A method and system for intelligent optimization of cloud service metering and billing strategies

Abstract

This application relates to the field of intelligent strategy optimization technology, and discloses a method and system for intelligent optimization of cloud service metering and billing strategies. The method includes: classifying target groups based on pre-acquired target group data from a cloud server and constructing a multi-chain blockchain; responding to metering and billing tasks of the target groups, analyzing the target types of the target groups and extracting task characteristics to generate metering impact factors and billing impact factors; matching corresponding target metering chain nodes and target billing chain nodes in the main blockchain to generate a first metering and billing strategy; filtering target sub-blockchains based on target types, matching target optimization chain nodes in the target sub-blockchains, and optimizing the first metering and billing strategy to obtain a second metering and billing strategy. This application can accurately optimize strategies for metering and billing tasks, improve the matching degree between metering and billing strategies and tasks, and enhance the accuracy and real-time performance of metering and billing strategies.

Description

Technical Field

[0001] This application relates to the field of intelligent strategy optimization technology, and more specifically to a method and system for intelligent optimization of cloud service metering and billing strategies. Background Technology

[0002] Currently, enterprises have increasingly diversified metering and billing needs for cloud services. Existing metering and billing models are costly to calculate, prone to rule omissions or calculation errors in complex scenarios, and lack data reliability and security. Furthermore, with numerous enterprise types, fixed metering and billing models cannot meet the diverse needs of different types of enterprises, resulting in strategies that fail to fulfill metering and billing requirements.

[0003] Existing technologies have the following problems: single-chain blockchains cannot simultaneously consider the general rules of metering and billing strategies and the different strategy formulation requirements of each task, resulting in a single strategy that cannot adapt to real-time changes in tasks; the use of fixed strategy optimization methods cannot dynamically optimize strategies in combination with the real-time needs of tasks, and cannot meet the real-time changes in task requirements; directly formulating metering and billing strategies without considering the relationship between metering and billing results in unreasonable strategy logic and high implementation difficulty; to solve at least one of the above problems, this application proposes a cloud service metering and billing strategy intelligent optimization method and system. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this application is to provide a method and system for intelligent optimization of cloud service metering and billing strategies, which can effectively solve the problems in the background technology. The specific technical solution of this application is as follows:

[0005] A method for intelligent optimization of cloud service metering and billing strategies includes:

[0006] Based on the target group data pre-acquired by the cloud server, the target group is classified and a multi-chain structure blockchain is constructed. The multi-chain structure blockchain includes a main blockchain for generating metering and billing strategies and multiple slave blockchains configured according to the target group type.

[0007] In response to the metering and billing tasks of the target group, the target group's target type is analyzed and task characteristics are extracted to generate metering impact factors and billing impact factors;

[0008] Based on the metering impact factor and the billing impact factor, the corresponding target metering chain node and target billing chain node are matched in the main blockchain respectively, and a first metering and billing strategy is generated according to the target metering chain node and the target billing chain node.

[0009] Based on the target type, the corresponding target is selected from the blockchain. Based on the metering impact factor and the billing impact factor, the corresponding target optimization chain node is matched in the target blockchain. The first metering and billing strategy is optimized to obtain the second metering and billing strategy, so as to intelligently optimize the metering and billing strategy.

[0010] Specifically, the step of classifying the target group based on the target group data pre-acquired by the cloud server and constructing a multi-chain blockchain structure includes:

[0011] Based on the target group data pre-acquired by the cloud server, the target group is classified through clustering to obtain the group type;

[0012] Construct a main blockchain and corresponding slave blockchains for each group type, wherein the main blockchain includes metering chain nodes and billing chain nodes, and the slave blockchain includes optimization chain nodes;

[0013] By combining the main blockchain and multiple slave blockchains, a multi-chain blockchain structure is obtained.

[0014] Specifically, the construction of the main blockchain and the corresponding slave blockchains for each group type includes:

[0015] Based on the preset metering and billing standards, generate a set of metering chain nodes and a set of billing chain nodes;

[0016] Analyze the correlation between metering and billing for the target group, connect metering chain nodes and billing chain nodes with a correlation greater than a preset correlation threshold, and use the output of the metering chain nodes as the input of the billing chain nodes to construct the main blockchain;

[0017] For each group type, configure a separate blockchain, generate optimized chain nodes based on the historical metering and billing data of the group type, and match them with the metering chain nodes and billing chain nodes in the main blockchain. Connect the optimized chain nodes of each slave blockchain with the matched metering chain nodes and billing chain nodes to obtain multiple slave blockchains.

[0018] Specifically, the metering and billing task in response to the target group involves analyzing the target group's target type and extracting task characteristics to generate metering impact factors and billing impact factors, including:

[0019] In response to the metering and billing tasks of the target group, task features are extracted and a task feature vector is constructed.

[0020] By combining the target group's target type and the task feature vector, the metering scale and billing demand are analyzed through a preset metering and billing analysis model, and the metering impact factor and billing impact factor are calculated.

[0021] Specifically, based on the metering impact factor and the billing impact factor, corresponding target metering chain nodes and target billing chain nodes are matched in the main blockchain, respectively. A first metering and billing strategy is generated according to the target metering chain node and the target billing chain node, including:

[0022] Analyze the metering capabilities of metering chain nodes and the billing capabilities of billing chain nodes in the main blockchain, and match the corresponding target metering chain nodes and target billing chain nodes based on the metering impact factors and billing impact factors, respectively.

[0023] Configure a policy generation mechanism to generate a first metering and billing policy based on the target metering chain node and the target billing chain node.

[0024] Specifically, the analysis of the metering capabilities of metering chain nodes and the billing capabilities of billing chain nodes in the main blockchain, and the matching of corresponding target metering chain nodes and target billing chain nodes based on the metering impact factor and the billing impact factor, includes:

[0025] For each metering chain node and billing chain node in the main blockchain, analyze the metering capability of the metering chain node and the billing capability of the billing chain node respectively, and construct the corresponding metering capability vector and billing capability vector.

[0026] According to the dimensions of the measurement capability vector and the billing capability vector, the measurement impact factor and the billing impact factor are decomposed and mapped respectively to obtain the corresponding target measurement vector and target billing vector.

[0027] Calculate the first similarity between the target measurement vector and the measurement capability vector, and filter out measurement chain nodes with a first similarity greater than a preset similarity threshold to obtain the first measurement chain node set;

[0028] Among the billing chain nodes connected to the metering chain nodes in the first metering chain node set, billing chain nodes whose second similarity between the target billing vector and the billing capability vector is greater than a preset similarity threshold are selected to obtain the first billing chain node set.

[0029] By combining the first similarity, the second similarity, and the correlation, target metering chain nodes and target billing chain nodes are selected from the first metering chain node set and the first billing chain node set.

[0030] Specifically, the configuration policy generation mechanism generates a first metering and billing policy based on the target metering chain node and the target billing chain node, including:

[0031] Based on the target metering chain node, the first metering result and billing constraint result are calculated using a pre-set strategy to generate a model.

[0032] Based on the first metering result and the billing constraint result, the target billing chain node calculates the first billing result;

[0033] By combining the first measurement result and the first billing result, the first measurement and billing strategy is obtained.

[0034] Specifically, based on the target type, the corresponding target from the blockchain is selected; based on the metering impact factor and the billing impact factor, the corresponding target optimization chain node is matched in the target from the blockchain; and the first metering and billing strategy is optimized to obtain the second metering and billing strategy, including:

[0035] Based on the target type, the corresponding target from the blockchain is selected, and the first optimized chain node set connected to the target metering chain node and the target billing chain node in the target from the blockchain is selected.

[0036] Based on the measurement impact factor and the billing impact factor, the corresponding target optimization chain node is matched in the first optimization chain node set;

[0037] The first metering and billing strategy is optimized based on the target optimization chain nodes to obtain the second metering and billing strategy.

[0038] Specifically, the first metering and billing strategy is optimized based on the target optimization chain nodes to obtain the second metering and billing strategy, including:

[0039] Based on the first metering and billing strategy, the target optimization chain nodes predict and simulate the strategy evolution results through a preset strategy simulation model;

[0040] Analyze the differences between the strategy evolution results and the metering and billing tasks to generate optimized strategy parameters;

[0041] The first metering and billing strategy is optimized based on the optimization strategy parameters to obtain the second metering and billing strategy.

[0042] A cloud service metering and billing strategy intelligent optimization system, used to implement the aforementioned cloud service metering and billing strategy intelligent optimization method, includes:

[0043] The blockchain construction module classifies the target group based on the target group data pre-acquired by the cloud server and constructs a multi-chain structure blockchain. The multi-chain structure blockchain includes a main blockchain for generating metering and billing strategies and multiple slave blockchains configured according to the target group type.

[0044] The task analysis module responds to the metering and billing tasks of the target group, analyzes the target group's target type and extracts task characteristics, and generates metering impact factors and billing impact factors.

[0045] The strategy generation module matches the target metering chain node and the target billing chain node in the main blockchain based on the metering impact factor and the billing impact factor, and generates a first metering and billing strategy according to the target metering chain node and the target billing chain node.

[0046] The strategy optimization module filters the corresponding target from the blockchain according to the target type, matches the corresponding target optimization chain node in the target from the blockchain based on the metering impact factor and the billing impact factor, optimizes the first metering and billing strategy, and obtains the second metering and billing strategy to intelligently optimize the metering and billing strategy.

[0047] The beneficial effects of this application are as follows: A multi-chain structure blockchain is constructed, with a main blockchain and slave blockchains working together. Metering and billing impact factors are calculated based on metering and billing tasks. Corresponding target metering chain nodes and target billing chain nodes are matched in the main blockchain to formulate a first metering and billing strategy. Then, corresponding target optimization chain nodes are matched in the slave blockchains of the corresponding target type to optimize the first metering and billing strategy, resulting in a second metering and billing strategy. The multi-chain structure blockchain allows for rapid formulation of metering and billing strategies. By combining metering and billing impact factors with the correlation between metering and billing chain nodes, the selected target metering and billing chain nodes are logically sound, improving the logic and feasibility of the formulated metering and billing strategy. Accurate optimization of the metering and billing strategy using the optimization chain nodes of the slave blockchain enables precise strategy optimization for metering and billing tasks, improving the matching degree between the metering and billing strategy and the task, and enhancing the accuracy and real-time performance of the metering and billing strategy. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating the intelligent optimization method for cloud service metering and billing strategies in an embodiment of this application.

[0049] Figure 2 This is a schematic diagram of the main blockchain in an embodiment of this application;

[0050] Figure 3 This is a schematic diagram of the blockchain in the embodiments of this application;

[0051] Figure 4 This is a schematic diagram of a multi-chain blockchain structure in an embodiment of this application;

[0052] Figure 5 This is a schematic diagram of the structure of a cloud service metering and billing strategy intelligent optimization system according to an embodiment of this application. Detailed Implementation

[0053] The present application will be further described in detail below with reference to the accompanying drawings and embodiments. In the embodiments of the present application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0054] Hereinafter, the terms "first," "second," and other generic terms are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0055] refer to Figure 1 The image shows a specific implementation of a cloud service metering and billing strategy intelligent optimization method according to this application, including:

[0056] S101. Based on the target group data pre-acquired by the cloud server, classify the target group and construct a multi-chain structure blockchain, wherein the multi-chain structure blockchain includes a main blockchain for generating metering and billing strategies and multiple slave blockchains configured according to the target group type.

[0057] S102. In response to the metering and billing tasks of the target group, analyze the target type of the target group and extract task characteristics to generate metering impact factors and billing impact factors.

[0058] S103. Based on the metering impact factor and the billing impact factor, match the corresponding target metering chain node and target billing chain node in the main blockchain respectively, and generate a first metering and billing strategy according to the target metering chain node and the target billing chain node.

[0059] S104. Filter the corresponding target from the blockchain according to the target type, match the corresponding target optimization chain node in the target from the blockchain based on the metering impact factor and the billing impact factor, optimize the first metering and billing strategy, and obtain the second metering and billing strategy to intelligently optimize the metering and billing strategy.

[0060] Cloud service users have diverse needs, and different enterprises have different requirements for metering and billing strategies. A uniform strategy formulation approach cannot meet the diverse metering and billing needs of different enterprises. This embodiment classifies target groups based on target group data and constructs a multi-chain blockchain structure with a main blockchain and slave blockchains working together. The main blockchain stores common metering and billing rules for users, used to quickly generate initial metering and billing strategies. Multiple slave blockchains are configured according to target group types, each corresponding to a specific metering and billing rule for a particular target group. Simultaneously, the immutability of the blockchain prevents data tampering and ensures data security. Target group data, including but not limited to identity size and resource usage behavior, is extracted from the cloud server backend. Target groups are classified by clustering based on this data. A multi-chain blockchain structure is constructed based on the target group types, and a connection is established between the main blockchain and slave blockchains. By constructing a multi-chain blockchain structure with a main blockchain and slave blockchains working together, when formulating strategies, the corresponding chain nodes can be matched according to task requirements to achieve precise matching. Based on the matched chain nodes, corresponding metering and billing strategies can be formulated, improving the efficiency of strategy formulation and the matching degree with tasks. When adding new group types, only the corresponding slave blockchain needs to be added, without modifying the main blockchain, which can improve the stability of the blockchain structure and its adaptability to group types and tasks.

[0061] When a metering and billing task is received from a target group, the task is analyzed. Based on the target group type's classification tags in the cloud server database, the target group is matched with the category to determine its target type. Task characteristics are extracted, including but not limited to CPU core count, memory size, and usage duration. Corresponding metering and billing impact factors are calculated based on these characteristics. Extracting task feature data avoids missing key features that could lead to metering and billing errors. Based on the target group type, personalized metering and billing strategies can be developed for different group types, improving the accuracy of the strategies.

[0062] Specifically, based on the measurement impact factor and the billing impact factor, the main blockchain is used to screen out the measurement requirements corresponding to the measurement impact factor and the billing requirements corresponding to the billing impact factor, respectively, to obtain the corresponding target measurement chain nodes and target billing chain nodes. According to the measurement and billing rules in the target measurement chain nodes and target billing chain nodes, the first measurement and billing strategy is generated. Combining the correlation between chain nodes to screen out the target measurement chain nodes and target billing chain nodes can improve the logic of the generated first measurement and billing strategy. By quickly matching the corresponding chain nodes and generating the corresponding strategy through the main blockchain, the efficiency of strategy formulation can be improved.

[0063] After formulating the first metering and billing strategy, corresponding target blockchains are selected based on the target type. Based on the metering and billing impact factors, corresponding target optimization chain nodes are matched within the target blockchains. The first metering and billing strategy is then decomposed, and the decomposed metering and billing strategies are optimized using the target optimization chain nodes to obtain the second metering and billing strategy, thus achieving intelligent optimization of the metering and billing strategy. By selecting target blockchains and target optimization chain nodes, the metering and billing strategy can be optimized specifically for each target type, making the optimized strategy more suitable for the needs of the target group. Real-time optimization of the strategy based on the blockchain does not require access to the main blockchain, improving the stability of the blockchain structure during the strategy optimization process.

[0064] This application constructs a multi-chain blockchain structure with a main blockchain and slave blockchains working together. Based on metering and billing tasks, it calculates metering and billing impact factors separately. In the main blockchain, it matches corresponding target metering chain nodes and target billing chain nodes to formulate a first metering and billing strategy. Then, in slave blockchains of the corresponding target type, it matches corresponding target optimization chain nodes to optimize the first metering and billing strategy, resulting in a second metering and billing strategy. The multi-chain blockchain structure allows for rapid formulation of metering and billing strategies. By combining metering and billing impact factors with the correlation between metering and billing chain nodes, the selected target metering and billing chain nodes are logically sound, improving the logic and feasibility of the formulated metering and billing strategy. Combined with the optimization chain nodes of the slave blockchain, the metering and billing strategy is accurately optimized, enabling precise strategy optimization for metering and billing tasks, improving the matching degree between the metering and billing strategy and the task, and enhancing the accuracy and real-time performance of the metering and billing strategy.

[0065] Furthermore, based on the target group data pre-acquired by the cloud server, the target group is classified, and a multi-chain blockchain structure is constructed, including:

[0066] S201. Based on the target group data pre-acquired by the cloud server, the target group is classified through clustering to obtain the group type;

[0067] S202. Construct a main blockchain and a slave blockchain corresponding to each group type, wherein the main blockchain includes metering chain nodes and billing chain nodes, and the slave blockchain includes optimization chain nodes;

[0068] S203. Combining the main blockchain and multiple slave blockchains, a multi-chain structure blockchain is obtained.

[0069] In this embodiment, based on the target group data pre-acquired by the cloud server, the target group is classified through clustering to obtain the group type; the target group data is obtained from the cloud server, and the data is preprocessed such as deduplication and normalization. The AP clustering algorithm is used to cluster the preprocessed target group data, grouping the target group into multiple categories, each corresponding to a target group category; clustering can quickly classify the target group and obtain accurate target group categories. Based on the target group categories, different metering and billing strategies can be formulated for different categories, improving the matching degree between the strategy and the needs of the target group.

[0070] like Figure 2 As shown, the main blockchain unifies the shared metering and billing logic for users, enabling the rapid formulation of initial metering and billing strategies. Then, specific metering and billing rules for each target group are stored in their respective slave blockchains. Three main chain nodes are deployed in the main blockchain: a technology node, a finance node, and a compliance node. The technology node maintains the metering rules, the finance node maintains the billing rules, and the compliance node reviews the legality of the rules. Metering chain nodes and billing chain nodes are created in the main blockchain. Different metering chain nodes are created based on different resource types, storing the metering rules for each resource type. Similarly, different billing chain nodes are created based on different fee types, storing the billing rules for each fee type. Each created metering and billing chain node requires joint review by the three main chain nodes before becoming effective, improving the quality of the metering and billing chain nodes and the accuracy and rationality of the metering and billing rules.

[0071] like Figure 3 As shown, a separate blockchain is built for each group type, with three slave chain nodes deployed in each blockchain: a technology department node, a finance department node, and a compliance department node. Representative groups corresponding to the cluster centers are selected from each group type. The metering and billing data of these representative groups are analyzed to determine their corresponding metering and billing needs. Optimized chain nodes are created based on these needs, and each optimized chain node requires joint review by the three slave chain nodes in its corresponding blockchain before it becomes effective. By building a separate blockchain for each group category, different categories are isolated, reducing policy deviations and preventing confusion caused by mixing rules from different categories. Furthermore, when changing the metering and billing rules for a particular group, only the optimized chain nodes of the corresponding blockchain need to be modified, improving modification efficiency and the stability of the multi-chain structure.

[0072] It is important to emphasize that by building a main blockchain and slave blockchains, the main blockchain is responsible for unifying the basic metering and billing rules, quickly formulating the initial metering and billing strategy, improving the efficiency of strategy formulation, and optimizing the initial metering and billing strategy based on the optimized chain nodes in the slave blockchain corresponding to the group type, thereby improving the matching degree between the formulated metering and billing strategy and the target group, and improving the accuracy and applicability of the metering and billing strategy.

[0073] Specifically, by combining the main blockchain and multiple slave blockchains, a multi-chain structure blockchain is constructed. This multi-chain structure allows the main blockchain to quickly find the slave blockchains corresponding to different group categories. At the same time, the inter-chain connections ensure that the data of the main and slave blockchains can be coordinated, enabling multi-chain collaboration for strategy formulation and improving the efficiency and accuracy of strategy formulation.

[0074] Furthermore, construct the main blockchain and the corresponding slave blockchains for each group type, including:

[0075] S301. Generate a set of metering chain nodes and a set of billing chain nodes according to the preset metering and billing standards;

[0076] S302. Analyze the correlation between metering and billing for the target group, connect metering chain nodes and billing chain nodes with a correlation greater than a preset correlation threshold, and use the output of the metering chain nodes as the input of the billing chain nodes to construct the main blockchain.

[0077] S303. Configure a slave blockchain for each group type, generate optimized chain nodes according to the historical metering and billing data of the group type, and match them with the metering chain nodes and billing chain nodes in the main blockchain. Connect the optimized chain nodes of each slave blockchain with the matched metering chain nodes and billing chain nodes to obtain multiple slave blockchains.

[0078] In this embodiment, a set of metering chain nodes and a set of billing chain nodes are generated according to preset metering and billing standards; corresponding metering and billing standards are formulated based on the enterprise's recognized basic basis. For metering standards, different metering standards are formulated according to different resource types. For example, CPU is metered by core hour, and memory is metered by GB. For billing standards, different billing standards are formulated according to different fee types. For example, different unit prices and surcharges are set according to different resource types.

[0079] In the main blockchain, metering chain nodes and billing chain nodes are created. Different metering chain nodes are created based on different resource types, and each metering chain node stores the metering standards for its corresponding resource type. Similarly, different billing chain nodes are created based on different fee types, and each billing chain node stores the billing standards for its corresponding fee type. This constructs a set of metering chain nodes and a set of billing chain nodes. By constructing corresponding chain nodes according to different resource types and fee types, each generated chain node corresponds to a clear metering or billing standard, which avoids ambiguity in metering and billing rules and improves the accuracy of the formulated metering and billing strategies.

[0080] Specifically, there is a correlation between metering and billing. Billing requires calculation based on the metering results. The correlation between metering and billing for the target group is analyzed, and metering chain nodes and billing chain nodes with a correlation greater than a preset correlation threshold are connected. This ensures that data in the main blockchain can be accurately transferred from the metering task to the billing task, avoiding logical errors caused by mismatch between metering and billing.

[0081] Based on the resource types used in the billing rules of the billing chain nodes, if the calculation of the billing chain node requires the measurement result of the resource type of the metering chain node, the correlation is high; if the calculation of the billing chain node does not require the measurement result of the resource type of the metering chain node, the correlation is low. By calculating the correlation between the billing process and the corresponding metering result, the correlation between the metering chain nodes and the billing chain nodes is obtained. A correlation threshold is set according to the metering and billing accuracy requirements. Metering chain nodes and billing chain nodes with a correlation greater than the preset correlation threshold are connected. The output of the metering chain node is used as the input of the billing chain node to construct the main blockchain. By analyzing the correlation between the metering chain nodes and the billing chain nodes to construct the main blockchain, a clear logical connection is established between the chain nodes, which can quickly realize the conversion from metering to billing, avoid billing errors, and improve data security and metering and billing accuracy.

[0082] like Figure 4 As shown, different group types have different needs. During the strategy optimization process, the initial metering and billing strategy generated based on the main blockchain is optimized, and the optimized chain nodes of the secondary blockchain must be matched with the chain nodes of the main blockchain. A secondary blockchain is configured for each group type. Representative groups corresponding to the cluster centers are selected from the corresponding group types. The metering and billing needs of the representative groups are analyzed, and based on the group metering and billing needs and historical metering and billing data, the metering and billing patterns of the corresponding groups are analyzed, and corresponding optimized chain nodes are created.

[0083] The process involves analyzing the resource types required by the corresponding group types for optimizing chain nodes, identifying matching metering chain nodes in the main blockchain, and selecting corresponding billing chain nodes based on the connection relationships between these metering and billing chain nodes in the main blockchain. Optimizing chain nodes are then connected to the selected metering and billing chain nodes, establishing a link between the main blockchain and each slave blockchain, resulting in multiple slave blockchains. Optimizing chain nodes generated based on group types and corresponding historical metering and billing data meet the actual needs of the corresponding group types. Matching and establishing links between these optimized chain nodes and the metering and billing chain nodes in the main blockchain allows for precise strategy optimization, enabling collaborative work between the main and slave blockchains. Independent optimization of each slave blockchain improves the stability of the blockchain structure during strategy formulation and optimization.

[0084] Furthermore, in response to the metering and billing tasks of the target group, the target group's target type is analyzed and task characteristics are extracted to generate metering impact factors and billing impact factors, including:

[0085] S401. In response to the metering and billing tasks of the target group, extract the task features and construct the task feature vector;

[0086] S402. Combining the target group's target type and the task feature vector, analyze the metering scale and billing demand through a preset metering and billing analysis model, and calculate the metering impact factor and billing impact factor.

[0087] In this embodiment, in response to the metering and billing task of the target group, the task is analyzed, and metering features and billing features are extracted respectively. Metering features include, but are not limited to, the number of CPU cores and memory size, while billing features include, but are not limited to, payment methods and additional services. The extracted features are converted into numerical values ​​according to the corresponding assignment mechanism and arranged in order to construct a task feature vector. By parsing the metering and billing task and constructing the task feature vector, the corresponding metering and billing strategy can be matched based on the task features, thereby improving the efficiency of strategy formulation and the matching degree between the strategy and the task.

[0088] Specifically, by combining the target group's target type and task feature vectors, a pre-defined metering and billing analysis model is used to analyze the metering scale and billing requirements, calculating metering impact factors and billing impact factors. This metering and billing analysis model includes, but is not limited to, decision tree models. A pre-trained decision tree model is trained using a large amount of historical metering and billing data. The target type and task feature vectors are then input into the pre-trained decision tree model, which analyzes the task's metering scale and billing requirements, outputting the calculated metering impact factors and billing impact factors. By combining the specific feature vectors of the group type and task to calculate the metering impact factors and billing impact factors, data references are provided for strategy formulation, enabling the rapid development of metering and billing strategies suitable for each task.

[0089] Furthermore, based on the metering impact factor and the billing impact factor, corresponding target metering chain nodes and target billing chain nodes are matched in the main blockchain, respectively. A first metering and billing strategy is generated according to the target metering chain node and the target billing chain node, including:

[0090] S501. Analyze the metering capability of the metering chain nodes and the billing capability of the billing chain nodes in the main blockchain, and match the corresponding target metering chain nodes and target billing chain nodes based on the metering impact factor and the billing impact factor, respectively.

[0091] S502, Configure the strategy generation mechanism to generate a first metering and billing strategy based on the target metering chain node and the target billing chain node.

[0092] In this embodiment, the metering chain nodes and billing chain nodes in the main blockchain each have specific metering and billing capabilities. For example, some metering chain nodes can only meter CPU cores, while some billing chain nodes can only bill basic fees. Before matching a task with a corresponding chain node, it is necessary to clarify the metering capability of each metering chain node and the billing capability of each billing chain node. By comparing the metering impact factor and the billing impact factor, metering chain nodes that meet the metering requirements of the metering impact factor and billing chain nodes that meet the billing requirements of the billing impact factor are matched. Matching the metering and billing capabilities of chain nodes with the corresponding metering and billing impact factors ensures that the matched metering and billing chain nodes can meet the metering and billing requirements of the task, improves the accuracy of metering and billing data, quickly matches the corresponding chain node, and improves the efficiency of strategy formulation.

[0093] Specifically, the configuration strategy generation mechanism prioritizes metering before billing. It generates metering results and billing constraints based on the target metering chain nodes, then inputs these results and constraints into the target billing chain nodes to generate the billing result, resulting in the first metering and billing strategy. This strategy generation mechanism provides a method for strategy formulation, enabling the rapid development of corresponding metering and billing strategies based on the target metering chain nodes and target billing chain nodes, thus improving the efficiency and accuracy of strategy formulation.

[0094] Furthermore, the metering capabilities of the metering chain nodes and the billing capabilities of the billing chain nodes in the main blockchain are analyzed. Based on the metering impact factor and the billing impact factor, corresponding target metering chain nodes and target billing chain nodes are matched, including:

[0095] S601. For each metering chain node and billing chain node in the main blockchain, analyze the metering capability of the metering chain node and the billing capability of the billing chain node respectively, and construct the corresponding metering capability vector and billing capability vector.

[0096] S602. According to the dimensions of the measurement capability vector and the billing capability vector, the measurement impact factor and the billing impact factor are decomposed and mapped respectively to obtain the corresponding target measurement vector and target billing vector.

[0097] S603. Calculate the first similarity between the target measurement vector and the measurement capability vector, and filter out measurement chain nodes with a first similarity greater than a preset similarity threshold to obtain the first measurement chain node set.

[0098] S604. Among the billing chain nodes connected to the metering chain nodes in the first metering chain node set, select the billing chain nodes whose second similarity between the target billing vector and the billing capability vector is greater than a preset similarity threshold, and obtain the first billing chain node set.

[0099] S605. Combining the first similarity, the second similarity, and the correlation, select the target metering chain node and the target billing chain node from the first metering chain node set and the first billing chain node set.

[0100] In this embodiment, for each metering chain node and billing chain node in the main blockchain, the metering capability of the metering chain node and the billing capability of the billing chain node are analyzed respectively, and corresponding metering capability vectors and billing capability vectors are constructed. The dimensions of the metering capability vector are set according to the core resource type and corresponding parameters of the cloud service, and the dimensions of the billing capability vector are set according to the cost composition. For each metering chain node and billing chain node, according to the corresponding capability dimension, if the chain node possesses the capability, it is assigned a value of 1; if the chain node does not possess the capability, it is assigned a value of 0. The metering capability of the metering chain node and the billing capability of the billing chain node are analyzed, and corresponding metering capability vectors and billing capability vectors are constructed. By constructing metering capability vectors and billing capability vectors, accurate data support is provided for selecting metering chain nodes and billing chain nodes that match the corresponding tasks, improving the accuracy of strategy formulation.

[0101] Specifically, based on the dimensions of the measurement capability vector and the billing capability vector, the measurement impact factor and the billing impact factor are decomposed and mapped respectively, transforming them into the same dimensions as the measurement capability vector and the billing capability vector. This allows for comparison, yielding the corresponding target measurement vector and target billing vector. If an impact factor contains the feature corresponding to that dimension, it is assigned a value of 1; otherwise, it is assigned a value of 0. Following the dimensions of the measurement capability vector, the measurement impact factor is compared with its corresponding dimension to construct the target measurement vector. Similarly, following the dimensions of the billing capability vector, the billing impact factor is compared with its corresponding dimension to construct the target billing vector. By unifying the dimensions of the target measurement vector, the target billing vector, and the corresponding measurement capability vector and billing capability vector, matching errors caused by different dimensions can be avoided, improving matching efficiency and the accuracy of matching results.

[0102] The target measurement vector is matched with the measurement capability vector, and the cosine similarity between the target measurement vector and the measurement capability vector is calculated to obtain the first similarity. A similarity threshold is set according to the accuracy requirements of the strategy calculation, and measurement chain nodes with the first similarity greater than the similarity threshold are selected to obtain the first set of measurement chain nodes. Through similarity calculation, mismatched measurement chain nodes can be quickly eliminated, and measurement chain nodes that can meet the measurement requirements can be selected.

[0103] It should be noted that billing chain nodes need to be connected to the metering chain nodes for data flow. Billing is performed based on metering results. After selecting the first set of metering chain nodes, the cosine similarity between the billing capability vector of each billing chain node connected to the metering chain nodes in the first set and the target billing vector is calculated to obtain a second similarity. Billing chain nodes with a second similarity greater than a similarity threshold are selected to obtain the first set of billing chain nodes. By limiting the selection range of billing chain nodes connected to the metering chain nodes, data flow between the metering and billing processes can be ensured, guaranteeing the smooth operation of the metering and billing processes. Furthermore, billing chain nodes that meet the billing task requirements can be selected, improving the selection efficiency and accuracy of both metering and billing chain nodes.

[0104] Specifically, considering that a single similarity or correlation score might lead to bias in the matched chain nodes, the first similarity, second similarity, and correlation scores are combined. Weights are assigned to each of these scores based on business needs. A weighted sum of these scores is then calculated to determine the chain node's overall score. The target metering and billing chain nodes with the highest overall scores are then selected from the first set of metering and the first set of billing chain nodes, respectively. By integrating multiple factors for chain node matching, the bias from single-indicator judgments can be reduced, and target metering and billing chain nodes with matching capabilities and data connectivity can be selected. This ensures the smooth operation of the metering and billing process and improves the accuracy of the formulated metering and billing strategies.

[0105] Furthermore, a configuration policy generation mechanism is established to generate a first metering and billing policy based on the target metering chain node and the target billing chain node, including:

[0106] S701. Based on the target metering chain node, calculate the first metering result and billing constraint result using a preset strategy generation model.

[0107] S702. Based on the first metering result and the billing constraint result, the target billing chain node calculates the first billing result;

[0108] S703. Combining the first metering result and the first billing result, a first metering and billing strategy is obtained.

[0109] In this embodiment, the target metering chain node stores the metering rules for the corresponding resources. Based on the target metering chain node, a preset strategy generation model combines the metering rules with the metering impact factors of the task to calculate the first metering result. The billing chain nodes associated with the target metering chain node are then analyzed to obtain the billing constraint result. The strategy generation model includes, but is not limited to, a linear regression model. A pre-trained linear regression model is trained using a large amount of historical metering and billing data. The metering rules and metering impact factors from the target metering chain node are input into the pre-trained linear regression model. The model analyzes the metering requirements of the task and calculates the first metering result. Billing-related constraints are extracted from the billing chain nodes associated with the target metering chain node and organized to obtain the billing constraint result. Calculating the first metering result provides a basis for calculating the billing result, improving the accuracy of the metering and billing result. The billing constraint result predefines the billing boundary, preventing billing nodes from arbitrarily adjusting unit prices and improving the stability and compliance of the billing calculation process.

[0110] Specifically, the first metering result and the billing constraint result are input into the target billing chain node. The target billing chain node calculates the first billing result based on the stored billing rules. The billing result is calculated according to the corresponding billing rules based on the first metering result. It is then determined whether the calculated billing result meets the billing constraints. If the billing result meets the constraints, it is output as the first billing result. If the billing result does not meet the constraints, it is adjusted until it does, thus obtaining the first billing result. Calculating the billing result by combining the metering result and the billing constraints ensures that the billing result matches the user's usage needs, avoids billing confusion, improves the accuracy of the billing result, and reduces billing failures.

[0111] Specifically, according to the metering logic and the billing logic, the first metering result and the first billing result are integrated to obtain the first metering and billing strategy; the first metering result is described according to the resource type, and the first billing result is described according to the corresponding metered resource. The description results are integrated to obtain the first metering and billing strategy; the metering result and the billing result are integrated to obtain the complete metering and billing strategy, which provides a clear strategy for the metering and billing tasks of the target group.

[0112] Furthermore, based on the target type, corresponding target blockchains are selected; based on the metering impact factor and billing impact factor, corresponding target optimization chain nodes are matched in the target blockchains; and the first metering and billing strategy is optimized to obtain a second metering and billing strategy, including:

[0113] S801. Filter the corresponding target from the blockchain according to the target type, and filter out the first optimized chain node set in the target from the blockchain that is connected to the target metering chain node and the target billing chain node.

[0114] S802. Based on the measurement impact factor and the billing impact factor, match the corresponding target optimization chain node in the first optimization chain node set;

[0115] S803. Optimize the first metering and billing strategy according to the target optimization chain node to obtain the second metering and billing strategy.

[0116] In this embodiment, each target group type has a corresponding slave blockchain. Based on the target type, the corresponding target slave blockchain is selected. Then, optimized chain nodes connected to the target metering chain node and the target billing chain node are selected from the target slave blockchain. These connected optimized chain nodes can obtain data from the first metering and billing strategy of the main blockchain, resulting in a first set of optimized chain nodes. Based on the target type, the corresponding target slave blockchain is selected from the multi-chain structure blockchain. Based on the connection relationship between the chain nodes of the main blockchain and slave blockchains in the multi-chain structure blockchain, optimized chain nodes connected to the target metering chain node and the target billing chain node in the main blockchain are extracted, resulting in a first set of optimized chain nodes. Selecting slave blockchains by target type avoids using the wrong optimization rules for the wrong group, ensuring accurate strategy optimization based on the target. By selecting optimized chain nodes connected to the target chain nodes of the main blockchain, it ensures that data from the first metering and billing strategy can be directly obtained during strategy optimization, improving strategy optimization efficiency.

[0117] Specifically, each optimization chain node in the first set of optimization chain nodes stores the corresponding optimization mechanism and optimization triggering conditions. Based on the metering impact factor and the billing impact factor, target optimization chain nodes that meet the metering and billing requirements and reach the optimization triggering conditions are matched in the first set of optimization chain nodes. Through precise matching and screening, it can be ensured that the selected target optimization chain nodes can meet the task requirements. Based on the task requirements, precise strategy optimization can improve the targeting of strategy optimization and improve the optimization effect.

[0118] The first metering and billing strategy is an initial strategy quickly formulated based on the metering and billing task. The target optimization chain nodes store the metering and billing rules specific to each group type. The first metering and billing strategy is optimized based on the target optimization chain nodes to obtain the second metering and billing strategy. Targeted optimization can be carried out on the basis of the quickly formulated initial strategy to improve the optimization effect.

[0119] Furthermore, the first metering and billing strategy is optimized based on the target optimization chain nodes to obtain a second metering and billing strategy, including:

[0120] S901. Based on the first metering and billing strategy, the target optimization chain node predicts and simulates the strategy evolution result through a preset strategy simulation model.

[0121] S902. Analyze the differences between the strategy evolution results and the metering and billing tasks, and generate optimized strategy parameters;

[0122] S903. Optimize the first metering and billing strategy according to the optimization strategy parameters to obtain the second metering and billing strategy.

[0123] In this embodiment, based on the first metering and billing strategy, the target optimization chain node predicts and simulates the strategy evolution result through a preset strategy simulation model. The strategy simulation model includes, but is not limited to, a multi-scenario enumeration simulation model. The data of the first metering and billing strategy, the rule range of the target optimization chain node, and the task constraints are input into the multi-scenario enumeration simulation model. The model lists all possible optimization scenarios according to the rule range and calculates the strategy evolution result for each optimization scenario. By enumerating multiple optimization scenarios in advance, the poor optimization effect caused by directly performing strategy optimization can be avoided, providing a clear direction for optimization and improving the optimization effect.

[0124] Specifically, the differences between the strategy evolution results and the metering and billing tasks are analyzed according to preset difference dimensions. These difference dimensions include, but are not limited to, profit margin differences and satisfaction differences. Based on the comparison results, the corresponding strategy adjustment items are extracted from the optimization scenario with the smallest difference as optimization strategy parameters. By comparing the differences between the optimization scenario and the task objective, optimization parameters that meet the task requirements can be extracted. During the strategy optimization process, the task requirements can be met, and the optimization effect can be optimized.

[0125] The first metering and billing strategy is optimized based on the optimization strategy parameters. The metering and billing parts are modified accordingly according to the optimization strategy parameters to obtain the second metering and billing strategy. The optimized metering and billing strategy meets the requirements of task requirements and cloud service metering compliance requirements, and improves the strategy optimization results and the adaptability between the metering and billing strategy and the task.

[0126] like Figure 5 As shown, a cloud service metering and billing strategy intelligent optimization system is used to implement a cloud service metering and billing strategy intelligent optimization method, including:

[0127] The blockchain construction module classifies the target group based on the target group data pre-acquired by the cloud server and constructs a multi-chain structure blockchain. The multi-chain structure blockchain includes a main blockchain for generating metering and billing strategies and multiple slave blockchains configured according to the target group type.

[0128] The task analysis module responds to the metering and billing tasks of the target group, analyzes the target group's target type and extracts task characteristics, and generates metering impact factors and billing impact factors.

[0129] The strategy generation module matches the target metering chain node and the target billing chain node in the main blockchain based on the metering impact factor and the billing impact factor, and generates a first metering and billing strategy according to the target metering chain node and the target billing chain node.

[0130] The strategy optimization module filters the corresponding target from the blockchain according to the target type, matches the corresponding target optimization chain node in the target from the blockchain based on the metering impact factor and the billing impact factor, optimizes the first metering and billing strategy, and obtains the second metering and billing strategy to intelligently optimize the metering and billing strategy.

[0131] In this embodiment, the blockchain construction module extracts data of the target group from the cloud server backend, divides the group, and constructs a multi-chain blockchain structure combining a main blockchain and multiple slave blockchains. The main blockchain stores the metering and billing rules common to users of the target group; each slave blockchain corresponds to a type of target group and stores the metering and billing rules for that type of group, resulting in a multi-chain blockchain structure. By classifying the groups and formulating strategies, different user needs can be accurately matched. The blockchain can ensure the security of metering and billing data and rules, avoiding disputes caused by malicious modifications. The division of labor between the main and slave blockchains is clear. When adding a new user type or adjusting the rules, only the corresponding slave blockchain needs to be added or modified, without reconstructing the main blockchain, which can improve the stability of the blockchain structure and the efficiency of strategy formulation.

[0132] The task analysis module responds in real time to cloud service metering and billing tasks for the target group. First, it determines the target group type to which the user belongs. Then, it extracts task features from the tasks, transforms these features into metering and billing impact factors, and binds the calculated impact factors to the corresponding tasks, storing them in the blockchain database. By parsing the metering and billing tasks, the module can extract the core information of the tasks, calculate the corresponding impact factors, provide data support for matching the main blockchain nodes and optimizing the blockchain, accurately match the tasks with the corresponding chain nodes, and formulate corresponding metering and billing strategies.

[0133] Specifically, the strategy generation module, based on the measurement impact factor and billing impact factor calculated by the task analysis module, first matches target metering chain nodes in the main blockchain that can handle the current metering impact factor. Then, it filters out target billing chain nodes that can handle the current billing impact factor and are data-connected to the target metering chain nodes. Based on a preset strategy generation mechanism, it generates the corresponding first metering and billing strategy. Matching chain nodes based on impact factors and node capabilities ensures that logically correct chain nodes are matched and highly compatible with the task, improving the accuracy of the generated strategy.

[0134] The strategy optimization module selects corresponding target blockchains from a multi-chain blockchain structure based on the target group type of the task. Based on measurement and billing impact factors, it matches target optimization chain nodes that fit the task scenario. Based on the measurement and billing rules of these optimized chain nodes, it optimizes and adjusts the billing portion of the first measurement and billing strategy to obtain a second measurement and billing strategy. By matching and optimizing chain nodes from the blockchain, corresponding optimizations can be performed for each task, making the optimized second measurement and billing strategy more suitable for task requirements.

[0135] The above description is merely a preferred embodiment of this application. The scope of protection of this application is not limited to the above embodiments. All technical solutions falling within the scope of this application's concept are within the scope of protection of this application. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of this application should also be considered within the scope of protection of this application.

Claims

1. A method for intelligent optimization of cloud service metering and billing strategies, characterized in that, include: Based on the target group data pre-acquired by the cloud server, the target group is classified and a multi-chain structure blockchain is constructed. The multi-chain structure blockchain includes a main blockchain for generating metering and billing strategies and multiple slave blockchains configured according to the target group type. In response to the metering and billing tasks of the target group, the target group's target type is analyzed and task characteristics are extracted to generate metering impact factors and billing impact factors; Based on the metering impact factor and the billing impact factor, the corresponding target metering chain node and target billing chain node are matched in the main blockchain respectively, and a first metering and billing strategy is generated according to the target metering chain node and the target billing chain node. Based on the target type, the corresponding target is selected from the blockchain. Based on the metering impact factor and the billing impact factor, the corresponding target optimization chain node is matched in the target blockchain. The first metering and billing strategy is optimized to obtain the second metering and billing strategy, so as to intelligently optimize the metering and billing strategy. The construction of a multi-chain blockchain includes: Based on the target group data pre-acquired by the cloud server, the target group is classified through clustering to obtain the group type; Construct a main blockchain and corresponding slave blockchains for each group type, wherein the main blockchain includes metering chain nodes and billing chain nodes, and the slave blockchain includes optimization chain nodes; By combining the main blockchain and multiple slave blockchains, a multi-chain blockchain structure is obtained; The construction of the main blockchain and the corresponding slave blockchains for each group type includes: Based on the preset metering and billing standards, generate a set of metering chain nodes and a set of billing chain nodes; Analyze the correlation between metering and billing for the target group, connect metering chain nodes and billing chain nodes with a correlation greater than a preset correlation threshold, and use the output of the metering chain nodes as the input of the billing chain nodes to construct the main blockchain; For each group type, configure a separate blockchain, generate optimized chain nodes based on the historical metering and billing data of the group type, and match them with the metering chain nodes and billing chain nodes in the main blockchain. Connect the optimized chain nodes of each slave blockchain with the matched metering chain nodes and billing chain nodes to obtain multiple slave blockchains.

2. The intelligent optimization method for cloud service metering and billing strategies according to claim 1, characterized in that, The metering and billing task in response to the target group analyzes the target group's target type and extracts task characteristics to generate metering impact factors and billing impact factors, including: In response to the metering and billing tasks of the target group, task features are extracted and a task feature vector is constructed. By combining the target group's target type and the task feature vector, the metering scale and billing demand are analyzed through a preset metering and billing analysis model, and the metering impact factor and billing impact factor are calculated.

3. The intelligent optimization method for cloud service metering and billing strategies according to claim 1, characterized in that, Based on the metering impact factor and the billing impact factor, corresponding target metering chain nodes and target billing chain nodes are matched in the main blockchain, respectively. A first metering and billing strategy is generated according to the target metering chain node and the target billing chain node, including: Analyze the metering capabilities of metering chain nodes and the billing capabilities of billing chain nodes in the main blockchain, and match the corresponding target metering chain nodes and target billing chain nodes based on the metering impact factors and billing impact factors, respectively. Configure a policy generation mechanism to generate a first metering and billing policy based on the target metering chain node and the target billing chain node.

4. The intelligent optimization method for cloud service metering and billing strategies according to claim 3, characterized in that, The analysis examines the metering capabilities of metering chain nodes and the billing capabilities of billing chain nodes in the main blockchain. Based on the metering impact factor and the billing impact factor, corresponding target metering chain nodes and target billing chain nodes are matched, including: For each metering chain node and billing chain node in the main blockchain, analyze the metering capability of the metering chain node and the billing capability of the billing chain node respectively, and construct the corresponding metering capability vector and billing capability vector. According to the dimensions of the measurement capability vector and the billing capability vector, the measurement impact factor and the billing impact factor are decomposed and mapped respectively to obtain the corresponding target measurement vector and target billing vector. Calculate the first similarity between the target measurement vector and the measurement capability vector, and filter out measurement chain nodes with a first similarity greater than a preset similarity threshold to obtain the first measurement chain node set; Among the billing chain nodes connected to the metering chain nodes in the first metering chain node set, billing chain nodes whose second similarity between the target billing vector and the billing capability vector is greater than a preset similarity threshold are selected to obtain the first billing chain node set. By combining the first similarity, the second similarity, and the correlation, target metering chain nodes and target billing chain nodes are selected from the first metering chain node set and the first billing chain node set.

5. The intelligent optimization method for cloud service metering and billing strategies according to claim 3, characterized in that, The configuration policy generation mechanism generates a first metering and billing policy based on the target metering chain node and the target billing chain node, including: Based on the target metering chain node, the first metering result and billing constraint result are calculated using a pre-set strategy to generate a model. Based on the first metering result and the billing constraint result, the target billing chain node calculates the first billing result; By combining the first measurement result and the first billing result, the first measurement and billing strategy is obtained.

6. The intelligent optimization method for cloud service metering and billing strategies according to claim 1, characterized in that, Based on the target type, the corresponding target from the blockchain is selected. Based on the metering impact factor and billing impact factor, the corresponding target optimization chain node is matched in the target from the blockchain. The first metering and billing strategy is optimized to obtain a second metering and billing strategy, including: Based on the target type, the corresponding target from the blockchain is selected, and the first optimized chain node set connected to the target metering chain node and the target billing chain node in the target from the blockchain is selected. Based on the measurement impact factor and the billing impact factor, the corresponding target optimization chain node is matched in the first optimization chain node set; The first metering and billing strategy is optimized based on the target optimization chain nodes to obtain the second metering and billing strategy.

7. The intelligent optimization method for cloud service metering and billing strategies according to claim 6, characterized in that, The first metering and billing strategy is optimized based on the target optimization chain nodes to obtain a second metering and billing strategy, including: Based on the first metering and billing strategy, the target optimization chain nodes predict and simulate the strategy evolution results through a preset strategy simulation model; Analyze the differences between the strategy evolution results and the metering and billing tasks to generate optimized strategy parameters; The first metering and billing strategy is optimized based on the optimization strategy parameters to obtain the second metering and billing strategy.

8. A cloud service metering and billing strategy intelligent optimization system, characterized in that, A method for intelligent optimization of cloud service metering and billing strategies as described in any one of claims 1 to 7, comprising: The blockchain construction module classifies the target group based on the target group data pre-acquired by the cloud server and constructs a multi-chain structure blockchain. The multi-chain structure blockchain includes a main blockchain for generating metering and billing strategies and multiple slave blockchains configured according to the target group type. The task analysis module responds to the metering and billing tasks of the target group, analyzes the target group's target type and extracts task characteristics, and generates metering impact factors and billing impact factors. The strategy generation module matches the target metering chain node and the target billing chain node in the main blockchain based on the metering impact factor and the billing impact factor, and generates a first metering and billing strategy according to the target metering chain node and the target billing chain node. The strategy optimization module filters the corresponding target from the blockchain according to the target type, matches the corresponding target optimization chain node in the target from the blockchain based on the metering impact factor and the billing impact factor, optimizes the first metering and billing strategy, and obtains the second metering and billing strategy to intelligently optimize the metering and billing strategy.

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