Enterprise asset panoramic management platform resource allocation method and system

Abstract

The invention relates to the technical field of resource allocation management, and particularly discloses a resource allocation method and system for an enterprise asset panoramic management platform, and the method comprises the steps: obtaining business process definition information and resource domain topological information representing the distribution characteristics and transmission characteristics of a resource pool; constructing a business process data flow diagram; generating a candidate resource allocation combination set conforming to the data sensitivity constraint; calculating the total end-to-end cost and total time consumption of the whole business process; based on a preset optimization target, selecting a resource allocation combination from the candidate resource allocation combination set according to the total cost and / or the total time consumption, and generating a resource allocation instruction according to the selected resource allocation combination; according to the method, the problem that end-to-end global optimization cannot be carried out due to the fact that tasks are processed in an isolated mode through an existing strategy and data dependence between the tasks and cross-resource pool transmission characteristics are neglected is solved, and the effects of reducing unnecessary overhead and time delay are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of resource allocation management, and in particular to a resource allocation method and system for an enterprise asset panoramic management platform. Background Art

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

[0003] However, existing resource allocation strategies still have significant shortcomings when handling complex business processes involving multiple interdependent tasks. These strategies often tend to handle resource requests for individual tasks in isolation, basing decisions primarily on resource availability or simple cost comparisons while failing to fully consider the inherent data dependencies between tasks in the business process. For example, in a business process involving both sensitive data processing and non-sensitive data analysis, existing strategies may 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 potential additional costs and delays associated with data transfer between tasks. In particular, when data needs to be transferred between different cloud environments (such as between a private cloud and a public cloud), it can incur high data egress fees and significant transmission delays. Furthermore, the transmission cost and transmission delay characteristics between different resource pools vary, and existing strategies fail to effectively incorporate these transmission characteristics into their comprehensive considerations. This neglect of data dependencies and transmission characteristics between tasks results in the inability to effectively optimize the end-to-end total cost and total time of the entire business process, thereby increasing unnecessary operational expenses and time delays, and severely limiting the improvement of the company's overall operational efficiency.

[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 to solve the problem that existing strategies process tasks in isolation, ignore data dependencies between tasks, and cross-resource pool transmission characteristics, resulting in the inability to perform end-to-end global optimization, thereby reducing unnecessary overhead and time delays.

[0006] In a first aspect, the present application provides a resource allocation method for an enterprise asset panoramic management platform, which is used to optimize business processes in an enterprise hybrid cloud environment, characterized in that the method includes the following steps: S1. Obtaining business process definition information and resource domain topology information that characterizes resource pool distribution and transmission characteristics; S2. Construct a business process data flow diagram based on the business process definition information; S3. Generate a candidate resource allocation combination set that meets data sensitivity constraints based on the business process data flow diagram and the resource domain topology information; S4. For each resource allocation combination in the candidate resource allocation combination set, calculate the end-to-end total cost and total time consumption of the entire business process; S5. Based on a preset optimization target, a resource allocation combination is selected from the candidate resource allocation combination set according to the total cost and / or the total time consumption, and a resource allocation instruction is generated according to the selected resource allocation combination.

[0007] In the resource allocation method for the enterprise asset panoramic management platform, 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, and step S2 includes: S21, converting the task sequence into a graph node in the business process data flow graph; S22, converting the inter-task data transmission relationship into an edge connecting the graph nodes; S23. Mark the data volume and data sensitivity information to the edges to construct the business process data flow graph.

[0008] In the resource allocation method for the enterprise asset panoramic management platform, the business process definition information further includes a task execution sequence, 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.

[0009] The resource allocation method of the enterprise asset panoramic 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 delay between different resource pools, and the types of resource pools include private cloud resource pools and public cloud resource pools.

[0010] The resource allocation method of the enterprise asset panoramic management platform, wherein step S3 includes: S31. For each task in the business process data flow graph, extract data sensitivity information on the corresponding input edge; S32. Based on the resource domain topology information, identify a resource pool having a data sensitivity processing capability that matches the extracted data sensitivity information, and form a set of candidate resource pools associated with the corresponding task; 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 constraint.

[0011] The resource allocation method of the enterprise asset panoramic management platform, wherein step S33 includes: S331. According to 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 as the key data transmission path, where the data sensitivity level is determined based on the data sensitivity information classification; S332: Acquire tasks corresponding to two graph nodes connected by 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 the goal of minimizing local cost and / or delay, select resource pools that match the key source task and the key target task respectively from the corresponding set of candidate resource pools as an initial resource allocation skeleton; S334. Based on the initial resource allocation skeleton, randomly combine 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 to form the candidate resource allocation combination set that meets the data sensitivity constraints.

[0012] The resource allocation method of the enterprise asset panoramic management platform, wherein step S4 includes: S41. For each candidate resource allocation combination in the candidate resource allocation combination set, calculate the task execution time based on 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 based on the edge connection relationship in the business process data flow graph and the transmission cost and transmission delay between different resource pools; S42. Accumulate all 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 all the inter-task transmission delays and all the task execution times corresponding to each candidate resource allocation combination as the end-to-end total time of the corresponding candidate resource allocation combination.

[0013] The resource allocation method of the enterprise asset panoramic management platform, wherein step S5 includes: S51. Evaluate the total cost and / or total time consumption of each candidate resource allocation combination in the set of candidate resource allocation combinations according to a preset optimization goal, and select the candidate resource allocation combination with the best evaluation result as the optimal resource allocation combination; S52: Generate a resource allocation instruction including task deployment information and resource configuration parameters according to the optimal resource allocation combination and the business process data flow diagram; S53: Send the resource allocation instruction to the resource pool involved in the optimal resource allocation combination.

[0014] The resource allocation method of the enterprise asset panoramic management platform, wherein step S52 includes: S521. Obtaining the execution order of tasks in the business process according to the business process data flow diagram; S522. Traverse each task in the optimal resource allocation combination in the order in which the tasks are executed, and generate the task deployment information and resource configuration parameters based on the data sensitivity information and data volume of the task in the business process data flow diagram and the resource pool type corresponding to the task; S523: Integrate the deployment information of each task and the resource configuration parameters to form the resource allocation instruction.

[0015] In a second aspect, the present application further provides a resource allocation system for an enterprise asset panorama management platform, for optimizing business processes in an enterprise hybrid cloud environment, the system comprising: An acquisition module, used to acquire business process definition information and resource domain topology information representing resource pool distribution characteristics and transmission characteristics; A graph building module, used to build a business process data flow graph based on the business process definition information; A screening module, configured to generate a candidate resource allocation combination set that meets data sensitivity constraints based on the business process data flow diagram and the resource domain topology information; A calculation module, configured to calculate the end-to-end total cost and total time consumption of the entire business process for each resource allocation combination in the candidate resource allocation combination set; The allocation module is configured to select a resource allocation combination from the candidate resource allocation combination set based on a preset optimization target, the total cost and / or the total time consumption, and generate a resource allocation instruction based on the selected resource allocation combination.

[0016] From the above, it can be seen that the present application provides a resource allocation method and system for an enterprise asset panoramic management platform, in which the method of the present 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, and on this basis generates a set of candidate resource allocation combinations that meet data sensitivity constraints, and then calculates the end-to-end total cost and total time of the entire business process for each combination, and finally makes a global selection based on the preset optimization goals, thereby solving the problem that existing strategies process tasks in isolation, ignore data dependencies between tasks and cross-resource pool transmission characteristics, resulting in the inability to perform end-to-end global optimization, and achieves the effect of reducing unnecessary overhead and time delays. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flowchart of the resource allocation method for the enterprise asset panoramic management platform provided in an embodiment of the present application.

[0018] Figure 2 This is a structural diagram of the resource allocation system of the enterprise asset panoramic management platform provided in an embodiment of the present application.

[0019] Reference numerals: 201, acquisition module; 202, mapping module; 203, screening module; 204, calculation module; 205, allocation module. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0021] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0022] First, please refer to Figure 1 Some embodiments of the present application provide a resource allocation method for an enterprise asset panorama management platform for globally optimizing business processes in an enterprise hybrid cloud environment. The method includes the following steps: S1. Obtaining business process definition information and resource domain topology information that characterizes resource pool distribution and transmission characteristics; S2. Construct a business process data flow diagram based on the business process definition information; S3. 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; S4. For each resource allocation combination in the candidate resource allocation combination set, calculate the end-to-end total cost and total time consumption of the entire business process; S5. Based on a preset optimization goal, a resource allocation combination is selected from the candidate resource allocation combination set according to the total cost and / or the total time consumption, and a resource allocation instruction is generated according to the selected resource allocation combination.

[0023] Specifically, business process definition information refers to a collection of data that describes the internal structure and execution logic of a business process. It provides a complete view of the business process and serves as the basis for subsequently constructing data flow diagrams and resource allocation. Resource domain topology information refers to a collection of data that describes the distribution, data sensitivity processing capabilities, and interconnectedness of various resource pools in an enterprise hybrid cloud environment. It provides a global view of the resource environment and provides a quantitative basis for resource allocation decisions.

[0024] More specifically, a business process data flow diagram refers to a graphical model that visualizes and structures business process definition information. It can be constructed in the form of a directed acyclic graph (DAG) or a flowchart to clearly display the dependencies and data flows between tasks, providing a structured foundation for subsequent global optimization.

[0025] More specifically, the set of candidate resource allocation combinations that comply with data sensitivity constraints refers to a set of selectable resource allocation schemes that meet the data sensitivity requirements in the business process, where each combination ensures that sensitive data is only allocated to resource pools with corresponding processing capabilities. It can be generated by rule-based filtering, constraint satisfaction programming, or heuristic search.

[0026] More specifically, the end-to-end total cost and total time refer to the cumulative value of the total overhead and total time consumption generated by all task execution and data transmission from the beginning to the end of the business process. The total cost can include the task execution cost and the inter-task transmission cost (such as cross-cloud data export fees), and the total time can include the task execution time and the inter-task transmission delay. 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.

[0027] Specifically, the method of the present application achieves global optimization of resource allocation through a series of collaborative steps. First, business process definition information is obtained, which contains the inherent structure and task relationships of the business process, and resource domain topology information that characterizes the distribution characteristics and transmission characteristics of the resource pool is obtained, which contains the capabilities, locations of different resource pools, and the cost and delay of data transmission between them. This information together forms the basis for subsequent global optimization and solves the problem of ignoring data dependencies between tasks and cross-cloud transmission costs in the existing technology. Then, a business process data flow graph is constructed based on the obtained business process definition information. This graph structure concretizes the abstract business process, clearly showing the various tasks in the business process and the data transmission relationship between them, as well as information such as the amount of data and data sensitivity that may be contained. Through this structure, the system can intuitively understand the dependencies between tasks and avoid the disadvantages of isolated processing tasks. Subsequently, the constructed business process data flow graph and resource domain topology information are used to generate a set of candidate resource allocation combinations that meet data sensitivity constraints. This step identifies the data sensitivity requirements of the business process and, in combination with the data sensitivity processing capabilities of each resource pool in the resource domain topology, intelligently selects resource pools that meet specific data sensitivity requirements. This ensures that all subsequently considered allocation solutions first meet 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 duration of the entire business process are calculated. This calculation no longer evaluates individual tasks in isolation, but instead comprehensively considers all factors, including task execution cost, inter-task data transmission costs (including cross-cloud data egress fees), task execution duration, and inter-task transmission latency. This end-to-end calculation approach directly addresses the existing art's inability to perform overall optimization due to the neglect of inter-task transmission characteristics, providing a quantitative basis for subsequent global optimal decision-making. Finally, based on the preset optimization objective, a resource allocation combination is selected from the set of candidate resource allocation combinations based on the calculated total cost and / or total duration, and resource allocation instructions are generated based on the selected combination. This selection is based on a global assessment of the entire business process, rather than a local optimum. This fundamentally addresses the overall inefficiency and unnecessary overhead caused by isolated decisions in existing technologies. Ultimately, the generated resource allocation instructions enable the optimization results to be deployed and executed, achieving a closed-loop management process from analysis to implementation.

[0028] Through the above processing, the method of the present application can effectively solve the problem that in an enterprise hybrid cloud environment, the existing resource allocation strategy cannot perform global optimization of the end-to-end total cost and total time for complex business processes containing multiple interdependent tasks due to isolated processing of tasks, neglect of data dependencies between tasks, data sensitivity and cross-resource pool transmission characteristics. The method of the present application comprehensively considers the dependencies and data sensitivity between tasks by constructing a business process data flow diagram, and ensures data security compliance by generating candidate combinations that meet data sensitivity constraints. At the same time, by calculating and evaluating the end-to-end total cost and total time of the entire business process, it optimizes the cross-resource pool transmission characteristics and overall performance, and can select the globally optimal resource allocation plan, significantly reducing unnecessary overhead and time delays, and improving the overall operating efficiency and economy of complex business processes in a hybrid cloud environment.

[0029] The method of the present 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, and on this basis generates a set of candidate resource allocation combinations that meet data sensitivity constraints, and then calculates the end-to-end total cost and total time of the entire business process for each combination, and finally makes a global selection based on the preset optimization goal, thereby solving the problem that existing strategies process tasks in isolation, ignore data dependencies between tasks and cross-resource pool transmission characteristics, resulting in the inability to perform end-to-end global optimization, and achieves the effect of reducing unnecessary overhead and time delays.

[0030] 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, and step S2 includes: S21, converting the task sequence into a graph node in a business process data flow graph; S22, converting the data transmission relationship between tasks into edges connecting graph nodes; S23. Label the data volume and data sensitivity information to the edges to construct a business process data flow graph.

[0031] Specifically, a task sequence refers to the set of all execution steps or operations in a business process, which can be represented by data structures such as lists, arrays, or sets. The data transmission relationship between tasks refers to the dependency of data input and output between tasks in a business process, which can be described by directed edges, data flow paths, or data dependency graphs. Data volume refers to the size or quantity of data transferred between tasks, which can be expressed in units of measurement such as number of bytes, file size, or number of records. Data sensitivity information refers to the security or privacy level that data must comply with during processing and transmission, and can be identified by security levels (such as public, internal, confidential, top secret), encryption requirements, or compliance labels.

[0032] Specifically, this solution specifies that business process definition information should include the business process's task sequence, inter-task data transfer relationships, data volume, and data sensitivity information. This requirement provides input for the subsequent construction of the business process data flow graph. By including the task sequence, the system can understand the components of the business process. By including inter-task data transfer relationships and data volumes, the system can identify inter-task dependencies and the scale of data flows, which is essential for assessing transmission costs and latency. The inclusion of data sensitivity information enables the system to adhere to data security compliance requirements when allocating resources, preventing the leakage or improper handling of sensitive data. Step S21 maps the task sequence into graph nodes, defining the boundaries and components of the business process and laying the foundation for subsequent analysis and allocation. Step S22 visualizes data transfer relationships as edges in the graph, allowing the system to identify which tasks interact with each other and the direction of data flow. This is crucial for understanding the overall structure and data dependency chain of the business process and provides a direct basis for subsequently calculating inter-task transmission costs and latency. Step S23, by annotating the data volumes on the edges, the system can quantify the scale of data transfer and thus estimate the resources and time required for transmission. At the same time, data sensitivity information is marked on the edge, so that the system can identify and follow data sensitivity constraints when generating resource allocation combinations, ensuring that sensitive data is only transmitted or processed between resource pools with processing capabilities, thereby avoiding data security risks.

[0033] By refining business process definition information and clarifying the data flow graph construction process, this solution enables the system to construct a business process data flow graph containing semantic information. This graph reflects the execution order and data dependencies of tasks, and also includes the properties of data transmission (data volume and sensitivity). This provides a data foundation for subsequent steps (such as generating candidate resource allocation combinations that meet data sensitivity constraints and calculating end-to-end total cost and total time), thus solving the problem of suboptimal resource allocation caused by insufficient information in business processes.

[0034] In some preferred implementations, the business process definition information further includes a task execution sequence, 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.

[0035] Specifically, the task execution order refers to the pre-set, logical, sequential execution sequence between tasks in a business process, which can be expressed as serial dependencies, parallel execution, or conditional branching. Determining the topological relationships of graph nodes involves establishing or adjusting the connections between graph nodes (representing tasks) in the business process data flow graph based on the acquired task execution order information to accurately reflect the logical dependencies and execution flow of the tasks. This can 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 aligns with the actual execution order.

[0036] Specifically, after steps S21 to S23 have completed the basic construction of the data flow graph, step S24 uses the acquired task execution order to determine the topological relationships between graph nodes. This topological relationship determination process overcomes the limitations of graph construction based solely on data transfer relationships. For example, some tasks may not directly transfer data between each other, but they have a strict 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 execute before task B, even if there is no data transfer between tasks A and B, the system will establish a topological connection from task A to task B in the data flow graph to ensure that this order is reflected. This allows the data flow graph to not only reflect the data flow path, but also more accurately map the actual execution process 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 provides a more reliable basis for subsequent resource allocation decisions. For example, when calculating the end-to-end total cost and total time of the entire business process, if the data flow diagram can accurately reflect the parallelism or seriality of the tasks, the system can more accurately evaluate the actual execution time and cost under different resource allocation combinations. For example, if two tasks can be executed in parallel, then they can be considered to be performed simultaneously in the total time calculation, rather than being accumulated serially. Conversely, if they must be executed serially, the system will correctly accumulate their time even if there is no data dependency. This precise topological relationship enables the resource allocation algorithm to generate a more optimal resource allocation combination, thereby improving the overall optimization effect.

[0037] Through the above solution, this application can more comprehensively and accurately construct a business process data flow diagram that can reflect the actual execution order of tasks and more complex topological dependencies. This solves the problem that when the diagram is constructed based solely on data transmission relationships, it cannot fully reflect the actual execution order of tasks and complex topological dependencies in the business process. As a result, 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, helping to generate more optimal resource allocation combinations and reducing unnecessary overhead and time delays.

[0038] In some of the above-mentioned solutions of the present application, it is proposed to obtain resource domain topology information to generate a set of candidate resource allocation combinations that meet data sensitivity constraints, and calculate the end-to-end total cost and total time of the entire business process. However, if the resource domain topology information fails to include in detail the type of resource pool, its data sensitivity processing capabilities, and the calculation method of the transmission cost and transmission delay between different types of resource pools (especially the data export fee of the public cloud, etc.), then the data sensitivity constraints may not be fully met when generating the candidate resource allocation combination, and when calculating the end-to-end total cost and total time, the lack of precise consideration of the transmission characteristics in the hybrid cloud environment will lead to inaccurate calculations, and thus the effective optimization of the entire business process cannot be achieved, and unnecessary overhead and time delays are added.

[0039] In this regard, 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 delay 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 the transmission cost and transmission delay between different resource pools. If a public cloud is used, the transmission cost includes the data export fee of the public cloud resource pool.

[0040] Specifically, the data sensitivity handling capability of a resource pool refers to the security and compliance capabilities of the resource pool when processing data of different sensitivity levels. This can be achieved through security level labels, compliance certification logos, or scoring systems to ensure that data is protected in the corresponding environment. Transmission cost refers to the fee required to transmit data between different resource pools. This can be achieved through a price per unit of data volume (such as 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. This can be achieved through network round-trip time (RTT), packet transmission rate, or an estimated value based on geographical distance and network congestion.

[0041] 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 transmission delay 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 association with the calculation method of transmission cost and transmission delay, 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 pool is utilized. The system can identify and filter resource pools with corresponding data sensitivity processing capabilities to ensure that sensitive data is not allocated to non-compliant environments, thereby meeting data sensitivity constraints and avoiding compliance risks.

[0042] In some preferred embodiments, step S3 includes: S31. For each task in the business process data flow graph, extract the data sensitivity information on the corresponding input edge; S32. Based on the resource domain topology information, identify a resource pool having a data sensitivity processing capability that matches the extracted data sensitivity information, and form a set of candidate resource pools associated with the corresponding task; 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 the data sensitivity constraints.

[0043] Specifically, the above steps are closely integrated with the entire resource allocation method, and their role is to pre-screen candidate combinations that meet the data sensitivity requirements before calculating the total cost and total time. The method of the present application ensures that subsequent cost and time calculations are only performed on compliant resource allocation combinations by utilizing the annotations on data sensitivity in the business process definition information and the data sensitivity processing capabilities of the resource pool in the resource domain topology information, thereby avoiding the calculation of 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 global optimization goals, and it makes the entire resource allocation process more intelligent and reliable. Step S31 ensures the basis for satisfying data sensitivity constraints. It clarifies the sensitivity of the data processed by each task and provides a key basis for the subsequent selection of a suitable resource pool. By accurately identifying the data sensitivity of each task, it is possible to avoid allocating sensitive data to a resource pool that does not have the corresponding processing capabilities, thereby ensuring data security. The data sensitivity processing capability of the resource pool refers to the level or category of data sensitivity information that the resource pool can process; step S32 uses the data about the data sensitivity processing capability of the resource pool in the resource domain topology information to perform a preliminary screening of the resource pool, that is, to analyze whether the data sensitivity information belongs to the level or category of data sensitivity information contained in the data sensitivity processing capability of the resource pool, so as to screen the resource pool. By matching the data sensitivity of the task with the processing capability of the resource pool, resource pools that do not meet the data security requirements can be effectively excluded, thereby constructing a list of potential resource pools that meet the data sensitivity constraints for each task, greatly narrowing 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 candidate resource allocation combination set that meets the data sensitivity constraints. On the premise that the candidate resource pool sets of each task have met the data sensitivity constraints, combining these sets can ensure that each generated candidate resource allocation combination naturally meets the data sensitivity requirements. This step-by-step screening and recombining approach avoids generating a large number of invalid combinations that do not meet the constraints, allowing subsequent calculation and optimization of total cost and total time to be performed in an effective and compliant set, thereby improving the efficiency of overall resource allocation and the quality of the final solution.

[0044] 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 comply with data sensitivity constraints. This significantly reduces the workload required to subsequently calculate the total end-to-end cost and total time of the entire business process, improving computational efficiency. At the same time, this method ensures the accuracy and compliance of the final resource allocation, preventing sensitive data from being allocated to resource pools that lack the corresponding processing capabilities, thereby improving data security.

[0045] In some of the above-mentioned schemes of the present 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 candidate resource allocation combination set that meets the 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 cause the number of generated candidate resource allocation combinations to grow exponentially. This exhaustive combination generation method will result 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 large impact on overall cost and time consumption, such as transmissions with large data volumes or high data sensitivity, resulting in the generated candidate combinations being unable to effectively focus on the optimal solution area, thereby limiting the efficiency of subsequent optimization steps and the quality of the final resource allocation scheme.

[0046] In this regard, in some preferred embodiments, 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 as key data transmission paths. The data sensitivity level is determined based on the data sensitivity information classification. S332. Obtain tasks corresponding to two graph nodes connected by 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 the goal of minimizing local cost and / or delay, select resource pools that match the key source task and the key target task respectively from the corresponding candidate resource pool set as the initial resource allocation skeleton; S334. Based on the initial resource allocation skeleton, randomly combine the candidate resource pool sets of the remaining tasks in the business process data flow diagram until a preset number of candidate resource allocation combinations are obtained to form a candidate resource allocation combination set that meets the data sensitivity constraints.

[0047] Specifically, the preset threshold refers to a set numerical value used to determine whether the data volume meets the criteria of the critical transmission path. The data sensitivity level is a pre-set grade or classification standard used to determine whether the data sensitivity meets the criteria of the critical transmission path, that is, it is determined based on the classification of data sensitivity information. The key source tasks and key 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 the data transmission and the other task is the end point of the data transmission. Minimizing local cost and / or delay refers to optimizing when selecting resource pools for key source tasks and key target tasks with the goal of reducing the cost of data transmission between these tasks or shortening the transmission delay.

[0048] 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 the greatest impact on performance. After identifying the critical data transmission paths, step S332 further determines the tasks connected by these paths, namely, the critical source tasks and the critical target tasks. These tasks are core links in the business process, and their resource allocation decisions have a decisive impact on overall performance. Next, for these critical source tasks and key target tasks, step S333 utilizes the transmission cost and transmission delay data contained in the resource domain topology information to select, from their respective candidate resource pool sets, a resource pool that minimizes local cost or shortens local delay. This selection and matching process includes obtaining all different resource pool combinations in the candidate resource pool sets corresponding to the critical source tasks and key target tasks, calculating the local cost and / or delay of each resource pool combination, and then selecting the optimal resource pool combination based on minimizing local cost and / or delay. The two resource pools from this optimal resource pool combination serve as the initial resource allocation framework. 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 the critical tasks, step S334 randomly combines this skeleton with the candidate resource pool set for the remaining tasks in the business process data flow graph. This combination approach avoids the computational overhead and time consumption associated with exhaustive combination searches for all tasks. Through random combinations, the system explores multiple allocation schemes for non-critical tasks while ensuring efficiency. By setting a preset number of candidate combinations, the size of the resulting set of combinations is effectively controlled. Through the above steps, this solution effectively generates a manageable number of high-quality candidate resource allocation combinations. This approach leverages the detailed information of the business process data flow graph and the rich transmission characteristic data in the resource domain topology information, prioritizing those with the greatest impact on overall performance when generating candidate combinations. This effectively narrows the subsequent search space and improves the quality of the final resource allocation solution. This phased, focused combination generation strategy effectively addresses the computational overhead and time consumption associated with simple exhaustive combination searches when the business process contains a large number of tasks, as well as the inability to effectively focus on the optimal solution region.

[0049] In some preferred embodiments, step S4 includes: S41. For each candidate resource allocation combination in the candidate resource allocation combination set, calculate the task execution time based on 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 based on the edge connection relationship in the business process data flow graph and the transmission cost and transmission delay between different resource pools; S42. Accumulate all 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 all inter-task transmission delays and all task execution times corresponding to each candidate resource allocation combination as the end-to-end total time of the corresponding candidate resource allocation combination.

[0050] Specifically, step S41 calculates the execution time of each task based on the resource pool type and the corresponding task's data volume. The resource pool type information is used to evaluate the execution efficiency of tasks in different environments. Furthermore, step S41 calculates the data transmission cost and delay between tasks based on the edge connections between tasks in the business process data flow graph, as well as the data transmission costs and delays between different resource pools. These transmission cost and delay calculations fully account for differences in resource pool types, such as potential data egress fees from public clouds, resulting in more realistic calculation results. Then, step S42 accumulates the data transmission costs between all tasks in each candidate resource allocation combination to obtain the end-to-end total cost for that combination. Simultaneously, the data transmission delays between all tasks and the execution time of all tasks are accumulated to obtain the end-to-end total time for that combination. This accumulation method ensures that all time and economic costs of the entire business process, from start to finish, are taken into account, particularly the additional costs and delays incurred by crossing different resource pool types.

[0051] Through the above scheme, the present application can solve the problem that in a hybrid cloud environment, the cost and time calculation deviates from the actual situation due to the failure to fully consider the different resource pool types and their unique transmission characteristics. Specifically, by calculating the task execution time based on the resource pool type and the amount of task data, and calculating the transmission cost and delay between tasks based on the connection relationship of the business process data flow diagram and the transmission cost and transmission delay between different resource pools, the basic data are made more realistic. Furthermore, by accumulating these calculated costs and time, the end-to-end total cost and total time that reflect the actual overhead and time consumption of the entire business process can be obtained, so that the evaluation of different candidate resource allocation combinations has a reference value, thereby supporting the generation of resource allocation decisions that meet expectations and avoiding unnecessary overhead and time delays.

[0052] In some preferred embodiments, step S5 includes: S51. Evaluate the total cost and / or total time consumption of each candidate resource allocation combination in the set of candidate resource allocation combinations according to a preset optimization goal, and select the candidate resource allocation combination with the best evaluation result as the optimal resource allocation combination; S52. Generate a resource allocation instruction including task deployment information and resource configuration parameters based on the optimal resource allocation combination and the business process data flow diagram; S53: Send the resource allocation instruction to the resource pools involved in the optimal resource allocation combination.

[0053] Specifically, task deployment information refers to detailed instructions about the specific resource pools in which each task in a business process should be placed. This information can be expressed in the form of a mapping between task IDs and target resource pool IDs, the logical location of the task within the resource pool, or the priority of the task within the resource pool. Resource configuration parameters refer to the specific resource specifications and environment settings required to ensure the normal operation of the task within the target resource pool. These parameters can be expressed in the form of computing resources, storage resources, network resources, security policies, or software environments.

[0054] More specifically, resource allocation instructions refer to converting the optimal resource allocation combination into a set of automated commands that can be executed by the resource pool, which is used to guide the resource pool to deploy tasks and configure resources.

[0055] Specifically, step S51 quantitatively evaluates the total cost and / or total time of each candidate resource allocation combination calculated in the previous step based on the preset optimization objective. This accurately identifies the resource allocation solution that performs best under the current constraints and determines it as the optimal resource allocation combination. Subsequently, step S52 generates resource allocation instructions containing task deployment information and resource configuration parameters based on the determined optimal resource allocation combination and the business process data flow graph. The business process data flow graph provides key contextual information such as inter-task dependencies, data volume, and data sensitivity. This information is combined with the target resource pool for each task in the optimal resource allocation combination. The generated instructions not only clearly define the resource pool to which each task should be deployed, but also specify the required resource specifications for compute, storage, network, and other resources, as well as possible security configurations. This business process context-based instruction generation method ensures the integrity 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 perform task deployment and resource configuration operations, thereby implementing the optimized resource allocation solution. 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 end-to-end optimization goal of business processes.

[0056] In some preferred embodiments, the optimization objectives are divided into minimizing the total cost, minimizing the total time consumption, and minimizing the weighted value of the total cost and the total time consumption.

[0057] Specifically, minimizing total cost means that the system prioritizes reducing overall financial overhead 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 values ​​of total cost and total time means that the system comprehensively considers total cost and total time by assigning different weights to each when evaluating candidate resource allocation combinations. This can be achieved using a weighted sum function.

[0058] Through the above design, this solution solves the problem of insufficient selection flexibility caused by unclear optimization objectives in existing solutions, ensuring that the selected resource allocation combination can accurately meet the optimization needs of specific business processes.

[0059] In some preferred embodiments, step S52 includes: S521. Obtain the execution order of tasks in the business process according to the business process data flow diagram; S522. Traverse each task in the optimal resource allocation combination in the order of task execution, and generate task deployment information and resource configuration parameters based on the data sensitivity information, data volume, and resource pool type corresponding to the task in the business process data flow diagram; S523: Integrate each task deployment information and resource configuration parameters to form a resource allocation instruction.

[0060] Specifically, after receiving the optimal resource allocation combination and the business process data flow graph, this solution first extracts the task execution order from the business process data flow graph in step S521. Subsequently, in step S522, each task in the optimal resource allocation combination is processed one by one according to the task execution order. This in-order traversal approach allows the completion status and data output of its predecessor tasks to be fully considered when generating deployment information and configuration parameters for the current task, thereby ensuring the logical correctness of resource allocation. When processing each task, the system comprehensively considers the data sensitivity information of the task in the business process data flow graph, the data volume, and the resource pool type to which the task has been assigned. 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. 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 to ensure instruction execution and resource utilization efficiency. Through this multi-dimensional and refined consideration, the deployment information and resource configuration parameters of each task can accurately reflect its specific needs and the characteristics of the environment in which it is located. Finally, step S523 integrates the deployment information and resource configuration parameters independently generated 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 a one-time and coordinated deployment of resources for the entire business process. In this way, this solution solves the problem that relying solely on the optimal resource allocation combination and the business process data flow diagram cannot systematically and accurately generate detailed task deployment information and resource configuration parameters for each task in the business process, ensuring that the generated instructions can accurately reflect the internal logic of the business process and the specific needs of the task, thereby effectively guiding task deployment and improving data security and resource utilization efficiency.

[0061] Second, please refer to Figure 2 Some embodiments of the present application further provide a resource allocation system for an enterprise asset panorama management platform, for optimizing business processes in an enterprise hybrid cloud environment. The system includes: An acquisition module 201 is configured to acquire business process definition information and resource domain topology information representing resource pool distribution characteristics and transmission characteristics; A graph building module 202 is used to build a business process data flow graph based on the business process definition information; The screening 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; The calculation module 204 is configured to calculate the end-to-end total cost and total time consumption of the entire business process for each resource allocation combination in the candidate resource allocation combination set; The allocation module 205 is configured to select a resource allocation combination from a set of candidate resource allocation combinations based on a preset optimization target, total cost and / or total time consumption, and generate a resource allocation instruction based on the selected resource allocation combination.

[0062] The system of the present 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, and on this basis generates a set of candidate resource allocation combinations that meet data sensitivity constraints, and then calculates the end-to-end total cost and total time of the entire business process for each combination, and finally makes a global selection based on the preset optimization goals, thereby solving the problem that existing strategies process tasks in isolation, ignore data dependencies between tasks and cross-resource pool transmission characteristics, resulting in the inability to perform end-to-end global optimization, and achieves the effect of reducing unnecessary overhead and time delays.

[0063] In addition, the units described as separate components may or may not be physically separate, and 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0064] Furthermore, the functional modules in each embodiment of the present 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.

[0065] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any actual relationship or order between these entities or operations.

[0066] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. 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 comprises the following steps: S1. Obtaining business process definition information and resource domain topology information that characterizes resource pool distribution and transmission characteristics; S2. Construct a business process data flow diagram based on the business process definition information; S3. Generate a candidate resource allocation combination set that meets data sensitivity constraints based on the business process data flow diagram and the resource domain topology information; S4. For each resource allocation combination in the candidate resource allocation combination set, calculate the end-to-end total cost and total time consumption of the entire business process; S5. Based on a preset optimization target, a resource allocation combination is selected from the candidate resource allocation combination set according to the total cost and / or the total time consumption, and a resource allocation instruction is generated according to the selected resource allocation combination.

2. The resource allocation method for an 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 data sensitivity information. Step S2 includes: S21, converting the task sequence into a graph node in the business process data flow graph; S22, converting the inter-task data transmission relationship into an edge connecting the graph nodes; S23. Mark the data volume and data sensitivity information to the edges to construct the business process data flow graph.

3. The resource allocation method for an enterprise asset panoramic management platform according to claim 2, characterized in that: The business process definition information also includes the task execution sequence, 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 an 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 delay between different resource pools. The types of resource pools include private cloud resource pools and public cloud resource pools.

5. The resource allocation method for an 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 graph, extract data sensitivity information on the corresponding input edge; S32. Based on the resource domain topology information, identify a resource pool having a data sensitivity processing capability that matches the extracted data sensitivity information, and form a set of candidate resource pools associated with the corresponding task; 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 constraint.

6. The method for allocating resources on an enterprise asset management platform according to claim 5, characterized in that: Step S33 includes: S331. According to 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 as key data transmission paths, where the data sensitivity level is determined based on the data sensitivity information classification; S332: Acquire tasks corresponding to two graph nodes connected by 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 the goal of minimizing local cost and / or delay, select resource pools that match the key source task and the key target task respectively from the corresponding set of candidate resource pools as an initial resource allocation skeleton; S334. Based on the initial resource allocation skeleton, randomly combine 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 to form the candidate resource allocation combination set that meets the data sensitivity constraints.

7. The resource allocation method for an 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 based on 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 based on the edge connection relationship in the business process data flow graph and the transmission cost and transmission delay between different resource pools; S42. Accumulate all 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 all the inter-task transmission delays and all the task execution times 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 an enterprise asset panoramic management platform according to claim 1, characterized in that: Step S5 includes: S51. Evaluate the total cost and / or total time consumption of each candidate resource allocation combination in the set of candidate resource allocation combinations according to a preset optimization goal, and select the candidate resource allocation combination with the best evaluation result as the optimal resource allocation combination; S52: Generate a resource allocation instruction including task deployment information and resource configuration parameters according to the optimal resource allocation combination and the business process data flow diagram; S53: Send the resource allocation instruction to the resource pool involved in the optimal resource allocation combination.

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

10. A resource allocation system for an enterprise asset panorama management platform, used to optimize business processes in an enterprise hybrid cloud environment, characterized in that: The system comprises: An acquisition module, used to acquire business process definition information and resource domain topology information representing resource pool distribution characteristics and transmission characteristics; A graph building module, used to build a business process data flow graph based on the business process definition information; A screening module, configured to generate a candidate resource allocation combination set that meets data sensitivity constraints based on the business process data flow diagram and the resource domain topology information; A calculation module, configured to calculate the end-to-end total cost and total time consumption of the entire business process for each resource allocation combination in the candidate resource allocation combination set; The allocation module is configured to select a resource allocation combination from the candidate resource allocation combination set based on a preset optimization target, the total cost and / or the total time consumption, and generate a resource allocation instruction based on the selected resource allocation combination.

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