A resource allocation method and system for an enterprise asset panoramic management platform

Abstract

This invention relates to the field of resource allocation management technology, specifically disclosing a resource allocation method and system for an enterprise asset panoramic management platform. The method includes the following steps: acquiring business process definition information and resource domain topology information characterizing the distribution and transmission characteristics of resource pools; constructing a business process data flow graph; generating a set of candidate resource allocation combinations that meet data sensitivity constraints; calculating the end-to-end total cost and total time of the entire business process; selecting a resource allocation combination from the candidate resource allocation combination set based on a preset optimization objective, according to the total cost and / or total time, and generating a resource allocation instruction based on the selected resource allocation combination. This method solves the problems of existing strategies that isolate tasks, ignore data dependencies between tasks, and fail to perform end-to-end global optimization due to cross-resource pool transmission characteristics, thus achieving the effect of reducing unnecessary overhead and time delays.

Description

Technical Field

[0001] This application relates to the field of resource allocation and management technology, and more specifically, to a resource allocation method and system for an enterprise asset panorama management platform. Background Technology

[0002] In enterprise hybrid cloud environments, resource allocation platforms have evolved from initial simple inventory-driven allocation to the introduction of cost optimization strategies and gradual adaptation to data security compliance requirements. As business complexity increases, platforms are beginning to support hybrid cloud architectures, integrating private and public cloud resources. The development trend points to more refined and integrated resource management to meet the growing needs of business processes.

[0003] However, existing resource allocation strategies still have significant shortcomings when dealing with complex business processes involving multiple interdependent tasks. These strategies tend to handle resource requests for individual tasks in isolation, with decisions primarily based on resource availability or simple cost comparisons, failing to adequately consider the inherent data dependencies between tasks within the business process. For example, in a business process involving both sensitive data processing and non-sensitive data analysis tasks, existing strategies might mechanically allocate sensitive tasks to a private cloud to ensure data security, while allocating non-sensitive tasks to a public cloud to leverage its elasticity or cost advantages. However, this isolated decision-making model often overlooks the additional costs and latency that may arise from data transfer between tasks, especially when data needs to be transferred between different cloud environments (such as between private and public clouds), potentially resulting in high data egress fees and significant transmission delays. Furthermore, the transmission costs and latency characteristics vary between different resource pools, and existing strategies fail to effectively incorporate these characteristics for comprehensive consideration. This neglect of data dependencies and transmission characteristics between tasks prevents the effective optimization of the end-to-end total cost and time of the entire business process, thereby increasing unnecessary operating expenses and time delays, and severely limiting the improvement of the overall operational efficiency of the enterprise.

[0004] There is currently no effective technical solution to the above problems. Summary of the Invention

[0005] The purpose of this application is to provide a resource allocation method and system for an enterprise asset panoramic management platform, so as to solve the problems that existing strategies handle tasks in isolation, ignore data dependencies between tasks and cross-resource pool transmission characteristics, which makes it impossible to perform end-to-end global optimization, thereby reducing unnecessary overhead and time delays.

[0006] Firstly, this application provides a resource allocation method for an enterprise asset panorama management platform, used to optimize business processes in an enterprise hybrid cloud environment, characterized in that the method includes the following steps:

[0007] S1. Obtain business process definition information and resource domain topology information that characterizes the distribution and transmission characteristics of the resource pool;

[0008] S2. Construct a business process data flow diagram based on the business process definition information;

[0009] S3. Based on the business process data flow diagram and the resource domain topology information, generate a set of candidate resource allocation combinations that meet data sensitivity constraints;

[0010] S4. For each resource allocation combination in the candidate resource allocation combination set, calculate the end-to-end total cost and total time of the entire business process.

[0011] S5. Based on the preset optimization objective, select a resource allocation combination from the candidate resource allocation combination set according to the total cost and / or the total time consumption, and generate a resource allocation instruction according to the selected resource allocation combination.

[0012] The resource allocation method for the enterprise asset panorama management platform, wherein the business process definition information includes the task sequence of the business process, the data transmission relationship between tasks, data volume, and data sensitivity information, and step S2 includes:

[0013] S21. Convert the task sequence into graph nodes in the business process data flow diagram;

[0014] S22. Convert the inter-task data transmission relationship into edges connecting the graph nodes;

[0015] S23. Label the data volume and data sensitivity information onto the edges to construct the business process data flow diagram.

[0016] The resource allocation method for the enterprise asset panoramic management platform, wherein the business process definition information further includes the task execution order, and step S2 further includes:

[0017] S24. Determine the topological relationship of the graph nodes according to the task execution order to improve the business process data flow graph.

[0018] The resource allocation method of the enterprise asset panorama management platform, wherein the resource domain topology information includes the type of resource pool, the data sensitivity processing capability of the resource pool, the transmission cost and transmission latency between different resource pools, and the type of resource pool includes private cloud resource pool and public cloud resource pool.

[0019] The resource allocation method for the enterprise asset panorama management platform, wherein step S3 includes:

[0020] S31. For each task in the business process data flow diagram, extract the data sensitivity information on the corresponding input edge;

[0021] S32. Based on the resource domain topology information, identify resource pools with data sensitivity processing capabilities that match the extracted data sensitivity information, and form a set of candidate resource pools associated with the corresponding tasks.

[0022] S33. Combine the candidate resource pool sets of all tasks in the business process data flow diagram to generate the candidate resource allocation combination set that meets the data sensitivity constraints.

[0023] The resource allocation method for the enterprise asset panorama management platform, wherein step S33 includes:

[0024] S331. Based on the business process data flow diagram, identify edges whose data volume exceeds a preset threshold and / or whose data sensitivity level is higher than a preset level, and use them as the key data transmission paths. The data sensitivity level is determined based on the data sensitivity information.

[0025] S332. Obtain the tasks corresponding to the two graph nodes connected to each path in the key data transmission path, as key source tasks and key target tasks;

[0026] S333. For the key source task and the key target task, based on the transmission cost and transmission delay in the resource domain topology information, and with the goal of minimizing local cost and / or delay, select resource pools from the corresponding candidate resource pool set that match the key source task and the key target task respectively as the initial resource allocation skeleton.

[0027] S334. Based on the initial resource allocation skeleton, randomly combine it with the candidate resource pool set of the remaining tasks in the business process data flow diagram until a preset number of candidate resource allocation combinations are obtained, forming the candidate resource allocation combination set that meets the data sensitivity constraints.

[0028] The resource allocation method for the enterprise asset panorama management platform, wherein step S4 includes:

[0029] S41. For each candidate resource allocation combination in the candidate resource allocation combination set, calculate the task execution time according to the type of resource pool and the data volume of the corresponding task, and calculate the inter-task transmission cost and inter-task transmission delay according to the connection relationship of the edges in the business process data flow diagram and the transmission cost and transmission delay between different resource pools.

[0030] S42. Accumulate the inter-task transmission costs corresponding to each candidate resource allocation combination as the end-to-end total cost of the corresponding candidate resource allocation combination, and accumulate the inter-task transmission delays and the execution time of all tasks corresponding to each candidate resource allocation combination as the end-to-end total time of the corresponding candidate resource allocation combination.

[0031] The resource allocation method for the enterprise asset panorama management platform, wherein step S5 includes:

[0032] S51. Based on the preset optimization objective, evaluate the total cost and / or total time consumption of each candidate resource allocation combination in the candidate resource allocation combination set, and select the candidate resource allocation combination with the best evaluation result as the optimal resource allocation combination.

[0033] S52. Based on the optimal resource allocation combination and the business process data flow diagram, generate a resource allocation instruction containing task deployment information and resource configuration parameters;

[0034] S53. Send the resource allocation instruction to the resource pool involved in the optimal resource allocation combination.

[0035] The resource allocation method for the enterprise asset panorama management platform, wherein step S52 includes:

[0036] S521. Obtain the execution order of tasks in the business process based on the business process data flow diagram;

[0037] S522. According to the execution order of the tasks, traverse each task in the optimal resource allocation combination, and generate the task deployment information and resource configuration parameters based on the data sensitivity information, data volume and resource pool type of the task in the business process data flow diagram.

[0038] S523. Integrate the deployment information and resource configuration parameters of each task to form the resource allocation instruction.

[0039] Secondly, this application also provides a resource allocation system for an enterprise asset panorama management platform, used to optimize business processes in an enterprise hybrid cloud environment, the system comprising:

[0040] The acquisition module is used to acquire business process definition information and resource domain topology information that characterizes the distribution and transmission characteristics of the resource pool;

[0041] The diagram building module is used to construct a business process data flow diagram based on the business process definition information;

[0042] The filtering module is used to generate a set of candidate resource allocation combinations that meet data sensitivity constraints based on the business process data flow diagram and the resource domain topology information.

[0043] The calculation module is used to calculate the end-to-end total cost and total time of the entire business process for each resource allocation combination in the candidate resource allocation combination set.

[0044] The allocation module is used to select a resource allocation combination from the candidate resource allocation combination set based on a preset optimization objective, according to the total cost and / or the total time consumed, and generate a resource allocation instruction based on the selected resource allocation combination.

[0045] As can be seen from the above, this application provides a resource allocation method and system for an enterprise asset panoramic management platform. The method of this application combines business process definition information and resource domain topology information to construct a business process data flow diagram to comprehensively characterize the data dependencies and sensitivities between tasks. Based on this, a set of candidate resource allocation combinations that meet data sensitivity constraints is generated. Then, the end-to-end total cost and total time of the entire business process are calculated for each combination. Finally, a global selection is made based on a preset optimization objective. This solves the problems of existing strategies that isolate tasks, ignore data dependencies between tasks, and fail to perform end-to-end global optimization due to cross-resource pool transmission characteristics. This achieves the effect of reducing unnecessary overhead and time delay. Attached Figure Description

[0046] Figure 1 A flowchart illustrating the resource allocation method for the enterprise asset panorama management platform provided in this application embodiment.

[0047] Figure 2 This is a schematic diagram of the structure of the enterprise asset panorama management platform resource allocation system provided in the embodiments of this application.

[0048] Attached reference numerals: 201, Acquisition module; 202, Mapping module; 203, Filtering module; 204, Calculation module; 205, Allocation module. Detailed Implementation

[0049] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0050] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0051] Firstly, please refer to Figure 1 This application provides a resource allocation method for an enterprise asset panorama management platform, used for global optimization of business processes in an enterprise hybrid cloud environment. The method includes the following steps:

[0052] S1. Obtain business process definition information and resource domain topology information that characterizes the distribution and transmission characteristics of the resource pool;

[0053] S2. Construct a business process data flow diagram based on the business process definition information;

[0054] S3. Based on the business process data flow diagram and resource domain topology information, generate a set of candidate resource allocation combinations that meet data sensitivity constraints;

[0055] S4. For each resource allocation combination in the candidate resource allocation combination set, calculate the end-to-end total cost and total time of the entire business process.

[0056] S5. Based on the preset optimization objective, select a resource allocation combination from the candidate resource allocation combination set according to the total cost and / or total time consumption, and generate a resource allocation instruction according to the selected resource allocation combination.

[0057] Specifically, business process definition information refers to a set of data describing the internal structure and execution logic of a business process. It provides a complete view of the business process, serving as the basis for subsequent data flow diagram construction and resource allocation. Resource domain topology information refers to a set of data describing the distribution, data sensitivity processing capabilities, and interconnection characteristics of various resource pools in an enterprise's hybrid cloud environment. It is used to provide a global view of the resource environment and provide a quantitative basis for resource allocation decisions.

[0058] More specifically, a business process data flow diagram is a graph model that visualizes and structures the definition information of a business process. It can be constructed using a directed acyclic graph (DAG) or a flowchart, etc., to clearly show the dependencies between tasks and the flow of data, providing a structured foundation for subsequent global optimization.

[0059] More specifically, the set of candidate resource allocation combinations that meet data sensitivity constraints refers to a set of available resource allocation schemes that satisfy the data sensitivity requirements in the business process. Each combination ensures that sensitive data is only allocated to resource pools with corresponding processing capabilities. These combinations can be generated using rule-based filtering, constraint satisfaction programming, or heuristic search.

[0060] More specifically, end-to-end total cost and total time refer to the cumulative value of the total overhead and total time consumed by all task execution and data transmission from the beginning to the end of the business process. The total cost may include task execution cost and inter-task transmission cost (such as cross-cloud data egress fees), and the total time may include task execution time and inter-task transmission latency. It can be calculated using path accumulation or graph traversal algorithms to perform a global quantitative evaluation of the entire business process and provide comprehensive performance indicators for optimal decision-making.

[0061] Specifically, the method of this application achieves global optimization of resource allocation through a series of collaborative steps. First, it acquires business process definition information, which includes the inherent structure and task relationships of the business process. Simultaneously, it acquires resource domain topology information characterizing the distribution and transmission characteristics of resource pools, including the capabilities and locations of different resource pools, as well as the cost and latency of data transmission between them. This information collectively forms the basis for subsequent global optimization, addressing the problem of neglecting data dependencies between tasks and cross-cloud transmission costs in existing technologies. Next, it constructs a business process data flow graph based on the acquired business process definition information. This graph structure visualizes the abstract business process, clearly showing the various tasks in the business process and their data transmission relationships, as well as information such as the potential data volume and data sensitivity. Through this structure, the system can intuitively understand the dependencies between tasks, avoiding the drawbacks of processing tasks in isolation. Subsequently, using the constructed business process data flow graph and resource domain topology information, it generates a set of candidate resource allocation combinations that meet data sensitivity constraints. This step intelligently filters resource pools that meet specific data sensitivity requirements by identifying data sensitivity requirements in the business process and combining this with the data sensitivity processing capabilities of each resource pool in the resource domain topology information. This ensures that all subsequent allocation schemes prioritize data security compliance and effectively narrows the search space. Furthermore, for each candidate resource allocation combination in the set, the end-to-end total cost and total time of the entire business process are calculated. This calculation no longer evaluates individual tasks in isolation but comprehensively considers all factors, including task execution cost, inter-task data transfer cost (including cross-cloud data egress fees), task execution time, and inter-task transmission latency. This end-to-end calculation method directly addresses the pain point of existing technologies that neglect inter-task transmission characteristics, leading to an inability to perform overall optimization, and provides a quantitative basis for subsequent global optimal decisions. Finally, based on the preset optimization objective, resource allocation combinations are selected from the candidate resource allocation combination set according to the calculated total cost and / or total time, and resource allocation instructions are generated based on the selected combinations. This approach, based on a holistic assessment of the entire business process rather than local optimization, fundamentally addresses the inefficiencies and unnecessary overhead caused by isolated decision-making in existing technologies. Ultimately, the generated resource allocation instructions enable the optimization results to be deployed and executed, achieving closed-loop management from analysis to practice.

[0062] Through the above processing, the method of this application can effectively solve the problem that existing resource allocation strategies in enterprise hybrid cloud environments, due to their isolated task processing and neglect of data dependencies, data sensitivity, and cross-resource pool transmission characteristics, cannot achieve global optimization of end-to-end total cost and total time for complex business processes containing multiple interdependent tasks. This is because such strategies fail to optimize the overall end-to-end total cost and time of complex business processes. The method of this application constructs a business process data flow graph, comprehensively considering the dependencies and data sensitivity between tasks. By generating candidate combinations that meet data sensitivity constraints, it ensures data security and compliance. Simultaneously, by calculating and evaluating the end-to-end total cost and total time of the entire business process, it optimizes cross-resource pool transmission characteristics and overall performance, selecting the globally optimal resource allocation scheme. This significantly reduces unnecessary overhead and time delays, improving the overall operational efficiency and economy of complex business processes in hybrid cloud environments.

[0063] The method of this application combines business process definition information and resource domain topology information to construct a business process data flow graph to comprehensively characterize the data dependencies and sensitivities between tasks. Based on this, a set of candidate resource allocation combinations that meet data sensitivity constraints is generated. Then, the end-to-end total cost and total time of the entire business process are calculated for each combination. Finally, a global selection is made based on a preset optimization objective. This solves the problems of existing strategies that process tasks in isolation, ignore data dependencies between tasks, and fail to perform end-to-end global optimization due to cross-resource pool transmission characteristics. This achieves the effect of reducing unnecessary overhead and time delay.

[0064] In some preferred embodiments, the business process definition information includes the task sequence of the business process, the data transmission relationship between tasks, the data volume, and data sensitivity information. Step S2 includes:

[0065] S21. Convert the task sequence into graph nodes in the business process data flow diagram;

[0066] S22. Convert the data transmission relationship between tasks into edges connecting the nodes of the graph;

[0067] S23. Label the data volume and data sensitivity information to the edges to construct a business process data flow diagram.

[0068] Specifically, a task sequence refers to the set of all execution steps or operations in a business process, which can be represented using data structures such as lists, arrays, or sets. Data transmission relationships between tasks refer to the dependencies between data inputs and outputs in a business process, which can be described using directed edges, data flow paths, or data dependency graphs. Data volume refers to the size or quantity of data transmitted between tasks, which can be expressed in units such as bytes, file size, or number of records. Data sensitivity information refers to the security or privacy levels that must be followed during data processing and transmission, which can be identified using security levels (e.g., public, internal, confidential, top secret), encryption requirements, or compliance labels.

[0069] Specifically, this solution clarifies that the business process definition information should include the task sequence of the business process, the data transmission relationship between tasks, the data volume, and data sensitivity information. This constraint provides input for the subsequent construction of the business process data flow diagram. By including the task sequence, the system can understand the components of the business process; by including the data transmission relationship and data volume between tasks, the system can identify the dependencies between tasks and the scale of data flow, which is indispensable for assessing transmission costs and latency; the introduction of data sensitivity information enables the system to comply with data security compliance requirements when allocating resources, avoiding the leakage or improper handling of sensitive data. Specifically, step S21 defines the boundaries and components of the business process by mapping the task sequence to graph nodes, laying the foundation for subsequent analysis and allocation. Step S22 visualizes the data transmission relationship as edges in the graph, enabling the system to identify which tasks have data interactions and the direction of data flow. This is crucial for understanding the overall structure of the business process and the data dependency chain, and is the direct basis for subsequent calculations of transmission costs and latency between tasks. Step S23 labels the data volume on the edges, allowing the system to quantify the scale of data transmission, thereby estimating the resources and time required for transmission. At the same time, data sensitivity information is marked on the edges, so that when the system generates resource allocation combinations, it can identify and follow data sensitivity constraints, ensuring that sensitive data is only transmitted or processed between resource pools with processing capabilities, thereby avoiding data security risks.

[0070] This solution, by refining the business process definition and clarifying the data flow diagram construction process, enables the system to build a business process data flow diagram containing semantic information. This diagram reflects the execution order of tasks and data dependencies, and also includes the attributes of data transmission (data volume and sensitivity), thus providing a data foundation for subsequent steps (such as generating candidate resource allocation combinations that meet data sensitivity constraints, calculating end-to-end total cost and total time), and solving the problem of suboptimal resource allocation due to insufficient information in the business process.

[0071] In some preferred embodiments, the business process definition information also includes the task execution order, and step S2 further includes:

[0072] S24. Determine the topological relationship of graph nodes according to the task execution order to improve the business process data flow graph.

[0073] Specifically, task execution order refers to the pre-defined, logical sequence of execution among tasks in a business process, which can be represented as serial dependency, parallel execution, or conditional branching. Determining the topological relationships of graph nodes refers to establishing or adjusting the connection relationships between graph nodes (representing tasks) in the business process data flow graph based on the obtained task execution order information, so as to accurately reflect the logical dependencies and execution flow of tasks. This may include adding new directed edges to represent non-data-dependent execution orders, or adjusting the direction and attributes of existing edges to ensure that the graph's topology is consistent with the actual execution order.

[0074] Specifically, after the basic construction of the data flow graph has been completed in steps S21 to S23, step S24 uses the obtained task execution order to determine the topological relationships between graph nodes. This process of determining topological relationships overcomes the limitations of relying solely on data transmission relationships to construct the graph. For example, some tasks may not have direct data transmission, but they have a strict sequential execution order in business logic. By introducing the task execution order, the system can identify and represent these non-data-dependent logical relationships, thereby establishing a more comprehensive inter-task dependency chain in the data flow graph. When the business process definition information includes the task execution order, the system can use the task execution order as an important topological constraint when constructing the data flow graph. For example, if task A must be executed before task B, even if there is no data transmission between task A and task B, the system will establish a topological connection from task A to task B in the data flow graph to ensure that this sequentiality is reflected. This makes the data flow graph not only reflect the path of data flow but also more accurately map the actual execution flow of the business process and the complex dependencies between tasks. In this way, the business process data flow graph is improved. A complete data flow graph can provide a more reliable basis for subsequent resource allocation decisions. For example, when calculating the end-to-end total cost and total time of an entire business process, if the data flow graph accurately reflects the parallelism or seriality of tasks, the system can more accurately assess the actual execution time and cost under different resource allocation combinations. For instance, if two tasks can be executed in parallel, they can be considered to be running simultaneously in the total time calculation, rather than being accumulated serially. Conversely, if they must be executed serially, the system will correctly accumulate their times even without data dependencies. This precise topological relationship enables resource allocation algorithms to generate better resource allocation combinations, thereby improving the overall optimization effect.

[0075] Through the above-described solution, this application can construct a more comprehensive and accurate business process data flow diagram, enabling it to reflect the actual execution order of tasks and more complex topological dependencies. This solves the problem that relying solely on data transmission relationships to construct the diagram cannot fully reflect the actual execution order of tasks and complex topological dependencies within the business process. Consequently, the constructed data flow diagram provides a more complete and accurate description of the business process, thereby improving the accuracy and optimization effect of subsequent resource allocation decisions. This helps generate better resource allocation combinations and reduces unnecessary overhead and time delays.

[0076] In some of the solutions described above in this application, resource domain topology information is obtained to generate a set of candidate resource allocation combinations that meet data sensitivity constraints, and the end-to-end total cost and total time of the entire business process are calculated. However, if the resource domain topology information does not include in detail the type of resource pool, its data sensitivity processing capability, and the calculation method of transmission costs and transmission delays between different types of resource pools (especially data egress fees of public clouds), the data sensitivity constraints may not be fully met when generating candidate resource allocation combinations. Furthermore, the calculation of the end-to-end total cost and total time may be inaccurate due to a lack of precise consideration of the transmission characteristics in a hybrid cloud environment, thus failing to effectively optimize the entire business process and increasing unnecessary overhead and time delays.

[0077] In some preferred embodiments, the resource domain topology information includes the type of resource pool, the data sensitivity processing capability of the resource pool, the transmission cost and transmission latency between different resource pools. The types of resource pools include private cloud resource pools and public cloud resource pools. The type of resource pool is used to distinguish the calculation method of transmission cost and transmission latency between different resource pools. For example, if a public cloud is used, the transmission cost includes the data egress fee of the public cloud resource pool.

[0078] Specifically, the data sensitivity processing capability of a resource pool refers to its security and compliance capabilities when processing data with different sensitivity levels. This can be achieved through security level labels, compliance certification marks, or scoring systems to ensure data protection within the appropriate environment. Transmission cost refers to the expense incurred in transferring data between different resource pools, which can be implemented using a price per unit of data (e.g., per GB), connection fees, or a billing model based on bandwidth usage. Transmission latency refers to the time required for data to be transmitted between different resource pools, which can be implemented using network round-trip time (RTT), packet transmission rate, or estimates based on geographical distance and network congestion.

[0079] Specifically, this solution clarifies that resource domain topology information should include the type of resource pool, the data sensitivity processing capability of the resource pool, and the transmission cost and latency between different resource pools. This resource domain topology information, especially the type of resource pool (including private cloud resource pools and public cloud resource pools) and its correlation with the calculation method of transmission cost and latency, provides important input for subsequent resource allocation decisions. When the system generates a set of candidate resource allocation combinations that meet data sensitivity constraints based on the business process data flow diagram and resource domain topology information, the data sensitivity processing capability information of the resource pools is utilized. The system can identify and filter resource pools with corresponding data sensitivity processing capabilities, ensuring that sensitive data is not allocated to non-compliant environments, thereby meeting data sensitivity constraints and avoiding compliance risks.

[0080] In some preferred embodiments, step S3 includes:

[0081] S31. For each task in the business process data flow diagram, extract the data sensitivity information on the corresponding input edge;

[0082] S32. Based on resource domain topology information, identify resource pools with data sensitivity processing capabilities that match the extracted data sensitivity information, and form a set of candidate resource pools associated with the corresponding tasks.

[0083] S33. Combine the candidate resource pool sets of all tasks in the business process data flow diagram to generate a candidate resource allocation combination set that meets data sensitivity constraints.

[0084] Specifically, the above steps are closely integrated with the overall resource allocation method, and their role is to pre-screen candidate combinations that meet data sensitivity requirements before calculating the total cost and total time. The method in this application ensures that subsequent cost and time calculations are performed only on compliant resource allocation combinations by utilizing the data sensitivity annotations in the business process definition information and the data sensitivity processing capabilities of resource pools in the resource domain topology information. This avoids calculations on a large number of invalid combinations, significantly improving the efficiency of the overall optimization process and the compliance of the final allocation results. This strategy of incorporating data sensitivity constraints at an early stage is a key link in achieving the global optimization goal, making the entire resource allocation process more intelligent and reliable. Step S31 ensures that the data sensitivity constraints are met; it clarifies the sensitivity of the data processed by each task, providing a crucial basis for subsequently selecting appropriate resource pools. By accurately identifying the data sensitivity of each task, sensitive data can be avoided from being allocated to resource pools that lack the corresponding processing capabilities, thereby ensuring data security. The data sensitivity processing capability of a resource pool refers to the level or category of data sensitivity information that the resource pool can process. Step S32 utilizes data on the data sensitivity processing capability of resource pools from the resource domain topology information to perform preliminary screening of resource pools. This involves analyzing whether the data sensitivity information belongs to the level or category of data sensitivity information included in the data sensitivity processing capability of the resource pool, thus filtering the resource pools accordingly. By matching the data sensitivity of tasks with the processing capability of resource pools, resource pools that do not meet data security requirements can be effectively excluded. This constructs a list of potential resource pools that meet data sensitivity constraints for each task, greatly reducing the search space for subsequent combinations and improving the efficiency and accuracy of generating candidate combinations. Finally, step S33 combines the candidate resource pool sets of all tasks in the business process data flow diagram to generate a set of candidate resource allocation combinations that meet data sensitivity constraints. Given that the candidate resource pool sets of each task already satisfy the data sensitivity constraints, combining these sets ensures that each generated candidate resource allocation combination naturally meets the data sensitivity requirements. This step-by-step screening and recombination method avoids generating a large number of invalid combinations that do not meet the constraints, allowing subsequent calculations and optimizations of total cost and total time to be carried out in a valid and compliant set, thereby improving the efficiency of overall resource allocation and the quality of the final solution.

[0085] Through the above processing, the method of this application ensures that all generated candidate resource allocation combinations meet data sensitivity requirements from the outset, avoiding the generation of a large number of invalid combinations that do not meet data sensitivity constraints. This significantly reduces the workload required for subsequent calculations of the end-to-end total cost and total time of the entire business process, improving computational efficiency. Simultaneously, this method guarantees the accuracy and compliance of the final resource allocation, preventing sensitive data from being allocated to resource pools lacking the corresponding processing capabilities, thereby enhancing data security.

[0086] In some of the solutions described above in this application, step S33 is proposed to combine the candidate resource pool sets of all tasks in the business process data flow diagram to generate a set of candidate resource allocation combinations that meet data sensitivity constraints. However, in this process, when the business process contains a large number of tasks, simply combining the candidate resource pool sets of all tasks will lead to an exponential increase in the number of generated candidate resource allocation combinations. This exhaustive combination generation method results in high computational overhead and long time consumption, making it difficult to effectively generate and evaluate all resource allocation schemes in practical applications. In addition, this indiscriminate combination method fails to identify and prioritize key data transmission paths in the business process that have a significant impact on overall cost and time consumption, such as transmissions with large data volumes or high data sensitivity. This results in the generated candidate combinations not being able to effectively focus on the optimal solution region, thereby limiting the efficiency of subsequent optimization steps and the quality of the final resource allocation scheme.

[0087] In some preferred embodiments, step S33 includes:

[0088] S331. Based on the business process data flow diagram, identify edges whose data volume exceeds a preset threshold and / or whose data sensitivity level is higher than a preset level, and use them as key data transmission paths. The data sensitivity level is determined based on the data sensitivity information classification.

[0089] S332. Obtain the tasks corresponding to the two graph nodes connected to each path in the critical data transmission path, and use them as the critical source task and the critical target task.

[0090] S333. For critical source tasks and critical target tasks, based on the transmission cost and transmission delay in the resource domain topology information, and with the goal of minimizing local cost and / or delay, select resource pools that match the critical source tasks and critical target tasks respectively from the corresponding candidate resource pool set as the initial resource allocation skeleton.

[0091] S334. Based on the initial resource allocation skeleton, randomly combine it with the candidate resource pool set of the remaining tasks in the business process data flow diagram until a preset number of candidate resource allocation combinations are obtained, forming a candidate resource allocation combination set that meets the data sensitivity constraints.

[0092] Specifically, the preset threshold refers to a set value used to determine whether the data volume meets the standards for a critical transmission path. The data sensitivity level is a pre-defined grade or classification standard used to determine whether the data sensitivity meets the standards for a critical transmission path; that is, it is determined based on data sensitivity information. Critical source tasks and critical target tasks refer to the tasks represented by the graph nodes connected at both ends of the critical data transmission path, where one task is the starting point of data transmission and the other is the ending point. Minimizing local costs and / or latency refers to optimizing the resource pool selection for critical source tasks and critical target tasks to reduce the cost of data transmission between these tasks or shorten transmission latency.

[0093] Specifically, step S331 determines critical data transmission paths by checking whether the data volume exceeds a preset threshold or whether the data sensitivity level is higher than a preset level. This allows the system to focus on the parts of the business process that have a significant impact on performance. After identifying the critical data transmission paths, step S332 further determines the tasks connected by these paths, namely, critical source tasks and critical target tasks. These tasks are core components of the business process, and their resource allocation decisions have a decisive impact on overall performance. Next, for these critical source tasks and critical target tasks, step S333 uses the transmission cost and transmission delay data contained in the resource domain topology information to select resource pools from their respective candidate resource pool sets that can minimize local costs or reduce local delays. This selection and matching process includes: obtaining all different combinations of resource pools in the candidate resource pool sets corresponding to the critical source tasks and critical target tasks, calculating the local costs and / or delays of different resource pool combinations, and then selecting the optimal resource pool combination based on minimizing local costs and / or delays as the selection objective. The two resource pools of this optimal resource pool combination are then used as the initial resource allocation skeleton. This step constructs an initial resource allocation skeleton, ensuring that the most critical data transmission bottlenecks in the business process are effectively addressed early on, providing an optimized foundation for subsequent combination generation. Finally, after establishing the initial resource allocation skeleton for critical tasks, step S334 randomly combines this skeleton with the candidate resource pool set of the remaining tasks in the business process data flow diagram. This combination method avoids the large computational load and long time consumption caused by exhaustive combination of all tasks. Through random combination, the system explores multiple allocation schemes for non-critical tasks while ensuring efficiency, and effectively controls the size of the final generated combination set by setting a preset number of candidate combinations. Through the above steps, this scheme can effectively generate a controllable number and high-quality set of candidate resource allocation combinations. This method utilizes detailed information from the business process data flow diagram and rich transmission characteristic data from the resource domain topology information, enabling priority to be given to parts with a significant impact on overall performance when generating candidate combinations, thereby effectively narrowing the subsequent search space and improving the quality of the final resource allocation scheme. This phased and focused combination generation strategy effectively solves the problems of high computational overhead and long time consumption caused by simple exhaustive combination when the business process contains a large number of tasks, as well as the inability to effectively focus on the optimal solution area.

[0094] In some preferred embodiments, step S4 includes:

[0095] S41. For each candidate resource allocation combination in the candidate resource allocation combination set, calculate the task execution time based on the resource pool type and the corresponding task data volume, and calculate the inter-task transmission cost and inter-task transmission delay based on the edge connection relationship in the business process data flow diagram and the transmission cost and transmission delay between different resource pools.

[0096] S42. Accumulate the transmission cost between all tasks corresponding to each candidate resource allocation combination as the end-to-end total cost of the corresponding candidate resource allocation combination. Accumulate the transmission delay between all tasks and the execution time of all tasks corresponding to each candidate resource allocation combination as the end-to-end total time of the corresponding candidate resource allocation combination.

[0097] Specifically, step S41 calculates the execution time of each task based on the type of resource pool and the data volume of the corresponding task. The resource pool type information is used to evaluate the execution efficiency of tasks under different environments. Simultaneously, step S41 calculates the data transmission cost and latency between tasks based on the edge connections between tasks in the business process data flow diagram, as well as the data transmission cost and latency between different resource pools. These calculations of transmission costs and latency fully consider the differences in resource pool types, such as the data egress fees that may arise from public clouds, making the calculation results more realistic. Then, step S42 sums up the data transmission costs between all tasks in each candidate resource allocation combination to obtain the end-to-end total cost of the combination. At the same time, it sums up the data transmission latency between all tasks and the execution time of all tasks to obtain the end-to-end total time of the combination. This summation method ensures that all time consumption and economic expenses of the entire business process from start to finish are taken into account, especially the additional expenses and latency incurred due to crossing different types of resource pools.

[0098] By employing the above-described solution, this application addresses the issue of discrepancies between calculated costs and execution times and actual conditions in hybrid cloud environments, stemming from insufficient consideration of different resource pool types and their unique transmission characteristics. Specifically, by calculating task execution time based on resource pool type and task data volume, and by calculating inter-task transmission costs and delays based on the connectivity of the business process data flow diagram and the transmission costs and delays between different resource pools, the underlying data becomes more realistic. Furthermore, by summing these calculated costs and execution times, an end-to-end total cost and total execution time reflecting the actual overhead and time consumption of the entire business process can be obtained. This provides valuable reference for evaluating different candidate resource allocation combinations, thereby supporting the generation of desired resource allocation decisions and avoiding unnecessary overhead and time delays.

[0099] In some preferred embodiments, step S5 includes:

[0100] S51. Based on the preset optimization objective, evaluate the total cost and / or total time of each candidate resource allocation combination in the candidate resource allocation combination set, and select the candidate resource allocation combination with the best evaluation result as the optimal resource allocation combination.

[0101] S52. Based on the optimal resource allocation combination and business process data flow diagram, generate resource allocation instructions that include task deployment information and resource configuration parameters;

[0102] S53. Send the resource allocation instruction to the resource pool involved in the optimal resource allocation combination.

[0103] Specifically, task deployment information refers to detailed instructions on which specific resource pool each task in the business process should be placed in. This can be represented by a mapping between task IDs and target resource pool IDs, the logical location of the task within the resource pool, or the task's priority within the resource pool. Resource configuration parameters refer to the specific resource specifications and environment settings required to ensure the normal operation of tasks in the target resource pool. These can be represented by computing resources, storage resources, network resources, security policies, or software environments.

[0104] More specifically, resource allocation instructions refer to a set of automated commands that transform the optimal resource allocation combination into an executable resource pool, guiding the resource pool in task deployment and resource configuration.

[0105] Specifically, step S51, based on the preset optimization objective, quantifies and evaluates the total cost and / or total time consumption of each candidate resource allocation combination calculated in the previous steps. This accurately identifies the best-performing resource allocation scheme under the current constraints and determines it as the optimal resource allocation combination. Subsequently, step S52, based on the determined optimal resource allocation combination and the business process data flow diagram, generates resource allocation instructions containing task deployment information and resource configuration parameters. The business process data flow diagram provides key contextual information such as dependencies between tasks, data volume, and data sensitivity. This information, combined with the target resource pool for each task in the optimal resource allocation combination, ensures that the generated instructions not only clearly specify which resource pool each task should be deployed to but also detail the required computing, storage, network, and other resource specifications, as well as possible security configurations. This instruction generation method based on business process context ensures the completeness and operability of the instructions. Step S53 sends the generated resource allocation instructions to each resource pool involved in the optimal resource allocation combination. These instructions guide the resource pools to automatically execute task deployment and resource configuration operations, thereby implementing the optimized resource allocation scheme. The entire process forms a closed loop, from the generation, evaluation, and optimal selection of candidate solutions to the generation and automated deployment of specific instructions, ensuring the accuracy of resource allocation decisions, the operability of instructions, and the effective implementation of solutions, thereby achieving the goal of end-to-end optimization of business processes.

[0106] In some preferred embodiments, the optimization objective is divided into minimizing total cost, minimizing total time, and minimizing a weighted average of total cost and total time.

[0107] Specifically, minimizing total cost means that the system prioritizes reducing overall financial expenditure when evaluating candidate resource allocation combinations. Minimizing total time means that the system prioritizes shortening the overall completion time of the business process when evaluating candidate resource allocation combinations. Minimizing the weighted average of total cost and total time means that the system comprehensively considers both total cost and total time by assigning different weights to them when evaluating candidate resource allocation combinations; this can be achieved using a weighted summation function.

[0108] Through the above design, this solution solves the problem of insufficient selection flexibility caused by unclear optimization objectives in existing solutions, and ensures that the selected resource allocation combination can accurately meet the optimization needs of specific business processes.

[0109] In some preferred embodiments, step S52 includes:

[0110] S521. Obtain the execution order of tasks in the business process based on the business process data flow diagram;

[0111] S522. According to the execution order of the tasks, traverse each task in the optimal resource allocation combination, and generate task deployment information and resource configuration parameters based on the data sensitivity information, data volume and resource pool type of the task in the business process data flow diagram.

[0112] S523. Integrate the deployment information and resource configuration parameters of each task to form a resource allocation instruction.

[0113] Specifically, after receiving the optimal resource allocation combination and the business process data flow diagram, this solution first extracts the task execution order from the business process data flow diagram in step S521. Then, in step S522, each task in the optimal resource allocation combination is processed one by one according to the task execution order. This sequential traversal method ensures that when generating deployment information and configuration parameters for the current task, the completion status and data output of its predecessor tasks are fully considered, thereby ensuring the logical correctness of resource allocation. When processing each task, the system comprehensively considers the data sensitivity information, data volume, and the type of resource pool to which the task has been allocated in the business process data flow diagram. For example, for tasks involving sensitive data, the generated deployment information will include stricter security configurations, such as mandatory data encryption and access control lists; for tasks processing large amounts of data, the resource configuration parameters will allocate larger storage space and higher network bandwidth accordingly; and based on the resource pool type corresponding to the task, the generated instructions will adapt to the API interfaces and deployment specifications of different cloud environments, ensuring the executability of the instructions and the efficiency of resource utilization. Through this multi-dimensional and refined consideration, the deployment information and resource configuration parameters of each task accurately reflect its specific needs and the characteristics of its environment. Finally, step S523 integrates the deployment information and resource configuration parameters generated independently for each task. This integration process brings together the scattered task-level configurations into a complete and coordinated resource allocation instruction. This instruction set can be directly executed by automated deployment tools or cloud management platforms, thereby achieving one-time, coordinated resource deployment throughout the entire business process. In this way, this solution solves the problem that relying solely on optimal resource allocation combinations and business process data flow diagrams cannot systematically and accurately generate detailed task deployment information and resource configuration parameters for each task in the business process. It ensures that the generated instructions accurately reflect the internal logic of the business process and the specific needs of the tasks, thereby effectively guiding task deployment and improving data security and resource utilization efficiency.

[0114] Secondly, please refer to Figure 2 Some embodiments of this application also provide a resource allocation system for an enterprise asset panorama management platform, used to optimize business processes in an enterprise hybrid cloud environment. The system includes:

[0115] The acquisition module 201 is used to acquire business process definition information and resource domain topology information that characterizes the distribution and transmission characteristics of the resource pool;

[0116] The diagram building module 202 is used to build a business process data flow diagram based on the business process definition information;

[0117] The filtering module 203 is used to generate a set of candidate resource allocation combinations that meet data sensitivity constraints based on the business process data flow diagram and resource domain topology information.

[0118] The calculation module 204 is used to calculate the end-to-end total cost and total time of the entire business process for each resource allocation combination in the candidate resource allocation combination set.

[0119] The allocation module 205 is used to select a resource allocation combination from the candidate resource allocation combination set based on the preset optimization target, the total cost and / or the total time, and generate a resource allocation instruction based on the selected resource allocation combination.

[0120] The system of this application combines business process definition information and resource domain topology information to construct a business process data flow graph to comprehensively characterize the data dependencies and sensitivities between tasks. Based on this, it generates a set of candidate resource allocation combinations that meet data sensitivity constraints. Then, it calculates the end-to-end total cost and total time of the entire business process for each combination. Finally, it makes a global selection based on a preset optimization objective. This solves the problems of existing strategies that process tasks in isolation, ignore data dependencies between tasks, and fail to perform end-to-end global optimization due to cross-resource pool transmission characteristics. It achieves the effect of reducing unnecessary overhead and time delay.

[0121] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0122] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0123] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0124] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A resource allocation method for an enterprise asset panoramic management platform, used to optimize business processes in an enterprise hybrid cloud environment, characterized in that, The method includes the following steps: S1. Obtain business process definition information and resource domain topology information that characterizes the distribution and transmission characteristics of the resource pool; S2. Construct a business process data flow diagram based on the business process definition information; S3. Based on the business process data flow diagram and the resource domain topology information, generate a set of candidate resource allocation combinations that meet data sensitivity constraints; S4. For each resource allocation combination in the candidate resource allocation combination set, calculate the end-to-end total cost and total time of the entire business process. S5. Based on the preset optimization objective, select a resource allocation combination from the candidate resource allocation combination set according to the total cost and / or the total time consumption, and generate a resource allocation instruction according to the selected resource allocation combination.

2. The resource allocation method for the enterprise asset panoramic management platform according to claim 1, characterized in that, The business process definition information includes the task sequence of the business process, the data transmission relationship between tasks, the data volume, and the data sensitivity information. Step S2 includes: S21. Convert the task sequence into graph nodes in the business process data flow diagram; S22. Convert the data transmission relationship between the tasks into edges connecting the graph nodes; S23. Label the data volume and data sensitivity information onto the edges to construct the business process data flow diagram.

3. The resource allocation method for the enterprise asset panoramic management platform according to claim 2, characterized in that, The business process definition information also includes the task execution order, and step S2 further includes: S24. Determine the topological relationship of the graph nodes according to the task execution order to improve the business process data flow graph.

4. The resource allocation method for the enterprise asset panoramic management platform according to claim 1, characterized in that, The resource domain topology information includes the type of resource pool, the data sensitivity processing capability of the resource pool, the transmission cost and transmission latency between different resource pools, and the types of resource pools include private cloud resource pools and public cloud resource pools.

5. The resource allocation method for the enterprise asset panoramic management platform according to claim 4, characterized in that, Step S3 includes: S31. For each task in the business process data flow diagram, extract the data sensitivity information on the corresponding input edge; S32. Based on the resource domain topology information, identify resource pools with data sensitivity processing capabilities that match the extracted data sensitivity information, and form a set of candidate resource pools associated with the corresponding tasks. S33. Combine the candidate resource pool sets of all tasks in the business process data flow diagram to generate the candidate resource allocation combination set that meets the data sensitivity constraints.

6. The resource allocation method for the enterprise asset panoramic management platform according to claim 5, characterized in that, Step S33 includes: S331. Based on the business process data flow diagram, identify edges whose data volume exceeds a preset threshold and / or whose data sensitivity level is higher than a preset level, and use them as key data transmission paths. The data sensitivity level is determined based on the data sensitivity information. S332. Obtain the tasks corresponding to the two graph nodes connected to each path in the key data transmission path, as key source tasks and key target tasks; S333. For the key source task and the key target task, based on the transmission cost and transmission delay in the resource domain topology information, and with the goal of minimizing local cost and / or delay, select resource pools from the corresponding candidate resource pool set that match the key source task and the key target task respectively as the initial resource allocation skeleton. S334. Based on the initial resource allocation skeleton, randomly combine it with the candidate resource pool set of the remaining tasks in the business process data flow diagram until a preset number of candidate resource allocation combinations are obtained, forming the candidate resource allocation combination set that meets the data sensitivity constraints.

7. The resource allocation method for the enterprise asset panoramic management platform according to claim 4, characterized in that, Step S4 includes: S41. For each candidate resource allocation combination in the candidate resource allocation combination set, calculate the task execution time according to the type of resource pool and the data volume of the corresponding task, and calculate the inter-task transmission cost and inter-task transmission delay according to the connection relationship of the edges in the business process data flow diagram and the transmission cost and transmission delay between different resource pools. S42. Accumulate the inter-task transmission costs corresponding to each candidate resource allocation combination as the end-to-end total cost of the corresponding candidate resource allocation combination, and accumulate the inter-task transmission delays and the execution time of all tasks corresponding to each candidate resource allocation combination as the end-to-end total time of the corresponding candidate resource allocation combination.

8. The resource allocation method for the enterprise asset panoramic management platform according to claim 1, characterized in that, Step S5 includes: S51. Based on the preset optimization objective, evaluate the total cost and / or total time consumption of each candidate resource allocation combination in the candidate resource allocation combination set, and select the candidate resource allocation combination with the best evaluation result as the optimal resource allocation combination. S52. Based on the optimal resource allocation combination and the business process data flow diagram, generate a resource allocation instruction containing task deployment information and resource configuration parameters; S53. Send the resource allocation instruction to the resource pool involved in the optimal resource allocation combination.

9. The resource allocation method for the enterprise asset panoramic management platform according to claim 8, characterized in that, Step S52 includes: S521. Obtain the execution order of tasks in the business process based on the business process data flow diagram; S522. According to the execution order of the tasks, traverse each task in the optimal resource allocation combination, and generate the task deployment information and resource configuration parameters based on the data sensitivity information, data volume and resource pool type of the task in the business process data flow diagram. S523. Integrate the deployment information and resource configuration parameters of each task to form the resource allocation instruction.

10. A resource allocation system for an enterprise asset panoramic management platform, used to optimize business processes in an enterprise hybrid cloud environment, characterized in that... The system includes: The acquisition module is used to acquire business process definition information and resource domain topology information that characterizes the distribution and transmission characteristics of the resource pool; The diagram building module is used to construct a business process data flow diagram based on the business process definition information; The filtering module is used to generate a set of candidate resource allocation combinations that meet data sensitivity constraints based on the business process data flow diagram and the resource domain topology information. The calculation module is used to calculate the end-to-end total cost and total time of the entire business process for each resource allocation combination in the candidate resource allocation combination set. The allocation module is used to select a resource allocation combination from the candidate resource allocation combination set based on a preset optimization objective, according to the total cost and / or the total time consumed, and generate a resource allocation instruction based on the selected resource allocation combination.

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