step S31 in

[0193] Step S311: Mine service association rules for information service nodes to obtain node association rule data;

[0194] In an embodiment of the present invention, by collecting service access records of users from the logs of information service nodes and data sources of monitoring systems, for example, when a user accesses service node data, arranging the service access records of each user in chronological order to form a service node access sequence, and using an association rule mining algorithm to analyze the service node access sequence to mine the association rules between service nodes.

[0195] Step S312: Identify connection service nodes for information service nodes based on the node association rule data to obtain connection nodes;

[0196] In an embodiment of the present invention, by setting the minimum support and confidence thresholds of the association rules according to actual requirements and the characteristics of the node association rule data for screening out rules with significant relevance, traversing each rule in the node association rule data in turn, for each rule, determining whether its support and confidence are both greater than or equal to the set thresholds. If the rule meets the thresholds, the antecedent node and the consequent node in the rule are identified as a pair of connection service nodes.

[0197] Step S313: Calculate the weights of the connection nodes to obtain connection node weight data;

[0198] In an embodiment of the present invention, by selecting appropriate metrics according to business requirements and the characteristics of the node association rules to calculate the weights of the connection nodes, or by combining multiple metrics for comprehensive calculation, calculating the weights of each pair of connection nodes according to the selected weight calculation metrics. For example, the confidence of the association rule can be used as the weight, indicating the likelihood that a user accesses the consequent node after accessing the antecedent node, and storing all pairs of connection nodes and their corresponding weights as connection node weight data.

[0199] Step S314: Construct a node connection topology for information service nodes based on the connection node weight data and the node association rule data to obtain a node connection topology structure;

[0200] In an embodiment of the present invention, by using a graph data structure to create an empty node connection topology graph for representing the connection relationship between service nodes, traversing each pair of nodes and their corresponding weights in the connection node weight data in turn, for each pair of nodes, adding them to the node connection topology graph and adding a directed edge between them according to the weight, the direction of the edge is from the antecedent node of the association rule to the consequent node, and the weight of the edge represents the connection strength between the nodes. After traversing all the connection node weight data, the node connection topology structure is obtained.

[0201] Step S315: Analyze the service node connection data of the node connection topology structure to obtain the service node connection data.

[0202] In the embodiment of the present invention, by analyzing the importance of each service node according to the nodes in the topology structure and analyzing the connection paths between service nodes, for example, finding all paths from one node to another node, and fitting the above two pieces of data to obtain the service node connection data.

[0203] Through service association rule mining, the present invention can discover the association and dependency relationships between information service nodes. By analyzing these association rules, it is possible to understand which service nodes are often accessed or used together, as well as their functional or data dependencies. This helps to identify potential service clusters or modules and can optimize service deployment and architecture design; by identifying the connection service nodes, it is possible to find the nodes that have close connections or frequently co-occur in the service association rules. These connection nodes represent the transition or interaction points between service nodes. By identifying these nodes, the service process and navigation path can be optimized, enabling users to access relevant services more smoothly and improving service availability; calculating the weights of the connection nodes to obtain the connection node weight data can calculate the weight of each connection node based on the node association rules and user access data, providing data support for the subsequent construction of the node connection topology. By calculating the connection node weights, the association strength between different nodes can be identified, which helps to determine the key service nodes and can accordingly allocate resources and optimize the deployment strategy; based on the connection node weight data and the node association rule data, constructing the node connection topology for the information service nodes to obtain the node connection topology structure can construct a connection relationship network including all information service nodes, providing basic data support for the subsequent node access scheduling; by constructing the node connection topology structure, the association relationship between nodes can be clearly displayed, the best access path can be identified, the user experience can be optimized, and the access efficiency can be improved; analyzing the node connection topology structure can obtain the service node connection data, including the connection strength, distance, and transmission efficiency between different nodes. By analyzing these data, the information transmission and interaction between service nodes can be optimized. This helps to improve the service response speed and reduce the delay during cross-node interaction.

[0204] Preferably, step S4 includes the following steps:

[0205] Step S41: Analyze the user access preference of the cloud feature access matrix to obtain the user access service preference data;

[0206] In an embodiment of the present invention, for each user, the access times and access duration metrics of different service category nodes in the cloud feature access matrix are counted, and the access preference of the user for each service category is calculated. For example, the proportion of the access times of a user on a certain service category to the total access times can be used to represent the preference degree of the user for this category. The access service preference data of all users is integrated to form user access service preference data, which can be represented using a matrix data structure. For example, a matrix can be used to store the access preferences of all users, where the rows represent users and the columns represent service categories, and the matrix elements represent the preference degree of the user for the corresponding service category.

[0207] Step S42: Construct an access user index for the user access service preference data to obtain an access user index;

[0208] In an embodiment of the present invention, by selecting a suitable data structure to construct the access user index, for example, an inverted index can be used, where the key is the service category and the value is a list of users with access preferences for this service category. Traverse each user and their access preferences in the user access service preference data in turn. For each user, add it to the user list corresponding to the service category it prefers. After traversing all users, a complete access user index is obtained.

[0209] Step S43: Obtain a user access request by acquiring a user access request from the computer network;

[0210] In an embodiment of the present invention, by listening on a specific port in the computer network to receive access requests sent by users, when a user sends an access request, the request data packet is received, and the user's identity information and the requested service type information are parsed. The parsed user access request information is recorded and the recorded user access request information is organized into user access request data.

[0211] Step S44: Upload the user access request to the cloud platform to obtain user access request cloud data;

[0212] In an embodiment of the present invention, a connection with the cloud platform is established using an interface method. For example, an access key provided by the cloud platform can be used for authentication. The user access request data is encapsulated in the format required by the cloud platform, and the encapsulated user access request data is uploaded to the cloud platform using the interface method provided by the cloud platform.

[0213] Step S45: Generate an information service scheduling flow for the access scheduling instruction tree based on the user access request cloud data and the access user index to obtain an information service scheduling flow;

[0214] In an embodiment of the present invention, by querying the user in the cloud data according to the user access request, searching for the service category or service combination preferred by the user in the access user index, and searching for the scheduling instruction matching the user-preferred service in the access scheduling instruction tree, for example, a call instruction including a specific service node, the matching scheduling instructions are traversed and combined according to the predetermined rules of the access scheduling instruction tree to generate an information service scheduling flow.

[0215] Step S46: Perform a preliminary scheduling of cloud platform services on the information service scheduling flow to obtain a preliminary scheduling result of cloud platform services.

[0216] In an embodiment of the present invention, by parsing the information service scheduling flow, extracting the service resource requirements corresponding to each scheduling instruction, and according to the service resource requirements, checking whether there are sufficient available resources on the cloud platform to meet the scheduling requirements, reserving the corresponding service resources, and feeding back the reservation result to the information service scheduling flow to prepare for subsequent actual service scheduling.

[0217] The present invention analyzes the cloud feature access matrix to identify the user's access preferences for different services, optimizes the cloud platform resource allocation according to the user's needs, provides a data basis for personalized service recommendation and resource optimization allocation, improves the user experience and system efficiency; by constructing an access user index, it can effectively organize and index user access data, which can achieve fast and accurate search and retrieval of user information, which is crucial for the management of large cloud platforms. This index can also enhance the access engine, provide personalized services for users, and improve their satisfaction; by obtaining the user's request to access the computer network, it can monitor and analyze the user's needs in real time, which can help the cloud platform maintain the ability to respond to changes in user needs, and ensure the timeliness and effectiveness of service responses; uploading the user access request to the cloud platform to achieve centralized management and processing, facilitating global optimization and resource scheduling, and improving the overall performance of the system; combining the user access request and the access user index to generate an information service scheduling flow, realizing personalized service scheduling based on user preferences and real-time needs, optimizing resource utilization, improving service quality and user experience; performing a preliminary scheduling of cloud platform services on the information service scheduling flow, making resource reservation and allocation in advance, shortening the service response time, improving the system throughput and concurrent processing ability, ensuring service stability and reliability, and optimizing the call order of information service nodes, thereby solving the problems of delayed response, multi-user competition for information service resources, limitations of scheduling strategies, and the resulting slow access response speed and waste of information service resources in information services.

[0218] Preferably, the present invention also provides a computer network information service management system based on cloud technology for executing the computer network information service management method based on cloud technology as described above. The computer network information service management system based on cloud technology includes:

[0219] An access order blockchain mapping module, which is used to set information service nodes for a computer network to obtain information service nodes; divide the information service nodes to obtain a central service node and edge service nodes; perform access order blockchain mapping on the central service node and the edge service nodes to obtain an access order blockchain;

[0220] A user access feature matrix uploading module, which is used to extract access user features from information service nodes to obtain user access feature data; construct a feature matrix for the user access feature data to obtain a user access feature matrix, and perform cloud platform uploading processing on the user access feature matrix to obtain a cloud feature access matrix;

[0221] An access scheduling instruction tree generating module, which is used to perform service node connection analysis on information service nodes to obtain service node connection data; extract access node block identifiers from the access order blockchain based on the user access feature matrix to obtain a priority access node identifier and an unextracted node identifier; generate an access scheduling instruction tree based on the service node connection data for the priority access node identifier and the unextracted node identifier to obtain an access scheduling instruction tree;

[0222] A user access pre-scheduling module, which is used to upload a user access request to a cloud platform for a computer network to obtain user access request cloud data; construct an access user index for the cloud feature access matrix to obtain an access user index; perform cloud platform service pre-scheduling on the access scheduling instruction tree based on the user access request cloud data and the access user index to obtain a cloud platform service pre-scheduling result.

[0223] In summary, the present invention provides a computer network information service management system based on cloud technology. The computer network information service management system based on cloud technology is composed of an access order blockchain mapping module, a user access feature matrix uploading module, an access scheduling instruction tree generating module, and a user access pre-scheduling module, and can implement any computer network information service management method described in the present invention. It is used to realize any computer network information service management method through the operations between computer programs running on each module. The internal structure of the system cooperates with each other, which can greatly reduce repetitive work and manpower investment, and can quickly and effectively provide a more accurate and efficient computer network information service management process based on cloud technology, thereby simplifying the operation process of the computer network information service management system based on cloud technology.

[0224] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A computer network information service management method based on cloud technology, characterized in that: The following steps are involved: Step S1: Setting information service nodes on the computer network to obtain information service nodes; dividing the information service nodes to obtain central service nodes and edge service nodes; mapping the central service nodes and edge service nodes to access order blockchain to obtain access order blockchain; Step S2: extracting access user features from the information service node to obtain user access feature data; constructing a feature matrix from the user access feature data to obtain a user access feature matrix; and uploading the user access feature matrix to the cloud platform to obtain a cloud feature access matrix; Step S3: Perform service node connection analysis on the information service node to obtain service node connection data; Extract access node block identifiers from the access order blockchain based on the user access feature matrix to obtain priority access node identifiers and unextracted node identifiers; Generate an access scheduling instruction tree for the priority access node identifier and the unextracted node identifier based on the service node connection data to obtain an access scheduling instruction tree; Step S4: Upload the user access request of the computer network to the cloud platform to obtain the user access request cloud data; construct the access user index of the cloud feature access matrix to obtain the access user index; perform cloud platform service preparation scheduling on the access scheduling instruction tree based on the user access request cloud data and the access user index to obtain the cloud platform service preparation scheduling result.

2. The computer network information service management method based on cloud technology according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Setting information service nodes on the computer network to obtain information service nodes; Step S12: Analyze the node centrality index data of the information service node to obtain the node centrality index data; Step S13: Analyze the access response performance of the information service nodes to obtain nodes with high access response performance and nodes with low access response performance; Step S14: generating a central service node for the node with high access response performance to obtain a central service node; Step S15: generating edge service nodes for nodes with low access response performance to obtain edge service nodes; Step S16: Based on the node centrality index data, the access order blockchain is mapped to the central service node and the edge service node to obtain the access order blockchain.

3. The computer network information service management method based on cloud technology according to claim 2 is characterized in that: Step S13 includes the following steps: Step S131: Perform access frequency threshold analysis on the information service node to obtain the access frequency threshold; Step S132: dividing the information service nodes into high-access nodes and low-access nodes based on the access frequency threshold, thereby obtaining high-access nodes and low-access nodes; Step S133: extracting node response delay characteristics of the information service node to obtain node response delay characteristic data; Step S134: performing node call stability evaluation on high-access nodes to obtain high-access node stability data; Step S135: performing high access response performance node evaluation on the high access node stability data and the node response delay characteristic data to obtain a high access response performance node; Step S136: evaluating the node request throughput of the low-access node to obtain the low-access node request throughput data; Step S137: perform low access response performance node evaluation on the low access node request throughput data and the node response delay characteristic data to obtain the low access response performance node.

4. The computer network information service management method based on cloud technology according to claim 3 is characterized in that: Step S134 includes the following steps: Step S1341: performing frequency domain conversion on the data of the high-access node to obtain a frequency spectrum of the high-access node; Step S1342: Calculate the spectrum amplitude of the high-access node spectrum to obtain high-access node amplitude data; Step S1343: Statistically analyzing the high-access node amplitude data to obtain a high-access node amplitude threshold; Step S1344: classify and calculate the high-access nodes based on the high-access node amplitude threshold to obtain a high-amplitude node spectrum and a low-amplitude node spectrum; Step S1345: performing time domain transformation on the high amplitude node spectrum and the low amplitude node spectrum respectively to obtain low stability node data and high stability node data; Step S1346: Mark the high-access node as a stability node based on the low-stability node data and the high-stability node data to obtain the high-access node stability data.

5. The computer network information service management method based on cloud technology according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: acquiring access user data from the information service node to obtain access user data; Step S22: extracting user access features from access user data to obtain user access feature data; Step S23: performing feature grouping on the user access feature data to obtain user access group feature data; Step S24: constructing a feature matrix for the access user data based on the user access group feature data to obtain a user access feature matrix; Step S25: Upload the user access feature matrix to the cloud platform to obtain a cloud feature access matrix.

6. The computer network information service management method based on cloud technology according to claim 5, characterized in that: Step S24 includes the following steps: Step S241: acquiring the node access process of the access user data to obtain the node access process data; Step S242: generating user characteristic attributes for the user access group characteristic data to obtain user characteristic attribute data; Step S243: Perform user behavior path analysis on the node access process data to obtain user behavior path data; Step S244: construct a user access feature matrix based on the user feature attribute data and the user behavior path data to obtain a user access feature matrix.

7. The computer network information service management method based on cloud technology according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Perform service node connection analysis on the information service node to obtain service node connection data; Step S32: extracting access node data from the user access feature matrix to obtain user feature access node data; Step S33: extracting the access node block identifier from the access order blockchain based on the user characteristic access node data to obtain a priority access node identifier; Step S34: Identify the unextracted node identifiers of the access order blockchain based on the priority access node identifiers to obtain the unextracted node identifiers; Step S35: generating a call instruction for the priority access node identifier and the unextracted node identifier to obtain a priority access node scheduling instruction and an unextracted node scheduling instruction; Step S36: constructing an access scheduling instruction tree for the priority access node scheduling instructions and the un-extracted node scheduling instructions based on the service node connection data to obtain an access scheduling instruction tree.

8. The computer network information service management method based on cloud technology according to claim 7 is characterized in that: Step S31 includes the following steps: Step S311: mining service association rules for information service nodes to obtain node association rule data; Step S312: identifying the connection service node of the information service node based on the node association rule data to obtain the connection node; Step S313: Calculate the connection node weight for the connection node to obtain the connection node weight data; Step S314: constructing a node connection topology for the information service nodes based on the connection node weight data and the node association rule data to obtain a node connection topology structure; Step S315: Analyze the service node connection data of the node connection topology structure to obtain the service node connection data.

9. The computer network information service management method based on cloud technology according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Perform user access preference analysis on the cloud feature access matrix to obtain user access service preference data; Step S42: constructing an access user index for the user access service preference data to obtain an access user index; Step S43: Obtaining a user access request from the computer network to obtain a user access request; Step S44: Upload the user access request to the cloud platform to obtain the user access request cloud data; Step S45: generating an information service scheduling flow for the access scheduling instruction tree based on the user access request cloud data and the access user index to obtain an information service scheduling flow; Step S46: Perform cloud platform service preparatory scheduling on the information service scheduling flow to obtain a cloud platform service preparatory scheduling result.

10. A computer network information service management system based on cloud technology, characterized in that: Used to execute the computer network information service management method based on cloud technology as claimed in claim 1, the computer network information service management system based on cloud technology comprises: The access order blockchain mapping module is used to set information service nodes on the computer network to obtain information service nodes; divide information service nodes into information service nodes to obtain central service nodes and edge service nodes; perform access order blockchain mapping on central service nodes and edge service nodes to obtain access order blockchain; The user access feature matrix upload module is used to extract the user features of the information service node to obtain the user access feature data; construct the feature matrix of the user access feature data to obtain the user access feature matrix, and upload the user access feature matrix to the cloud platform to obtain the cloud feature access matrix; The access scheduling instruction tree generation module is used to perform service node connection analysis on the information service node to obtain service node connection data; extract the access node block identifier from the access order blockchain based on the user access feature matrix to obtain the priority access node identifier and the unextracted node identifier; generate the access scheduling instruction tree for the priority access node identifier and the unextracted node identifier based on the service node connection data to obtain the access scheduling instruction tree; The user access pre-scheduling module is used to upload user access requests to the computer network to the cloud platform to obtain user access request cloud data; to construct access user indexes for the cloud feature access matrix to obtain access user indexes; and to perform cloud platform service pre-scheduling on the access scheduling instruction tree based on the user access request cloud data and the access user index to obtain cloud platform service pre-scheduling results.

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