Enterprise Full-process Service Resource Scheduling Optimization Method and System Based on Big Data

Abstract

The present invention relates to the field of resource scheduling, and particularly to an optimization method and system for enterprise full-process service resource scheduling based on big data. The method includes the following steps: obtaining enterprise business logs and enterprise operation service resources; analyzing the distribution of business processes in the enterprise business logs to construct a business full-process structure diagram; classifying the enterprise operation service resources by resource type to obtain multi-type enterprise service resource data; fusing heterogeneous resources of the multi-type enterprise service resource data to construct an enterprise heterogeneous resource library; analyzing the resource requirements of each link in the business full-process structure diagram to generate link node resource requirement data; generating business link simulation data according to the link node resource requirement data; predicting the resource requirements of each link for the business link simulation data to obtain multiple link resource requirement prediction data; identifying resource shortage links according to the multiple link resource requirement prediction data; The present invention improves the utilization rate and efficiency of enterprise resource scheduling.

Description

Technical Field

[0001] The present invention relates to the field of resource scheduling, and particularly to an optimization method and system for enterprise full-process service resource scheduling based on big data. Background Art

[0002] With the rapid development of information technology and the intensification of global competition among enterprises, when providing full-process services, enterprises involve multiple links and resources, including human resources, material resources, equipment resources, etc. The reasonable scheduling and optimization of these resources are crucial for improving enterprise service quality, reducing costs, and enhancing competitiveness. However, currently, enterprises are facing increasingly complex resource scheduling and optimization challenges. Traditional resource scheduling methods are often based on experience and rules, and it is difficult to cope with large-scale service demands and dynamically changing environments. There are often problems such as low resource scheduling efficiency and inaccurate scheduled resource quantities. In order to meet the enterprise's needs for efficient, flexible, and intelligent resource scheduling, an intelligent enterprise resource scheduling method is required. Summary of the Invention

[0003] To solve the above technical problems, the present invention proposes an optimization method and system for enterprise full-process service resource scheduling based on big data to solve at least one of the above technical problems.

[0004] To achieve the above object, the present invention provides an optimization method for enterprise full-process service resource scheduling based on big data, including the following steps:

[0005] Step S1: Obtain enterprise business logs and enterprise operation service resources; analyze the distribution of business links in the enterprise business logs, and construct a business full-process structure diagram;

[0006] Step S2: Classify the enterprise operation service resources by resource type to obtain multi-type enterprise service resource data; fuse heterogeneous resources of the multi-type enterprise service resource data to construct an enterprise heterogeneous resource library;

[0007] Step S3: Analyze the resource requirements of each link in the business full-process structure diagram to generate link node resource requirement data; generate business link simulation data according to the link node resource requirement data;

[0008] Step S4: Forecast the resource requirements of each link for the business link simulation data to obtain multiple link resource requirement forecast data; identify resource shortage links according to the multiple link resource requirement forecast data;

[0009] Step S5: Make a global resource dynamic configuration decision for the resource shortage links based on the enterprise heterogeneous resource library, and construct a global resource configuration strategy;

[0010] Step S6: Based on the global resource configuration strategy, perform scheduling parameter optimization modeling to construct an adaptive dynamic resource scheduling model for executing enterprise resource scheduling optimization tasks.

[0011] The present invention obtains enterprise business logs and enterprise operation service resources to obtain the data basis regarding enterprise operation status and resource utilization. It conducts a distribution analysis of business processes to understand the business processes of the enterprise and the dependencies between links, constructs a full-process business structure diagram for subsequent resource scheduling optimization decisions, classifies the enterprise operation service resources into different resource types for subsequent resource demand analysis and scheduling optimization, integrates different types of enterprise service resources through heterogeneous resource fusion to construct an enterprise heterogeneous resource library for comprehensive management and optimal utilization of different types of resources. By conducting resource demand analysis on the full-process business structure diagram, it understands the demand for different types of resources in each link, provides a basis for subsequent resource scheduling, generates simulated data for business links to simulate the resource demand situations of different links, helps evaluate and optimize the effect of resource scheduling strategies, conducts resource demand prediction for the simulated data of business links to predict the future resource demand in each link, provides a prediction basis for resource scheduling decisions, identifies resource shortage links to determine the links where resource supply will be insufficient in the future, helps the enterprise take measures in advance to avoid problems caused by resource shortages, makes global resource configuration decisions for resource shortage links based on the enterprise heterogeneous resource library by comprehensively considering the supply situations of different types of resources to ensure the reasonable allocation and utilization of resources, constructs a global resource configuration strategy to help the enterprise rationally arrange the allocation priorities and scheduling strategies of resources under limited resource supply, improve resource utilization efficiency and business operation effects, perform scheduling parameter optimization modeling based on the global resource configuration strategy to optimize the decision-making process of resource scheduling, improve resource utilization efficiency and business response capabilities, and construct an adaptive dynamic resource scheduling model for dynamic scheduling according to real-time resource demand and supply situations to adapt to the changing business environment and resource status, realizing the optimization of enterprise resource scheduling and improving business efficiency and operation results.

[0012] Preferably, step S1 includes the following steps:

[0013] Step S11: Obtain enterprise business logs and enterprise operation service resources;

[0014] Step S12: Filter sensitive information from the enterprise business logs to generate filtered business logs;

[0015] Step S13: Conduct a distribution analysis of business processes on the filtered business logs to generate business process distribution data;

[0016] Step S14: Conduct a dependency analysis of business process execution on the generated business process distribution data to obtain the dependency data of each business process execution;

[0017] Step S15: Reconstruct the overall business process structure based on the execution dependency data of each business link to obtain the overall business process structure diagram.

[0018] In the present invention, by acquiring enterprise business logs and enterprise operation service resources, a data basis regarding the enterprise operation status and resource utilization is obtained. These data provide information for subsequent sensitive information filtering, business analysis, and resource scheduling optimization. Sensitive information filtering excludes sensitive data in the business logs to ensure data security and privacy protection, generating filtered business logs so that subsequent analysis and processing can be carried out under the premise of protecting privacy. By analyzing the filtered business logs, the business processes of the enterprise and the execution conditions of each link are understood, generating business link distribution data to provide information such as the quantity, sequence, and execution frequency of business links, providing a basis for subsequent reconstruction of the overall business process structure. By analyzing the business link distribution data, the execution dependency relationships between business links are determined, that is, which links need to start after other links are executed, obtaining the execution dependency data of each business link to reveal the execution logic and critical path in the business process, providing a basis for subsequent reconstruction of the overall business process structure. Reconstructing the overall business process structure based on the execution dependency data of business links reorganizes the relationships between business links to form a clearer overall business process structure diagram. The overall business process structure diagram shows the overall business process of the enterprise and highlights the dependency relationships between different links, providing a visual basis for subsequent resource scheduling optimization.

[0019] Preferably, the specific steps of step S14 are as follows:

[0020] Extract link feature extraction from the filtered business logs to obtain business event types and business link action behavior features;

[0021] Perform action feature annotation on the business link action behavior features based on the business event types to obtain action behavior data of each type of link;

[0022] Calculate the average duration for the action behavior data of each type of link to generate the average processing duration of link actions;

[0023] Mine business paths from the business link distribution data to obtain business link processing paths;

[0024] Perform execution dependency analysis on the business link processing paths based on the average processing duration of link actions, thereby obtaining the execution dependency data of each business link.

[0025] Through extracting link characteristics from the filtered business logs, the present invention identifies different business event types and the action behavior characteristics of business links. The business event types and action behavior characteristics provide a more fine-grained understanding of the business process, laying a foundation for subsequent action feature annotation and execution dependency analysis. According to the business event types, the action behavior characteristics of business links are annotated, dividing the business links into different types, and classifying the corresponding action behaviors into each type, obtaining the action behavior data of each type of link, providing the behavior patterns of different types of links in the business process, and providing a data basis for subsequent average duration calculation and execution dependency analysis. By calculating the average duration of the action behavior data of each type of link, the average processing time of different types of links in the business process is understood. The average processing duration of link actions is an important indicator for resource scheduling and optimization, used to evaluate and compare the processing efficiency and resource requirements of different links. By performing business path mining on the business link distribution data, common or typical processing paths in the business process are discovered, obtaining the business link processing paths to help understand the execution sequence and common paths of the business process, providing a basis for subsequent execution dependency analysis and resource scheduling optimization. Based on the average processing duration of link actions, the execution dependency relationships between business links are analyzed, that is, which links need to start after other links are completed, obtaining the execution dependency data of each business link, revealing the execution logic and critical paths in the business process, and providing a basis for subsequent resource scheduling optimization decisions to ensure the reasonable allocation and utilization of resources.

[0026] Preferably, the specific steps of step S2 are as follows:

[0027] Step S21: Divide the enterprise operation service resources into resource types to obtain multi-type enterprise service resource data;

[0028] Step S22: Quantify the resource capabilities of the multi-type enterprise service resource data based on the preset enterprise resource quantification rules to obtain multi-type resource quantification values;

[0029] Step S23: Perform a standardized mapping transformation on the multi-type enterprise service resource data to obtain an enterprise resource table;

[0030] Step S24: Perform heterogeneous resource fusion on the enterprise resource table according to the multi-type resource quantification values to construct an enterprise heterogeneous resource library.

[0031] The present invention classifies enterprise operation service resources by resource type, classifying resources into different types, such as enterprise service personnel, equipment, venues, logistics resources, etc. The classification of multi-type enterprise service resource data provides a breakdown perspective for different resource types, facilitating subsequent resource capacity quantification and resource scheduling optimization. By quantifying multi-type enterprise service resource data based on preset enterprise resource quantification rules, the capabilities of resources are numerically represented. The quantification values of multi-type resources provide an assessment of the capacity and availability of different resource types, providing a basis for subsequent resource scheduling decisions. Standardized mapping transformation is performed on multi-type enterprise service resource data to unify different types of resource data into one format or standard, facilitating subsequent data processing and analysis. The enterprise resource table provides detailed information and metrics for each resource type of the enterprise, including resource name, quantity, location, status, etc., providing a data foundation for resource scheduling and optimization. Based on the quantification values of multi-type resources, different resource types in the enterprise resource table are comprehensively considered and integrated to construct a heterogeneous resource library containing multi-type resources. The enterprise heterogeneous resource library provides an understanding of the association and dependency relationships between different resource types, providing a more comprehensive perspective and comprehensive consideration for resource scheduling and optimization decisions.

[0032] Preferably, the specific steps of step S3 are as follows:

[0033] Step S31: Mark key links in the business full-process structure diagram to obtain multiple business link nodes;

[0034] Step S32: Calculate the resource occupancy of multiple business link nodes to obtain the link resource occupancy;

[0035] Step S33: Based on the link resource occupancy, analyze the resource requirements of each link to generate link node resource requirement data;

[0036] Step S34: According to the link node resource requirement data, simulate the operation of business links in the business full-process structure diagram to generate business link simulation data.

[0037] The present invention identifies the key link nodes in the business process by marking the key links in the business process structure diagram, obtains multiple business link nodes, provides the decomposition and organization of the business process, and provides a basis for subsequent resource occupancy calculation and resource scheduling optimization. By calculating the resource occupancy of multiple business link nodes, the quantity and type of resources required for each link during operation are understood. The link resource occupancy quantifies the demand degree of each link for resources, providing data support for subsequent resource demand analysis and resource scheduling. Based on the link resource occupancy, the resource demand of each link is analyzed to evaluate the resource demand of each link, including resource type, quantity, duration, etc. The link node resource demand data provides detailed information on the resource demand of each link, providing a basis for subsequent resource scheduling decision-making and resource optimization. According to the link node resource demand data, the operation of the business link is simulated to simulate the operation of each link in the business process, including the occupancy and release of resources. The business link simulation data provides the simulation results of resource occupancy and operation in the business process, providing reference and verification for resource scheduling and optimization decisions.

[0038] Preferably, the specific steps of step S4 are as follows:

[0039] Step S41: Calculate the resource peak time for the business link simulation data to obtain the resource peak time of each link;

[0040] Step S42: Analyze the resource share distribution of the business link simulation data to generate link resource share distribution data;

[0041] Step S43: Based on the link resource share distribution data and the resource peak time of each link, predict the resource demand of each link to obtain multiple link resource demand prediction data;

[0042] Step S44: According to the multiple link resource demand prediction data, conduct a resource shortage evolution analysis on the business process structure diagram to identify resource shortage links.

[0043] The present invention determines the time period when the highest peak of resource demand appears in each link by calculating the resource peak time for the simulated data of business links. The determination of the resource peak time for each link helps the enterprise understand in which time periods more resources need to be allocated to meet business requirements, providing guidance in time for resource scheduling and optimization. The resource share distribution analysis of the simulated data of business links is carried out to understand the share distribution of each link in resource usage. The link resource share distribution data provides the relative proportion and distribution of resource usage for each link, providing a basis for subsequent resource demand prediction and resource scheduling. Based on the link resource share distribution data and the resource peak time for each link, resource demand prediction is carried out to predict the future resource demand for each link. The resource demand prediction data for multiple links provides an estimate of the resource demand for each link, providing a prediction basis for subsequent resource scheduling decisions and resource optimization. According to the resource demand prediction data for multiple links, resource shortage evolution analysis is carried out on the business full-process structure diagram to identify the links where resource shortages occur in the business process. Identifying the resource shortage links helps the enterprise make preparations for resource replenishment and scheduling in advance to avoid the impact on business operations caused by the imbalance between resource supply and demand.

[0044] Preferably, the specific steps of step S43 are as follows:

[0045] Based on the link resource share distribution data, resource demand prediction is carried out to obtain the resource demand prediction value;

[0046] Perform time series peak change analysis on the resource peak time for each link to obtain the peak change rule;

[0047] Based on the peak change rule, predict the resource demand peak points to generate resource peak point prediction data;

[0048] Traverse all business links to obtain the resource demand prediction value and resource peak point prediction data for each business link;

[0049] Based on the resource demand prediction value and resource peak point prediction data for each business link, perform integration to obtain the resource demand prediction data for multiple links.

[0050] The present invention estimates the specific resource requirements of each link by predicting the resource requirements based on the distribution data of the link resource shares. The resource demand prediction value provides an estimate of the amount of resources required by each link in the future time period, guiding resource scheduling and allocation. The analysis of the time-series peak value changes reveals the time variation law of the resource demand peak of each link, enabling the enterprise to predict which links will have peak resource demands in different time periods, providing a time reference for resource scheduling and optimization. Based on the peak value change law, the prediction of the resource demand peak point determines the time point when the resource demand peak of each link appears. The resource peak point prediction data provides the time point prediction of the resource demand peak of each link, enabling the enterprise to reasonably arrange the resource supply. By traversing all business links and comprehensively considering the resource demand prediction value and the resource peak point prediction data of each link, the resource demand prediction value and the resource peak point prediction data of each business link are obtained, providing a global basis for resource scheduling and optimization decisions. Integrating the resource demand prediction values and the resource peak point prediction data of each business link obtains more comprehensive link resource demand prediction data. The multiple link resource demand prediction data provides a comprehensive estimate of the resource demand in the entire business process, providing a more accurate reference for the decision-making of resource scheduling and optimization.

[0051] Preferably, the specific steps of step S5 are as follows:

[0052] Step S51: Based on the enterprise heterogeneous resource library, perform resource scheduling matching calculation on the resource shortage links to generate the matching degree of idle resource scheduling;

[0053] Step S52: Based on the matching degree of idle resource scheduling, perform local resource scheduling on the resource shortage links to generate local resource scheduling data for the shortage links;

[0054] Step S53: Analyze the resource scheduling error of the local resource scheduling data for the shortage links to obtain the local scheduling error value;

[0055] Step S54: Based on the local scheduling error value, make a global resource dynamic configuration decision on the business full-process structure diagram and construct a global resource configuration strategy.

[0056] The present invention finds idle resources suitable for filling resource shortage links through resource scheduling matching calculation based on an enterprise heterogeneous resource library. The idle resource scheduling matching degree evaluates the matching degree of each idle resource with the resource shortage link, providing a basis for subsequent resource scheduling decisions. Based on the idle resource scheduling matching degree, local resource scheduling is performed to allocate appropriate idle resources to the resource shortage links. The local resource scheduling data for the shortage links indicates which resources should be allocated to which shortage links, providing a specific scheduling plan for the balance of resource supply and demand. Resource scheduling error analysis is performed on the local resource scheduling data for the shortage links to evaluate the difference between the actual scheduling result and the expected scheduling result. The obtained local scheduling error value provides an evaluation of the accuracy of resource scheduling, helping the enterprise understand the effect of resource scheduling and providing feedback and improvement directions for further optimization. Based on the local scheduling error value, global resource dynamic configuration decisions are made to perform overall optimization and configuration adjustment of resources according to the actual scheduling effect. The constructed global resource configuration strategy is based on the actual scheduling error value, helping the enterprise adjust the resource allocation plan and further improve the utilization efficiency and service quality of resources.

[0057] Preferably, the specific steps of step S6 are as follows:

[0058] Step S61: Based on the resource shortage links, perform optimal link logic optimization on the business full-process structure diagram to obtain an optimal process structure;

[0059] Step S62: Use the global resource configuration strategy to monitor resource scheduling for the optimal process structure and generate resource scheduling monitoring data;

[0060] Step S63: Perform quantitative evaluation processing on the resource scheduling monitoring data to generate a resource scheduling evaluation value;

[0061] Step S64: Optimize the scheduling parameters of the global resource configuration strategy according to the resource scheduling evaluation value, and construct an adaptive dynamic resource scheduling model to execute the enterprise resource scheduling optimization operation.

[0062] The present invention optimizes the logic of the optimal link based on the resource shortage link, optimizes resource utilization and service efficiency by adjusting the business process structure, improves the resource scheduling effect with the obtained optimal process structure, reduces the occurrence of resource shortage links and bottleneck links, improves the execution efficiency of the overall process, monitors the resource scheduling of the optimal process structure using the global resource allocation strategy, monitors the allocation situation and execution effect of resources in real time, provides real-time feedback and monitoring of the resource scheduling process through the resource scheduling monitoring data, helps the enterprise understand the execution situation of the scheduling plan, quantitatively evaluates the scheduling effect of the resource scheduling monitoring data, objectively evaluates the performance and effect of the resource scheduling plan, judges the effectiveness and improvement space of the scheduling plan, optimizes the scheduling parameters of the global resource allocation strategy according to the resource scheduling evaluation value, dynamically optimizes the resource scheduling strategy according to the actual scheduling effect, and adjusts the resource scheduling parameters of the constructed adaptive dynamic resource scheduling model according to real-time data and evaluation results to achieve more accurate and efficient resource scheduling optimization.

[0063] In this specification, a system for optimizing the resource scheduling of the entire enterprise process based on big data is provided, which is used to execute the method for optimizing the resource scheduling of the entire enterprise process based on big data as described above, and includes:

[0064] The business process distribution module is used to obtain the enterprise business logs and enterprise operation service resources; perform business process distribution analysis on the enterprise business logs, and construct a business process structure diagram;

[0065] The heterogeneous resource module is used to classify the types of enterprise operation service resources to obtain multi-type enterprise service resource data; perform heterogeneous resource fusion on the multi-type enterprise service resource data, and construct an enterprise heterogeneous resource library;

[0066] The demand analysis module is used to analyze the resource requirements of each link in the business process structure diagram, generate link node resource requirement data; generate business process simulation data according to the link node resource requirement data;

[0067] The demand prediction module is used to predict the resource requirements of each link for the business process simulation data, obtain multiple link resource requirement prediction data; identify resource shortage links according to the multiple link resource requirement prediction data;

[0068] The dynamic configuration module is used to make global resource dynamic configuration decisions for resource shortage links based on the enterprise heterogeneous resource library, and construct a global resource allocation strategy;

[0069] The resource scheduling model module is used to optimize the scheduling parameter modeling based on the global resource allocation strategy, construct an adaptive dynamic resource scheduling model, and execute the enterprise resource scheduling optimization operation.

[0070] The present invention obtains enterprise business logs and operation service resources, understands the business processes and available resources of the enterprise, conducts a distribution analysis of business links on the enterprise business logs to identify each business link of the enterprise, and establishes a business full-process structure diagram. The constructed business full-process structure diagram helps the enterprise comprehensively understand the business processes, provides a basis for subsequent resource scheduling optimization. The enterprise operation service resources are classified by resource types, different types of resources are classified and managed, and heterogeneous resource fusion is used to construct an enterprise heterogeneous resource library to integrate different types of resources together to form a unified resource library, which is convenient for resource scheduling and management. The resource requirements of each link are analyzed for the business full-process structure diagram to understand the resource requirements of each link. The resource requirement data of link nodes provides a detailed description of the resource requirements, and provides a basis for subsequent resource scheduling decisions. Based on the resource requirement data of link nodes, business link simulation data simulates the resource requirement situations of different links to help evaluate and optimize the resource scheduling plan. The resource requirements of each link are predicted for the business link simulation data to predict the resource requirements of each future link. The generation of resource requirement prediction data for multiple links helps the enterprise identify resource shortage links, that is, links where resource requirements exceed supply. Based on the enterprise heterogeneous resource library, global resource dynamic configuration decisions are made for resource shortage links, and resource scheduling optimization is carried out according to the real-time resource situation and requirements. The constructed global resource configuration strategy ensures that resources are reasonably allocated in shortage links, improves the overall resource utilization efficiency and service quality. Based on the global resource configuration strategy, scheduling parameter optimization modeling is carried out to dynamically optimize the resource scheduling strategy according to the actual scheduling effect. The constructed adaptive dynamic resource scheduling model adjusts the resource scheduling parameters according to real-time data and evaluation results to achieve more accurate and efficient resource scheduling optimization. The enterprise resource scheduling optimization operation is executed to ensure that resources are reasonably configured and allocated in each link, and the overall resource scheduling effect and business operation efficiency are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a schematic diagram of the step flow of a method for optimizing the scheduling of enterprise full-process service resources based on big data according to the present invention;

[0072] Figure 2 It is a schematic diagram of the detailed implementation steps of step S1;

[0073] Figure 3 It is a schematic diagram of the detailed implementation steps of step S2;

[0074] Figure 4 It is a schematic diagram of the detailed implementation steps of step S3. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0075] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0076] The embodiments of the present application provide an optimization method and system for enterprise full-process service resource scheduling based on big data. The execution entities of the optimization method and system for enterprise full-process service resource scheduling based on big data include, but are not limited to, the following general computing nodes of this application: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. The data processing platform includes, but is not limited to, at least one of: an audio and image management system, an information management system, and a cloud data management system.

[0077] Please refer to Figures 1 to 4 , the present invention provides an optimization method for enterprise full-process service resource scheduling based on big data. The optimization method for enterprise full-process service resource scheduling based on big data includes the following steps:

[0078] Step S1: Obtain enterprise business logs and enterprise operation service resources; analyze the business process distribution of the enterprise business logs, and construct a business full-process structure diagram;

[0079] Step S2: Classify the enterprise operation service resources by resource type to obtain multi-type enterprise service resource data; fuse the heterogeneous resources of the multi-type enterprise service resource data to construct an enterprise heterogeneous resource library;

[0080] Step S3: Analyze the resource requirements of each link in the business full-process structure diagram to generate link node resource requirement data; generate business link simulation data according to the link node resource requirement data;

[0081] Step S4: Forecast the resource requirements of each link for the business link simulation data to obtain multiple link resource requirement forecast data; identify resource shortage links according to the multiple link resource requirement forecast data;

[0082] Step S5: Make a global resource dynamic configuration decision for the resource shortage links based on the enterprise heterogeneous resource library, and construct a global resource configuration strategy;

[0083] Step S6: Optimize the scheduling parameter modeling based on the global resource configuration strategy, and construct an adaptive dynamic resource scheduling model to execute the enterprise resource scheduling optimization task.

[0084] The present invention obtains enterprise business logs and enterprise operation service resources, acquires the data basis regarding the enterprise operation status and resource utilization, conducts business process distribution analysis to understand the business processes of the enterprise and the dependency relationships between the processes, constructs a full-process business structure diagram for subsequent resource scheduling optimization decisions, classifies the enterprise operation service resources into different resource types to facilitate subsequent resource demand analysis and scheduling optimization, integrates different types of enterprise service resources through heterogeneous resource fusion, constructs an enterprise heterogeneous resource library to comprehensively manage and optimize the utilization of different types of resources, conducts resource demand analysis on the full-process business structure diagram to understand the demand for different types of resources in each process, provides a basis for subsequent resource scheduling, generates business process simulation data to simulate the resource demand situations of different processes, helps evaluate and optimize the effect of resource scheduling strategies, conducts resource demand prediction for the business process simulation data to predict the future resource demands of each process, provides a prediction basis for resource scheduling decisions, identifies resource shortage processes to determine the processes where resource supply will be insufficient in the future, helps the enterprise take preventive measures in advance to avoid problems caused by resource shortages, makes a global resource allocation decision for the resource shortage processes by comprehensively considering the supply situations of different types of resources based on the enterprise heterogeneous resource library, ensures the reasonable allocation and utilization of resources, constructs a global resource allocation strategy to help the enterprise reasonably arrange the allocation priorities and scheduling strategies of resources in the case of limited resource supply, improves resource utilization efficiency and business operation effects, conducts scheduling parameter optimization modeling based on the global resource allocation strategy to optimize the decision-making process of resource scheduling, improves resource utilization efficiency and business response capabilities, constructs an adaptive dynamic resource scheduling model to conduct dynamic scheduling according to real-time resource demands and supply situations to adapt to the changing business environment and resource status, realizes the optimization of enterprise resource scheduling, and improves business efficiency and operation results.

[0085] In an embodiment of the present invention, referring to Figure 1 , it is a schematic diagram of the step flow of a method and system for optimizing the full-process service resource scheduling of an enterprise based on big data. In this example, the steps of the method for optimizing the full-process service resource scheduling of an enterprise based on big data include:

[0086] Step S1: Obtain enterprise business logs and enterprise operation service resources; conduct business process distribution analysis on the enterprise business logs to construct a full-process business structure diagram;

[0087] In this embodiment, business log data generated by various business systems within the enterprise is collected, including various interactive activities such as order records, service requests, work order processing, etc., ensuring that these log data can reflect the overall picture of the enterprise's business, covering the entire business process from customer order placement to service delivery. The various types of resources required for the enterprise to provide services are sorted out, including human resources, IT systems, production equipment, warehousing facilities, etc., and detailed information about these resources is collected, such as resource type, quantity, attributes, usage status, etc. The business logs are analyzed in depth, and the various business activities therein are classified into different business processes, such as order acceptance, inventory management, distribution services, etc. The proportion and time distribution of each business process in the entire business process are counted, laying a foundation for constructing a full business process structure diagram subsequently. According to the foregoing analysis results of the business process distribution, an end-to-end full business process of the enterprise is drawn. This flow chart can clearly show the logical relationships, time sequences, resource requirements, etc. between the various business processes. This flow chart will be used as an important reference basis for subsequent resource requirement analysis and optimal scheduling.

[0088] Step S2: Classify the enterprise operation service resources by resource type to obtain multi-type enterprise service resource data; fuse the heterogeneous resources of the multi-type enterprise service resource data to construct an enterprise heterogeneous resource library;

[0089] In this embodiment, a comprehensive type classification of these resources is carried out, and classification is performed according to dimensions such as the attributes and uses of the resources, such as human resources, IT system resources, production equipment resources, warehousing facility resources, etc., ensuring that all service resource types required for various business processes of the enterprise are covered. For each type of service resource, its detailed attribute information, such as quantity, performance parameters, usage status, etc., is collected and integrated, ensuring that these data can comprehensively reflect the specific conditions of various resources, providing a basis for subsequent resource requirement analysis and dynamic allocation. Since these resource data come from different business systems and management departments, there are significant differences in data formats and storage methods. Technologies such as data integration and data modeling need to be used to fuse these heterogeneous data into a unified database or data warehouse, ensuring that these heterogeneous resource data can communicate with each other and support resource allocation decisions within the enterprise's global scope. The fused multi-type enterprise service resource data is organized into a structured and queryable resource library. This resource library should be able to provide detailed information queries for various resources, support dynamic resource status monitoring and resource scheduling optimization.

[0090] Step S3: Analyze the resource requirements of each link in the full business process structure diagram to generate link node resource requirement data; generate business process simulation data according to the link node resource requirement data;

[0091] In this embodiment, various types of resources required for each business process are analyzed one by one. Combining with the established enterprise heterogeneous resource library, the resource type, quantity, performance indicators and other requirements corresponding to each process are queried. The resource requirement information is sorted out and summarized to form detailed resource requirement data for process nodes. The resource requirement information of each process obtained from the above analysis is organized into a structured data table or a database table. Each process node corresponds to a record, which records the various types of resources and quantity requirements required by the node. Using professional simulation tools or self-developed simulation systems, with the resource requirement data of process nodes as input, the actual operation of each business process is simulated, including key indicators such as resource consumption, process duration, and process output, generating a series of business process simulation data to reflect the dynamic change process of each process. The business process simulation data is deeply analyzed to identify problems such as resource bottlenecks and time delays in each process. Combining with the business objectives and KPI indicators of the enterprise, the overall operation efficiency of the current business process is evaluated.

[0092] Step S4: Forecast the resource requirements of each business process for the simulation data, obtaining multiple resource requirement forecast data for the processes; identify the resource shortage processes based on the multiple resource requirement forecast data;

[0093] In this embodiment, the resource requirements of each process are forecasted. Methods such as time series analysis and machine learning are used to forecast the resource requirements of each process within a certain period in the future based on the historical data trend, ensuring that the forecast model can fully consider the impacts of business environment changes, seasonal factors, etc. on resource requirements. According to the methods and models of demand forecasting, multiple resource requirement forecast data for the processes are obtained. These data reflect the forecast of the resources required by each process within a certain period in the future, ensuring that the forecast data corresponds to the business processes, including the process names and the corresponding resource requirement forecast values. Comparing with the enterprise heterogeneous resource library, check whether the forecasted resource requirements of each process can be met by the existing resources. For the processes where the resource requirements exceed the current resource supply capacity, they are identified as the key processes with resource shortages. For these processes with resource shortages, it is necessary to further analyze the degree of their impact on the entire business process, evaluate the impact of resource shortages on the current business process, including output decline, delivery time delay, etc., and analyze the transmission of the bottleneck effect caused by resource shortages.

[0094] Step S5: Make a global resource dynamic allocation decision for the processes with resource shortages based on the enterprise heterogeneous resource library, and construct a global resource allocation strategy;

[0095] In this embodiment, check the supply situation of the corresponding resources in the enterprise heterogeneous resource library, analyze whether the current resource supply can meet the needs of these shortage links. If it cannot be met, global resource dynamic allocation decisions need to be made. Consider the overall resource allocation priority and scheduling strategy of the enterprise, comprehensively evaluate the resource requirements of each link, find the optimal global resource allocation plan, and formulate global resource dynamic allocation decisions based on the identified resource shortage links and the available resource situation. This involves the reallocation, scheduling or optimization of resources. The goal of the decision is to solve the shortage problem through the best resource allocation plan and improve the efficiency and performance of the business process. When making global resource dynamic allocation decisions, various factors need to be considered, including the type, priority, availability, cost, etc. of the resources. It is also necessary to analyze the impact of resource allocation on business links to ensure the rationality and feasibility of resource allocation. According to the results of the resource dynamic allocation decision, construct a global resource allocation strategy, which includes determining the rules, priorities and scheduling strategies of resource allocation, etc., to ensure that the resource allocation strategy can meet business requirements and consider the scalability and flexibility of resources.

[0096] Step S6: Based on the global resource allocation strategy, perform optimization modeling of scheduling parameters, and construct an adaptive dynamic resource scheduling model to execute the enterprise resource scheduling optimization task.

[0097] In this embodiment, according to the global resource allocation strategy, determine the key parameters that need to be optimized for scheduling parameters. These parameters include task execution time, resource allocation ratio, task priority, etc. Model each parameter, define the value range and optimization goal of the parameter. Using the results of the scheduling parameter optimization modeling, construct an adaptive dynamic resource scheduling model, which is a mathematical model, optimization algorithm or machine learning model used to make resource scheduling decisions according to real-time resource conditions and business requirements. Provide input data for the adaptive dynamic resource scheduling model, which includes real-time resource data, task execution status, business priority and other information, and ensure the accuracy and timeliness of the input data to support the decision-making process of the model. Use the constructed adaptive dynamic resource scheduling model to execute the enterprise resource scheduling optimization task, and dynamically adjust the resource allocation, task execution order or priority according to the decision results of the model to optimize resource utilization and business performance.

[0098] In this embodiment, refer to Figure 2 For the detailed implementation step flow diagram of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0099] Step S11: Obtain enterprise business logs and enterprise operation service resources;

[0100] Step S12: Filter sensitive information from the enterprise business logs to generate filtered business logs;

[0101] Step S13: Analyze the distribution of business processes in the filtered business logs to generate business process distribution data;

[0102] Step S14: Analyze the execution dependencies of business processes in the business process distribution data to obtain the execution dependency data for each business process;

[0103] Step S15: Reconstruct the overall business process structure based on the execution dependency data for each business process to obtain the overall business process structure diagram.

[0104] In this embodiment, after obtaining the enterprise authorization, collect the business log data of the enterprise, including various business system, application program, or device log files, and ensure that the obtained business log data contains key operation and event information for subsequent analysis and processing. Obtain the enterprise's operation service resource data, including various resources used by the enterprise, such as human resources, device resources, and venue resources, and ensure that the obtained resource data can reflect the overall operation service infrastructure of the enterprise. Filter sensitive information from the enterprise's business logs to protect the security and privacy of sensitive data. Use methods such as text processing, regular expressions, or machine learning algorithms to identify and filter sensitive information, such as personal identity information and confidential data. Generate the filtered business log data according to the results of the sensitive information filtering, ensuring that the filtered business logs do not contain sensitive information while retaining key operation and event information to support subsequent analysis and processing. Define the concept and scope of business processes according to the business characteristics and requirements of the enterprise. A business process is a specific step, operation, or functional module in the enterprise's business process. Analyze the filtered business logs to identify and extract the business process information involved. Determine indicators such as the execution times and durations of each business process by statistically analyzing the operations or events in the business logs, and generate business process distribution data to reflect the execution status and frequency of each business process. Define the execution dependency relationships between business processes according to the mutual relationships and execution sequences between business processes. Execution dependencies can be sequential relationships, parallel relationships, or conditional relationships, etc. Analyze the execution dependency relationships between business processes based on the business process distribution data, and obtain the execution dependency data for each business process by identifying and analyzing the operation sequences and collaboration relationships between business processes. Reconstruct the overall business process structure based on the execution dependency data for each business process. According to the execution sequence and dependency relationships between business processes, construct the overall business process structure diagram to ensure that the overall business process structure diagram can clearly display the execution sequence and dependency relationships of business processes for subsequent business analysis, optimization, and decision-making.

[0105] In this embodiment, the specific steps of Step S14 are as follows:

[0106] Extract the feature of the business process from the filtered business logs to obtain the business event type and the action behavior feature of the business process;

[0107] Perform action feature annotation on the action behavior characteristics of business process links based on business event types to obtain action behavior data for each type of link;

[0108] Calculate the average duration of action behavior data for each type to generate the average processing duration of link actions;

[0109] Mine the business path from the business process distribution data to obtain the business process handling path;

[0110] Perform execution dependency analysis on the business process handling path based on the average processing duration of link actions to obtain the execution dependency data for each business process.

[0111] In this embodiment, analyze the filtered business logs, identify the characteristic information of each business process, extract the types of business events, such as sales order creation, outbound delivery, customer payment collection, etc., and at the same time extract the specific action behavior characteristics involved in each business process, such as entering order information, packing goods, approving payments, etc. According to the business event types and the action behavior characteristics of business process links, annotate the action behaviors of business process links, assign corresponding labels or categories to the action behaviors of each business process link to distinguish different types of action behaviors. According to the results of action feature annotation, generate action behavior data for each type of link, classify and organize the action behavior data of the same type for subsequent analysis and calculation. For the action behavior data of each business process link, calculate its average processing duration. According to the start time and end time of each action behavior, calculate the duration of each action behavior, and average the durations of action behaviors of the same type. Based on the business process distribution data, mine and extract the business path. By analyzing the execution order and dependency relationship between business process links, identify typical business processing paths to ensure that the mined business paths can cover common business processes and operation sequences. Based on the average processing duration data of link actions, perform execution dependency analysis on the business process handling path. According to the processing duration of each business process and the execution order of the business path, determine the execution dependency relationship between business process links, identify the execution dependency relationship between each business process link, including pre-dependency and post-dependency. According to the results of execution dependency analysis, generate the execution dependency data for each business process to ensure that the data can accurately reflect the execution order and dependency relationship between business process links.

[0112] In this embodiment, refer to Figure 3 , which is a schematic diagram of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0113] Step S21: Divide the enterprise operation service resources into resource types to obtain multi-type enterprise service resource data;

[0114] Step S22: Quantify the resource capabilities of multi-type enterprise service resource data based on a preset enterprise resource quantification rule to obtain multi-type resource quantification values;

[0115] Step S23: Perform a standardized mapping transformation on the multi-type enterprise service resource data to obtain an enterprise resource table;

[0116] Step S24: Perform heterogeneous resource fusion on the enterprise resource table according to the multi-type resource quantification values to construct an enterprise heterogeneous resource library.

[0117] In this embodiment, the service resource data of the enterprise is collected, classified and sorted according to the resource type. For example, the personnel information of enterprise employees is collected, including positions, skills, qualifications, etc.; the models, quantities, performance parameters of equipment are collected; the area, location, availability of venues are collected; the transportation capacity, distribution range of logistics resources are collected, etc., to ensure that the collected data can completely and accurately describe the situation of various types of resources. According to the resource type and enterprise requirements, a resource quantification rule is formulated. The resource quantification rule is a method and specification for converting resource capabilities into specific values. The skill level of enterprise employees is quantified into scores or grades; the performance parameters of equipment are converted into index values; the availability of venues is defined as available time periods, etc. According to the preset resource quantification rule, the capabilities of multi-type enterprise service resource data are quantified. According to the rule, the characteristics and attributes of the resources are mapped into corresponding numerical values or indicators. According to the skills and qualifications of employees, their ability scores are calculated; according to the performance parameters of equipment, their index values are calculated to ensure that the results of resource capacity quantification can objectively and accurately reflect the actual capacity level of the resources. According to the resource standardization definition, the multi-type enterprise service resource data is mapped and transformed, and the data of various types of resources are converted and integrated according to the standardized format to generate an enterprise resource table, ensuring that the resource table can uniformly and consistently represent the information of various types of resources of the enterprise. Perform heterogeneous resource fusion calculation on the enterprise resource table, weight or combine the quantification values of various types of resources to obtain comprehensive enterprise resource indicators, ensuring that the results of heterogeneous resource fusion can reflect the overall resource capabilities and advantages of the enterprise. The results of heterogeneous resource fusion are stored as an enterprise heterogeneous resource library, which is a database or system that centrally stores and manages the information of various types of resources of the enterprise.

[0118] In this embodiment, refer to Figure 4 , which is a schematic diagram of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0119] Step S31: Mark the key links of the business full-process structure diagram to obtain multiple business link nodes;

[0120] Step S32: Calculate the resource occupancy of multiple business process nodes to obtain the process resource occupancy;

[0121] Step S33: Analyze the resource requirements of each process based on the process resource occupancy to generate process node resource requirement data;

[0122] Step S34: Perform business process operation simulation on the business full - process structure diagram according to the process node resource requirement data to generate business process simulation data.

[0123] In this embodiment, according to the importance and impact degree of the business, mark the key processes in the business full - process structure diagram. Key processes are usually important nodes that determine the success or failure of the business and require special attention and analysis. Carefully analyze each process in the business process, identify the key business process nodes, mark and number these key process nodes to form a business process set containing multiple nodes. For each marked business process node, analyze various types of resources required by each node, such as manpower, equipment, venue, etc. According to the obtained resource quantification values, calculate the actual occupancy of each node in various resources. Summarize and organize these process resource occupancy data to form a data set of process resource occupancy. Analyze the process resource occupancy data in combination with the actual resource supply situation of the enterprise to identify the resource requirement gaps existing in each business process node and quantify them as specific resource requirement data. These process node resource requirement data provide a basis for subsequent business process simulation. Through simulation analysis, predict the actual operation situation of each business process under the constraint of resource conditions, simulate the resource occupancy and operation situation of each process node, and generate business process simulation data. These data include the operation time, resource utilization situation, efficiency indicators, etc. of each process node.

[0124] In this embodiment, step S4 includes the following steps:

[0125] Step S41: Calculate the resource peak time of the business process simulation data to obtain the resource peak time of each process;

[0126] Step S42: Analyze the resource share distribution of the business process simulation data to generate process resource share distribution data;

[0127] Step S43: Forecast the resource requirements of each process based on the process resource share distribution data and the resource peak time of each process to obtain multiple process resource requirement forecast data;

[0128] Step S44: Perform resource shortage evolution analysis on the business full - process structure diagram according to multiple process resource requirement forecast data to identify resource - short - age processes.

[0129] In this embodiment, the peak time points of each link in the use of various resources are analyzed. For key resources such as human resources, equipment, and venues, the resource peak time of each link is calculated respectively. The data of the resource peak time of each link are sorted out and summarized to form a complete "resource peak time" data set. The occupancy shares of each link on different resources are analyzed. For key resources such as human resources, equipment, and venues, the occupancy ratio of each link on this resource is calculated. These resource share distribution data are sorted out and summarized to form a complete "link resource share distribution" data set. The demand situations of each link for different resources are analyzed. Considering the future business development plan of the enterprise, the demand change trends of each link in resource use are predicted to form a detailed "link resource demand prediction" data, which provides a basis for subsequent resource shortage analysis. According to the resource demand prediction data of multiple links, the resource demands of each link in the business process structure diagram are compared and evaluated to find out the key links with resource shortages in the business process and predict the evolution trend of resource shortages, and identify the links where the resource demand exceeds the available resources, that is, the resource shortage links.

[0130] In this embodiment, the specific steps of step S43 are as follows:

[0131] Based on the link resource share distribution data, predict the resource demand to obtain the resource demand prediction value;

[0132] Conduct a time-series peak change analysis on the resource peak time of each link to obtain the peak change rule;

[0133] Based on the peak change rule, predict the resource demand peak points to generate resource peak point prediction data;

[0134] Traverse all business links to obtain the resource demand prediction values and resource peak point prediction data of each business link;

[0135] Integrate based on the resource demand prediction values and resource peak point prediction data of each business link to obtain the resource demand prediction data of multiple links.

[0136] In this embodiment, the usage ratios of different resources in each link are analyzed. Combining with the future business development plan of the enterprise, it is predicted how the resource demands in each link will change. These prediction data are sorted into "resource demand prediction values". The peak usage times of various resources in each link are analyzed, and time series analysis is performed on these peak time data to find out their variation rules, such as whether the peak time will advance or delay, etc. These peak variation rules are sorted out and summarized to provide a basis for subsequent resource peak point prediction. Based on the peak variation rules, the future resource peak points are predicted. According to the characteristics such as periodicity, trend or mutation in the rules, combined with historical data or relevant models, the time of future resource peak points is predicted. According to the results of resource demand peak point prediction, the future resource peak point prediction data of each link are obtained and recorded for subsequent analysis. Each link in the business process is traversed. For each link, there are its corresponding "resource demand prediction values" and "resource peak point prediction data", which are stored using data structures (such as lists or tables). The resource peak point prediction data of each business link are integrated, integrated in chronological order, and the resource peak point prediction data of each business link are merged to obtain the resource peak point prediction data of multiple links. By integrating the resource demand prediction data and the resource peak point prediction data, the resource demand prediction data of multiple links are obtained. These data are used for subsequent resource planning, scheduling or decision-making tasks.

[0137] In this embodiment, the specific steps of step S5 are as follows:

[0138] Step S51: Based on the enterprise heterogeneous resource library, resource scheduling matching calculation is performed on the resource shortage links to generate the idle resource scheduling matching degree;

[0139] Step S52: Based on the idle resource scheduling matching degree, local resource scheduling is performed on the resource shortage links to generate local resource scheduling data for the shortage links;

[0140] Step S53: Resource scheduling error analysis is performed on the local resource scheduling data for the shortage links to obtain the local scheduling error value;

[0141] Step S54: Based on the local scheduling error value, global resource dynamic configuration decision is made on the business full-process structure diagram to construct a global resource configuration strategy.

[0142] In this embodiment, for the resource - shortage links, according to the attributes and quantities of the required resources, suitable idle resources are found from the enterprise's heterogeneous resource library for scheduling and matching. The basis for matching is the matching degree of resource attributes, the matching degree of available resource quantities, etc. Through resource - scheduling matching calculation, an idle - resource scheduling matching degree is generated for each resource - shortage link. This matching degree represents the matching degree between the available idle resources and the resources required by the shortage link. According to the idle - resource scheduling matching degree, the idle resource with the highest matching degree is selected and scheduled to the corresponding resource - shortage link. The scheduling methods include physical transfer of resources, re - allocation of tasks, etc., to meet the resource requirements of the shortage link. Analyze the local resource - scheduling data of each shortage link, and compare the difference between the actually scheduled resources and the required resources. Calculate the scheduling error, using absolute error, relative error or other appropriate metrics. Through resource - scheduling error analysis, the local scheduling error value of each shortage link is obtained. This value represents the accuracy or deviation degree of local resource scheduling. Combine the local scheduling error value and the business - process overall - structure diagram to make dynamic configuration decisions for global resources. According to the scheduling - error situation of the shortage link and the dependency relationship of the process structure, decide whether to re - allocate resources, adjust the priority of resources, etc. Based on the dynamic configuration decision of global resources, formulate specific global - resource configuration strategies. This strategy includes resource re - allocation plans, resource - priority adjustment plans, resource - scheduling time arrangements, etc., to achieve the efficient operation of the entire business process and the rational utilization of resources.

[0143] Example: A company has three types of resources: human resources, material resources, and equipment resources. The resource library records the attributes and available quantities of each type of resource. Suppose there is a resource - shortage link that requires 2 human resources, 10 material resources, and 1 equipment resource. Through matching calculation on the enterprise's heterogeneous resource library, the idle resources that best match the resource requirements are found. There are 5 idle employees in the human - resource library.

[0144] There are 15 idle materials in the material - resource library.

[0145] There are 3 idle devices in the equipment - resource library. According to the matching calculation, the following idle - resource scheduling matching degrees are obtained:

[0146] Human - resource matching degree: 2 / 5 = 0.4

[0147] Material - resource matching degree: 10 / 15 = 0.67

[0148] Equipment - resource matching degree: 1 / 3 ≈ 0.33

[0149] According to the idle - resource scheduling matching degree, select the idle resources with the highest matching degree for scheduling. In this example, select 2 idle employees, 10 idle materials, and 1 idle device.

[0150] Example data:

[0151] Idle employees: Employee A, Employee B, Employee C, Employee D, Employee E.

[0152] Idle materials: Material 1, Material 2, Material 3, Material 4, Material 5, Material 6, Material 7, Material 8, Material 9, Material 10, Material 11, Material 12, Material 13, Material 14, Material 15.

[0153] Idle equipment: Equipment X, Equipment Y, Equipment Z.

[0154] Human resource scheduling: Employee A, Employee B

[0155] Material resource scheduling: Material 1, Material 2, Material 3, Material 4, Material 5, Material 6, Material 7, Material 8, Material 9, Material 10

[0156] Equipment resource scheduling: Equipment X

[0157] Analyze the local resource scheduling data for each shortage link, and compare the difference between the actually scheduled resources and the required resources. Calculate the scheduling error, using absolute error, relative error or other appropriate metrics.

[0158] Human resource scheduling error: 2 employees are actually scheduled, which exactly matches the required 2 employees, and the error is 0.

[0159] Material resource scheduling error: 10 materials are actually scheduled, which exactly matches the required 10 materials, and the error is 0.

[0160] Equipment resource scheduling error: 1 piece of equipment is actually scheduled, which exactly matches the required 1 piece of equipment, and the error is 0.

[0161] Human resource scheduling error: 0

[0162] Material resource scheduling error: 0

[0163] Equipment resource scheduling error: 0

[0164] It can be seen that the current resource scheduling has met the requirements of all links, and the error is 0. Therefore, no adjustment is required in the global resource allocation strategy.

[0165] In this embodiment, the specific steps of step S6 are as follows:

[0166] Step S61: Based on the resource shortage links, perform optimal link logic optimization on the business full-process structure diagram to obtain the optimal process structure;

[0167] Step S62: Use the global resource allocation strategy to monitor the resource scheduling of the optimal process structure and generate resource scheduling monitoring data;

[0168] Step S63: Quantitatively evaluate the scheduling effect of the resource scheduling monitoring data to generate a resource scheduling evaluation value;

[0169] Step S64: Optimize the scheduling parameters of the global resource configuration strategy based on the resource scheduling evaluation value, and construct an adaptive dynamic resource scheduling model to execute the enterprise resource scheduling optimization task.

[0170] In this embodiment, logical optimization is performed on the resource shortage links. By adjusting the link order, merging or splitting links, optimizing resource requirements, etc., the resource shortage problem is minimized to the greatest extent, and the overall process efficiency is improved. According to the obtained optimal process structure, the global resource configuration strategy is applied to this process structure for resource scheduling monitoring. According to the actual resource scheduling situation, data such as the resource scheduling situation and resource utilization rate of each link are recorded. By analyzing the resource scheduling monitoring data, the scheduling effect is evaluated. Various indicators, such as resource utilization rate, average waiting time, completion time, etc., are used to quantitatively evaluate the performance of resource scheduling. Analyze the resource scheduling evaluation value to find existing problems and improvement space. For example, if the resource utilization rate is low, the resource allocation strategy needs to be optimized; if the average waiting time is long, the link order needs to be adjusted or resource allocation increased. According to the analysis results, design a scheduling parameter optimization model. This model is rule-based, optimization algorithm-based, or a machine learning model. The goal of the model is to maximize the resource utilization rate and minimize indicators such as the average waiting time and completion time. According to the scheduling parameter optimization model, determine the optimization strategy for resource scheduling. For example, adjust the priority of resource allocation, reallocate the proportion of resources, etc. According to the optimization strategy, optimize the resource scheduling. Manually adjust the resource allocation or execute the optimization task with the help of an automated resource scheduling system. After implementing the resource scheduling optimization task, monitor and evaluate the scheduling effect. Compare indicators such as the resource utilization rate, average waiting time, and completion time before and after optimization to judge the effectiveness of the optimization effect.

[0171] Example: In the business full-process structure diagram, there are four links A, B, C, and D, and link B is the resource shortage link. After the optimal link logic optimization, the following optimal process structure is obtained:

[0172] A ->C ->D ->B

[0173] The optimal process structure is A ->C ->D ->B. According to the global resource configuration strategy, the following data is obtained after resource scheduling monitoring:

[0174] Link A: 2 employees, 5 materials, and 1 device are used.

[0175] Link C: 3 employees, 8 materials, and 1 device are used.

[0176] Step D: 4 employees, 10 materials and 2 devices are used.

[0177] Step B: 2 employees, 5 materials and 1 device are used.

[0178] The utilization rate of employee resources is (2 + 3 + 4 + 2) / (24) ≈ 0.875, the utilization rate of material resources is (5 + 8 + 10 + 5) / (154) = 0.85, and the utilization rate of device resources is (1 + 1 + 2 + 1) / (3 * 4) ≈ 0.833.

[0179] Statistically analyze the average waiting time for each step according to the actual situation.

[0180] The utilization rate of employee resources is increased to 0.9, the utilization rate of material resources is increased to 0.88, and the utilization rate of device resources is increased to 0.85.

[0181] The average waiting time for Step B is reduced by 30%.

[0182] The completion time of the entire process is reduced by 10%.

[0183] In this embodiment, an enterprise full - process service resource scheduling optimization system based on big data is provided, which is used to execute the enterprise full - process service resource scheduling optimization method as described above, including:

[0184] A business process distribution module, which is used to obtain enterprise business logs and enterprise operation service resources; perform business process distribution analysis on the enterprise business logs, and construct a business full - process structure diagram;

[0185] A heterogeneous resource module, which is used to classify the types of enterprise operation service resources to obtain multi - type enterprise service resource data; perform heterogeneous resource fusion on the multi - type enterprise service resource data, and construct an enterprise heterogeneous resource library;

[0186] A demand analysis module, which is used to analyze the resource requirements of each step in the business full - process structure diagram to generate step - node resource requirement data; generate business process simulation data according to the step - node resource requirement data;

[0187] A demand prediction module, which is used to predict the resource requirements of each step for the business process simulation data to obtain multiple step - resource requirement prediction data; identify resource - shortage steps according to the multiple step - resource requirement prediction data;

[0188] A dynamic configuration module, which is used to make global resource dynamic configuration decisions for the resource - shortage steps based on the enterprise heterogeneous resource library, and construct a global resource configuration strategy;

[0189] A resource scheduling model module is used to optimize the modeling of scheduling parameters based on a global resource allocation strategy, construct an adaptive dynamic resource scheduling model, and execute enterprise resource scheduling optimization operations.

[0190] In the present invention, by obtaining enterprise business logs and operation service resources, understanding the business processes and available resources of the enterprise, analyzing the distribution of business links in the enterprise business logs to identify each business link of the enterprise, and establishing a business full-process structure diagram, the constructed business full-process structure diagram helps the enterprise comprehensively understand the business processes and provides a basis for subsequent resource scheduling optimization. Classify the enterprise operation service resources by resource type, classify and manage different types of resources, and integrate heterogeneous resources to construct an enterprise heterogeneous resource library, integrating different types of resources together to form a unified resource library, which is convenient for resource scheduling and management. Analyze the resource requirements of each link in the business full-process structure diagram to understand the resource requirements of each link. The resource requirement data of the link nodes provides a detailed description of the resource requirements and provides a basis for subsequent resource scheduling decisions. Based on the resource requirement data of the link nodes, simulate the resource requirements of different links to help evaluate and optimize the resource scheduling plan. Forecast the resource requirements of each link for the business link simulation data to predict the resource requirements of each future link. The generation of multiple link resource requirement forecast data helps the enterprise identify resource shortage links, that is, links where resource requirements exceed supply. Based on the enterprise heterogeneous resource library, make global resource dynamic allocation decisions for the resource shortage links, optimize resource scheduling according to the real-time resource situation and requirements. The constructed global resource allocation strategy ensures the reasonable allocation of resources in the shortage links, improves the overall resource utilization efficiency and service quality. Optimize the modeling of scheduling parameters based on the global resource allocation strategy, dynamically optimize the resource scheduling strategy according to the actual scheduling effect. The constructed adaptive dynamic resource scheduling model adjusts the resource scheduling parameters according to real-time data and evaluation results to achieve more accurate and efficient resource scheduling optimization, and execute enterprise resource scheduling optimization operations to ensure the reasonable configuration and allocation of resources in each link, improving the overall resource scheduling effect and business operation efficiency.

[0191] Therefore, in any regard, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to encompass all changes within the meaning and scope of the equivalent elements of the application documents in the present invention.

[0192] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. An optimization method for enterprise full-process service resource scheduling based on big data, characterized in that, It includes the following steps: Step S1: Obtain enterprise business logs and enterprise operation service resources; conduct an analysis of the business link distribution of enterprise business logs, and construct a business full-process structure diagram; Step S2: Classify the enterprise operation service resources by resource type to obtain multi-type enterprise service resource data; Fuse heterogeneous resources of the multi-type enterprise service resource data to construct an enterprise heterogeneous resource library; Step S3: Analyze the resource requirements of each link in the business full-process structure diagram to generate link node resource requirement data; Generate business link simulation data based on the link node resource requirement data; Step S4: Forecast the resource requirements of each link for the business link simulation data to obtain multiple link resource requirement forecast data; identify resource shortage links based on the multiple link resource requirement forecast data; Step S5: Make a global resource dynamic allocation decision for the resource shortage links based on the enterprise heterogeneous resource library, and construct a global resource allocation strategy; Step S6: Optimize the scheduling parameter modeling based on the global resource allocation strategy to construct an adaptive dynamic resource scheduling model to execute the enterprise resource scheduling optimization task; Among them, the specific steps of Step S1 are: Step S11: Obtain enterprise business logs and enterprise operation service resources; Step S12: Filter sensitive information from the enterprise business logs to generate filtered business logs; Step S13: Conduct an analysis of the business link distribution of the filtered business logs to generate business link distribution data; Step S14: Conduct an analysis of the execution dependencies of the business links for the business link distribution data to obtain the execution dependency data of each business link; Step S15: Reconstruct the business full-process structure based on the execution dependency data of each business link to obtain the business full-process structure diagram; Among them, the specific steps of Step S14 are: Extract link characteristics from the filtered business logs to obtain business event types and business link action behavior characteristics; Label the action characteristics of the business link action behavior characteristics based on the business event types to obtain various types of link action behavior data; Calculate the average duration of each type of link action behavior data to generate the average processing duration of link actions; Mine the business paths from the business link distribution data to obtain business link processing paths; Conduct an analysis of the execution dependencies of the business link processing paths based on the average processing duration of link actions to obtain the execution dependency data of each business link.

2. The method for optimizing the resource scheduling of the enterprise full-process service based on big data according to claim 1, wherein The specific steps of Step S2 are: Step S21: Classify the enterprise operation service resources by resource type to obtain multi-type enterprise service resource data; Step S22: Quantify the resource capabilities of the multi-type enterprise service resource data based on the preset enterprise resource quantification rules to obtain multi-type resource quantification values; Step S23: Perform a standardized mapping conversion on the multi-type enterprise service resource data to obtain an enterprise resource table; Step S24: Fuse heterogeneous resources for the enterprise resource table according to the multi-type resource quantification values to construct an enterprise heterogeneous resource library.

3. The method for optimizing the scheduling of enterprise full-process service resources based on big data according to claim 1, wherein The specific steps of Step S3 are: Step S31: Mark the key links in the business full-process structure diagram to obtain multiple business link nodes; Step S32: Calculate the resource occupancy of multiple business process nodes to obtain the resource occupancy of each process; Step S33: Based on the resource occupancy of each process, conduct resource requirement analysis for each process to generate resource requirement data for process nodes; Step S34: According to the resource requirement data of process nodes, perform business process operation simulation on the business full-process structure diagram to generate business process simulation data.

4. The method for optimizing the scheduling of enterprise full-process service resources based on big data according to claim 1, characterized in that, The specific steps of Step S4 are as follows: Step S41: Calculate the resource peak time for the business process simulation data to obtain the resource peak time for each process; Step S42: Conduct resource share distribution analysis on the business process simulation data to generate resource share distribution data for each process; Step S43: Based on the resource share distribution data for each process and the resource peak time for each process, conduct resource requirement prediction for each process to obtain multiple resource requirement prediction data for processes; Step S44: According to the multiple resource requirement prediction data for processes, perform resource shortage evolution analysis on the business full-process structure diagram to identify resource shortage processes.

5. The method for optimizing the scheduling of enterprise full-process service resources based on big data according to claim 4, characterized in that, The specific steps of Step S43 are as follows: Based on the resource share distribution data for each process, conduct resource requirement prediction to obtain resource requirement prediction values; Conduct time-series peak change analysis on the resource peak time for each process to obtain peak change rules; Based on the peak change rules, conduct resource requirement peak point prediction to generate resource peak point prediction data; Traverse all business processes to obtain the resource requirement prediction values and resource peak point prediction data for each business process; Based on the resource requirement prediction values and resource peak point prediction data for each business process, conduct integration to obtain multiple resource requirement prediction data for processes.

6. The method for optimizing the resource scheduling of the enterprise full-process service based on big data according to claim 1, wherein The specific steps of Step S5 are as follows: Step S51: Based on the enterprise heterogeneous resource library, conduct resource scheduling matching calculation for the resource shortage processes to generate idle resource scheduling matching degrees; Step S52: Based on the idle resource scheduling matching degrees, conduct local resource scheduling for the resource shortage processes to generate local resource scheduling data for shortage processes; Step S53: Conduct resource scheduling error analysis on the local resource scheduling data for shortage processes to obtain local scheduling error values; Step S54: Based on the local scheduling error values, conduct global resource dynamic configuration decision-making on the business full-process structure diagram to construct a global resource configuration strategy.

7. The method for optimizing the resource scheduling of the enterprise full-process service based on big data according to claim 1, wherein, The specific steps of Step S6 are as follows: Step S61: Based on the resource shortage processes, conduct optimal process logic optimization on the business full-process structure diagram to obtain an optimal process structure; Step S62: Use the global resource configuration strategy to conduct resource scheduling monitoring on the optimal process structure to generate resource scheduling monitoring data; Step S63: Conduct scheduling effect quantitative evaluation processing on the resource scheduling monitoring data to generate resource scheduling evaluation values; Step S64: According to the resource scheduling evaluation values, conduct scheduling parameter optimization modeling on the global resource configuration strategy to construct an adaptive dynamic resource scheduling model to execute enterprise resource scheduling optimization operations.

8. An enterprise full-process service resource scheduling optimization system based on big data, characterized in that, For executing the big data-based enterprise full-process service resource scheduling optimization method as described in Claim 1, including: A business process distribution module, used to obtain enterprise business logs and enterprise operation service resources; conduct business process distribution analysis on the enterprise business logs to construct a business full-process structure diagram; Heterogeneous resource module, which is used to classify the types of enterprise operation service resources to obtain multi-type enterprise service resource data; fuse the heterogeneous resources of the multi-type enterprise service resource data to construct an enterprise heterogeneous resource library; Requirement analysis module, which is used to analyze the resource requirements of each link in the business full-process structure diagram to generate link node resource requirement data; generate business link simulation data according to the link node resource requirement data; Requirement prediction module, which is used to predict the resource requirements of each link in the business link simulation data to obtain multiple link resource requirement prediction data; identify resource shortage links according to the multiple link resource requirement prediction data; Dynamic configuration module, which is used to make global resource dynamic configuration decisions on resource shortage links based on the enterprise heterogeneous resource library and construct a global resource configuration strategy; Resource scheduling model module, which is used to optimize the scheduling parameters based on the global resource configuration strategy to construct an adaptive dynamic resource scheduling model to execute enterprise resource scheduling optimization operations.

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