A system and method for monitoring the running status of RPA editor processes

Abstract

The present invention discloses a system and method for monitoring the running status of an RPA editor process, and relates to the field of data monitoring and processing. The present invention obtains the number of executions of a corresponding RPA sub-process and the time consumed for the execution from log data; determines whether it is a redundant sub-process according to the number of executions; then optimizes the determined redundant RPA sub-process, and uses the execution time consumed by the entire RPA process as a judgment criterion; compares the sub-process data of the RPA total process to be monitored with the RPA sub-process set matrix collected in the previous steps, and selects a method for optimizing the sub-process of the RPA total process to be monitored according to the comparison result, thereby reducing unnecessary processing and making the monitoring more efficient; after the optimization is completed, the sub-process of the RPA total process to be monitored and the optimized result are added to the original data, thereby improving the richness of the original data; making the subsequent monitoring of other RPA processes more efficient.

Description

Technical Field

[0001] The present invention belongs to the field of data monitoring and processing, and in particular, relates to a system and method for monitoring the running status of an RPA editor process. Background Art

[0002] Chinese patent CN117406972B discloses an RPA high-value process instance discovery method based on fit analysis, which obtains interaction logs and preprocesses them to obtain new interaction logs; clusters the new interaction logs to obtain a process model set; uses a process executor to simulate the running status of each process model in the process model set to obtain a process instance set, and then calculates a high-value process instance set based on the process instance set; and converts the high-value process instance set into an RPA executable script.

[0003] During the construction of the RPA editor process, since the process builders' understanding of the current business type process is usually limited and one-sided, there may be some unnecessary redundant steps in the constructed RPA business process. If these redundant steps cannot be monitored and optimized in real time, it will have a significant impact on the operating efficiency of the entire RPA business process, and even cause the operation of the entire RPA business process to fail. Summary of the invention

[0004] In response to the problems in the related technology, the present invention proposes a system and method for monitoring the operation status of an RPA editor process to overcome the above-mentioned technical problems existing in the existing related technology.

[0005] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention is a method for monitoring the running status of an RPA editor process, comprising the following steps:

[0007] S1. Collect RPA sub-process data of various business types to obtain an RPA sub-process set matrix; extract and calculate the corresponding average execution probability based on the process log data of the RPA sub-process set matrix to obtain an average execution probability data set matrix;

[0008] Calculate the average time consumption data of each total process in the RPA sub-process set matrix to obtain the average time consumption data matrix;

[0009] S2. Optimize the RPA sub-process set matrix according to the average execution probability data set matrix and the average consumption time data matrix to obtain an optimized RPA sub-process set matrix;

[0010] S3. Collect sub-process data of the RPA total process to be monitored to obtain a sub-process set to be monitored; compare the sub-process set to be monitored with the RPA sub-process set matrix to select a method for processing the sub-process set to be monitored, and obtain a method selection result; determine whether to execute according to the method selection result; if executed, optimize the sub-process set to be monitored according to the RPA sub-process set matrix and the optimized RPA sub-process set matrix to obtain an adjusted sub-process to be monitored; otherwise, optimize the sub-process set to be monitored according to the log data of the sub-process set to be monitored to obtain an optimized sub-process set to be monitored;

[0011] S4, comparing the sub-process set to be monitored with the adjusted sub-process set to be monitored or the optimized sub-process set to be monitored and performing a visual display;

[0012] By collecting RPA sub-process data of various business types, the RPA sub-process data of various business types can be optimized later, making this solution more universal and robust; the log data of each RPA sub-process records the execution status of the process and the execution time consumption, so the execution times and execution time consumption of the corresponding RPA sub-process can be obtained from the log data; the execution times can reflect the utilization rate of the current RPA sub-process. If the utilization rate is small, it means that the RPA sub-process is relatively redundant. On the contrary, it means that the RPA sub-process is frequently used, indispensable and not redundant. Then optimize the determined RPA redundant sub-process, and use the overall execution time consumption of the RPA process as the judgment standard to determine whether the current optimization operation of the RPA redundant sub-process makes the RPA process as a whole optimal; during the optimization process, when the overall execution time consumption of the RPA process meets the requirements, The optimization is completed; when it is necessary to optimize the sub-process of the RPA overall process to be monitored, first compare the sub-process data of the RPA overall process to be monitored with the RPA sub-process set matrix collected in the previous step. If the same data exists, the result of optimizing the RPA sub-process set matrix is ​​directly applied to the RPA overall process to be monitored, which simplifies the optimization steps; if the same data does not exist, it is necessary to optimize the sub-process of the RPA overall process to be monitored based on the log data of the sub-process of the RPA overall process to be monitored; after the optimization is completed, the sub-process of the RPA overall process to be monitored and the optimized results are added to the original data, thereby improving the richness of the original data and making the subsequent monitoring of other RPA processes more efficient; finally, the comparison results of the sub-process set to be monitored with the adjusted sub-process to be monitored or the optimized sub-process set to be monitored are visualized, so that operators can find problems and corresponding optimization solutions in time.

[0013] Preferably, the S1 comprises the following steps:

[0014] S11. Set a business type set; construct multiple RPA editor sub-processes for each business type in the business type set to obtain an RPA sub-process set matrix;

[0015] S12. Set a first historical time period set; collect process log data of each RPA sub-process in the RPA sub-process set matrix in each historical time period according to the first historical time period set to obtain a first process log data matrix;

[0016] S13, according to the first process log data matrix Obtain the execution probability of each RPA sub-process to obtain an execution probability data matrix; calculate the average execution probability of each RPA sub-process according to the execution probability data matrix to obtain an average execution probability data set matrix;

[0017] S14. Obtain the execution time consumption data of the total process of the RPA editor constructed for each business type according to the first process log data matrix and the first historical time period set to obtain a consumption time data set matrix; calculate the average consumption time data of the total process of the RPA editor constructed for each business type according to the consumption time data set matrix to obtain an average consumption time data matrix;

[0018] Business types mainly include manufacturing, service, retail, wholesale, finance, real estate, tourism, information technology, energy, communications, logistics, agriculture, construction, culture and media, and healthcare, etc. The above business types cover almost most business types. By setting the first historical time period set, the log data of RPA sub-processes in multiple time periods is collected, which reduces the sporadic nature of the collected data and improves the accuracy of subsequent optimization. At the same time, it provides data support for the subsequent calculation of the average execution probability and the average consumption time.

[0019] Preferably, S2 comprises the following steps:

[0020] S21. Set an average execution probability threshold; compare each average execution probability data in the average execution probability data set matrix with the average execution probability threshold; extract the RPA sub-processes corresponding to the average execution probability data in the average execution probability data set matrix that is less than the average execution probability threshold, and obtain an RPA redundant sub-process set matrix;

[0021] S22. Optimize each editor total process in the RPA sub-process set matrix according to the RPA redundant sub-process set matrix to obtain an optimized RPA sub-process set matrix; extract the RPA sub-processes that do not meet the conditions as redundant sub-processes, so that targeted optimization of redundant sub-processes can be achieved, thereby improving optimization efficiency.

[0022] Preferably, the S22 comprises the following steps:

[0023] S221, construct a first snake population; set the maximum number of iterations of the first snake population to , recorded as the first maximum number of iterations, and the current number of iterations is , recorded as the first current iteration number;

[0024] S222, setting the initial position of each snake in the first snake population according to the RPA redundant sub-process set matrix to obtain an initial position matrix set;

[0025] S223, setting an average consumption time data threshold matrix according to the average consumption time data matrix; setting a fitness function of the first snake population according to the average consumption time data threshold matrix and the average consumption time data matrix;

[0026] S224, start iteration; before iteration, set the first current iteration number to 1; in each round of iteration, calculate the fitness value of each snake in the first snake population according to the fitness function to obtain a first fitness value set; take the position of the snake corresponding to the maximum fitness value in the first fitness value set as the global optimal position of the current round of iteration, and take the global optimal position of the current round of iteration as the first final global optimal position; randomly update the position of each snake in the first snake population in each round of iteration;

[0027] When the fitness value corresponding to the global best position in the next iteration is greater than or equal to the fitness value corresponding to the global best position in the current iteration, the first final global best position is updated to the global best position in the next iteration; otherwise, the first final global best position does not need to be updated; after the iteration is completed, the first current iteration number is increased by one;

[0028] S225, when When , the iteration is stopped and the global optimal position set matrix is ​​output; the global optimal position set matrix is ​​used as the RPA sub-process state data set matrix;

[0029] S226: Calculate the RPA redundant sub-process set matrix according to the RPA sub-process state data set matrix. Perform optimization to obtain the optimized RPA sub-process set matrix;

[0030] The snake swarm optimization algorithm has strong global search capabilities, can effectively avoid falling into local optimal solutions in the search space, has low implementation complexity, and is easy to parallelize, which helps to accelerate the optimization process. Therefore, by using the snake swarm optimization algorithm to optimize the state of the RPA redundant sub-process set matrix, the optimization efficiency is improved; the overall consumption time of the RPA process can be quickly made to meet the requirements.

[0031] Preferably, the S226 includes the following process:

[0032] Traverse the RPA sub-process state data set matrix; when the RPA sub-process state data in the RPA sub-process state data set matrix is ​​0, remove the RPA redundant sub-process corresponding to the RPA redundant sub-process set matrix; when the RPA sub-process state data in the RPA sub-process state data set matrix is ​​1, add a judgment branch before the RPA redundant sub-process corresponding to the RPA redundant sub-process set matrix d;

[0033] After the traversal is completed, the optimized RPA sub-process set matrix is ​​obtained; by assigning two quantitative values ​​of 0 and 1 to the RPA redundant sub-processes, the optimization process can be conveniently performed; since directly deleting the corresponding RPA redundant sub-processes may affect other sub-processes; therefore, in addition to directly deleting the RPA redundant sub-processes, this solution also adds a solution of adding a judgment branch before the RPA redundant sub-process, and compares the time consumption of the two solutions to select a more appropriate optimization method.

[0034] Preferably, S3 comprises the following steps:

[0035] S31, setting the RPA overall process to be monitored; extracting each sub-process of the RPA overall process to be monitored to obtain a sub-process set to be monitored;

[0036] S32. Compare the sub-process set to be monitored with each RPA editor total process in the RPA sub-process set matrix;

[0037] If there is an RPA editor total process that is exactly the same as the sub-process set to be monitored in the RPA sub-process set matrix, the sub-process set to be monitored is adjusted to the optimized RPA sub-process set corresponding to the RPA editor total process in the optimized RPA sub-process set matrix, to obtain the adjusted sub-process set to be monitored, and enter S4; if there is no RPA editor total process that is exactly the same as the sub-process set to be monitored in the RPA sub-process set matrix, enter S33;

[0038] S33, setting a second historical time period set; collecting process log data of each sub-process in the sub-process set to be monitored in each historical time period according to the second historical time period set, to obtain a second process log data matrix;

[0039] S34, obtaining the execution probability of each sub-process to be monitored in each historical time period according to the second process log data matrix and the second historical time period set, and obtaining the execution probability data matrix to be monitored; calculating the average execution probability of each sub-process to be monitored according to the execution probability data matrix to be monitored, and obtaining the average execution probability data set;

[0040] S35. Obtain the execution consumption time data of the RPA total process to be monitored in each historical time period according to the second process log data matrix and the second historical time period set to obtain a consumption time data set; calculate the average consumption time data of the RPA total process to be monitored according to the consumption time data set to obtain the average consumption time data to be monitored;

[0041] Compare each average execution probability data in the average execution probability data set with the average execution probability threshold; extract the sub-processes to be monitored corresponding to the average execution probability data in the average execution probability data set that are less than the average execution probability threshold, and obtain a set of redundant sub-processes to be monitored;

[0042] S36, optimizing the RPA total process to be monitored according to the redundant sub-process set to be monitored to obtain an optimized sub-process set to be monitored;

[0043] S37, adding the redundant sub-process set to be monitored and the optimized sub-process set to be monitored to the RPA sub-process set matrix and the optimized RPA sub-process set matrix respectively;

[0044] According to whether the RPA sub-process set matrix collected in the previous step contains the RPA total process to be monitored, if it is included, the RPA total process to be monitored is directly optimized according to the previous optimization results; otherwise, it is processed separately; thereby reducing unnecessary processing and making monitoring more efficient.

[0045] Preferably, the S36 comprises the following steps:

[0046] S361, construct a second snake population; set the maximum number of iterations of the second snake population to , recorded as the second maximum number of iterations, and the current number of iterations is , recorded as the second current iteration number;

[0047] S362, setting the initial position of each snake in the second snake population according to the redundant sub-process set to be monitored to obtain an initial position matrix;

[0048] S363, setting a threshold value of the average consumption time data to be monitored according to the average consumption time data to be monitored; and setting a threshold value of the average consumption time data to be monitored according to the average consumption time data to be monitored and the average consumption time data to be monitored. Set the fitness function of the second snake population;

[0049] S364, start iteration; before iteration, set the second current iteration number to 1; in each round of iteration, calculate the fitness value of each snake in the second snake population according to the fitness function to obtain a second fitness value set; take the position of the snake corresponding to the maximum fitness value in the second fitness value set as the global optimal position of the current round of iteration, and take the global optimal position of the current round of iteration as the second final global optimal position; randomly update the position of each snake in the second snake population in each round of iteration;

[0050] When the fitness value corresponding to the global best position in the next iteration is greater than or equal to the fitness value corresponding to the global best position in the current iteration, the second final global best position is updated as the global best position in the next iteration; otherwise, the second final global best position does not need to be updated; after the iteration is completed, the second current iteration number is increased by one;

[0051] S365, when When , the iteration is stopped and the global optimal position set is output; the global optimal position set is used as the RPA sub-process state data set to be monitored;

[0052] S366. Optimize the RPA overall process to be monitored according to the RPA sub-process state data set to be monitored to obtain an optimized sub-process set to be monitored;

[0053] By adopting the snake swarm optimization algorithm to optimize the status of the redundant sub-process set to be monitored, the optimization efficiency is improved; the overall consumption time of the total RPA process to be monitored can be quickly made to meet the requirements.

[0054] Preferably, the S366 includes the following process:

[0055] Traversing the RPA sub-process status data set to be monitored; when the RPA sub-process status data to be monitored in the RPA sub-process status data set to be monitored is 0, removing the redundant sub-process to be monitored corresponding to the redundant sub-process set to be monitored; when the RPA sub-process status data to be monitored in the RPA sub-process status data set to be monitored is 1, adding a judgment branch before the redundant sub-process to be monitored corresponding to the redundant sub-process set to be monitored; after the traversal is completed, the optimized sub-process set to be monitored is obtained;

[0056] By assigning two quantitative values, 0 and 1, to the status of the RPA sub-process to be monitored, 0 means deletion and 1 means adding a judgment branch, the optimization process can be easily executed. The time consumption of the two solutions is compared to select a more appropriate optimization method.

[0057] Preferably, S4 comprises the following steps:

[0058] S41, comparing the adjusted sub-process set to be monitored or the optimized sub-process set to be monitored with the sub-process set to be monitored to obtain a comparison result;

[0059] S42, visually displaying the sub-process set to be monitored, the adjusted sub-process set to be monitored, or the optimized sub-process set to be monitored, and the comparison result;

[0060] By visually displaying the results of comparing the sub-process set to be monitored with the adjusted sub-process set to be monitored or the optimized sub-process set to be monitored, it is convenient for operators to find problems and corresponding optimization solutions in time.

[0061] A system for monitoring the running status of an RPA editor process includes a first RPA sub-process data collection module, an execution probability data acquisition module, a consumption time data acquisition module, a first process optimization module, a second RPA sub-process data collection module, a judgment and selection module, a direct comparison optimization module, a second process optimization module and a visualization display module.

[0062] The present invention has the following beneficial effects:

[0063] 1. In the present invention, the number of executions of the corresponding RPA sub-process and the time consumed for the execution are obtained from the log data; whether it is a redundant sub-process is determined according to the number of executions; the determined RPA redundant sub-process is then optimized, and the execution time consumed by the entire RPA process is used as the judgment standard to determine whether the current optimization operation of the RPA redundant sub-process makes the RPA process as a whole optimal; when it is necessary to optimize the sub-process of the RPA total process to be monitored, the sub-process data of the RPA total process to be monitored is first compared with the RPA sub-process set matrix collected in the previous step, and the method for optimizing the sub-process of the RPA total process to be monitored is selected according to the comparison result, thereby reducing unnecessary processing and making the monitoring more efficient; after the optimization is completed, the sub-process of the RPA total process to be monitored and the optimized results are added to the original data, thereby improving the richness of the original data; making the subsequent monitoring of other RPA processes more efficient.

[0064] 2. In the present invention, the RPA sub-process status to be monitored is assigned two quantitative values ​​of 0 and 1, 0 for deletion and 1 for adding a judgment branch, so that the optimization process can be conveniently executed. The time consumption of the two schemes is compared to select a more appropriate optimization method.

[0065] 3. In the present invention, the state of the RPA redundant sub-process set matrix is ​​optimized by adopting the snake swarm optimization algorithm, thereby avoiding falling into the local optimal solution and improving the optimization efficiency; the overall consumption time of the RPA process can be quickly made to meet the requirements.

[0066] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0068] Figure 1 A schematic diagram of a process for monitoring the running status of an RPA editor process based on an RPA editor process running status monitoring method of the present invention. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the invention to clearly and completely describe the technical solutions in the embodiments of the invention. Obviously, the described embodiments are only part of the embodiments of the invention, not all of the embodiments. Based on the embodiments in the invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the invention.

[0070] In the description of the present invention, it is necessary to understand that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0071] Embodiment 1

[0072] This embodiment is a method for monitoring the running status of an RPA editor process, including the following steps:

[0073] S1. Collect RPA sub-process data of various business types to obtain an RPA sub-process set matrix; extract and calculate the corresponding average execution probability based on the process log data of the RPA sub-process set matrix to obtain an average execution probability data set matrix;

[0074] Calculate the average time consumption data of each total process in the RPA sub-process set matrix to obtain the average time consumption data matrix;

[0075] The S1 comprises the following steps:

[0076] S11. Set the service type set , Indicates the set i-th business type, Indicates the total number of set business types;

[0077] For the business type set For each business type in , construct multiple RPA editor sub-processes respectively, and obtain the RPA sub-process set matrix a; as follows,

[0078] ;

[0079] in, It represents the total process of the jth RPA editor built for the i-th business type. Indicates the total number of RPA editor processes built for each business type; , express The kth RPA sub-process in express The total number of RPA sub-processes in

[0080] S12. Setting the first historical time period set , Indicates the i-th historical time period in the set first historical time period. Indicates the total number of historical time periods in the set first historical time period;

[0081] According to the first historical time period Collect the process log data of each RPA sub-process in each historical time period in the RPA sub-process set matrix a to obtain the first process log data matrix ;as follows,

[0082] ; ;

[0083] in, The first process log data matrix representing the total process of the jth RPA editor constructed for the i-th business type; express The kth RPA subprocess in Process log data for a historical period;

[0084] S13, according to the first process log data matrix Get the execution probability of each RPA sub-process and obtain the execution probability data matrix c; as follows,

[0085] ; ;

[0086] in, represents the execution probability data matrix in the total process of the j-th RPA editor built for the i-th business type, express The kth RPA subprocess in Execution probability data for historical time periods;

[0087] According to the execution probability data matrix c, the average execution probability of each RPA sub-process is calculated to obtain the average execution probability data set matrix ;as follows,

[0088] ;

[0089] in, represents the average execution probability dataset of the jth RPA editor total process built for the i-th business type; , express The average execution probability data of the kth RPA sub-process in; the calculation formula is as follows,

[0090] ;

[0091] S14, according to the first process log data matrix And the first historical period set Obtain the execution time data of the total process of the RPA editor built for each business type and obtain the time consumption data set matrix ;as follows,

[0092] ;

[0093] in, represents the time consumption dataset of the total process of the j-th RPA editor built for the i-th business type, , express The consumption time data of the kth historical time period;

[0094] According to the consumption time data set matrix Calculate the average time consumption data of the total process of the RPA editor built for each business type and obtain the average time consumption data matrix ;as follows,

[0095] ;

[0096] in, Indicates the average time consumed by the total process of the j-th RPA editor built for the i-th business type;

[0097] S2. Optimize the RPA sub-process set matrix according to the average execution probability data set matrix and the average consumption time data matrix to obtain an optimized RPA sub-process set matrix;

[0098] The S2 comprises the following steps:

[0099] S21, set the average execution probability threshold; the average execution probability data set matrix Each average execution probability data in is compared with the average execution probability threshold; the average execution probability data set matrix The RPA sub-processes corresponding to the average execution probability data that is less than the average execution probability threshold are extracted to obtain the RPA redundant sub-process set matrix d; as follows,

[0100] ;

[0101] in, represents the set of redundant sub-processes in the j-th RPA editor total process built for the i-th business type, , express The kth RPA redundant sub-process in express The total number of redundant RPA sub-processes in the

[0102] S22: Optimize each editor total process in the RPA sub-process set matrix a according to the RPA redundant sub-process set matrix d to obtain an optimized RPA sub-process set matrix ;as follows,

[0103] ;

[0104] in, It represents the total process of the jth RPA editor constructed for the i-th business type after optimization. , express The kth RPA sub-process in express The total number of RPA sub-processes in

[0105] The S22 comprises the following steps:

[0106] S221. Constructing the first snake population , represents the i-th snake in the first snake population, represents the size of the first snake population; sets the maximum number of iterations of the first snake population to , recorded as the first maximum number of iterations, and the current number of iterations is , recorded as the first current iteration number, the search space dimension is ;

[0107] S222: Set the first snake population according to the RPA redundant sub-process set matrix d. The initial position of each snake in the equation is obtained by taking the initial position matrix set , Represents the first snake population The initial position set matrix of the i-th snake in is as follows:

[0108] ;

[0109] in, for , represents the initial position set of the redundant sub-process state data set in the k-th RPA editor total process constructed by the j-th business type in d, , for Indicated in Middle The initial position component of the redundant sub-process state data; is a random number, ;

[0110] S223, according to the average consumption time data matrix Set the average consumption time data threshold matrix ;as follows,

[0111] ;

[0112] in, Indicates for The set average consumption time data threshold;

[0113] According to the average consumption time data threshold matrix And the average consumption time data matrix Set the first snake population The fitness function ;as follows,

[0114] ;

[0115] Among them, β is a positive number, indicating the first correction parameter;

[0116] S224, start iteration; before iteration, set the first current iteration number Set to 1; in each iteration, the fitness function Calculating the first snake population The fitness value of each snake in the first fitness value set is obtained; the position of the snake corresponding to the largest fitness value in the first fitness value set is taken as the global optimal position of the current round of iteration, and the global optimal position of the current round of iteration is taken as the first final global optimal position; each round of iteration is performed on the first snake population The position of each snake is randomly updated;

[0117] When the fitness value corresponding to the global best position in the next iteration is greater than or equal to the fitness value corresponding to the global best position in the current iteration, the first final global best position is updated to the global best position in the next iteration; otherwise, there is no need to update the first final global best position; after the iteration is completed, the first current iteration number is updated. Perform plus one;

[0118] S225, when When , the iteration is stopped and the global optimal position set matrix is ​​output; the global optimal position set matrix is ​​used as the RPA sub-process state data set matrix ;as follows,

[0119] ;

[0120] in, Represents the redundant sub-process state dataset in the j-th RPA editor total process constructed by the i-th business type in d; , To express The status data of the kth redundant subprocess in;

[0121] S226: Based on the RPA sub-process status data set matrix Redundant sub-process set matrix for RPA Optimize and obtain the optimized RPA sub-process set matrix ;

[0122] The S226 includes the following process:

[0123] Traverse the RPA sub-process status data set matrix ;

[0124] When the RPA sub-process status dataset matrix When the RPA sub-process state data in is 0, the RPA redundant sub-process corresponding to the RPA redundant sub-process set matrix d is removed; when the RPA sub-process state data set matrix When the RPA sub-process state data in is 1, a judgment branch is added before the RPA redundant sub-process corresponding to the RPA redundant sub-process set matrix d;

[0125] After the traversal is completed, the optimized RPA sub-process set matrix is ​​obtained ;

[0126] S3. Collect sub-process data of the RPA total process to be monitored to obtain a sub-process set to be monitored; compare the sub-process set to be monitored with the RPA sub-process set matrix to select a method for processing the sub-process set to be monitored, and obtain a method selection result; determine whether to execute according to the method selection result; if executed, optimize the sub-process set to be monitored according to the RPA sub-process set matrix and the optimized RPA sub-process set matrix to obtain an adjusted sub-process to be monitored; otherwise, optimize the sub-process set to be monitored according to the log data of the sub-process set to be monitored to obtain an optimized sub-process set to be monitored;

[0127] The S3 comprises the following steps:

[0128] S31, setting the RPA overall process to be monitored; extracting each sub-process of the RPA overall process to be monitored, and obtaining a sub-process set to be monitored. , represents the i-th sub-process of the RPA overall process to be monitored, Indicates the total number of sub-processes of the RPA overall process to be monitored;

[0129] S32, the sub-process set to be monitored Compare with the total process of each RPA editor in the RPA sub-process set matrix a;

[0130] If the RPA sub-process set matrix a contains the RPA editor total process and the sub-process set to be monitored If they are exactly the same, the sub-process set to be monitored Adjust to the optimized RPA sub-process set matrix The optimized RPA sub-process set corresponding to the RPA editor total process is obtained to obtain the adjusted sub-process set to be monitored, and then enter S4; if the RPA sub-process set matrix a does not contain the RPA editor total process and the sub-process set to be monitored When they are exactly the same, enter S33;

[0131] S33, setting the second historical time period set , represents the i-th historical time period in the second historical time period set, Indicates the total number of historical time periods in the set second historical time period;

[0132] According to the second historical time period Collect the sub-process set to be monitored The process log data of each sub-process in each historical time period is obtained to obtain the second process log data matrix ;as follows,

[0133] ;

[0134] in, Indicates that the i-th sub-process to be monitored of the RPA total process to be monitored is in the second historical time period set Process log data of the jth historical time period;

[0135] S34, according to the second process log data matrix And the second historical period set Obtain the execution probability of each sub-process to be monitored in each historical time period, and obtain the execution probability data matrix to be monitored ;as follows,

[0136] ;

[0137] in, Indicates that the i-th sub-process of the RPA total process to be monitored is in the second historical time period set The execution probability data of the jth historical time period in the matrix; according to the execution probability data matrix to be monitored Calculate the average execution probability of each sub-process to be monitored and obtain the average execution probability data set , Represents the average execution probability data of the i-th sub-process to be monitored; the calculation formula is as follows:

[0138] ;

[0139] S35, according to the second process log data matrix And the second historical period set Obtain the execution time data of the total RPA process to be monitored in each historical time period to obtain the consumption time data set , Indicates the total RPA process to be monitored in the second historical time period The execution time of the i-th historical time period;

[0140] According to the consumption time data set Calculate the average time consumption data of the total RPA process to be monitored and obtain the average time consumption data to be monitored ; The calculation formula is as follows,

[0141] ;

[0142] The average execution probability dataset Each average execution probability data in is compared with the average execution probability threshold; the average execution probability data set is The sub-processes to be monitored corresponding to the average execution probability data less than the average execution probability threshold are extracted to obtain the redundant sub-process set to be monitored. , represents the i-th redundant sub-process to be monitored of the total RPA process to be monitored, Indicates the total number of redundant sub-processes to be monitored of the RPA total process to be monitored;

[0143] S36, according to the redundant sub-process set to be monitored Optimize the overall RPA process to be monitored and obtain the optimized sub-process set to be monitored , represents the i-th sub-process to be monitored in the optimized RPA total process to be monitored. Indicates the total number of sub-processes to be monitored in the optimized RPA total process to be monitored;

[0144] The S36 comprises the following steps:

[0145] S361. Constructing the second snake population , represents the i-th snake in the second snake population, represents the size of the second snake population; sets the maximum number of iterations of the second snake population to , recorded as the second maximum number of iterations, and the current number of iterations is , recorded as the second current iteration number, the search space dimension is ;

[0146] S362: Based on the redundant sub-process set to be monitored Set up the second snake population The initial position of each snake in the equation is obtained, and the initial position matrix is ​​obtained. ;as follows,

[0147] ;

[0148] in, represents the component of the i-th snake in the second snake population in the j-th search space dimension; is a random number, ;

[0149] S363: Based on the average consumption time data to be monitored Set the average consumption time data threshold to be monitored ; According to the average consumption time data to be monitored And the average consumption time data threshold to be monitored Set up the second snake population The fitness function ;as follows,

[0150] ;

[0151] Wherein, δ is a positive number, indicating the second correction parameter;

[0152] S364, start iteration; before iteration, set the second current iteration number Set to 1; in each iteration, the fitness function Calculate the second snake population The fitness value of each snake in the second fitness value set is obtained; the position of the snake corresponding to the largest fitness value in the second fitness value set is taken as the global optimal position of the current round of iteration, and the global optimal position of the current round of iteration is taken as the second final global optimal position; each round of iteration is performed on the second snake population The position of each snake is randomly updated;

[0153] When the fitness value corresponding to the global best position in the next iteration is greater than or equal to the fitness value corresponding to the global best position in the current iteration, the second final global best position is updated to the global best position in the next iteration; otherwise, there is no need to update the second final global best position; after the iteration is completed, the second current iteration number is updated. Perform plus one;

[0154] S365, when When , the iteration is stopped and the global optimal position set is output; the global optimal position set is used as the RPA sub-process state data set to be monitored , Represents the status data of the i-th redundant sub-process to be monitored in the RPA overall process to be monitored;

[0155] S366: Based on the RPA sub-process status data set to be monitored Optimize the overall RPA process to be monitored and obtain the optimized sub-process set to be monitored ;

[0156] The S366 includes the following processes:

[0157] Traverse the RPA sub-process status data set to be monitored ; When the RPA sub-process status data set to be monitored When the state data of the RPA sub-process to be monitored in is 0, the redundant sub-process set to be monitored The corresponding redundant sub-process to be monitored is removed; when the RPA sub-process status data set to be monitored When the state data of the RPA sub-process to be monitored in is 1, the redundant sub-process set to be monitored Add a judgment branch before the corresponding redundant sub-process to be monitored; after the traversal is completed, the optimized sub-process set to be monitored is obtained ;

[0158] S37, the redundant sub-process set to be monitored And the optimized set of sub-processes to be monitored Add them to the RPA sub-process set matrix a and the optimized RPA sub-process set matrix respectively middle;

[0159] S4, comparing the sub-process set to be monitored with the adjusted sub-process set to be monitored or the optimized sub-process set to be monitored and performing a visual display;

[0160] The S4 comprises the following steps:

[0161] S41, the adjusted sub-process set to be monitored or the optimized sub-process set to be monitored and the sub-process set to be monitored Perform comparison and obtain comparison results;

[0162] S42, the sub-process set to be monitored , the adjusted set of sub-processes to be monitored or the optimized set of sub-processes to be monitored And the comparison results are visualized.

[0163] Embodiment 2

[0164] This embodiment discloses a system for monitoring the running status of an RPA editor process, which can implement the method of the above embodiment, including a first RPA sub-process data collection module, an execution probability data acquisition module, a consumption time data acquisition module, a first process optimization module, a second RPA sub-process data collection module, a judgment and selection module, a direct comparison optimization module, a second process optimization module and a visualization display module;

[0165] The first RPA sub-process data collection module is used to collect RPA sub-process data of various business types to obtain an RPA sub-process set matrix;

[0166] The execution probability data acquisition module is used to extract and calculate the corresponding average execution probability according to the process log data of the RPA sub-process set matrix to obtain the average execution probability data set matrix;

[0167] The consumption time data acquisition module is used to calculate the average consumption time data of each total process in the RPA sub-process set matrix to obtain the average consumption time data matrix;

[0168] The first process optimization module is used to optimize the RPA sub-process set matrix according to the average execution probability data set matrix and the average consumption time data matrix to obtain an optimized RPA sub-process set matrix;

[0169] The second RPA sub-process data collection module is used to collect sub-process data of the RPA overall process to be monitored to obtain a sub-process set to be monitored;

[0170] The determination and selection module is used to compare the sub-process set to be monitored with the RPA sub-process set matrix to select a processing method for the sub-process set to be monitored and obtain a method selection result;

[0171] The direct comparison optimization module is used to determine whether to execute according to the mode selection result; if executed, the sub-process set to be monitored is optimized according to the RPA sub-process set matrix and the optimized RPA sub-process set matrix to obtain the adjusted sub-process to be monitored; otherwise, it is not executed;

[0172] The second process optimization module is used to determine whether to execute according to the mode selection result; if executed, the sub-process set to be monitored is optimized according to the log data of the sub-process set to be monitored to obtain the optimized sub-process set to be monitored; otherwise, it is not executed;

[0173] The visualization display module is used to compare the sub-process set to be monitored with the adjusted sub-process set to be monitored or the optimized sub-process set to be monitored and perform visualization display.

[0174] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0175] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A method for monitoring the running status of an RPA editor process, characterized in that: The following steps are involved: S1. Collect RPA sub-process data of various business types to obtain an RPA sub-process set matrix; extract and calculate the corresponding average execution probability based on the process log data of the RPA sub-process set matrix to obtain an average execution probability data set matrix; Calculate the average time consumption data of each total process in the RPA sub-process set matrix to obtain the average time consumption data matrix; S2. Optimize the RPA sub-process set matrix according to the average execution probability data set matrix and the average consumption time data matrix to obtain an optimized RPA sub-process set matrix; The S2 comprises the following steps: S21. Set an average execution probability threshold; extract the RPA sub-processes corresponding to the average execution probability data set matrix that is less than the average execution probability threshold, and obtain an RPA redundant sub-process set matrix; S22. Optimize each editor total process in the RPA sub-process set matrix according to the RPA redundant sub-process set matrix to obtain an optimized RPA sub-process set matrix; The S22 comprises the following steps: S221, construct a first snake population; set the maximum number of iterations of the first snake population to , the current number of iterations is ; S222, setting the initial position of each snake in the first snake population according to the RPA redundant sub-process set matrix to obtain an initial position matrix set; S223, setting an average consumption time data threshold matrix according to the average consumption time data matrix; setting a fitness function of the first snake population according to the average consumption time data threshold matrix and the average consumption time data matrix; S224, start iteration; in each round of iteration, according to the fitness function of the first snake population, the position of the snake corresponding to the maximum fitness value in the first snake population is used as the global optimal position of the current round of iteration, and the position of each snake in the first snake population is randomly updated; S225, when When , the iteration is stopped and the global optimal position set matrix is ​​output; the global optimal position set matrix is ​​used as the RPA sub-process state data set matrix; S226. Optimize the RPA redundant sub-process set matrix according to the RPA sub-process state data set matrix to obtain the optimized RPA sub-process set matrix; The S226 includes the following process: When the RPA sub-process status data in the RPA sub-process status data set matrix is ​​0, the RPA redundant sub-process corresponding to the RPA redundant sub-process set matrix is ​​removed; when the RPA sub-process status data in the RPA sub-process status data set matrix is ​​1, a judgment branch is added before the RPA redundant sub-process corresponding to the RPA redundant sub-process set matrix; after the traversal is completed, the optimized RPA sub-process set matrix is ​​obtained; S3. Collect sub-process data of the RPA total process to be monitored to obtain a sub-process set to be monitored; compare the sub-process set to be monitored with the RPA sub-process set matrix to select a method for processing the sub-process set to be monitored, and obtain a method selection result; determine whether to execute according to the method selection result; if executed, optimize the sub-process set to be monitored according to the RPA sub-process set matrix and the optimized RPA sub-process set matrix to obtain an adjusted sub-process to be monitored; otherwise, optimize the sub-process set to be monitored according to the log data of the sub-process set to be monitored to obtain an optimized sub-process set to be monitored; S4. Compare the sub-process set to be monitored with the adjusted sub-process set to be monitored or the optimized sub-process set to be monitored and display them visually.

2. According to claim 1, a method for monitoring the running status of an RPA editor process is characterized in that: The S1 comprises the following steps: S11. Set a business type set; construct multiple RPA editor sub-processes for each business type in the business type set to obtain an RPA sub-process set matrix; S12. Collect process log data of each RPA sub-process in each historical time period in the RPA sub-process set matrix to obtain a first process log data matrix; S13. Obtain the execution probability of each RPA sub-process according to the first process log data matrix to obtain an execution probability data matrix; calculate the average execution probability of each RPA sub-process according to the execution probability data matrix to obtain an average execution probability data set matrix; S14. Obtain the execution time consumption data of the total process of each RPA editor according to the first process log data matrix and the first historical time period set to obtain a consumption time data set matrix; calculate the average consumption time data of the total process of each RPA editor according to the consumption time data set matrix to obtain an average consumption time data matrix.

3. According to claim 2, a method for monitoring the running status of an RPA editor process is characterized in that: The S3 comprises the following steps: S31, setting the RPA overall process to be monitored; extracting each sub-process of the RPA overall process to be monitored to obtain a sub-process set to be monitored; S32, compare the sub-process set to be monitored with each RPA editor total process in the RPA sub-process set matrix; if there is an RPA editor total process in the RPA sub-process set matrix that is exactly the same as the sub-process set to be monitored, adjust the sub-process set to be monitored to the optimized RPA sub-process set corresponding to the RPA editor total process in the optimized RPA sub-process set matrix, obtain the adjusted sub-process set to be monitored, and enter S4; if there is no RPA editor total process in the RPA sub-process set matrix that is exactly the same as the sub-process set to be monitored, enter S33; S33, setting a second historical time period set; collecting process log data of each sub-process in the sub-process set to be monitored in each historical time period according to the second historical time period set, to obtain a second process log data matrix; S34, obtaining the execution probability of each sub-process to be monitored in each historical time period according to the second process log data matrix and the second historical time period set, and obtaining the execution probability data matrix to be monitored; calculating the average execution probability of each sub-process to be monitored according to the execution probability data matrix to be monitored, and obtaining the average execution probability data set; S35. Obtain the execution consumption time data of the RPA total process to be monitored in each historical time period according to the second process log data matrix and the second historical time period set to obtain a consumption time data set; calculate the average consumption time data of the RPA total process to be monitored according to the consumption time data set to obtain the average consumption time data to be monitored; compare each average execution probability data in the average execution probability data set with the average execution probability threshold; extract the sub-process to be monitored corresponding to the average execution probability data in the average execution probability data set that is less than the average execution probability threshold to obtain a redundant sub-process set to be monitored; S36, optimizing the RPA total process to be monitored according to the redundant sub-process set to be monitored to obtain an optimized sub-process set to be monitored; S37. Add the redundant sub-process set to be monitored and the optimized sub-process set to be monitored to the RPA sub-process set matrix and the optimized RPA sub-process set matrix respectively.

4. According to claim 3, a method for monitoring the running status of an RPA editor process is characterized in that: The S36 comprises the following steps: S361, construct a second snake population; set the maximum number of iterations of the second snake population to , the current number of iterations is ; S362, setting the initial position of each snake in the second snake population according to the redundant sub-process set to be monitored to obtain an initial position matrix; S363, setting a threshold of the average consumption time data to be monitored according to the average consumption time data to be monitored; setting a fitness function of the second snake population according to the average consumption time data to be monitored and the threshold of the average consumption time data to be monitored; S364, start iteration; in each round of iteration, according to the fitness function of the second snake population, take the position of the snake corresponding to the maximum fitness value in the second snake population as the global optimal position of the current round of iteration and randomly update the position of each snake in the second snake population; S365, when When , the iteration is stopped and the global optimal position set is output; the global optimal position set is used as the RPA sub-process state data set to be monitored; S366. Optimize the RPA overall process to be monitored according to the RPA sub-process status data set to be monitored to obtain an optimized sub-process set to be monitored.

5. According to claim 4, a method for monitoring the running status of an RPA editor process is characterized in that: The S366 includes the following processes: When the RPA sub-process status data to be monitored in the RPA sub-process status data set to be monitored is 0, the redundant sub-process to be monitored corresponding to the redundant sub-process set to be monitored is removed; when the RPA sub-process status data to be monitored in the RPA sub-process status data set to be monitored is 1, a judgment branch is added before the redundant sub-process to be monitored corresponding to the redundant sub-process set to be monitored; After the traversal is completed, the optimized sub-process set to be monitored is obtained.

6. According to claim 5, a method for monitoring the running status of an RPA editor process is characterized in that: The S4 comprises the following steps: S41, comparing the adjusted sub-process set to be monitored or the optimized sub-process set to be monitored with the sub-process set to be monitored to obtain a comparison result; S42, visually displaying the sub-process set to be monitored, the adjusted sub-process set to be monitored or the optimized sub-process set to be monitored and the comparison result.

7. A system for implementing the RPA editor process running status monitoring method according to any one of claims 1 to 6.

Images