CN116451987A - Workflow-oriented intelligent arrangement method - Google Patents
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Abstract
The invention relates to an intelligent arrangement method facing workflow, which belongs to the technical field of service resource integration, and comprises the following steps: forming a historical operation behavior data set, and respectively acquiring a condition attribute and a decision attribute corresponding to each behavior data item in the historical operation behavior data set; extracting a plurality of frequent items in the historical operation behavior data set according to the condition attribute and forming a frequent item set; acquiring a behavior correlation set according to the formed frequent item set; taking the decision attribute as a user operation node, and establishing a user behavior flow chart; and arranging the workflow through the established user behavior flow chart. The method provided by the application can provide practical and reliable recommended options for the workflow of the user in the complex scene, so that the user can more reasonably consider the trend of the next flow, the sinking cost caused by overlong business processing is shortened, the accurate prediction of the future operation links of the user is realized, and the arrangement of the intelligent workflow can be realized.
Description
Technical Field
The invention relates to the technical field of service resource integration, in particular to an intelligent arrangement method facing workflow.
Background
In the prior art, workflow task execution of enterprises often depends on time sequence information, and effective pushing and circulation can not be performed according to actual service scenes under complex working scenes, so that the time-sequence workflow processing mode is difficult to have intelligent scene application, and particularly, automation and intellectualization can not be realized on timely scheduling of workflow task arrangement and information, and timely adjustment can not be performed according to actual conditions and occurrence of events, so that optimal effects of performance and efficiency are difficult to achieve in complex working scenes.
Currently, for workflow task processing, a unified webservers is deployed to open task receiving APIs of a workflow of a plurality of business systems, after acquisition is completed, task processing is performed according to time sequence information, and then trigger points are set at regular time or in real time according to resources to wait for task execution or starting. However, when facing the service processing of the multi-system workflow, the current method for scheduling tasks according to the time sequence information firstly has the problems of small granularity and easy error of the workflow; secondly, multiple workflow instances may generate complex logic relationships, which are difficult to monitor and manage; finally, the workflow is complex and easily confused by the user, and if the user needs to use the workflow, the user needs to design a specific form or flow chart to guide the user to operate, which greatly aggravates the work and fault tolerance of the dispatcher and the configurator of the workflow.
Disclosure of Invention
The invention aims to provide an intelligent arrangement method facing workflow, which solves the defects in the prior art, and the technical problem to be solved by the invention is realized by the following technical scheme.
The intelligent arranging method facing to the workflow provided by the invention comprises the following steps:
step S1: integrating a plurality of pieces of historical operation behavior data of a user in a system to form a historical operation behavior data set, and respectively acquiring condition attributes and decision attributes corresponding to each behavior data item in the historical operation behavior data set;
step S2: extracting a plurality of frequent items in the historical operation behavior data set according to the condition attribute, and forming a frequent item set according to the extracted frequent items;
step S3: acquiring a behavior correlation set according to the formed frequent item set;
step S4: the decision attribute is used as a user operation node, labels are given to the user operation node through a relevance set, the user operation node is connected through the labels respectively corresponding to the user operation node, and a user behavior flow chart is established;
step S5: and arranging the workflow through the established user behavior flow chart.
In the above-described scheme, the condition attribute includes a time scenario attribute, a role scenario attribute, and a node scenario attribute.
In the above scheme, step S2 includes:
acquiring the total number of condition attributes corresponding to each behavior data item in the historical operation behavior data set;
acquiring the number of condition attributes corresponding to each behavior data item in the historical operation behavior data set;
the number of the condition attributes corresponding to each behavior data item is obtained and the ratio of the number to the total number is calculated;
and taking the proportion of the number of the condition attributes corresponding to each behavior data item to the total number as the frequent support percentage corresponding to each behavior data item.
In the above scheme, step S2 further includes:
comparing the obtained frequent support percentage with a threshold value of the frequent support percentage, and extracting a plurality of frequent items in the historical operation behavior data set according to the comparison result;
and forming a frequent item set according to the extracted frequent items.
In the above-described aspect, extracting the plurality of frequent items in the historical operating behavior data set according to the comparison result includes:
and taking the data items with the frequent support percentage being larger than the threshold value of the frequent support percentage as frequent items, and rejecting the data items with the corresponding proportion being smaller than the frequent threshold value.
In the above solution, the set of behavior relatedness is expressed as:
wherein S is a behavior correlation set, O (·) represents a time complexity calculation function, b i For the frequent support percentage corresponding to the i-th frequent item in the frequent item set, L m Counting the positions of the ith frequent item in the frequent item set in the corresponding historical operation behavior data set, wherein n is the number of all the frequent items in the frequent item set, L n The n-th frequent item in the frequent item set is counted in the corresponding historical operation behavior data set.
In the above scheme, step S4 includes:
the decision attribute is used as a user operation node, each item in the relevance set is converted into a vector, and labels are given to the user operation node corresponding to each item through the vector corresponding to each item;
calculating the association degree between the user operation nodes through the labels of the user operation nodes;
according to the association degree calculation result among the user operation nodes, connection among the user operation nodes is carried out;
and establishing a user behavior flow chart.
In the above-described scheme, when there is a term in the relevance set for which the user operation node does not have a correspondence, the label given to the user operation node for which the term in the relevance set does not have a correspondence is a 0 vector.
In the above scheme, the relevance between the user operation nodes is calculated according to a user operation node relevance calculation formula, where the user operation node relevance calculation formula is:
wherein v is x For the user to operate the label corresponding to the node x, v y For the label corresponding to the user operation node y, processing link (v x ,v y ) For the degree of association between the user operation node x and the user operation node y, TM (v j ) Label set corresponding to all user operation nodes, < ->For the total number of labels corresponding to all user operation nodes, sim (v x ,v y ) The similarity between the label corresponding to the user operation node x and the label corresponding to the user operation node y is obtained.
In the above-mentioned scheme, performing connection between the user operation nodes according to the correlation degree calculation result between the user operation nodes includes:
traversing all the operation nodes, and sorting the association degree calculation results between the currently traversed operation node and other operation nodes from large to small;
connecting the operation nodes which are sequenced in the front and the operation nodes which are traversed currently;
sorting the association degree calculation results between the corresponding operation nodes with the previous sorting and other operation nodes remained after the traversing is completed from big to small;
connecting related operation nodes according to the sequencing result;
and repeating the steps to finish the connection between all the operation nodes.
The embodiment of the invention has the following advantages:
according to the workflow-oriented intelligent arrangement method provided by the embodiment of the invention, the decision attribute and the condition attribute comprising the time scene attribute, the role scene attribute and the node scene attribute are processed to establish the user behavior flow chart, so that a practical and reliable recommendation option can be given to the workflow of the user in a complex scene, the trend of the next flow can be considered more reasonably by the user, the sinking cost caused by overlong business processing is shortened, the accurate prediction of the future operation links of the user is realized, the arrangement of the intelligent workflow is realized, and the efficiency and the performance of a business system are effectively improved.
Drawings
FIG. 1 is a step diagram of a workflow-oriented intelligent orchestration method of the present invention.
FIG. 2 is a diagram of steps for forming frequent item sets in accordance with the present invention.
FIG. 3 is a step diagram of the present invention for establishing a user behavior flow chart.
Fig. 4 is a step diagram of the present invention for making a connection between user operated nodes.
FIG. 5 is a schematic diagram of one embodiment of the present invention for establishing a user behavior flow chart.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
As shown in fig. 1, the present invention provides a workflow-oriented intelligent orchestration method, including:
step S1: integrating a plurality of pieces of historical operation behavior data of a user in a system to form a historical operation behavior data set, and respectively acquiring condition attributes and decision attributes corresponding to all behavior data items in the historical operation behavior data set, wherein the condition attributes comprise time scene attributes, role scene attributes and node scene attributes.
Step S2: and extracting a plurality of frequent items in the historical operation behavior data set according to the condition attribute, and forming a frequent item set according to the extracted frequent items.
As shown in fig. 2, step S2 includes:
step S21: acquiring the total number of condition attributes corresponding to each behavior data item in the historical operation behavior data set;
step S22: acquiring the number of condition attributes corresponding to each behavior data item in the historical operation behavior data set;
step S23: the number of the condition attributes corresponding to each behavior data item is obtained and the ratio of the number to the total number is calculated;
step S24: taking the proportion of the number of the condition attributes corresponding to each behavior data item to the total number as the frequent support percentage corresponding to each behavior data item;
step S25: comparing the obtained frequent support percentage with a threshold value of the frequent support percentage, and extracting a plurality of frequent items in the historical operation behavior data set according to the comparison result;
step S26: and forming a frequent item set according to the extracted frequent items.
Specifically, the threshold for the frequent support percentage in the present invention is 0.55.
Wherein extracting the plurality of frequent items in the historical operational behavior dataset according to the comparison result comprises:
and taking the data items with the frequent support percentage being larger than the threshold value of the frequent support percentage as frequent items, and rejecting the data items with the corresponding proportion being smaller than the frequent threshold value.
Step S3: and acquiring a behavior correlation set according to the formed frequent item set.
Wherein, the behavior relatedness set is expressed as:
wherein S is a behavior correlation set, O (·) represents a time complexity calculation function, b i For the frequent support percentage corresponding to the i-th frequent item in the frequent item set, L m Counting the positions of the ith frequent item in the frequent item set in the corresponding historical operation behavior data set, wherein n is the number of all the frequent items in the frequent item set, L n The n-th frequent item in the frequent item set is counted in the corresponding historical operation behavior data set.
Step S4: and taking the decision attribute as a user operation node, giving labels to the user operation node through the relevance set, and connecting the user operation node through the labels respectively corresponding to the user operation node to establish a user behavior flow chart.
As shown in fig. 3, step S4 includes:
step S41: the decision attribute is used as a user operation node, each item in the relevance set is converted into a vector, and a label is given to the user operation node corresponding to each item through the vector corresponding to each item, wherein when the user operation node does not have the item in the corresponding relevance set, the label given by the user operation node without the item in the corresponding relevance set is a 0 vector;
step S42: calculating the association degree between the user operation nodes through the labels of the user operation nodes;
step S43: according to the association degree calculation result among the user operation nodes, connection among the user operation nodes is carried out;
step S44: and establishing a user behavior flow chart.
Specifically, the relevance between the user operation nodes is calculated through a user operation node relevance calculation formula, wherein the user operation node relevance calculation formula is as follows:
wherein v is x For the user to operate the label corresponding to the node x, v y For the label corresponding to the user operation node y, processing link (v x ,v y ) For the degree of association between the user operation node x and the user operation node y, TM (v j ) Label set corresponding to all user operation nodes, < ->For the total number of labels corresponding to all user operation nodes, sim (v x ,v y ) The similarity between the label corresponding to the user operation node x and the label corresponding to the user operation node y is obtained.
As shown in fig. 4, step S43 includes:
step S431: traversing all the operation nodes, and sorting the association degree calculation results between the currently traversed operation node and other operation nodes from large to small;
step S432: connecting the operation nodes which are sequenced in the front and the operation nodes which are traversed currently;
step S433: sorting the association degree calculation results between the corresponding operation nodes with the previous sorting and other operation nodes remained after the traversing is completed from big to small;
step S434: connecting related operation nodes according to the sequencing result;
step S435: and repeating the steps to finish the connection between all the operation nodes.
As shown in fig. 5, in one embodiment of the present invention, all the operation nodes include an operation node a, an operation node B, an operation node C, an operation node D, an operation node G, an operation node H, and an operation node N, respectively, and the association degree calculation results between the operation node a currently traversed and other operation nodes are ordered from large to small, and the operation node B orders first, so that the operation node B is connected with the operation node a currently traversed; then sorting the association degree calculation results between the operation node B and other operation nodes remained after the traversing is finished from big to small, and sorting the operation node D, the operation node E and the operation node F in parallel to be first, so as to connect the operation node D, the operation node E and the operation node F with the operation node B; sorting the association degree calculation results among the operation node D, the operation node E and the operation node F and other operation nodes except the operation node A and the operation node B from large to small respectively, connecting the operation node G with the operation node D and the operation node E respectively according to the sorting results, connecting the operation node N with the operation node E, and connecting the operation node H with the operation node F; and sorting from big to small according to the association degree calculation results among the operation node G, the operation node H and the rest of the operation nodes, and respectively connecting the operation node N with the operation node G and the operation node H.
Step S5: and arranging the workflow through the established user behavior flow chart.
It should be noted that the foregoing detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular is intended to include the plural unless the context clearly indicates otherwise. Furthermore, it will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, steps, operations, devices, components, and/or groups thereof.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or otherwise described herein.
Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Spatially relative terms, such as "above … …," "above … …," "upper surface at … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial location relative to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "above" or "over" other devices or structures would then be oriented "below" or "beneath" the other devices or structures. Thus, the exemplary term "above … …" may include both orientations of "above … …" and "below … …". The device may also be positioned in other different ways, such as rotated 90 degrees or at other orientations, and the spatially relative descriptors used herein interpreted accordingly.
In the above detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, like numerals typically identify like components unless context indicates otherwise. The illustrated embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A workflow-oriented intelligent orchestration method, the method comprising:
step S1: integrating a plurality of pieces of historical operation behavior data of a user in a system to form a historical operation behavior data set, and respectively acquiring condition attributes and decision attributes corresponding to each behavior data item in the historical operation behavior data set;
step S2: extracting a plurality of frequent items in the historical operation behavior data set according to the condition attribute, and forming a frequent item set according to the extracted frequent items;
step S3: acquiring a behavior correlation set according to the formed frequent item set;
step S4: the decision attribute is used as a user operation node, labels are given to the user operation node through a relevance set, the user operation node is connected through the labels respectively corresponding to the user operation node, and a user behavior flow chart is established;
step S5: and arranging the workflow through the established user behavior flow chart.
2. The workflow-oriented intelligent orchestration method according to claim 1, wherein the condition attributes include a time context attribute, a role context attribute, and a node context attribute.
3. The workflow-oriented intelligent orchestration method according to claim 1, wherein step S2 comprises:
acquiring the total number of condition attributes corresponding to each behavior data item in the historical operation behavior data set;
acquiring the number of condition attributes corresponding to each behavior data item in the historical operation behavior data set;
the number of the condition attributes corresponding to each behavior data item is obtained and the ratio of the number to the total number is calculated;
and taking the proportion of the number of the condition attributes corresponding to each behavior data item to the total number as the frequent support percentage corresponding to each behavior data item.
4. The workflow-oriented intelligent orchestration method according to claim 3, wherein step S2 further comprises:
comparing the obtained frequent support percentage with a threshold value of the frequent support percentage, and extracting a plurality of frequent items in the historical operation behavior data set according to the comparison result;
and forming a frequent item set according to the extracted frequent items.
5. The workflow-oriented intelligent orchestration method according to claim 4, wherein extracting a plurality of frequent items in the historical operational behavior dataset according to the comparison result comprises:
and taking the data items with the frequent support percentage being larger than the threshold value of the frequent support percentage as frequent items, and rejecting the data items with the corresponding proportion being smaller than the frequent threshold value.
6. The workflow-oriented intelligent orchestration method according to claim 4, wherein the set of behavioral relevance is expressed as:
wherein S is a behavior correlation set, O (·) represents a time complexity calculation function, b i For the frequent support percentage corresponding to the i-th frequent item in the frequent item set, L m Counting the positions of the ith frequent item in the frequent item set in the corresponding historical operation behavior data set, wherein n is the number of all the frequent items in the frequent item set, L n The n-th frequent item in the frequent item set is counted in the corresponding historical operation behavior data set.
7. The workflow-oriented intelligent orchestration method according to claim 1, wherein step S4 comprises:
the decision attribute is used as a user operation node, each item in the relevance set is converted into a vector, and labels are given to the user operation node corresponding to each item through the vector corresponding to each item;
calculating the association degree between the user operation nodes through the labels of the user operation nodes;
according to the association degree calculation result among the user operation nodes, connection among the user operation nodes is carried out;
and establishing a user behavior flow chart.
8. The workflow-oriented intelligent orchestration method according to claim 7, wherein when there is a user operation node without an item in the corresponding relevance set, the label assigned to the user operation node without an item in the corresponding relevance set is a 0 vector.
9. The workflow-oriented intelligent orchestration method according to claim 7, wherein the relevance between each user operation node is calculated by a user operation node relevance calculation formula, wherein the user operation node relevance calculation formula is:
wherein v is x For the user to operate the label corresponding to the node x, v y For the label corresponding to the user operation node y, processing link (v x ,v y ) For the degree of association between the user operation node x and the user operation node y, TM (v j ) Label set corresponding to all user operation nodes, < ->For the total number of labels corresponding to all user operation nodes, sim (v x ,v y ) The similarity between the label corresponding to the user operation node x and the label corresponding to the user operation node y is obtained.
10. The workflow-oriented intelligent orchestration method according to claim 7, wherein making connections between user operation nodes according to the degree of association calculation results between the respective user operation nodes comprises:
traversing all the operation nodes, and sorting the association degree calculation results between the currently traversed operation node and other operation nodes from large to small;
connecting the operation nodes which are sequenced in the front and the operation nodes which are traversed currently;
sorting the association degree calculation results between the corresponding operation nodes with the previous sorting and other operation nodes remained after the traversing is completed from big to small;
connecting related operation nodes according to the sequencing result;
and repeating the steps to finish the connection between all the operation nodes.
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| CN106327323A (en) * | 2016-08-19 | 2017-01-11 | 清华大学 | Bank frequent item mode mining method and bank frequent item mode mining system |
| US20180302306A1 (en) * | 2017-04-12 | 2018-10-18 | Battelle Memorial Institute | Complementary workflows for identifying one-hop network behavior and multi-hop network dependencies |
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