CN116737823A - Task mining method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses a task mining method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring an original operation log data set; the original operation log data set comprises a plurality of log records used for representing operation events, and each log record at least comprises a task identifier and a position identifier used for calibrating the occurrence position of the operation event; determining various operation events under various position identifications according to various log records in the original operation log data set; the operation event is obtained by mapping the log record; determining key operation events under each task identifier based on the operation events under each position identifier and the task identifier; and generating a flow chart according to the key operation event under each task identifier. The method and the system improve the granularity of the events expressed by the log records, and the generated flow chart is concise and can represent the specific execution condition of a certain task, so that a user can understand the task and optimize the task conveniently.
Description
Technical Field
The present invention relates to the field of task mining technologies, and in particular, to a task mining method, a device, an electronic device, and a storage medium.
Background
Task mining is a technique that enables an organization to capture detailed steps of tasks performed on a user's desktop, whether performed independently or in collaboration with colleagues. By analyzing the recorded user operations, task mining allows organizations to learn how they perform tasks, discover common errors that occur during task execution, and determine tasks that can be automated, all of which can help optimize their business processes.
In the prior art, the flow chart generated by the existing task mining method has a plurality of nodes, so that a user cannot conveniently understand and optimize tasks.
Disclosure of Invention
The invention provides a task mining method and device, which are used for solving the technical problems that the generated flow chart in the prior art is too many in nodes, so that a user cannot conveniently understand tasks and optimize the tasks.
In a first aspect, the present invention provides a task mining method, including:
acquiring an original operation log data set; the original operation log data set comprises a plurality of log records used for representing operation events, and each log record at least comprises a task identifier and a position identifier used for calibrating the occurrence position of the operation event;
determining various operation events under each position mark aiming at each log record in the original operation log data set; the operation event is obtained by mapping the log record;
determining key operation events under each task identifier based on the operation events under each position identifier and the task identifier;
and generating a flow chart according to the key operation event under each task identifier.
According to the technical scheme, the event granularity expressed by the log record is improved through the operation event, the key operation event under the task identification is determined by combining the task identification and the position identification, the generated flow chart is concise, the specific execution condition of a certain task can be represented, and the user can understand the task and optimize the task conveniently.
Optionally, the location identifier includes a window title or an application name.
Optionally, the log record further includes an element tag, and the step of determining the operation event under each location identifier includes:
determining the log records under each position identifier aiming at each log record in the original operation log data set;
and mapping the log records with the same element label into the same class of operation event aiming at the log records under the same position identification.
Optionally, the step of determining the key operation event under each task identifier includes:
determining operation events and corresponding occurrence times under each task identifier based on the operation events under each position identifier and the task identifiers; the occurrence times are the number of log records corresponding to each type of operation event;
calculating importance parameters of the operation events under each task identifier based on the occurrence times of the operation events;
and determining the operation event meeting the importance parameter threshold as a key operation event.
Optionally, a word frequency and inverse text frequency index algorithm is used to calculate the importance parameters of each operation event under each task identifier.
Optionally, the step of generating a flowchart according to the key operation event under each task identifier includes:
based on the key operation event under each task identifier, filtering the log records in the original operation log data set;
generating a corresponding flow chart by using the filtered operation log data set; the nodes in the flow chart are obtained by merging log records based on the position identification.
In a second aspect, the present invention provides a data mining apparatus comprising:
the acquisition module is used for acquiring an original operation log data set; the original operation log data set comprises a plurality of log records used for representing operation events, and each log record at least comprises a task identifier and a position identifier used for calibrating the occurrence position of the operation event;
the classification module is used for determining various operation events under each position identifier aiming at each log record in the original operation log data set; the operation events are obtained by classifying the log records;
the determining module is used for determining key operation events under each task identifier based on the operation events under each position identifier and the task identifier;
and the generating module is used for generating a flow chart according to the key operation event under each task identifier.
In a third aspect, the present invention provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the task mining method according to the first aspect as described above when executing the program.
In a fourth aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the task mining method as described in the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
1. the method of the invention determines various operation events under each position mark aiming at each log record in the original operation log data set, the step uses the position mark as a unit, mapping operation is carried out on the log record to obtain the operation event, and the granularity of the event expressed by the log record is improved through the operation event.
2. The method of the invention determines key operation events under each task identifier based on the operation events under each position identifier and the task identifiers; and generating a flow chart according to the key operation event under each task identifier. The method considers the task identifier, combines the task identifier and the position identifier, determines the key operation event under the task identifier, and generates a flow chart which is concise and can represent the specific execution condition of a certain task, thereby facilitating the user to understand the task and optimize the task.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow diagram of a task mining method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a task mining apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the related art, a task mining technique may automatically discover and extract tasks from collected operation log data. In the existing task mining product, after analysis is performed on input data, a flow chart with an operation window as a view angle can be generated finally, and a flow chart with an operation event as a view angle can be generated. After the analyst obtains the flow chart of the analyst, analysis can be performed on the flow chart, for example, analysis tasks such as return on investment (ReturnOnInvestment, ROI) analysis, task analysis, time-consuming analysis, flow comparison and the like, so as to optimize the operation of the existing tasks. Both analysts and analysts are collectively referred to as users.
In specific practice, the operation log data collected for the user operation often involves different applications and different windows, and the operation log data also covers different tasks. And for log records in operation log data are collected and recorded by an operation event recorder in an operation system, such as Windows, the operation event recorder records all operations of a user on a current terminal in an extremely small way, granularity of the operation event recorded by the log records is often too detailed, for example, a scene that a user executes reimbursement is considered, information such as an amount of money to be filled in, reimbursement type, reimbursement person name and the like is required in a window of a reimbursement bill, for which, some users can be directly input by a keyboard, some are input through a sticky board, some are input once, some are input after the input is finished, and the like are modified for many times. For these operation details, the operation event recorder will record all, so for the user to understand and analyze a task, the event granularity in the operation log data is too much to affect the user to understand and analyze a task, and therefore, the number of nodes in the flow chart is too much, which is inconvenient for the user to analyze the overall situation of the task.
Aiming at the problem, the invention provides a task mining method, which aims at each log record in the original operation log data set to determine various operation events under each position mark, and the step aims at classifying the log records by taking the position mark as a unit, and characterizing the same operation event by the log records classified into one type to realize the combination of the log records so as to reduce the data volume in the original operation log data set. Then, the method determines key operation events under each task identifier based on the operation events under each position identifier and the task identifiers; and generating a flow chart according to the key operation event under each task identifier. According to the method, the key operation events under the task identifications are considered, the key operation events under the task identifications are determined, and finally, the corresponding flow chart is generated based on the key operation events, so that the data volume in the original operation log data set is further reduced, the generated flow chart is simpler than the flow chart in the prior art, and accordingly, the method, the device and the system can represent the overall execution condition of a certain task although simple by considering the key operation events under the task identifications, and can help a user to understand the task and analyze the task relatively quickly.
As shown in fig. 1, the task mining method provided by the invention includes the following steps:
step 100, acquiring an original operation log data set; the original operation log data set comprises a plurality of log records used for representing operation events, and each log record at least comprises a task identifier and a position identifier used for calibrating the occurrence position of the operation event.
In this embodiment, the original operation log data set includes a plurality of log records for characterizing user operation events, and the log data may have a plurality of attributes, such as a unique identifier, a task identifier, a time stamp, a location identifier, an element tag, and an operation event type, etc. of each log record. The task identifier is used for distinguishing task categories to which different log records belong, and the position identifier is used for calibrating the occurrence position of the operation event, for example, the window title, the application name and the like. Element tags are objects to which a user operation is directed, such as the amount, type of reimbursement, reimbursement person, etc., described above. The operation event type may be a mouse click, a keyboard input, a clipboard input, and so on.
In this embodiment, the log record may be obtained by an operation event recorder in an operating system, for example, an operation event recorder in Windows. Or a screen recording mode is adopted to acquire the desktop operation screenshot of the user, then text recognition is carried out on the desktop operation screenshot, and corresponding information is extracted to supplement an original operation log data set. The original operation log data set comprises a plurality of log records, wherein the log records can be records of a user executing different tasks, or records obtained by executing the same tasks or different tasks by a plurality of users.
Step 101, determining various operation events under the position identifiers according to each log record in the original operation log data set; the operation event is obtained by mapping each log record.
In this embodiment, the operation event is mapped based on one or more key attributes in the log record, where the key attributes are semantic information that can represent the operation event corresponding to the log record. Thus, log records of the same key attribute may represent the same operational event.
In this embodiment, the key attributes may be an element tag and an operation event type, and mapping may be performed only according to the element tag, or mapping may be performed according to an element tag+an operation event type.
Specifically, the log records with the same element tag and operation event type are mapped to the same operation event type under a certain position mark, for example, under a window title. The mapping may be, for example, in the scenario where the user performs the reimbursement, in the corresponding original operation log data set, the user performs an operation of inputting a name in a window title of the reimbursement ticket, for example, the name of the user is: the A, B and C are input by the user in a keyboard input mode. The method can be that the "jacquard" is input through a keyboard, then the "jacquard" is deleted, then the "A" is input, and then the "ethylene-propylene" is input. For the user to execute the operation of inputting "A, B and C" through the keyboard, the original operation log data set may have more than 10 operation records, and then the method of the embodiment may be mapped to the same class or the same operation event, that is, the "input name".
In this embodiment, the key attribute may be an element tag and a flag of whether the content is changed, that is, no matter what event operation type is adopted by the user, for example, the key may be input multiple times through a keyboard, or even input after deletion, or may be completed through one input through a clipboard, for example, the clipboard is directly used to paste "ethylmethyl" or "ethylmethyl", so long as the content is changed, that is, the same operation event is considered. The method considers a situation that a user copies and pastes contents in a real operation, specifically, the user often copies and pastes contents in a plurality of windows, selects one or a plurality of fields for one window, then copies by right keys, switches to another window for pasting, and does not modify any contents for the former window, if only element labels are considered, and whether the contents change is not considered, log records in the cases are mapped to the same type of operation event, but the real semantics of the user operation are not reflected.
In this embodiment, the location identifier may be an application name or a window title, preferably a window title, or other identifier that can accurately mark that a user operates in a UI (user interface) interface.
In some cases, there is no element tag in the log record, for example, more and more applications have built-in intelligent form filling functionality, i.e. the user provides a button through intelligent form filling, and the user clicks the button, i.e. fills multiple content boxes simultaneously. Also taking the above scenario as an example, in a CRM (Customer RelationshipManagement ) with an intelligent form filling function, the user fills in contents such as an amount, a reimbursement type, a reimbursement name, etc. through a button of the intelligent form filling. In this scenario, there is no element tag for the amount, type of reimbursement, and reimbursement name in the corresponding log record. Thus, the user's click on a button may be mapped to a user input amount, user selection of a reimbursement type, user input name by predefined rules.
The purpose of the above steps is to merge the operation events by means of a location identifier, such as a window title, the operation events under the same window title being displayed on one node in the flow chart. Secondly, aiming at each log record in the original operation log data set, a plurality of log records are mapped into one operation event by a mapping method, so that the data volume is reduced, and the granularity perceived by a user is improved.
Step 102, determining key operation events under each task identifier based on the operation events under each position identifier and the task identifier.
In this embodiment, the above-mentioned key operation event is semantic information that can represent a task corresponding to the operation event, and the key operation event under each task identifier may be determined by the following method:
and 1021, determining the operation event and the corresponding occurrence times under each task identifier based on the operation event under each position identifier and the task identifier.
Specifically, the number of occurrences of a certain operation event is represented as the number of log records corresponding to the operation event, that is, the operation event mapped by how many log records, and the number of occurrences of the operation event is the number.
Step 1022, calculating importance parameters of the operation events under each task identifier based on the occurrence times of the operation events.
In this embodiment, one possible way is: in step 1021, the operation event and the corresponding occurrence number under each task identifier can be obtained, so that the occurrence number of all operation events under a certain task identifier can be added as a denominator, and the occurrence number of each operation event is used as a numerator, thereby obtaining the weight parameter of each operation event under the task identifier. Based on the calculation method, the weight parameters of each operation event under each task identifier can be further obtained. The weight parameter may be defined as a first weight parameter. In this embodiment, the first weight parameter measures the frequency of occurrence of a certain operation event under a certain task identifier, and accordingly, the frequency of occurrence of a certain operation event under a certain task identifier is high, which does not mean that the operation event is a key operation event capable of representing the task semantic information, and secondly, the frequency of occurrence of a certain operation event under a certain task identifier is not high, which does not mean that the operation event is a key operation event capable of representing the task semantic information. For example, in the above scenario where the user performs reimbursement, under a task identifier, the frequency of performing operation events such as input amount, reimbursement type, reimbursement name, etc. by the user is often much higher than the frequency of performing clicking the reimbursement single submit button, but accordingly, clicking the reimbursement single submit button is a key operation event capable of characterizing semantic information of the task. Therefore, the lateral comparison under multiple task identities also needs to be considered. After determining the first weight parameter of each operation event under each task identity, a second weight parameter of each operation event in the original operation log data set may be calculated, for example, a log function log (x) may be constructed, where x is the total number of operation events in the original operation log data set divided by the number of certain operation events in the original operation log data set. And multiplying the first weight parameter by the second weight parameter to obtain the importance parameter of the operation event under each task identifier.
Another possible way is: using TF-IDF (term-inverse document frequency index algorithm), the first weight parameter is TF value, and then IDF value is a log function log (x) that is the total number of tasks in the original operation log data set divided by the number of tasks with a certain operation event.
Step 1023, determining the operation event meeting the importance parameter threshold as a key operation event.
In this embodiment, in addition to determining key operation events under each task identifier by the above method, there is a possible way to: setting a first threshold value, wherein the first threshold value is used for filtering out operation events which occur too few times in an original operation log data set; for excluding operational events that occur too little in their entirety in the original oplog dataset. And filtering operation events which are not met in the occurrence times of each task through a second threshold value, wherein the reserved operation events are key operation events.
Step 103, generating a flow chart according to the key operation event under each task identifier.
In this embodiment, the step 103 may include: based on the key operation event under each task identifier, filtering the log records in the original operation log data set; generating a corresponding flow chart by using the filtered operation log data set; the nodes in the flow chart are obtained by merging log records based on the position identification. Specifically, the log records in the original oplog dataset are filtered by determining key operational events, so that the original oplog dataset is simplified, and a flow chart can be obtained based on the simplified oplog dataset by using a flow mining algorithm, such as an indictveminer algorithm. The generated flow chart is more concise, and meanwhile, the overall execution condition of a certain task can be represented, so that a user can be helped to understand the task and analyze the task relatively quickly.
The task mining apparatus provided by the present invention will be described below, and the task mining apparatus described below and the task mining method described above may be referred to correspondingly to each other.
As shown in fig. 2, the task mining apparatus includes the following modules:
an acquisition module 200, configured to acquire an original operation log data set; the original operation log data set comprises a plurality of log records used for representing operation events, and each log record at least comprises a task identifier and a position identifier used for calibrating the occurrence position of the operation event;
the classification module 210 is configured to determine, for each log record in the original operation log data set, each type of operation event under each location identifier; the operation events are obtained by classifying the log records;
a determining module 220, configured to determine key operation events under each task identifier based on the operation events under each location identifier and the task identifier;
the generating module 230 is configured to generate a flowchart according to the key operation events under the task identifiers.
Optionally, the location identifier includes a window title or an application name.
Optionally, the log record further includes an element tag, and the step of determining the operation event under each location identifier includes:
determining the log records under each position identifier aiming at each log record in the original operation log data set;
for log records under the same location identification, the log records with at least partially identical element tags are mapped to the same class of operation events.
Optionally, the step of determining the key operation event under each task identifier includes:
determining operation events and corresponding occurrence times under each task identifier based on the operation events under each position identifier and the task identifiers; the occurrence times are the number of log records corresponding to each type of operation event;
calculating importance parameters of the operation events under each task identifier based on the occurrence times of the operation events;
and determining the operation event meeting the importance parameter threshold as a key operation event.
Optionally, a word frequency and inverse text frequency index algorithm is used to calculate the importance parameters of each operation event under each task identifier.
Optionally, the step of generating a flowchart according to the key operation event under each task identifier includes:
based on the key operation event under each task identifier, filtering the log records in the original operation log data set;
generating a corresponding flow chart by using the filtered operation log data set; the nodes in the flow chart are obtained by merging log records based on the position identification.
Fig. 3 illustrates a physical schematic diagram of an electronic device, as shown in fig. 3, where the electronic device may include: processor 310, communication interface 320, memory 330 and communication bus 340, wherein processor 310, communication interface 320 and memory 330 communicate with each other via communication bus 340. The processor 310 may invoke logic instructions in the memory 330 to perform a task mining method comprising:
acquiring an original operation log data set; the original operation log data set comprises a plurality of log records used for representing operation events, and each log record at least comprises a task identifier and a position identifier used for calibrating the occurrence position of the operation event;
determining various operation events under each position mark aiming at each log record in the original operation log data set; the operation event is obtained by mapping the log record;
determining key operation events under each task identifier based on the operation events under each position identifier and the task identifier;
and generating a flow chart according to the key operation event under each task identifier.
Further, the logic instructions in the memory 330 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-only memory (ROM), a random access memory (RAM, randomAccessMemory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In still another aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor is implemented to perform the task mining method provided by the above methods, the method comprising:
acquiring an original operation log data set; the original operation log data set comprises a plurality of log records used for representing operation events, and each log record at least comprises a task identifier and a position identifier used for calibrating the occurrence position of the operation event;
determining various operation events under each position mark aiming at each log record in the original operation log data set; the operation event is obtained by mapping the log record;
determining key operation events under each task identifier based on the operation events under each position identifier and the task identifier;
and generating a flow chart according to the key operation event under each task identifier.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. A method of task mining, comprising:
acquiring an original operation log data set; the original operation log data set comprises a plurality of log records used for representing operation events, and each log record at least comprises a task identifier and a position identifier used for calibrating the occurrence position of the operation event;
determining various operation events under each position mark aiming at each log record in the original operation log data set; the operation event is obtained by mapping the log record;
determining key operation events under each task identifier based on the operation events under each position identifier and the task identifier;
and generating a flow chart according to the key operation event under each task identifier.
2. A method of task mining according to claim 1, wherein the location identity comprises a window title or an application name.
3. A method of task mining according to claim 1, wherein said log record further includes element tags, and wherein the step of determining operational events under each of said location identifiers comprises:
determining the log records under each position identifier aiming at each log record in the original operation log data set;
and mapping the log records with the same element label into the same class of operation event aiming at the log records under the same position identification.
4. A method of task mining according to claim 3, wherein the step of determining key operational events under each task identity comprises:
determining operation events and corresponding occurrence times under each task identifier based on the operation events under each position identifier and the task identifiers; the occurrence times are the number of log records corresponding to each type of operation event;
calculating importance parameters of the operation events under each task identifier based on the occurrence times of the operation events;
and determining the operation event meeting the importance parameter threshold as a key operation event.
5. The method of claim 4, wherein the importance parameters of each operation event under each task identifier are calculated using word frequency and inverse text frequency index algorithms.
6. The method according to claim 1, wherein the step of generating a flowchart according to the key operation events under the respective task identifications comprises:
based on the key operation event under each task identifier, filtering the log records in the original operation log data set;
generating a corresponding flow chart by using the filtered operation log data set; the nodes in the flow chart are obtained by merging log records based on the position identification.
7. A data mining apparatus, comprising:
the acquisition module is used for acquiring an original operation log data set; the original operation log data set comprises a plurality of log records used for representing operation events, and each log record at least comprises a task identifier and a position identifier used for calibrating the occurrence position of the operation event;
the classification module is used for determining various operation events under each position identifier aiming at each log record in the original operation log data set; the operation events are obtained by classifying the log records;
the determining module is used for determining key operation events under each task identifier based on the operation events under each position identifier and the task identifier;
and the generating module is used for generating a flow chart according to the key operation event under each task identifier.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the data mining method of any of claims 1 to 6 when the program is executed by the processor.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the data mining method according to any one of claims 1 to 6.
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| CN202310852671.XA CN116737823A (en) | 2023-07-12 | 2023-07-12 | Task mining method and device, electronic equipment and storage medium |
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| CN202310852671.XA CN116737823A (en) | 2023-07-12 | 2023-07-12 | Task mining method and device, electronic equipment and storage medium |
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| CN116737823A true CN116737823A (en) | 2023-09-12 |
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| CN202310852671.XA Pending CN116737823A (en) | 2023-07-12 | 2023-07-12 | Task mining method and device, electronic equipment and storage medium |
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| CN (1) | CN116737823A (en) |
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