CN112700112A - RPA flow adjusting method, device, electronic equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a method, a device, electronic equipment and a storage medium for RPA flow adjustment, wherein the method comprises the following steps: monitoring the process index of the current process; when the process index meets the adjusting condition, matching at least one candidate process from a preset process library based on the characteristic information of the current process, wherein each process and the characteristic information of each process are stored in the preset process library; updating the current flow based on the at least one candidate flow. The technical scheme provided by the embodiment of the invention can monitor and adjust a certain type of flow in a targeted manner, and if the input data and the like are changed, the automatic adjustment of the flow can be realized, manual operation is not needed, the manual error is reduced, and the efficiency of flow adjustment is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of artificial intelligence, in particular to a method and a device for RPA flow adjustment, electronic equipment and a storage medium.
Background
Robot Process Automation (RPA) refers to the automation of business processing implemented by software robots, and is a popular field in the current technology field. The RPA robot captures data and simulates human operation of the application using a user interface, allowing for a variety of repetitive tasks including logging into the application, moving files or folders, negating or pasting data information, etc.
In the traditional RPA process design, the business needs to be understood very thoroughly in the early stage of design, the business processing process is required to be unchanged, and the designed process can be put into use after being tested. If the later service processing flow is changed, the automatic flow needs to be adjusted by experienced workers, the subjectivity is strong, and the matching time is long.
Disclosure of Invention
The invention provides an RPA flow adjusting method, an RPA flow adjusting device, an electronic device and a storage medium, which are used for analyzing and adjusting each link of a flow, improving the efficiency and the accuracy of flow adjustment and improving the practicability of the flow.
In a first aspect, an embodiment of the present invention provides an RPA flow adjustment method, where the method includes:
monitoring the process index of the current process;
when the process index meets the adjusting condition, matching at least one candidate process from a preset process library based on the characteristic information of the current process, wherein each process and the characteristic information of each process are stored in the preset process library;
updating the current flow based on the at least one candidate flow.
In a second aspect, an embodiment of the present invention further provides an RPA flow adjusting apparatus, where the apparatus includes:
the flow index monitoring module is used for monitoring the flow index of the current flow;
a candidate process determining module, configured to match at least one candidate process from a preset process library based on the feature information of the current process when the process indicator satisfies an adjustment condition, where each process and the feature information of each process are stored in the preset process library;
a flow updating module for updating the current flow based on the at least one candidate flow.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for RPA flow adjustment according to any of the embodiments of the present invention.
In a fourth aspect, embodiments of the present invention further provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a method for RPA flow adjustment according to any one of the embodiments of the present invention.
According to the technical scheme of the embodiment of the invention, the flow indexes of the current flow are continuously monitored and judged, when the flow indexes meet the adjustment condition, at least one candidate flow is matched from a preset flow base based on the characteristic information of the current flow, and the current flow is updated based on the at least one candidate flow. The technical scheme provided by the embodiment of the invention can monitor and adjust a certain type of flow in a targeted manner, and if the input data and the like are changed, the automatic adjustment of the flow can be realized, manual operation is not needed, the manual error is reduced, and the efficiency of flow adjustment is improved.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, a brief description is given below of the drawings used in describing the embodiments. It should be clear that the described figures are only views of some of the embodiments of the invention to be described, not all, and that for a person skilled in the art, other figures can be derived from these figures without inventive effort.
Fig. 1 is a schematic flow chart of an RPA flow adjustment method according to a first embodiment of the present invention;
fig. 2 is a schematic flow chart of an RPA flow adjustment method according to a second embodiment of the present invention;
fig. 3 is a schematic flow chart of an RPA flow adjustment method according to a third embodiment of the present invention;
FIG. 4 is an exemplary diagram of a fuzzy membership function according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of an RPA flow adjustment apparatus according to a fourth embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device in a fifth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a schematic flow diagram of an RPA flow sorting method according to an embodiment of the present invention, where the embodiment of the present invention is applicable to a case where corresponding adjustment is performed when an automation flow runs in a specific type, and the method may be performed by an RPA flow adjusting apparatus, and the apparatus may be implemented in a form of software and/or hardware.
As illustrated in fig. 1, the method of the embodiment of the present invention includes the steps of:
and S110, monitoring the process indexes of the current process.
It should be noted that the flow in the embodiment of the present invention refers to an RPA flow. Current flow refers to a flow used in a certain type, including but not limited to robotic automation flows. For example, in a financial scenario, a company performs invoice verification through a robot automation process. The process index is a processing effect that data can be processed through a process in a certain type, and the process index includes, but is not limited to, the execution efficiency of the whole process, the execution efficiency of each link in the process, the execution quality of the whole process, the execution quality of each link in the process, tasks completed by a robot in the process, tasks completed by a person, and the like. When the process is started, the process index of the current process is continuously monitored so as to judge the effect of the process processing data through the process index.
And S120, when the process indexes meet the adjusting conditions, matching at least one candidate process from a preset process library based on the characteristic information of the current process.
And the preset process library stores each process and the characteristic information of each process. Optionally, different types of flows and characteristic information of the flows are stored in the flow library, for example, flows such as invoice approval and payroll accounting in the financial category. The adjustment condition refers to that the current process cannot achieve the expected data processing effect, and the set adjustment condition includes, but is not limited to, a process name, a name of a key link in the process, a name of data processed by the key link in the process, key step information in the link, and the like. Optionally, the setting of the feature information in the present invention includes: the flow identification of the flow and the link identification of the key link of the flow. And when the process indexes meet the adjusting conditions, matching the characteristic information of each process in a preset process library based on the characteristic information of the current process, and determining at least one candidate process. It should be understood that the candidate flow may be of the same type as the current flow or of a different type.
Optionally, the matching at least one candidate process from a preset process library based on the feature information of the current process includes: respectively determining the similarity between the characteristic information of the current process and the characteristic information of each process in the preset process library; and sequencing the flows in the preset flow library based on the similarity, and determining at least one flow in a preset sequencing range as a candidate flow.
The processes in the preset process library refer to various types of processes, and when the feature information of the current process is matched with the feature information of each process in the preset process library, the feature information of the current process is matched with the feature information of each process in different types, and the similarity is determined. Of course, the sorting method is not limited to the above-described method, and may be a small-to-large sorting method.
And S130, updating the current flow based on the at least one candidate flow.
The updating manner includes, but is not limited to, replacing, adjusting, and the like. After determining at least one candidate flow, the current flow may be updated based on the candidate flows, and optionally, when there are multiple candidate flows, the current flow is sequentially updated by the multiple candidate flows, and the multiple candidate flows are compared to determine that one of the multiple candidate flows is preferable to update the current flow.
According to the technical scheme of the embodiment of the invention, the flow indexes of the current flow are continuously monitored and judged, when the flow indexes meet the adjustment condition, at least one candidate flow is matched from a preset flow base based on the characteristic information of the current flow, and the current flow is updated based on the at least one candidate flow. The technical scheme provided by the embodiment of the invention can monitor and adjust a certain type of flow in a targeted manner, and if the input data and the like are changed, the automatic adjustment of the flow can be realized, manual operation is not needed, the manual error is reduced, and the efficiency of flow adjustment is improved.
Example two
Fig. 2 is a schematic flow chart of an RPA flow adjustment method provided in an embodiment of the present invention, where the embodiment of the present invention is a refinement performed on the basis of the alternatives of the foregoing embodiments, and optionally, step 110 includes: acquiring link characteristics, execution efficiency and execution quality of each link in the current flow, and determining link scores of each link according to the link characteristics, the execution efficiency and the execution quality; and determining the flow index score of the current flow based on the link scores of all the links. When the process index meets the regulation condition, the method further comprises the following steps: determining at least one abnormal link with the link score lower than a preset link score threshold; and determining the abnormal reason of the at least one abnormal link, and generating prompt information and sending the prompt information to a user terminal based on the link characteristics, the execution efficiency and the execution quality of each abnormal link and the abnormal reason. Technical terms identical or similar to those of the above embodiments will not be described again.
As shown in fig. 2, the RPA flow adjustment method according to the embodiment of the present invention includes the following steps:
s210, acquiring link characteristics, execution efficiency and execution quality of each link in the current process, and determining link scores of each link according to the link characteristics, the execution efficiency and the execution quality; and determining the flow index score of the current flow based on the link scores of all the links.
And the link score is obtained by performing matrix calculation according to the link characteristics, the execution efficiency and the execution quality. The process includes at least one element, and the nature of the element includes, but is not limited to, an operational step. The execution efficiency includes, but is not limited to, a historical execution efficiency, an execution efficiency of the current link, and the like, and the historical execution efficiency may be a historical execution efficiency of the current link or a historical execution efficiency of the flow. The execution quality includes, but is not limited to, a historical execution error rate, a current link execution error rate, and the like, and the historical execution error rate may be a historical execution error rate of the current link or a historical execution error rate of the process. The various parts of the process may be machine operations, manual operations, or both manual and machine operations. Therefore, if the link is operated by a machine and a human, the operation steps in the link characteristics are divided into the operation steps of the machine and the manual operation steps. Similarly, execution quality and execution efficiency also need to be partitioned on a machine and human basis. The process index score is used for evaluating the data processing capacity of the current process, the process can be divided into different grades, such as high, medium and low, each grade comprises a process index score in a preset score range, when the calculated process index score falls into which grade preset score range, the grade of the current process is determined, for example, the preset score range of the high grade is 8-10, the preset score of the medium grade is 4-6, the preset score of the low grade is 0-2, a fuzzy theory is applied, different boundary points and different curves are set, middle linear cross points among three areas of the high grade, the medium grade and the low grade are fuzzy points, a gravity center method or a linear proportion method can be used for calculating the membership degree, and the grade preset score range of the process index score is determined. Therefore, when the calculated flow index score falls into which preset score range, the corresponding grade can be known. After the link characteristics, the execution efficiency and the execution quality of each link in the current flow are obtained, the link score of each link is determined based on the link characteristics, the execution efficiency and the execution quality of each link. Optionally, the link characteristics, the execution efficiency, and the execution quality may be calculated in a matrix manner to obtain the link score. Illustratively, a link characteristic score, an execution efficiency score and an execution quality score of each link are obtained firstly, and then the link scores of each link are obtained through the scores in a matrix form. In obtaining the score of the link characteristic, a threshold of the operation step may be set first, optionally, the threshold range is greater than 0 and less than 10, for example, the operation step score corresponding to the number of the operation steps being less than or equal to 5 is 1, the operation step score corresponding to the number of the operation steps being greater than or equal to 5 and less than or equal to 10 is 5, and the operation step score corresponding to the number of the operation steps being greater than 5 is 9. This allows the operation step score to be obtained based on the specific operation step. It should be noted that the manual operation step score calculation mode may be the same as the operation step score calculation mode of the machine, and certainly, different thresholds may also be set, which are not specifically limited herein, and the link characteristic score may be obtained according to the operation step score block. Similarly, respective thresholds are set for the current link execution error rate and the historical error rate in the execution quality, and the current link execution efficiency and the historical execution efficiency in the execution efficiency, and optionally, the threshold range of each threshold is greater than 0 and less than 10. Calculating an execution quality score and an execution efficiency score, calculating a link score based on the obtained link characteristic score, the execution quality score and the execution efficiency score, and calculating a process index score of the current process based on each link score, wherein the calculation modes include but are not limited to averaging, maximum value taking, minimum value taking and the like.
And S220, when the process indexes meet the adjusting conditions, matching at least one candidate process from a preset process library based on the characteristic information of the current process.
And S230, updating the current flow based on the at least one candidate flow.
Optionally, the updating the current process based on the at least one candidate process includes: covering the current process based on each candidate process, and acquiring a process index score of each candidate process in the operation process; determining a maximum process index score, and determining a candidate process corresponding to the maximum process index score as a target process when the maximum process index score is larger than a preset score threshold; overlaying the current flow based on the target flow.
The target process refers to a process which can replace the current process by performing data processing to achieve a data processing effect better than that of the current process according to the type of the current process and exceeding a preset score threshold. The method comprises the steps of covering a current flow with at least one candidate flow respectively, carrying out data processing based on each candidate flow to obtain a total flow index score of each candidate flow in an operation process, determining a maximum value in the at least one flow index score, comparing the maximum flow index score with a preset score threshold, determining a flow corresponding to the maximum flow index score as a target flow when the maximum flow index score is larger than the preset score threshold, and covering the target flow with the current flow to realize adjustment of the current flow, so that when the result of processing data of the current flow is poor, the current flow can be adjusted in time without manually analyzing flow links, adjusting the flow, improving the efficiency of flow adjustment, and avoiding errors caused by manual operation.
S240, when the process index meets the adjusting condition, determining at least one abnormal link of which the link score is lower than a preset link score threshold; and determining the abnormal reason of the at least one abnormal link, and generating prompt information and sending the prompt information to a user terminal based on the link characteristics, the execution efficiency and the execution quality of each abnormal link and the abnormal reason.
The abnormal link refers to a link with a link score lower than a preset link score threshold value. The user terminal comprises a mobile terminal, a computer, a tablet computer and the like. The presentation form of the prompt message includes, but is not limited to, text, image, table, etc. For example, the prompt message is: and (3) the superior leader audit process in the financial invoice reimbursement audit is abnormal, the authenticity of the invoice cannot be identified due to the generated reason, wherein the link characteristic score of the link is 5, the execution efficiency score is 2, and the execution quality score is 2. Optionally, an abnormal link library is preset, where the abnormal link library includes different types of flows, and steps that may cause an abnormality in each link in each flow and reasons for the abnormality, and when it is determined that the link score of a certain link is lower than the preset link score, the step of causing an abnormality in the link is obtained, and according to the corresponding type, the corresponding flow and the corresponding link in the abnormal link library, the abnormality reason that the step causes an abnormality is determined. And generating prompt information based on the link characteristics, the execution efficiency, the execution quality and the abnormal reasons of each abnormal link, and sending the prompt information to the user terminal. Optionally, the prompt message further includes a step of generating an exception. Optionally, the level of the prompt information may be determined according to the total number of the loop segments and the number of the abnormal loop segments in the current flow, and when the proportion of the number of the abnormal loop segments in the total number of the loop segments in the current flow is greater than a first preset value, the prompt information of the first level is sent to a user, which indicates that the flow may have a large error in processing data, and is not suitable for the type of data processing, and the flow needs to be urgently checked and adjusted. When the proportion of the abnormal link number in the current process is less than or equal to the first preset value, the abnormal link number is sent to the user by the prompt message of the second level, which indicates that the current process has abnormality but has little influence on data transmission, and the user can be left for a long time to adjust the process.
Optionally, updating the current flow based on the at least one candidate flow includes: matching the updating links of the abnormal links from the at least one candidate process based on the characteristic information of the abnormal links; correspondingly updating the abnormal links in the current flow based on the updating links to form an updating flow; and when the flow index score of the updating flow meets a preset score threshold, covering the current flow based on the updating flow.
The method comprises the steps of determining characteristic information of each link in a candidate process based on the characteristic information of the abnormal link to be matched, determining an updating link matched with the abnormal link, replacing the abnormal link in the current process with the updating link to form an updating process, and operating the updating process to perform data processing so as to obtain a process index score of the updating process, wherein the process index score of the updating process meets a preset score threshold, for example, the process index score of the updating process is larger than the preset score threshold. The current flow is covered based on the updating flow, so that the current flow is adjusted, and the adjustment mode can locally adjust the current flow, reduce the calculation amount and reduce the time cost.
The technical scheme provided by the embodiment of the invention comprises the steps of obtaining the link characteristics, the execution efficiency and the execution quality of each link of the current process, determining the link scores of each link according to the link characteristics, the execution efficiency and the execution quality, determining the process index score of the current process based on the link scores of each link, matching at least one candidate process from a preset process library based on the characteristic information of the current process when the current process index score is less than a preset score threshold value, covering the current process based on each candidate process, respectively obtaining the process index score of each candidate process in the operation process, determining the maximum process index score, determining the candidate process corresponding to the maximum process index score as a target process when the maximum process index score is greater than the preset score threshold value, covering the target process with the current process, or determining the abnormal reason of at least one abnormal link with the link score lower than the preset link score threshold value, and generating prompt information and sending the link characteristics, the execution efficiency and the execution quality of each abnormal link and the abnormal reason to a user terminal. The technical scheme of the embodiment of the invention can realize automatic adjustment of the whole process and improve the efficiency and accuracy of process adjustment. The method and the device can also realize that various information of the abnormal link is sent to the user terminal in a prompt message mode, so that the user can adjust the abnormal link according to the prompt message, the user can locally adjust according to the actual situation, and the efficiency of flow adjustment is improved.
EXAMPLE III
Fig. 3 is a schematic flow chart illustrating an RPA flow adjustment method according to an embodiment of the present invention. The embodiments of the present invention are preferred embodiments of alternatives to the embodiments described above. The method comprises the steps of presetting the abnormity and the abnormity reasons of part or all links in different types of flows, for example, in the financial invoice auditing flow, the links of reporting a bill on the sky street and uploading an invoice image, wherein the abnormally uploaded invoice image is not clear, the abnormity reasons are caused, and the invoice type is uncertain. The number of operation steps of the operation steps in the link characteristics and the corresponding operation step score are preset, for example, the operation step score corresponding to the number of operation steps being less than or equal to 5 is 1, the operation step score corresponding to the number of operation steps being greater than 5 and less than or equal to 10 is 5, and the operation step score corresponding to the number of operation steps being greater than 5 is 9. Of course, a link may include both a machine operation step and a manual operation step, and the determination of the scores for the operation steps may be calculated in the manner described above. The execution efficiency comprises the historical execution efficiency of the current link and the execution efficiency of the current link, the average step execution time of the machine operation is less than 0.55 second, the historical execution efficiency score of the machine is 8, the execution time is more than or equal to 0.55 second and less than 15 seconds, the historical execution efficiency score of the machine is 6, the execution time is more than 15 seconds, and the historical execution efficiency score of the machine is 4. According to the same principle, the execution time length of the average step of manual operation is set to be less than 1 minute, the artificial history execution efficiency score is set to be 8, the execution time length of the average step of manual operation is greater than or equal to 1 minute and less than 10 minutes, the artificial history execution efficiency score is set to be 6, the execution time length of the average step of manual operation is greater than or equal to 10 minutes and less than 1 hour, the artificial history execution efficiency score is set to be 4, the execution time length of the average step of manual operation is greater than 1 hour, and the artificial history execution efficiency score is set to be 2. The human and machine averaging step execution scores of the current link are set on the same principle and are not described in detail here. The execution quality comprises a historical execution error rate of a current link and a current execution error rate of the current link, the historical execution error rate comprises machine operation and manual operation, the machine or manual operation comprises data problems, environmental problems, processing overtime problems, other problems and the like, and different scores are set according to each problem.
When the flow is analyzed, the scores of the items are calculated by a rule engine, and optionally, the flow is adjusted by an interface configuration mode. And acquiring link characteristics through the configuration file, and acquiring execution efficiency and execution quality through the flow execution data. Then, setting each rule factor node to correspond to a Java class method for writing the called rule template and fact data, wherein each rule factor node has a plurality of adjustment conditions, such as: the execution efficiency has 3 adjusting conditions, each condition has 1 score, the rule engine determines that the score of the rule factor node can be obtained according to 1 adjusting condition, and the 3 conditions are respectively: the execution efficiency is manually intervened, the execution efficiency is not manually intervened and is less than 0.1 (unit: second/number of operation steps), and the execution efficiency is not manually intervened and is more than or equal to 0.1 (unit: second/number of operation steps); each adjustment condition corresponds to a plurality of regular expressions, and the regular expressions are composed of: rule entry + operator + score composition, e.g., an execution efficiency without human intervention and with a value less than 0.1 (units: seconds/number of operation steps) consists of 2 rule expressions: 1) no manual intervention (0); 2) the execution time of the link divided by the number of the operation steps is less than 0.1 (the execution time of the link/the number of the operation steps is less than 0.1).
And calculating a two-dimensional matrix according to the historical execution error rate and the current execution error rate to obtain an execution quality score, obtaining an execution efficiency score according to the same principle, and calculating a link characteristic score, an execution efficiency score and an execution quality score through a three-dimensional matrix to obtain a link score. For example, the scores corresponding to the link characteristics, the execution efficiency and the execution quality are 7.75, 2.0 and 1.75 respectively. And obtaining 5.6 of the link scores through three-dimensional matrix dereferencing, and after the link scores of all links are sequentially obtained, averaging the link scores to obtain the process index score. When three dimensions including link characteristics, execution efficiency and execution quality are calculated, each dimension has three conditions of high, medium and low, and the node values of the membership functions corresponding to the link characteristics, the execution efficiency and the execution quality are as follows: 0.2, 4, 6, 8 and 10. The score is low in an interval of 0-2, medium in an interval of 4-6 and high in an interval of 8-10, and the higher the analysis value is, the better the effect of processing data in a link is. The corresponding piecewise function is:
wherein x is1The parameter setting of the segment interval is that the node value representing the corresponding membership function is: 0.2, 4, 6, 8 and 10. x is a fractional value of three dimensions, y1Is the ratio of the value of the first dimension configuration to be high, y'1Is the ratio of the values of the first dimension configuration low.
For historical execution efficiency (machine operation and/or manual operation) in execution efficiency, current execution efficiency (machine operation and/or manual operation) in a current link are calculated to obtain historical execution quality (machine operation and/or manual operation) in execution efficiency and execution quality and current execution quality (machine operation and/or manual operation) in the current link are calculated to obtain execution quality, fuzzy theory is applied in calculation, the calculation is divided into three stages of { low, medium and high }, different boundary points and different curves are set, a middle linear intersection point is a fuzzy point, and a gravity center method or a linear proportion method can be used for calculating to obtain the membership degree. Fuzzy membership functions are shown in FIG. 4, such as x-3, intermediate between 2 and 4, when y1 and y'1Are all 0.5, when x is 3.5, the distance between x and 4 is less than the distance between x and 2, and y1 is 0.75, y'10.25. For example, the scores corresponding to the link characteristics, the execution efficiency and the execution quality are respectively 7.75, 2.0 and 1.75, and the link characteristics and the execution efficiency are usedCalculating the corresponding scores from the three matrixes according to the piecewise function, and calculating the execution quality score through the three-dimensional matrix specifically comprises the following steps:
H1＝(y′1*10+y1*9)*y′2+(y′1*8+y1*7)y2＝((0.5*7.75-3)*10+(-0.5*7.75+4)*9)*0+((0.5*7.75-3)*8+(-0.5*7.75+4)*7)*1＝0.875*8+0.125*7＝7.875
H2＝(y′1*8+y1*7)*y′2+(y′1*7+y1*5)y2＝((0.5*7.75-3)*8+(-0.5*7.75+4)*7)*0+((0.5*7.75-3)*7+(-0.5*7.75+4)*5)*1＝0.875*7+0.125*5＝6.75
H3＝(y′1*8+y1*7)*y′2+(y′1*7+y1*5)y2＝((0.5*7.75-3)*7+(-0.5*7.75+4)*5)*0+((0.5*7.75-3)*6+(-0.5*7.75+4)*3)*1＝0.875*6+0.125*3＝5.625
wherein: y is1: the first dimension is configured with a high value-taking ratio, y'1: the first dimension is configured with a low value-taking ratio, y2: the second dimension is configured with a high value-taking proportion, y'2: the second dimension configures a low ratio of values. Obtaining three values of high execution quality as H1＝＝7.875、H2＝＝6.75、H3From the membership function of 5.625, y1 is 1, y 'when x is 1.75'1And when the flow index score is lower than a preset score threshold value, the flow needs to be adjusted.
And searching matched processes or links in a process library through the process characteristic information, and finding out the first 5 links or processes with the highest matching degree of the same type and different types but the highest matching degree for a machine (only needing the highest matching degree) or a user to select. The calculation formula of the matching time is as follows: y ═ X1H1+X2H2. Wherein X1＝1，X2Fraction of link/10, H1+H21. Wherein: x1: fixed value 1 of the same type; h1: the weight of the same type; x2: different types of analysis result values/10; h2: different types of weights; y: and calculating the total similarity of the flows. Thus, the larger the calculated Y value is, the better the result we want to find. And after finding out the Y values, listing the first 5 with the maximum Y values for selection. Wherein H1And H2The configuration can be carried out according to the actual situation.
According to the technical scheme of the embodiment of the invention, the link scores are obtained by obtaining the link characteristics, the execution quality and the execution efficiency, the process index scores are calculated based on the link scores, when the process index scores are smaller than the preset score threshold, similarity matching is carried out in the process library through the characteristic information, the current process is covered by the first five processes with high matching degree, and a proper process is finally obtained through calculation. By the RPA flow adjusting method, the reasonability and the effectiveness of the flow can be analyzed and judged before the flow is designed and executed or during and after the flow is executed. If the flow is unreasonable, the flow needs to be adjusted properly, the method of the embodiment of the invention can realize automatic adjustment without manually searching the flow problem source, quickens the flow adjustment speed, does not manually participate in adjusting the flow, and reduces errors generated by manual operation.
Example four
Fig. 5 is a schematic structural diagram of an RPA flow adjustment apparatus according to an embodiment of the present invention, which is capable of executing an RPA flow adjustment method according to any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method. As shown in fig. 5, the RPA flow adjustment apparatus according to the embodiment of the present invention includes: a process indicator monitoring module 510, a candidate process determination module 520, and a process update module 530, wherein:
a process index monitoring module 510, configured to monitor a process index of a current process;
a candidate process determining module 520, configured to match at least one candidate process from a preset process library based on the feature information of the current process when the process index meets an adjustment condition, where the preset process library stores each process and feature information of each process;
a process update module 530 configured to update the current process based on the at least one candidate process.
Further, the process index monitoring module 510 includes:
the flow index score determining submodule is used for acquiring link characteristics, execution efficiency and execution quality of each link in the current flow and determining the link score of each link according to the link characteristics, the execution efficiency and the execution quality; determining a process index score of the current process based on the link scores of the links; and the link score is obtained by performing matrix calculation according to the link characteristics, the execution efficiency and the execution quality.
Further, the feature information includes: the flow identification of the flow and the link identification of the key link of the flow.
Further, the candidate process determining module 520 includes:
the similarity determining submodule is used for respectively determining the similarity between the characteristic information of the current process and the characteristic information of each process in the preset process library;
and the candidate process determining submodule is used for sequencing each process in the preset process library based on the similarity and determining at least one process in a preset sequencing range as a candidate process.
Further, the flow updating module 530 includes:
a flow index score determination and acquisition submodule, configured to cover the current flow based on each candidate flow, and acquire a flow index score of each candidate flow in an operation process;
the target process determining submodule is used for determining the maximum process index score, and determining the candidate process corresponding to the maximum process index score as the target process when the maximum process index score is larger than a preset score threshold;
a first current flow determination submodule configured to override the current flow based on the target flow.
Further, the apparatus further comprises:
the prompt information sending module is used for determining at least one abnormal link with the link score lower than a preset link score threshold; and determining the abnormal reason of the at least one abnormal link, and generating prompt information and sending the prompt information to a user terminal based on the link characteristics, the execution efficiency and the execution quality of each abnormal link and the abnormal reason.
Further, the flow updating module 530 includes:
an updating link matching sub-module, configured to match updating links of the different abnormal links from the at least one candidate process based on feature information of the different abnormal links;
an updating process forming submodule, configured to correspondingly update an abnormal link in the current process based on the updating link, so as to form an updating process;
and the second current flow determining submodule is used for covering the current flow based on the updating flow when the flow index of the updating flow meets a preset score threshold value.
According to the technical scheme of the embodiment of the invention, the flow indexes of the current flow are continuously monitored and judged, when the flow indexes meet the adjustment condition, at least one candidate flow is matched from a preset flow base based on the characteristic information of the current flow, and the current flow is updated based on the at least one candidate flow. The technical scheme provided by the embodiment of the invention can monitor and adjust a certain type of flow in a targeted manner, and if the input data and the like are changed, the automatic adjustment of the flow can be realized, manual operation is not needed, the manual error is reduced, and the efficiency of flow adjustment is improved.
It should be noted that, the units and modules included in the system are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the embodiment of the invention.
EXAMPLE five
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. FIG. 6 illustrates a block diagram of an exemplary device 60 suitable for use in implementing embodiments of the present invention. The device 60 shown in fig. 6 is only an example and should not bring any limitation to the function and scope of use of the embodiments of the present invention.
As shown in FIG. 6, device 60 is embodied in a general purpose computing device. The components of the device 60 may include, but are not limited to: one or more processors or processing units 601, a system memory 602, and a bus 603 that couples various system components including the system memory 602 and the processing unit 601.
The system memory 602 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)604 and/or cache memory 605. The device 60 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 606 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 603 by one or more data media interfaces. Memory 602 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 608 having a set (at least one) of program modules 607 may be stored, for example, in memory 602, such program modules 607 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. The program modules 607 generally perform the functions and/or methods of the described embodiments of the invention.
The processing unit 601 executes various functional applications and data processing by running a program stored in the system memory 602, for example, to implement the RPA flow adjustment method provided by the embodiment of the present invention.
EXAMPLE six
Embodiments of the present invention also provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a method for RPA flow tuning.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. An RPA flow adjustment method, comprising:
monitoring the process index of the current process;
when the process index meets the adjusting condition, matching at least one candidate process from a preset process library based on the characteristic information of the current process, wherein each process and the characteristic information of each process are stored in the preset process library;
updating the current flow based on the at least one candidate flow.
2. The method of claim 1, wherein monitoring a process indicator of a current process comprises:
acquiring link characteristics, execution efficiency and execution quality of each link in the current flow, and determining link scores of each link according to the link characteristics, the execution efficiency and the execution quality;
determining a process index score of the current process based on the link scores of the links;
and the link score is obtained by performing matrix calculation according to the link characteristics, the execution efficiency and the execution quality.
3. The method of claim 1, wherein the feature information comprises: the flow identification of the flow and the link identification of the key link of the flow.
4. The method of claim 1, wherein the matching at least one candidate process from a predetermined process library based on the feature information of the current process comprises:
respectively determining the similarity between the characteristic information of the current process and the characteristic information of each process in the preset process library;
and sequencing the flows in the preset flow library based on the similarity, and determining at least one flow in a preset sequencing range as a candidate flow.
5. The method of claim 2, wherein the updating the current flow based on the at least one candidate flow comprises:
covering the current process based on each candidate process, and acquiring a process index score of each candidate process in the operation process;
determining a maximum process index score, and determining a candidate process corresponding to the maximum process index score as a target process when the maximum process index score is larger than a preset score threshold;
overlaying the current flow based on the target flow.
6. The method of claim 2, wherein when the process indicator satisfies an adjustment condition, the method further comprises:
determining at least one abnormal link with the link score lower than a preset link score threshold;
and determining the abnormal reason of the at least one abnormal link, and generating prompt information and sending the prompt information to a user terminal based on the link characteristics, the execution efficiency and the execution quality of each abnormal link and the abnormal reason.
7. The method of claim 6, wherein the updating the current flow based on the at least one candidate flow comprises:
matching the updating links of the abnormal links from the at least one candidate process based on the characteristic information of the abnormal links;
correspondingly updating the abnormal links in the current flow based on the updating links to form an updating flow;
and when the flow index score of the updating flow meets a preset score threshold, covering the current flow based on the updating flow.
8. An RPA procedure adjustment apparatus, comprising:
the flow index monitoring module is used for monitoring the flow index of the current flow;
a candidate process determining module, configured to match at least one candidate process from a preset process library based on the feature information of the current process when the process indicator satisfies an adjustment condition, where each process and the feature information of each process are stored in the preset process library;
a flow updating module for updating the current flow based on the at least one candidate flow.
9. An electronic device, characterized in that the device comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the RPA flow adaptation method of any one of claims 1-7.
10. A storage medium containing computer-executable instructions for performing the RPA procedure adaptation method of any one of claims 1-7 when executed by a computer processor.
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