Flow execution efficiency adjusting method and device based on data analysis

Abstract

The embodiment of the invention provides a process execution efficiency adjusting method and device based on data analysis. The method comprises the steps that element sub-management and control nodes are determined according to a current role; determining management and control parameters and node data based on the historical service execution data; performing data analysis based on the management and control parameters and the node data, determining a data analysis result, and generating a central control instrument panel based on the element sub-management and control nodes and the data analysis result; performing parameter adjustment based on the data analysis result, and determining an adjustment result; and performing validity evaluation based on the adjustment result, determining an evaluation result, and adjusting the management and control parameters based on the evaluation result. Through the technical means of big data analysis, management parameter intelligent recommendation, a beforehand control early warning model, an intelligent central control instrument panel, time standard intelligent parameter adjustment and the like, the end-to-end full life cycle automatic management and upgrading of the process are realized, and the effective and efficient requirements of the user process and cross-process management seminiferous management are systematically met.

Description

Technical Field

[0001] The embodiments of this specification relate to the technical field of data processing, and particularly to a method for adjusting the execution efficiency of a process based on data analysis. Background Art

[0002] Current technologies often perform data analysis, process scheduling task processing, automatic reminder, etc., but do not achieve end-to-end full-life-cycle automation management and escalation of the process, do not reflect user-centeredness, and do not support the smallest business unit to the greatest extent.

[0003] Taking the supplier identification often found in supply chain operations as an example, simple workflows and visualization processing of workflows are often performed, or some pre-alarm processing is done; however, there are many deficiencies in terms of the granularity of management, the abundance of data tool applications, the effectiveness of warnings, the continuity of pre-event / in-event / post-event control and notification, and the secondary empowerment of data at execution nodes, etc. As a result, the reporting is good, but the actual effect is not very obvious after use. At the same time, since it is not possible to fully control and warn specific individuals in execution and cannot empower the smallest organizational unit of business operations with autonomy, the actual solutions either do not manage the places where problems actually occur or the tools provided are not liked by front-line management organizations and are considered inefficient.

[0004] Therefore, there is an urgent need for a better solution. Summary of the Invention

[0005] In view of this, the embodiments of this specification provide a method for adjusting the execution efficiency of a process based on data analysis. One or more embodiments of this specification also relate to a device for adjusting the execution efficiency of a process based on data analysis, a computing device, a computer-readable storage medium, and a computer program to solve the technical deficiencies existing in the prior art.

[0006] According to the first aspect of the embodiments of this specification, a method for adjusting the execution efficiency of a process based on data analysis is provided, including: Determine meta-sub-control nodes according to the current role; Determine control parameters and node data based on historical business execution data; Perform data analysis based on the control parameters and node data to determine the data analysis result, and generate a central control dashboard based on the meta-sub-control nodes and the data analysis result; Adjust parameters based on the data analysis result to determine the adjustment result; Perform effectiveness evaluation based on the adjustment result to determine the evaluation result, and adjust the control parameters based on the evaluation result.

[0007] In a possible implementation, determining meta-sub-control nodes according to the current role includes: Determine the initial process and identify relevant task steps based on the current role in the initial process; Determine relevant role steps based on the relevant task steps; Determine the meta-control node based on the relevant task steps and relevant role steps.

[0008] In a possible implementation, determine the control parameters and node data based on historical business execution data, including: Determine the passing rate, rework rate, historical processing situation, historical delay rate, and average processing duration based on historical business execution data; Determine the control parameters based on the passing rate, rework rate, historical processing situation, historical delay rate, and average processing duration; Determine the node data from the historical business execution data based on the control parameters.

[0009] In a possible implementation, perform data analysis based on the control parameters and node data, determine the data analysis result, and generate a central control dashboard based on the meta-control node and the data analysis result, including: Determine the starting checkpoint, the number of checkpoints, and the check calculation deviation threshold corresponding to the process based on the control parameters; Perform data analysis based on the starting checkpoint, the number of checkpoints, and the check calculation deviation threshold to determine the process warning result; Determine the handler portrait and node portrait based on the process warning result; Determine the portrait result based on the handler portrait and node portrait; Generate the data analysis result based on the process warning result and the portrait result, and generate a central control dashboard based on the data analysis result.

[0010] In a possible implementation, perform parameter adjustment based on the data analysis result to determine the adjustment result, including: Determine the historical time period data based on the data analysis result; Determine the benchmark value based on the historical time period data and the coverage ratio; Perform parameter adjustment based on the benchmark value to determine the adjustment result.

[0011] In a possible implementation, perform parameter adjustment based on the benchmark value to determine the adjustment result, including: Perform standard time adjustment based on the benchmark value to determine the tunability; Perform parameter adjustment based on the tunability to determine the adjustment result.

[0012] In a possible implementation, it is characterized in that perform effectiveness evaluation based on the adjustment result to determine the evaluation result, and adjust the control parameters based on the evaluation result, including: Track the node processing result based on the adjustment result; Determine the warning relevance based on the node processing result; Conduct an effectiveness assessment based on the warning relevance, determine the assessment result, and adjust the control parameters based on the assessment result.

[0013] According to the second aspect of the embodiments of this specification, there is provided a device for adjusting the process execution efficiency based on data analysis, including: A node determination module configured to determine a meta-sub control node according to the current role; A data determination module configured to determine control parameters and node data based on historical business execution data; A data analysis module configured to perform data analysis based on the control parameters and node data, determine the data analysis result, and generate a central control dashboard based on the meta-sub control node and the data analysis result; A parameter adjustment module configured to adjust parameters based on the data analysis result and determine the adjustment result; An effectiveness assessment module configured to conduct an effectiveness assessment based on the adjustment result, determine the assessment result, and adjust the control parameters based on the assessment result.

[0014] According to the third aspect of the embodiments of this specification, there is provided a computing device, including: A memory and a processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the above method for adjusting the process execution efficiency based on data analysis are implemented.

[0015] According to the fourth aspect of the embodiments of this specification, there is provided a computer-readable storage medium storing computer-executable instructions, and when the instructions are executed by a processor, the steps of the above method for adjusting the process execution efficiency based on data analysis are implemented.

[0016] According to the fifth aspect of the embodiments of this specification, there is provided a computer program, wherein when the computer program is executed in a computer, the computer is made to execute the steps of the above method for adjusting the process execution efficiency based on data analysis.

[0017] The embodiments of this specification provide a method and device for adjusting the process execution efficiency based on data analysis. The method includes: determining a meta-node control node according to the current role; determining control parameters and node data based on historical business execution data; performing data analysis based on the control parameters and node data to determine the data analysis result, and generating a central control dashboard based on the meta-node control node and the data analysis result; performing parameter adjustment based on the data analysis result to determine the adjustment result; performing effectiveness evaluation based on the adjustment result to determine the evaluation result, and adjusting the control parameters based on the evaluation result. Through technical means such as big data analysis, intelligent recommendation of management parameters, pre-control warning model, intelligent central control dashboard, and intelligent tuning of time standards, the end-to-end full life cycle of the process is automated and upgraded, and the requirements for the refined and effective management of user processes and cross-processes are systematically solved. Description of the Drawings

[0018] Figure 1 is a flowchart of a method for adjusting the process execution efficiency based on data analysis provided by an embodiment of this specification; Figure 2 is an overall logic diagram of a method for adjusting the process execution efficiency based on data analysis provided by an embodiment of this specification; Figure 3 is a schematic diagram of the principle of node slicing and segmentation of a method for adjusting the process execution efficiency based on data analysis provided by an embodiment of this specification; Figure 4 is a schematic diagram of the cross-process principle of a method for adjusting the process execution efficiency based on data analysis provided by an embodiment of this specification; Figure 5 is a schematic diagram of the display of the central control dashboard of a method for adjusting the process execution efficiency based on data analysis provided by an embodiment of this specification; Figure 6 is a schematic diagram of the over-due trace of a method for adjusting the process execution efficiency based on data analysis provided by an embodiment of this specification; Figure 7 is a schematic diagram of the focused display of the time-limit exception of a method for adjusting the process execution efficiency based on data analysis provided by an embodiment of this specification; Figure 8 is a schematic diagram of the optimization of the time standard of a method for adjusting the process execution efficiency based on data analysis provided by an embodiment of this specification; Figure 9 is a schematic diagram of the recommendation of the time standard of a method for adjusting the process execution efficiency based on data analysis provided by an embodiment of this specification; Figure 10It is a schematic diagram of readjustment after the time standard parameter of a method for adjusting the process execution efficiency based on data analysis provided by an embodiment of this specification; Figure 11 It is a schematic structural diagram of a device for adjusting the process execution efficiency based on data analysis provided by an embodiment of this specification; Figure 12 It is a structural block diagram of a computing device provided by an embodiment of this specification. Detailed implementation manners

[0019] Many specific details are set forth in the following description in order to provide a thorough understanding of this specification. However, this specification can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the connotation of this specification. Therefore, this specification is not limited by the specific implementations disclosed below.

[0020] The terms used in one or more embodiments of this specification are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of this specification. The singular forms "a" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more of the associated listed items.

[0021] It should be understood that although the terms first, second, etc. may be used in one or more embodiments of this specification to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0022] In this specification, a method for adjusting the process execution efficiency based on data analysis is provided. This specification also relates to a device for adjusting the process execution efficiency based on data analysis, a computing device, and a computer-readable storage medium, which will be described in detail one by one in the following embodiments.

[0023] Refer to Figure 1 , Figure 1 shows a flowchart of a method for adjusting the process execution efficiency based on data analysis provided by an embodiment of this specification, which specifically includes the following steps.

[0024] Step 101: Determine the meta-control node according to the current role.

[0025] In a possible implementation, determining the meta-sub control node according to the current role includes: determining the initial process, determining the relevant task steps in the initial process based on the current role; determining the relevant role steps based on the relevant task steps; and determining the meta-sub control node based on the relevant task steps and the relevant role steps.

[0026] Among them, the process is the process in a certain task. For example, in an office system, role M can execute a work process, and the work process includes the task steps of approval.

[0027] In practical applications, refer to Figure 2 , this solution realizes the end-to-end full life cycle automation management and upgrading of the process through technical means such as big data analysis, intelligent recommendation of management parameters, pre-control warning model, intelligent central control dashboard, and intelligent parameter adjustment of time standards, and solves the management requirements of user processes and cross-processes.

[0028] This solution includes automatically defining the meta-sub control solution: automatically matching roles to automatically generate meta-sub control nodes, and the benchmark for setting meta-sub control nodes is the handover point after the end of a continuous operation of the same account and switching to another role for operation; automatically generating meta-sub control points, automatically defining two logically adjacent meta-sub control nodes as meta-sub control points, and automatically associating the meta-sub control points with the process.

[0029] Specifically, the system automatically grabs and defines the critical point where the same role continuously and uninterruptedly executes several steps in a process and then switches to the next role for operation as the meta-sub control node, and defines two adjacent meta-sub control nodes as meta-sub control points. And based on this, slice and segment the efficiency and quality monitoring nodes, and determine the management granularity. See the specific schematic diagram in Figure 3 。

[0030] Such as Figure 3 shown: In Plan A, set the control point from a to b; in Plan B, set the control point from a to c; in Plan C, set the control point from a to b; in Plan D, set the control point from a to c. The difference between Plan A and Plan C is that the setting standard of the interval time from a to b is different due to different organizations.

[0031] The difference between Plan B and Plan D is that the setting standard of the interval time from a to c is different due to different organizations. a1 and a2, a3, b1 and b2, b3, etc. are the same logical time points, and different stamps are made due to different setting organizations.

[0032] Furthermore, as Figure 4As shown, it includes three processes, namely Process 1, Process 2, and Process 3. Process 1 includes nodes a and b, Process 2 includes nodes c, d, and e, and Process 3 includes nodes f and g. It also includes Solution A, Solution B, and Solution C. Among them, the nodes include quality portrait points and ordinary nodes.

[0033] Among them, Solution A is cross-process connection monitoring between point b of Process 1 and point c of Process 2; Solution B is cross-process and multi-node monitoring between point a of Process 1 and point d of Process 2; Solution C is cross-multi-process and multi-node monitoring between point a of Process 1 and point g of Process 3. Thus, seamless connection and supervision between any two nodes can be achieved.

[0034] Step 102: Determine control parameters and node data based on historical business execution data.

[0035] In a possible implementation, determining control parameters and node data based on historical business execution data includes: determining the passing rate, rework rate, historical processing situation, historical delay rate, and average processing duration based on historical business execution data; determining control parameters based on the passing rate, rework rate, historical processing situation, historical delay rate, and average processing duration; and determining node data from historical business execution data based on the control parameters.

[0036] In practical applications, the passing rate, rework rate, historical processing situation, historical delay rate, and average processing duration of a certain node can be obtained through the analysis of the historical business execution data of a certain role. For example, role A1 has processed node a 10 times, and 6 of them have passed, so the passing rate is sixty percent. The rework rate is the return frequency of the role for this node, and the historical delay rate is the delay frequency of the role for node processing. For example, role A1 has processed node a 10 times, and 6 of them are delayed, so the delay rate is sixty percent.

[0037] Based on the above passing rate, rework rate, historical processing situation, historical delay rate, and average processing duration, the control parameters can be determined by means of threshold judgment. For example, role A1 has processed node a 10 times, and 6 of them are delayed, exceeding the set threshold of fifty percent, then the historical delay rate is determined as the control parameter. Subsequently, the delay data of the node can be obtained according to the historical delay rate.

[0038] Step 103: Perform data analysis based on the control parameters and node data, determine the data analysis results, and generate a central control dashboard based on the meta-control nodes and the data analysis results.

[0039] In a possible implementation, data analysis is performed based on control parameters and node data to determine the data analysis result, and a central control dashboard is generated based on the meta control node and the data analysis result, including: determining the starting checkpoint, the number of checkpoints, and the deviation threshold for inspection calculation corresponding to the process based on the control parameters; performing data analysis based on the starting checkpoint, the number of checkpoints, and the deviation threshold for inspection calculation to determine the process warning result; determining the portrait of the handler and the node portrait based on the process warning result; determining the portrait result based on the portrait of the handler and the node portrait; generating the data analysis result based on the process warning result and the portrait result, and generating the central control dashboard based on the data analysis result.

[0040] Among them, the checkpoint is a point in the process that needs to be analyzed and judged, and it can be a time point.

[0041] In practical applications, the starting checkpoint, the number of checkpoints, and the deviation threshold for inspection calculation corresponding to the process can be determined based on the control parameters. For example, obtain the starting checkpoint, the number of checkpoints, and the deviation threshold for inspection calculation corresponding to the historical delay rate, and then perform process warning analysis. The process warning includes the following steps: Obtain the starting checkpoint + the number of checkpoints + the deviation threshold for inspection calculation, and automatically trigger the process warning. After the starting checkpoint, the second-to-last checkpoint will automatically calculate and compare whether the remaining time of the standard time is greater than the total time of the nodes to be completed, and compare the calculation deviation degree with the deviation degree that initially triggers the warning. If it further increases, the warning will be upgraded.

[0042] Among them, the warning calculation formula is: (current time - process start time) - sum of the times of the nodes that have been completed / sum of the times of the nodes that have been completed - deviation threshold. If it is greater than or equal to 0, it is defaulted to trigger the pre-process warning. If it is less than 0, it is defaulted that no warning is generated. The checkpoint setting method can be fixed according to the process time. For example, the first checkpoint is defaulted to be at 1 / 4 of the process duration, and then every 1 / 4 of the process duration, the number of checkpoints + 1.

[0043] Furthermore, the portrait of the current node handler and the current node portrait can be generated based on the result of the warning calculation. Among them, the portrait of the node handler and the current node portrait can be generated through the passing rate of the node. For the passing rate lower than the warning threshold and the delay rate higher than the warning threshold, a reminder can be given. Finally, the data analysis result can be generated through the process warning result and the portrait result, and the central control dashboard can be generated based on the data analysis result.

[0044] See Figure 5 , in one implementation, the content displayed on the central control dashboard includes the following information: Warning documents: The details of the process approaching deadline warning are presented in a linked display.

[0045] Average rate: Displays the ratio of the average execution duration to the standard duration.

[0046] Execution rate: Displays the average execution duration of the current user for this process divided by the standard duration.

[0047] Self-driving rate: Displays the standard duration of the current user divided by the average duration.

[0048] Abnormal point X: Displays the details of the atomic control points with execution anomalies.

[0049] Among them, the red, yellow, and green colors of point X indicate the health level. Among them, red indicates unhealthy, yellow indicates sub-healthy, and green indicates healthy. The calculation rule for red, yellow, and green is based on the comparison between the number of abnormal points in the current statistical control point (being executed) / the total number of the current statistical control points (being executed) and the health threshold range. For example, the color corresponding to 70% - 100% is red.

[0050] Self-driving rate = self-driving process duration / average duration × weight A + self-driving process duration / standard process duration × (1 - weight A). If it is greater than 1, it means the self-driving ability is weak.

[0051] Execution rate = average execution duration of the current user / average duration. If it is greater than 1, it means the execution rate is low.

[0052] Warning documents can view the processes that are currently being executed and are considered to have a risk of delay.

[0053] Point X can view the control points that are currently overdue.

[0054] The embodiments of this specification adopt the methods of threshold setting and simulation calculation to identify in advance the nodes with a risk of delay, improving the compliance rate of the process to be completed on schedule.

[0055] In one embodiment, it may further include warning process warning escalation: Combining multiple checkpoints set in the process, a multi-checkpoint warning grading control mechanism is set. Warning messages are sent starting from the penultimate checkpoint, and the highest warning level is reminded at the last checkpoint and transferred to manual processing.

[0056] Furthermore, credit management is carried out for the current handler objects with a high delay rate and a low passing rate.

[0057] Specifically, if the historical extension ratio exceeds 30% in the past cycle of the acquisition object, it is defined as execution time limit B, and a business warning list information is automatically pushed to the role corresponding to the business process after more than 2 / 3 cycles; if the historical extension ratio is within 30%, and the pass rate is greater than or equal to 90%, it is defined as execution time limit A, and no business warning monitoring list is generated after more than 2 / 3 cycles. When the cycle is exceeded, a warning list is automatically generated for the role corresponding to the business process. Rule adjustment frequency: The system background calculates once every quarter and updates the reminder rules implemented to people once.

[0058] Furthermore, portrait management is carried out for processes and branch nodes with high extension rates and low pass rates.

[0059] Specifically, in the past cycle of the node, if the historical extension ratio exceeds 30%, it is defined as execution time limit B, and it takes more than 2 / 3 of the cycle to automatically generate a business warning monitoring list for the role corresponding to the business process; if the historical extension ratio is within 30%, and the pass rate is greater than or equal to 90%, it is defined as execution time limit A, and no warning monitoring list will be generated for the role corresponding to the business process after more than 2 / 3 of the cycle. Only when the cycle is exceeded will a warning monitoring list be generated and a to-do message reminder be sent. Rule adjustment frequency: The system background calculates once every quarter and automatically updates the reminder rules implemented to people. The method of locking the node: process + branch. The advantage of setting this rule: Provide timely information reminders to business promoters.

[0060] See also Figure 6 In the node alarm, you can also include the plan of leaving traces after the expiration date. The specific steps are as follows: (1) When the to-do items are expedited and triggered by node warnings, the processor is processing the document. The system automatically verifies that this is the case. When the processor submits the document, a pop-up window will appear, which is a required field.

[0061] (2) The process initiator is the first person to initiate the process and check the process against the process and process details checklist.

[0062] (3) For the corresponding business name and business ID, first call the business name and ID number of the process initiation document, and then check it through the process progress query table.

[0063] (4) The organization where the process initiator is located. Find the process initiator, the initiator ID, and then call the organization data permission to find the organization where the initiator is located.

[0064] (5) The current node is displayed as the node that the business handler is currently handling (also the node that has been identified as expired).

[0065] (6) The number of days that the current node has exceeded the due date is automatically calculated. The data is fetched as the difference between the current time and the start time of the current node compared with the reference time of the current node. The start time of the current node is the end time of the previous node. When there is no reference time set for the current node, by default, the average processing time of the current node in the past 3 months stored in the background is called for substitution. If it is exactly the first business, this item is defaulted to 5 days (5 days is the initialization value for the system to process the first business to prevent infinite repeated calculation).

[0066] (7) Selection of the reason for exceeding the due date, and the optional values are one of the reasons such as failure to log in to the system in time, failure to receive reminder messages, incomplete information, and others.

[0067] (8) Explanation of the reason for exceeding the due date: The reason for exceeding the due date must be filled in.

[0068] Further, referring to Figure 7 and Table 1, a display table can also be formed by summarizing according to the above analysis results.

[0069] Table 1 is the ranking of the processing flow efficiency

[0070] Step 104: Based on the data analysis results, perform parameter adjustment to determine the adjustment result.

[0071] In a possible implementation manner, based on the data analysis results, perform parameter adjustment to determine the adjustment result, including: determining the historical time period data based on the data analysis results; determining the benchmark value based on the historical time period data and the coverage ratio; performing parameter adjustment based on the benchmark value to determine the adjustment result.

[0072] Specifically, performing parameter adjustment based on the benchmark value to determine the adjustment result includes: adjusting the standard time based on the benchmark value to determine the tunability; determining to perform parameter adjustment based on the tunability to determine the adjustment result.

[0073] In practical applications, referring to Figure 8 , the automatic calculation step of time standard tuning adopts the average value probability distribution logic, that is, judging different standard value levels, estimating how much data is expected to be within the 80% timely rate horizontal line, that is, focusing on retrieving the set time standard to see if 80% of the actual data can be completed within this time standard, and judging the data of different calibers and precisions in the forms of weekly, monthly, quarterly data, etc.

[0074] The core algorithm uses data with three calibers: weekly, monthly, and quarterly. It respectively counts whether 80% of the actual executions in a week meet the set time standard, whether the average value within the counted week is less than the newly set standard time, whether more than 80% of the actual execution data points within a week are within the newly set standard time, and whether there are a consecutive set number of weeks with actual average values all within the newly set standard time period. After defining the weekly caliber, the monthly and quarterly calibers are further defined in the same way. If all meet the criteria, it is determined that the newly set time standard theoretically meets the requirements. The specific steps are as follows: Statistical weekly data, continuously count 32 weeks of data. First, monitor and use distribution statistics to find the value point where the benchmark value can cover more than 80% of the weeks below this standard value. This value - the standard time of the monitored node - 1 adjustment unit. If it is greater than 0, further identify the data ratio where the actual average time is less than the time standard by 1 adjustment parameter. If this data ratio is greater than or equal to 24×0.8, rounded up to 20, and there are 6 consecutive weeks of data that all meet the actual time being less than the time standard by 1 adjustment parameter; then it is considered that this time standard has the potential for optimization.

[0075] Multi-dimensional analysis of indicators. After tightening the standard by 1 time adjustment parameter (default 1 day), simultaneously monitor whether more than 80% of the data points are less than this time standard, and compare the first 24 weeks of actual data from continuous statistics with the new standard. If it still results in more than or equal to 20 data within the new standard time, and 6 consecutive weeks of data all meet the time being less than the new time standard; at the same time, use the average value of the first 2 weeks of data × 50% + the average value of the first 6 weeks of data × 30% + the average value of the first 12 weeks of data × 20% to calculate the average value of the next 2 weeks of data. If it is found that this predicted data is also less than the new time standard, then it is considered that this time standard can be optimized and can be adjusted by at least 1 time adjustment parameter.

[0076] Based on the above time standard, on the basis of tightening 1 time parameter, further tighten 1 time parameter, that is, the new time standard is obtained by subtracting 2 time parameters from the original time standard. Similarly, simultaneously monitor whether more than 80% of the data points are less than this time standard, and compare the 24 weeks of actual data from continuous statistics with the new standard. If it still results in more than or equal to 20 data within the new standard time, and 6 consecutive weeks of data all meet the time being less than the new time standard; at the same time, use the average value of the first 2 weeks of data × 50% + the average value of the first 6 weeks of data × 30% + the average value of the first 12 weeks of data × 20% to calculate the average value of the next 2 weeks of data. If it is found that this predicted data is also less than the new time standard, then it is considered that this time standard can be optimized and can be adjusted by at least 2 time adjustment parameters. If one of the above four data characteristics is not met, it is considered that adjusting 2 time adjustment parameters is not advisable; a reasonable recommended value is to tighten the original standard by 1 time adjustment parameter. After the adjustment is completed, the time standard recommendation table is as Figure 9as shown

[0077] Step 105: Perform effectiveness evaluation based on the adjustment result, determine the evaluation result, and adjust the control parameter based on the evaluation result.

[0078] In a possible implementation, performing effectiveness evaluation based on the adjustment result, determining the evaluation result, and adjusting the control parameter based on the evaluation result includes: tracking the node processing result based on the adjustment result; determining the warning correlation based on the node processing result; performing effectiveness evaluation based on the warning correlation, determining the evaluation result, and adjusting the control parameter based on the evaluation result.

[0079] In practical applications, refer to Figure 10 , after parameter adjustment, automatically monitor the operation effectiveness, automatically monitor whether the monitoring effect meets the standard, and perform big data analysis based on this to correct the upper limit value of parameter adjustment.

[0080] Specifically, the system automatically identifies type A parameters and type B parameters. Among them, type A parameters are time standards, and type B parameters are parameters for assisting in parameter adjustment, including but not limited to the passing rate of the meta-control node based on the user portrait, the rework rate, the processing habit statistics, the historical delay rate, the average processing duration, the first warning point rule of the process, the warning escalation rule, the average processing duration of the meta-control point and the corresponding minimum time adjustment unit, the adjusted time parameter, the business processing timeliness rate threshold, the effectiveness verification weight of parameter adjustment, the empirical parameter adjustment threshold limit, and the node warning trigger point, etc. An automatic monitoring model can be set for each type B parameter to perform effectiveness monitoring, and automatically process and update the recommended value or roll back the recommended value. The effectiveness of type A parameters is the same as the intelligent parameter adjustment calculation logic in the fourth point. Taking the node warning rule as an example, track whether there is a positive correlation between the reduction of the proportion of overdue nodes triggered by the current rule reminder, specifically check whether the proportion exceeds 80% and the statistical period is a decrease compared to the base point value. If so, no adjustment is required; otherwise, further reduce the rule value.

[0081] The embodiments of this specification provide a method and device for adjusting the process execution efficiency based on data analysis. The method based on it includes: determining the meta-control node according to the current role; determining the control parameter and node data based on the historical business execution data; performing data analysis based on the control parameter and node data, determining the data analysis result, and generating a central control dashboard based on the meta-control node and the data analysis result; performing parameter adjustment based on the data analysis result, determining the adjustment result; performing effectiveness evaluation based on the adjustment result, determining the evaluation result, and adjusting the control parameter based on the evaluation result. Through technical means such as big data analysis, intelligent recommendation of management parameters, pre-control warning model, intelligent central control dashboard, and intelligent parameter adjustment of time standards, realize the end-to-end full-life cycle automation management and escalation of the process, and systematically solve the requirements of refined and effective management of user processes and cross-processes.

[0082] Corresponding to the above method embodiments, this specification also provides embodiments of a device for adjusting the process execution efficiency based on data analysis. Figure 11 FIG. shows a schematic structural diagram of a device for adjusting the process execution efficiency based on data analysis provided by an embodiment of this specification. As Figure 11 shown, the device includes: A node determination module 2001, configured to determine a meta-sub control node according to the current role; A data determination module 2002, configured to determine control parameters and node data based on historical business execution data; A data analysis module 2003, configured to perform data analysis based on the control parameters and node data, determine a data analysis result, and generate a central control dashboard based on the meta-sub control node and the data analysis result; A parameter adjustment module 2004, configured to adjust parameters based on the data analysis result and determine an adjustment result; An effectiveness evaluation module 2005, configured to perform an effectiveness evaluation based on the adjustment result, determine an evaluation result, and adjust the control parameters based on the evaluation result.

[0083] In a possible implementation manner, determining a meta-sub control node according to the current role includes: Determining an initial process, and determining relevant task steps in the initial process based on the current role; Determining relevant role steps based on the relevant task steps; Determining a meta-sub control node based on the relevant task steps and the relevant role steps.

[0084] In a possible implementation manner, determining control parameters and node data based on historical business execution data includes: Determining a pass rate, a rework rate, historical processing conditions, a historical delay rate, and an average processing duration based on historical business execution data; Determining control parameters based on the pass rate, the rework rate, the historical processing conditions, the historical delay rate, and the average processing duration; Determining node data from the historical business execution data based on the control parameters.

[0085] In a possible implementation manner, performing data analysis based on the control parameters and node data, determining a data analysis result, and generating a central control dashboard based on the meta-sub control node and the data analysis result includes: Determining a starting checkpoint, the number of checkpoints, and a check calculation deviation threshold corresponding to the process based on the control parameters; Performing data analysis based on the starting checkpoint, the number of checkpoints, and the check calculation deviation threshold to determine a process warning result; Determine the handler portrait and node portrait based on the process warning result; Determine the portrait result based on the handler portrait and node portrait; Generate the data analysis result based on the process warning result and the portrait result, and generate the central control dashboard based on the data analysis result.

[0086] In a possible implementation, perform parameter adjustment based on the data analysis result to determine the adjustment result, including: Determine the historical time period data based on the data analysis result; Determine the benchmark value based on the historical time period data and the coverage ratio; Perform parameter adjustment based on the benchmark value to determine the adjustment result.

[0087] In a possible implementation, perform parameter adjustment based on the benchmark value to determine the adjustment result, including: Perform standard time adjustment based on the benchmark value to determine the tunability; Determine to perform parameter adjustment based on the tunability to determine the adjustment result.

[0088] In a possible implementation, it is characterized in that perform effectiveness evaluation based on the adjustment result to determine the evaluation result, and adjust the control parameters based on the evaluation result, including: Track the node processing result based on the adjustment result; Determine the warning correlation based on the node processing result; Perform effectiveness evaluation based on the warning correlation to determine the evaluation result, and adjust the control parameters based on the evaluation result.

[0089] The above is a schematic solution of a process execution efficiency adjustment device based on data analysis in this embodiment. It should be noted that the technical solution of the process execution efficiency adjustment device based on data analysis belongs to the same concept as the technical solution of the above-mentioned process execution efficiency adjustment method based on data analysis. For the details not described in the technical solution of the process execution efficiency adjustment device based on data analysis, reference can be made to the description of the technical solution of the above-mentioned process execution efficiency adjustment method based on data analysis.

[0090] Figure 12 The structural block diagram of a computing device 2100 provided according to an embodiment of this specification is shown. The components of the computing device 2100 include but are not limited to a memory 2110 and a processor 2120. The processor 2120 is connected to the memory 2110 through a bus 2130, and a database 2150 is used to store data.

[0091] The computing device 2100 also includes an access device 2140, which enables the computing device 2100 to communicate via one or more networks 2160. Examples of such networks include the Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or a combination of communication networks such as the Internet. The access device 2140 may include one or more of any type of wired or wireless network interface (e.g., a network interface card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC) interface.

[0092] In one embodiment of the present specification, the above components of the computing device 2100 and Figure 12 other components not shown therein may also be connected to each other, for example, via a bus. It should be understood that Figure 12 the block diagram of the computing device shown is for illustrative purposes only and is not a limitation on the scope of the present specification. Those skilled in the art may add or replace other components as needed.

[0093] The computing device 2100 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smartphone), a wearable computing device (e.g., a smartwatch, smart glasses, etc.) or other types of mobile devices, or a stationary computing device such as a desktop computer or a Personal Computer (PC). The computing device 2100 can also be a mobile or stationary server.

[0094] Among them, the processor 2120 is configured to execute the following computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the above method for adjusting the process execution efficiency based on data analysis are implemented. The above is a schematic solution of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the above method for adjusting the process execution efficiency based on data analysis belong to the same concept. For the details not described in detail in the technical solution of the computing device, reference can be made to the description of the technical solution of the above method for adjusting the process execution efficiency based on data analysis.

[0095] An embodiment of this specification also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, the steps of the above method for adjusting the process execution efficiency based on data analysis are implemented.

[0096] The above is a schematic solution of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium and the technical solution of the above method for adjusting the process execution efficiency based on data analysis belong to the same concept. For the details not described in detail in the technical solution of the storage medium, reference can be made to the description of the technical solution of the above method for adjusting the process execution efficiency based on data analysis.

[0097] An embodiment of this specification also provides a computer program. When the computer program is executed on a computer, the computer is made to execute the steps of the above method for adjusting the process execution efficiency based on data analysis.

[0098] The above is a schematic solution of a computer program according to this embodiment. It should be noted that the technical solution of this computer program and the technical solution of the above method for adjusting the process execution efficiency based on data analysis belong to the same concept. For the details not described in detail in the technical solution of the computer program, reference can be made to the description of the technical solution of the above method for adjusting the process execution efficiency based on data analysis.

[0099] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain implementations, multitasking and parallel processing are also possible or may be advantageous.

[0100] The computer instructions include computer program code, which may be in the form of source code, object code, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0101] It should be noted that for the foregoing method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the embodiments of this specification are not limited by the described action sequence, because according to the embodiments of this specification, some steps may be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments of this specification.

[0102] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0103] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The alternative embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. The embodiments selected and specifically described in this specification are to better explain the principles and practical applications of the embodiments of this specification, so that those skilled in the art can well understand and utilize this specification. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A process execution efficiency adjustment method based on data analysis, characterized in that: include: Determine the meta-control node based on the current role; Determine control parameters and node data based on historical business execution data; Performing data analysis based on the control parameters and the node data, determining data analysis results, and generating a central control dashboard based on the meta-sub control nodes and the data analysis results; Perform parameter adjustment based on the data analysis result and determine the adjustment result; An effectiveness assessment is performed based on the adjustment result, an assessment result is determined, and the control parameters are adjusted based on the assessment result.

2. The method according to claim 1, characterized in that: Determining the meta-sub management and control node according to the current role includes: Determine an initial process, and determine relevant task steps in the initial process based on the current role; Determining relevant role steps based on the relevant task steps; A meta-sub management and control node is determined based on the relevant task steps and the relevant role steps.

3. The method according to claim 1, characterized in that The determining of control parameters and node data based on historical business execution data includes: Determine the pass rate, rework rate, historical processing status, historical delay rate and average processing time based on historical business execution data; Determining control parameters based on the pass rate, the rework rate, the historical processing situation, the historical delay rate, and the average processing time; Node data is determined from the historical business execution data based on the management and control parameters.

4. The method according to claim 1, characterized in that: The performing of data analysis based on the control parameters and the node data, determining the data analysis results, and generating a central control dashboard based on the meta-sub control nodes and the data analysis results, includes: Determine the starting checkpoint, number of checkpoints and check calculation deviation threshold corresponding to the process based on the control parameters; Performing data analysis based on the starting checkpoint, the number of checkpoints, and the check calculation deviation threshold to determine a process warning result; Determine the person handling the process and the node portrait based on the process warning results; Determine a portrait result based on the handler portrait and the node portrait; A data analysis result is generated based on the process warning result and the portrait result, and a central control instrument panel is generated based on the data analysis result.

5. The method according to claim 1, characterized in that Performing parameter adjustment based on the data analysis result and determining the adjustment result includes: Determine historical time period data based on the data analysis result; Determine a benchmark value based on the historical time period data and the coverage ratio; Parameter adjustment is performed based on the reference value, and an adjustment result is determined.

6. The method according to claim 5, characterized in that The step of adjusting the parameters based on the reference value and determining the adjustment result includes: Performing standard time adjustment based on the reference value to determine tunability; Based on the tunability, it is determined to adjust the parameters and determine the adjustment result.

7. The method according to claim 1, characterized in that The performing effectiveness evaluation based on the adjustment result, determining the evaluation result, and adjusting the control parameter based on the evaluation result includes: Tracking node processing results based on the adjustment results; Determining warning relevance based on the node processing result; An effectiveness assessment is performed based on the warning correlation, an assessment result is determined, and the control parameters are adjusted based on the assessment result.

8. A process execution efficiency adjustment device based on data analysis, characterized in that: include: A node determination module, configured to determine a meta-sub control node according to a current role; A data determination module, configured to determine control parameters and node data based on historical business execution data; A data analysis module is configured to perform data analysis based on the control parameters and the node data, determine data analysis results, and generate a central control dashboard based on the meta-control nodes and the data analysis results; A parameter adjustment module, configured to perform parameter adjustment based on the data analysis result and determine an adjustment result; The effectiveness evaluation module is configured to perform effectiveness evaluation based on the adjustment result, determine the evaluation result, and adjust the control parameter based on the evaluation result.

9. A computing device, characterized in that include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the process execution efficiency adjustment method based on data analysis as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the process execution efficiency adjustment method based on data analysis as described in any one of claims 1 to 7.

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