CN-121526538-B - Multi-source data-based process parameter iterative optimization method and system

CN121526538BCN 121526538 BCN121526538 BCN 121526538BCN-121526538-B

Abstract

The invention relates to the technical field of business data and discloses a multi-source data-based process parameter iterative optimization method and a multi-source data-based process parameter iterative optimization system. And taking the resource as a main body, fusing task time sequence and dependency relationship, and constructing a resource cooperation map. After mapping the preliminary scheme to the map, the model can be used for simulating and estimating the load change of all nodes. And comparing the load increase value of the nodes in the full-node load change set with the available resource capacity of the nodes, and identifying a resource overload risk set of the business process. Adjusting the preliminary optimization scheme set according to the resource overload risk set to obtain a collaborative optimization execution sequence of the target business process; the invention can improve the accuracy of optimizing the technological parameters of the target business process.

Inventors

GAO HAO
LIU DONG
WU LONG
WANG QIANTING
ZHANG ENLAI

Assignees

三明学院

Dates

Publication Date: 20260508
Application Date: 20260114

Claims (8)

1. The iterative optimization method of the process parameters based on the multi-source data is characterized by comprising the following steps: collecting multi-source business data, a resource association table and a task time sequence set of a target business process; Based on steady state performance characteristics and abnormal fluctuation characteristics in the multi-source business data, respectively determining a resource allocation adjustment interval and a task item to be improved of the target business process to obtain a preliminary optimization scheme set of the target business process; Taking a resource main body in the multi-source business data as an object, and constructing a resource cooperation map of the target business process according to the execution sequence of the task time sequence set and the dependency relationship in the resource association table; Mapping the preliminary optimization scheme set to the resource cooperation map to obtain a full-node load change set of the target business process; Comparing the load increase value of the nodes in the full-node load change set with the available resource capacity of the nodes, and identifying a resource overload risk set of the business process, wherein the method comprises the following steps: Acquiring available resource capacity and load rate of a corresponding node in the resource main body from the resource association table; And carrying out relative load assessment on the available resource capacity and the load rate based on the total load increase value of the node to obtain an overload risk index of the target business process, wherein a calculation formula of the overload risk index is as follows: For the overload risk index to be the same, For the node index to be a function of the node index, For the index of the neighbor node, Is a node Is added to the total load increase value of (a), Is a node Is used to determine the available resource capacity of the (c) system, Is a node Is used for the load-bearing capacity of the (c), In order to dynamically buffer the coefficients of the coefficients, In order for the co-ordination to influence the coefficients, Is a node Is set of the neighbor node set of (c), For slave neighbour nodes Pointing node Is a weight of the strength of relationship of the edges of (c), Is a neighbor node Is a load factor of (2); Taking the node with the overload risk index exceeding a critical threshold as a resource overload risk set of the target business process; And adjusting the preliminary optimization scheme set according to the resource overload risk set to obtain a collaborative optimization execution sequence of the target business process.
2. The iterative optimization method of process parameters based on multi-source data of claim 1, wherein said collecting multi-source business data, resource association table and task time sequence set of a target business process comprises: Separating multi-source business data in a target business process from material records and interaction data in the target business process; fusing the transaction triggering record and the resource state change record in the interaction data to obtain a resource association table of the target business process; And extracting the time stamp of the task creation and completion event in the interaction data to obtain a task time sequence set of the target business process.
3. The iterative optimization method of process parameters based on multi-source data according to claim 1, wherein the determining the resource allocation adjustment interval and the task item to be improved of the target business process based on the steady state performance feature and the abnormal fluctuation feature in the multi-source business data to obtain the preliminary optimization scheme set of the target business process comprises: Identifying steady state performance characteristics and abnormal fluctuation characteristics of the target business process from the multi-source business data; Taking the resource utilization rate range corresponding to the steady-state performance characteristics as a resource allocation adjustment interval of the target business process; screening task items to be improved of the target business process from the task time sequence set based on the time stamp of the abnormal fluctuation characteristic; and associating the resource allocation adjustment interval with the task item to be improved, and directly generating a preliminary optimization scheme set of the target business process.
4. The iterative optimization method of process parameters based on multi-source data of claim 3, wherein said separating steady state performance characteristics and abnormal fluctuation characteristics of said target business process from said multi-source business data comprises: Trend separation is carried out on the multi-source business data, and steady-state performance characteristics of the target business process are obtained; Performing point-by-point difference on the multi-source service data and the steady-state performance characteristics to generate a residual sequence of the target service flow; and taking the standard deviation of the residual sequence as a discrimination threshold of the target business process, and identifying the point exceeding the discrimination threshold in the residual sequence as the abnormal fluctuation feature.
5. The iterative optimization method of process parameters based on multi-source data according to claim 1, wherein the constructing a resource cooperation map of the target business process based on the dependency relationship between the execution sequence of the task time sequence set and the resource association table by using the resource main body in the multi-source business data as an object comprises: Fusing material data, equipment data and personnel data in the multi-source business data to obtain a node set of the target business process; connecting node pairs corresponding to the task time sequence set in the node set according to the execution sequence in the task time sequence set to obtain a time sequence edge set of the target business process; According to the dependency relationship in the resource association table, connecting node pairs corresponding to the resource association table in the node set to obtain a dependency edge set of the target business process; And integrating the node set, the time sequence edge set and the dependency edge set to obtain a resource cooperation map of the target business process.
6. The iterative optimization method of process parameters based on multi-source data according to claim 1, wherein mapping the preliminary optimization scheme set to the resource collaboration map to obtain a full-node load variation set of the target business process comprises: taking the optimal configuration parameters in the preliminary optimal scheme set as expected load increment of the target business process; Weighting and distributing the expected load increment based on the relation strength of edges in the resource collaboration graph to obtain an indirect load increment of the target business process; Accumulating the expected load increment and the indirect load increment to obtain a total load increment value of the target business process; and summarizing the total load increase value to obtain a full-node load change set of the target business process.
7. The iterative optimization method of process parameters based on multi-source data according to claim 1, wherein said adjusting said preliminary optimization scheme set according to said resource overload risk set to obtain a collaborative optimization execution sequence of said target business process comprises: The nodes in the resource overload risk set are ordered in descending order according to the overload risk index, and the risk priority processing order of the target business process is obtained; screening adjustment instructions aiming at the resource overload risk concentration nodes in the preliminary optimization scheme based on the risk priority processing order; Matching the adjustment instruction with a replacement resource main body in the target business process according to the resource cooperation map; And adjusting the preliminary optimization scheme set based on the replacement resource main body to obtain a collaborative optimization execution sequence of the target business process.
8. A multi-source data based process parameter iterative optimization system for implementing the multi-source data based process parameter iterative optimization method of claim 1, said system comprising: The data acquisition module acquires multi-source business data, a resource association table and a task time sequence set of a target business process; The preliminary optimization scheme set module is used for respectively determining a resource allocation adjustment interval and a task item to be improved of the target business process based on steady state performance characteristics and abnormal fluctuation characteristics in the multi-source business data to obtain a preliminary optimization scheme set of the target business process; The resource collaboration spectrum module takes a resource main body in the multi-source business data as an object, and constructs a resource collaboration spectrum of the target business process according to the execution sequence of the task time sequence set and the dependency relationship in the resource association table; The full-node load change set module maps the preliminary optimization scheme set to the resource cooperation map to obtain a full-node load change set of the target business process; And a resource overload risk set module for comparing the load increment value of the nodes in the all-node load change set with the available resource capacity of the nodes, and identifying a resource overload risk set of the business process, comprising: Acquiring available resource capacity and load rate of a corresponding node in the resource main body from the resource association table; And carrying out relative load assessment on the available resource capacity and the load rate based on the total load increase value of the node to obtain an overload risk index of the target business process, wherein a calculation formula of the overload risk index is as follows: For the overload risk index to be the same, For the node index to be a function of the node index, For the index of the neighbor node, Is a node Is added to the total load increase value of (a), Is a node Is used to determine the available resource capacity of the (c) system, Is a node Is used for the load-bearing capacity of the (c), In order to dynamically buffer the coefficients of the coefficients, In order for the co-ordination to influence the coefficients, Is a node Is set of the neighbor node set of (c), For slave neighbour nodes Pointing node Is a weight of the strength of relationship of the edges of (c), Is a neighbor node Is a load factor of (2); Taking the node with the overload risk index exceeding a critical threshold as a resource overload risk set of the target business process; And the collaborative optimization execution sequence module is used for adjusting the preliminary optimization scheme set according to the resource overload risk set to obtain the collaborative optimization execution sequence of the target business process.

Description

Multi-source data-based process parameter iterative optimization method and system Technical Field The invention relates to the technical field of business data, in particular to a process parameter iterative optimization method and system based on multi-source data. Background In the field of optimizing process parameters of a target business process, the prior art often has difficulty in comprehensively integrating core information such as materials, equipment, personnel and the like in multi-source business data, and an optimization scheme is formulated only by relying on single-dimension data or partial process fragments, so that optimization measures lack consideration of overall operation logic of the business process. The schemes can not effectively comb the time sequence relation and the resource dependency relation of task execution, can not accurately identify the steady-state characteristics and the abnormal fluctuation sources in the process, so that the optimization direction deviates from the actual demand, the problem of process bottleneck is difficult to solve fundamentally, and finally, the resource allocation is unbalanced, and the task execution efficiency is low. Meanwhile, the prior art lacks systematic evaluation of the load change of all nodes before the implementation of an optimization scheme, only focuses on the direct load influence of single nodes, ignores the indirect load conduction effect caused by cooperative association among nodes, and leads to the condition that part of node resources are overloaded and part of node resources are idle after the implementation of the optimization measures. And the adjustment aiming at overload risk lacks a definite priority guiding and accurate resource replacement matching mechanism, the dynamic adjustment capability of an optimization scheme is insufficient, the complex change of a business process is difficult to adapt, the stability and the persistence of an optimization effect are finally influenced, and the cooperative and efficient operation of the business process cannot be realized. Disclosure of Invention The invention provides a multi-source data-based process parameter iterative optimization method and a multi-source data-based process parameter iterative optimization system, and mainly aims to solve the problem of lower accuracy in multi-source data-based process parameter iterative optimization. In order to achieve the above object, the method for iterative optimization of process parameters based on multi-source data provided by the present invention comprises: collecting multi-source business data, a resource association table and a task time sequence set of a target business process; Based on steady state performance characteristics and abnormal fluctuation characteristics in the multi-source business data, respectively determining a resource allocation adjustment interval and a task item to be improved of the target business process to obtain a preliminary optimization scheme set of the target business process; Taking a resource main body in the multi-source business data as an object, and constructing a resource cooperation map of the target business process according to the execution sequence of the task time sequence set and the dependency relationship in the resource association table; Mapping the preliminary optimization scheme set to the resource cooperation map to obtain a full-node load change set of the target business process; Comparing the load increment value of the nodes in the full-node load change set with the available resource capacity of the nodes, and identifying a resource overload risk set of the business process; And adjusting the preliminary optimization scheme set according to the resource overload risk set to obtain a collaborative optimization execution sequence of the target business process. In a preferred embodiment, the collecting the multi-source business data, the resource association table and the task time sequence set of the target business process includes: Separating multi-source business data in a target business process from material records and interaction data in the target business process; fusing the transaction triggering record and the resource state change record in the interaction data to obtain a resource association table of the target business process; And extracting the time stamp of the task creation and completion event in the interaction data to obtain a task time sequence set of the target business process. In a preferred embodiment, the determining the resource allocation adjustment interval and the task item to be improved of the target business process based on the steady state performance feature and the abnormal fluctuation feature in the multi-source business data respectively, to obtain the preliminary optimization scheme set of the target business process includes: Identifying steady state performance characteristics and abnormal fluctuation characteristics of