Process execution efficiency adjustment method and device based on data analysis

Abstract

The embodiment of the specification provides a process execution efficiency adjustment method and device based on data analysis, wherein the method comprises the following steps: determining a meta-sub-control node according to a current role; determining control parameters and node data based on historical business execution data; performing data analysis based on the control parameters and the 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; adjusting the parameters based on the data analysis result, determining an adjustment result; performing effectiveness evaluation based on the adjustment result, determining an 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 adjustment of time standards, automatic management and upgrading of the whole life cycle of the process end-to-end are realized, and the demand for effective and efficient management of user processes and cross-process management is systematically solved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present specification relate to the technical field of data processing, in particular to a process execution efficiency adjustment method based on data analysis. BACKGROUND

[0002] Current technologies often perform data analysis, process timing task processing, automatic reminders, etc., but do not realize end-to-end full life cycle automation management and promotion, do not reflect user-centeredness, and do not maximize support for the smallest unit of business.

[0003] For example, in the supply chain business, the supplier qualification is often done with some simple workflow and workflow visualization processing, or some early warning processing; but there are many deficiencies in the granularity of management, the richness of data tool application, the effectiveness of early warning, the continuity of pre-event / mid-event / post-event control and reporting, and the data secondary empowerment of execution nodes: which leads to good reporting but not very obvious results after actual use. At the same time, because it cannot support complete control and early warning to specific individuals and cannot empower the smallest business management organization unit with autonomy, the actual solution either does not manage to the place where the problem actually occurs or provides tools that are not liked by the actual front-line management organization and is considered to be low in efficiency.

[0004] Therefore, a better solution is needed. SUMMARY

[0005] Therefore, the embodiments of the present specification provide a process execution efficiency adjustment method based on data analysis. One or more embodiments of the present specification also relate to a process execution efficiency adjustment device based on data analysis, a computing device, a computer-readable storage medium, and a computer program to solve the technical defects in the prior art.

[0006] According to a first aspect of the embodiments of the present specification, a process execution efficiency adjustment method based on data analysis is provided, comprising:

[0007] determining a meta-sub-control node according to a current role;

[0008] determining control parameters and node data based on historical business execution data;

[0009] performing data analysis based on the control parameters and the 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;

[0010] performing parameter adjustment based on the data analysis result, and determining an adjustment result;

[0011] Based on the adjustment result, effectiveness evaluation is performed, an evaluation result is determined, and the management and control parameter is adjusted based on the evaluation result.

[0012] In a possible implementation, the meta-sub management and control node is determined according to the current role, including:

[0013] The initial flow is determined, and the relevant task step is determined in the initial flow based on the current role;

[0014] The relevant role step is determined based on the relevant task step;

[0015] The meta-sub management and control node is determined based on the relevant task step and the relevant role step.

[0016] In a possible implementation, the management and control parameter and the node data are determined based on the historical business execution data, including:

[0017] The pass rate, rework rate, historical processing situation, historical delay rate and average processing time are determined based on the historical business execution data;

[0018] The management and control parameter is determined based on the pass rate, rework rate, historical processing situation, historical delay rate and average processing time;

[0019] The node data is determined from the historical business execution data based on the management and control parameter.

[0020] In a possible implementation, data analysis is performed based on the management and control parameter and the node data, a data analysis result is determined, and a central control dashboard is generated based on the meta-sub management and control node and the data analysis result, including:

[0021] The starting checkpoint, the number of checkpoints and the inspection calculation deviation threshold corresponding to the flow are determined based on the management and control parameter;

[0022] Data analysis is performed based on the starting checkpoint, the number of checkpoints and the inspection calculation deviation threshold, and a flow warning result is determined;

[0023] The handling person portrait and the node portrait are determined based on the flow warning result;

[0024] The portrait result is determined based on the handling person portrait and the node portrait;

[0025] The data analysis result is generated based on the flow warning result and the portrait result, and the central control dashboard is generated based on the data analysis result.

[0026] In a possible implementation, parameter adjustment is performed based on the data analysis result, an adjustment result is determined, including:

[0027] The historical time period data is determined based on the data analysis result;

[0028] The baseline value is determined based on historical time period data and coverage ratio;

[0029] Adjust the parameters based on the baseline value and determine the adjustment result.

[0030] In one possible implementation, parameter adjustments are made based on a baseline value, and the adjustment result is determined, including:

[0031] Adjust the standard time based on the baseline value to determine the tunability;

[0032] Based on the tunability, parameter adjustments are determined, and the adjustment results are confirmed.

[0033] In one possible implementation, the method is characterized by conducting an effectiveness assessment based on the adjustment results, determining the assessment results, and adjusting the control parameters based on the assessment results, including:

[0034] The processing results of the tracking nodes are based on the adjustment results;

[0035] Determine the relevance of early warnings based on node processing results;

[0036] The effectiveness of the early warning is assessed based on its relevance, the assessment results are determined, and the control parameters are adjusted accordingly.

[0037] According to a second aspect of the embodiments of this specification, a process execution efficiency adjustment device based on data analysis is provided, comprising:

[0038] The node determination module is configured to determine the sub-control nodes based on the current role;

[0039] The data determination module is configured to determine control parameters and node data based on historical business execution data;

[0040] The data analysis module is configured to perform data analysis based on control parameters and node data, determine the data analysis results, and generate a central control dashboard based on the sub-control nodes and the data analysis results.

[0041] The parameter adjustment module is configured to adjust parameters based on data analysis results and determine the adjustment result.

[0042] The effectiveness assessment module is configured to perform effectiveness assessments based on the adjustment results, determine the assessment results, and adjust the control parameters based on the assessment results.

[0043] According to a third aspect of the embodiments of this specification, a computing device is provided, comprising:

[0044] Memory and processor;

[0045] The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, which, when executed by the processor, implement the steps of the method for adjusting execution efficiency of a process based on data analysis.

[0046] According to a fourth aspect of the embodiments of the present specification, a computer readable storage medium is provided, which stores computer executable instructions, which, when executed by a processor, implement the steps of the method for adjusting execution efficiency of a process based on data analysis.

[0047] According to a fifth aspect of the embodiments of the present specification, a computer program is provided, which, when executed in a computer, causes the computer to perform the steps of the method for adjusting execution efficiency of a process based on data analysis.

[0048] The embodiments of the present specification provide a method and device for adjusting execution efficiency of a process based on data analysis, wherein the method comprises: determining a meta-sub-control node according to a current role; determining control parameters and node data based on historical business execution data; performing data analysis based on the control parameters and the 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; adjusting parameters based on the data analysis result, determining an adjustment result; performing effectiveness evaluation based on the adjustment result, determining an 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 adjustment of time standards, end-to-end full life cycle automation management and upgrading of a process are realized, and the effective and efficient demand for user process and cross-process management is systematically solved. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 is a flowchart of a method for adjusting execution efficiency of a process based on data analysis provided by an embodiment of the present specification;

[0050] Figure 2 is a whole logic diagram of a method for adjusting execution efficiency of a process based on data analysis provided by an embodiment of the present specification;

[0051] Figure 3 is a node slicing segmentation principle diagram of a method for adjusting execution efficiency of a process based on data analysis provided by an embodiment of the present specification;

[0052] Figure 4 is a cross-process principle diagram of a method for adjusting execution efficiency of a process based on data analysis provided by an embodiment of the present specification;

[0053] Figure 5is a central control panel display schematic diagram of a process execution efficiency adjustment method based on data analysis provided by an embodiment of the present specification;

[0054] Figure 6 is an overstay trace schematic diagram of a process execution efficiency adjustment method based on data analysis provided by an embodiment of the present specification;

[0055] Figure 7 is a time effectiveness abnormality focusing display schematic diagram of a process execution efficiency adjustment method based on data analysis provided by an embodiment of the present specification;

[0056] Figure 8 is a time standard tuning schematic diagram of a process execution efficiency adjustment method based on data analysis provided by an embodiment of the present specification;

[0057] Figure 9 is a time standard recommendation indication schematic diagram of a process execution efficiency adjustment method based on data analysis provided by an embodiment of the present specification;

[0058] Figure 10 is a time standard parameter after adjustment schematic diagram of a process execution efficiency adjustment method based on data analysis provided by an embodiment of the present specification;

[0059] Figure 11 is a structural schematic diagram of a process execution efficiency adjustment device based on data analysis provided by an embodiment of the present specification;

[0060] Figure 12 is a structural block diagram of a computing device provided by an embodiment of the present specification. DETAILED DESCRIPTION

[0061] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present specification. However, the present specification can be practiced without the specific details, other than in the examples, set forth in this description. Those skilled in the art, in light of the description, can implement the present specification without limiting the same to the specific details disclosed.

[0062] The terminology used in one or more embodiments of the present specification is for the purpose of describing particular embodiments only and is not intended to be limiting of one or more embodiments of the present specification. As used in one or more embodiments of the present specification and the accompanying claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in one or more embodiments of the present specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0063] It should be understood that, although the terms first, second, etc. can be employed in describing various information in one or more embodiments of the present specification, the information should not be limited to such terms. These terms are only used to differentiate one piece of information from another piece of information of the same type. For example, without departing from the scope of one or more embodiments of the present specification, first can also be referred to as second, and similarly, second can also be referred to as first. Depending on the context, the word "if' as used herein can be interpreted to mean "when" or "upon" or "in response to determining".

[0064] In the present specification, a process execution efficiency adjustment method based on data analysis is provided, the present specification also relates to a process execution efficiency adjustment device based on data analysis, a computing device, and a computer readable storage medium, which are described in detail one by one in the following embodiments.

[0065] Referring to Figure 1 , Figure 1 A flowchart of a process execution efficiency adjustment method based on data analysis according to one embodiment of the present specification is shown, which specifically includes the following steps.

[0066] Step 101: Determine the meta-sub-control node according to the current role.

[0067] In one possible implementation, determining the meta-sub-control node according to the current role includes: determining an initial process, determining a relevant task step in the initial process based on the current role; determining a relevant role step based on the relevant task step; and determining the meta-sub-control node based on the relevant task step and the relevant role step.

[0068] Wherein, the process is a process in a task, for example, in an office system, a role M can execute a work process, which includes a task step of approval.

[0069] In actual application, referring to Figure 2 , the present scheme realizes automatic management and upgrading of the whole life cycle of the process through technical means such as big data analysis, intelligent recommendation of management parameters, pre-control early warning model, intelligent central control instrument panel, and intelligent adjustment of time standard, and solves the management needs of users' processes and cross-processes.

[0070] The present scheme includes automatic definition of meta-sub-control scheme: automatic matching of roles to automatically generate meta-sub-control nodes, the benchmark set for the meta-sub-control node is the delivery point after the end of a continuous operation in the same account and the operation switching to another role; the meta-sub-control point is automatically generated, two logically adjacent meta-sub-control nodes are automatically defined as meta-sub-control points, and the meta-sub-control points are automatically associated with the process.

[0071] Specifically, the system automatically captures the critical point where the system switches to the next role to operate after continuously and uninterruptedly performing several steps in one process according to the same role as a meta-sub-control node, and defines two adjacent meta-sub-control nodes as a meta-sub-control point. And according to this, the efficiency and quality monitoring node slice segmentation, the management granularity is determined. The specific principle diagram is shown in Figure 3 .

[0072] As shown in Figure 3 : A scheme, set control point a to b; B scheme, set control point a to c; C scheme, set control point a to b; D scheme, set control point a to c. The difference between A scheme and C scheme is that the interval time setting standard of a to b is different due to the difference of organization.

[0073] The difference between B scheme and D scheme is that the interval time setting standard of a to c is different due to the difference of organization. a1 and a2, a3, b1 and b2, b3, etc. are the same logical time point, and the difference is marked due to the difference of setting organization.

[0074] Further, as shown in Figure 4 , which includes three processes, process 1, process 2 and process 3, process 1 includes a and b nodes, process 2 includes c, d, e nodes, and process 3 includes f, g nodes. It also includes scheme A, scheme B and scheme C. Among them, the nodes include quality image points and ordinary nodes.

[0075] Among them, scheme A is the cross-process connection monitoring between b point of process 1 and c point of process 2; scheme B is the cross-process multi-node monitoring between a point of process 1 and d point of process 2; scheme C is the multi-process multi-node monitoring between a point of process 1 and g point of process 3. Thus, seamless connection and supervision between any two nodes can be realized.

[0076] Step 102: determining control parameters and node data based on historical business execution data.

[0077] In one possible implementation, determining control parameters and node data based on historical business execution data includes: determining pass rate, rework rate, historical processing situation, historical delay rate and average processing time based on historical business execution data; determining control parameters based on pass rate, rework rate, historical processing situation, historical delay rate and average processing time; determining node data from historical business execution data based on control parameters.

[0078] In practical applications, the pass rate, rework rate, historical processing condition, historical delay rate and average processing duration of a node can be obtained by analyzing the historical business execution data of a certain role. For example, the pass rate of the A1 role at the a node is 60% because the A1 role has processed the a node for 10 times and passed 6 times.

[0079] Based on the pass rate, rework rate, historical processing condition, historical delay rate and average processing duration, the control parameter can be determined by threshold determination, for example, the historical delay rate of the A1 role at the a node is determined as the control parameter because the A1 role has processed the a node for 10 times and delayed 6 times, which exceeds the set threshold of 50%. Subsequently, the delay data of the node can be obtained based on the historical delay rate.

[0080] Step 103: performing data analysis based on the control parameter and the 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.

[0081] In a possible implementation, the data analysis based on the control parameter and the node data, the determination of the data analysis result, and the generation of the central control dashboard based on the meta-sub control node and the data analysis result include: determining the starting checkpoint, the number of checkpoints and the check calculation deviation threshold corresponding to the process based on the control parameter; performing data analysis based on the starting checkpoint, the number of checkpoints and the check calculation deviation threshold to determine a process warning result; determining the handler portrait and the node portrait based on the process warning result; determining the portrait result based on the handler portrait 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.

[0082] The checkpoint is a point in the process that needs to be analyzed and determined, which can be a time point.

[0083] In practical applications, the starting checkpoint, the number of checkpoints and the check calculation deviation threshold corresponding to the process can be determined based on the control parameter, for example, the starting checkpoint, the number of checkpoints and the check calculation deviation threshold corresponding to the historical delay rate are obtained, and then the process warning analysis is performed. The process warning includes the following steps: obtaining the starting checkpoint + the number of checkpoints + the check calculation deviation threshold, automatically triggering the process warning, after the starting checkpoint, the second last checkpoint automatically calculates whether the remaining time of the comparison standard duration is greater than the total duration of the to-be-completed node, and compares the calculation deviation degree with the initial search to generate a warning deviation degree. If the deviation degree is further increased, the warning is upgraded.

[0084] The early warning calculation formula includes:

(current time-process start time)-sum of current executed node time

[0085] Further, the current node handler portrait and the current node portrait can be generated based on the early warning calculation result, wherein the node handler portrait and the current node portrait can be generated by the pass rate of the node, and for the pass rate lower than the early warning threshold and the delay rate higher than the early warning threshold, a reminder can be given. Finally, the data analysis result can be generated by the process early warning result and the portrait result, and the central control dashboard can be generated based on the data analysis result.

[0086] Referring to Figure 5 In an implementation mode, the content displayed on the central control dashboard includes the following information:

[0087] Early warning document: link display presents the details of the process near-term early warning.

[0088] Average rate: shows the ratio of average execution time / standard time.

[0089] Execution rate: shows the average execution time / standard time of the current user for this process.

[0090] Self-driving rate: shows the standard time / average time of the current user.

[0091] Abnormal point X: shows the details of the abnormal sub-control point.

[0092] The red, yellow and green display of point X represents the health degree, wherein red represents unhealthy, yellow represents sub-healthy, and green represents healthy. The calculation rule of red, yellow and green is to compare the number of abnormal points (being executed) of the current statistical control point with the total number of the current statistical control point (being executed) with the health degree threshold interval, for example, the color corresponding to 70%-100% is red.

[0093] Self-driving rate= self-driving process time / average time×weight A+ self-driving process time / standard process time×(1-weight A), if greater than 1, it means that the self-driving nature is weak.

[0094] Execution rate= current user average execution time / average time, if greater than 1, it means that the execution rate is low.

[0095] The early warning document can view the current process being executed which is considered to have a delay risk.

[0096] Point X can view the current control points that are overdue.

[0097] The embodiment of the present specification adopts a threshold setting and simulation calculation method to identify nodes at risk of delay in advance, thereby improving the compliance rate of process completion on time.

[0098] In an embodiment, a pre-warning process pre-warning escalation can also be included, which sets a multi-checkpoint pre-warning grading control mechanism in combination with multiple checkpoints set in the process. Warning messages are sent at the second-to-last checkpoint, and the highest pre-warning level is reminded at the last checkpoint and transferred to manual processing.

[0099] Further, credit management is carried out for current handlers with high delay rate and low pass rate.

[0100] Specifically, if the historical delay rate exceeds 30% in the past cycle of the object, it is defined as execution timeliness B, and a business pre-warning list information needs to be automatically pushed to the role corresponding to the business process more than 2 / 3 of the cycle; if the historical delay rate is within 30% and the pass rate is greater than or equal to 90%, it is defined as execution timeliness A, and no business pre-warning monitoring list is generated more than 2 / 3 of the cycle, and a pre-warning list is automatically generated for the role corresponding to the business process when the cycle is exceeded. The rule adjustment frequency is calculated once every quarter in the system background, and the reminder rule implemented to the person is updated once.

[0101] Further, portrait management is carried out for nodes of processes and branches with high delay rate and low pass rate.

[0102] Specifically, if the historical delay rate exceeds 30% in the past cycle of the node, it is defined as execution timeliness B, and a business pre-warning monitoring list needs to be automatically generated for the role corresponding to the business process more than 2 / 3 of the cycle; if the historical delay rate is within 30% and the pass rate is greater than or equal to 90%, it is defined as execution timeliness A, and no pre-warning monitoring list is generated for the role corresponding to the business process more than 2 / 3 of the cycle, and a pre-warning monitoring list is generated and a to-do message reminder is sent when the cycle is exceeded. The rule adjustment frequency is calculated once every quarter in the system background, and the reminder rule implemented to the person is automatically updated once. The method of locking the node: process + branch. The advantage of setting this rule: providing timely information reminders to business promoters.

[0103] Reference Figure 6 In node alarm, a scheme of over-time trace can also be included, and the specific steps are as follows:

[0104] (1) In the to-do urgent task triggered by the pre-warning of the pass node, the handler will automatically check the system when processing the document, and a pop-up operation window will be popped up when submitting, which is a mandatory item.

[0105] (2) Process initiator, take the first initiator of the process, and check according to the process and the process detail check table.

[0106] (3) Corresponding business name, business ID, first call through the business name and ID number of the process initiation document, and secondly check through the process progress query table.

[0107] (4) Organization of the process initiator, find the process initiator, initiator ID, and then call the organization data authority to find the organization of the initiator.

[0108] (5) Current node, which is the node being handled by the business handler (also the identified overdue node).

[0109] (6) Current node overdue days, automatically calculated, the difference between the current time-current node start time and the current node reference time. The current node start time is the end time of the previous node; when there is no reference time set in the current node, the average processing time of the current node stored in the background for the past 3 months is called by default to replace it. If it is the first business, this item defaults to 5 days (5 days for the system to handle the first business to prevent infinite repeated calculation initialization value).

[0110] (7) Overdue reason selection, one of the optional values is not timely login system, not received prompt message, incomplete data, and other reasons.

[0111] (8) Overdue reason explanation: must fill in the overdue reason.

[0112] Further, see Figure 7 and Table 1, and the analysis results can be summarized to form a display table.

[0113] Table 1 is the ranking of process efficiency

[0114]

[0115] Step 104: Based on the data analysis result, the parameter adjustment is determined.

[0116] In one possible implementation, based on the data analysis result, the parameter adjustment is determined, including: determining the historical period data based on the data analysis result; determining the reference value based on the historical period data and the coverage ratio; adjusting the parameters based on the reference value to determine the adjustment result.

[0117] Specifically, based on the reference value, the parameter adjustment is determined, including: adjusting the standard time based on the reference value to determine the tunability; determining the parameter adjustment based on the tunability to determine the adjustment result.

[0118] In practical applications, referring to Figure 8 The time standard optimization automatic calculation step adopts average value probability distribution logic, that is, judging whether there will be how many data within the 80% level line of the timeliness rate, that is, whether the set time standard of the key search conforms to the actual data that can be completed within the time standard of 80%, and respectively looking at weekly, monthly, and quarterly data to judge different precision data of different caliber.

[0119] The core algorithm is to use weekly, monthly, and quarterly data to respectively count whether 80% of the actual execution of the week meets the set time standard, whether the average value of the counted week is less than the newly set standard time, whether more than 80% of the actual execution data points are within the newly set standard time, and whether there are a continuous number of weeks of actual average values within the newly set standard time period; after defining the week caliber, further define the month caliber and quarter caliber by the same method, and all of them meet the requirements, then it is determined that the newly set time standard theoretically meets the requirements. The specific steps include the following:

[0120] Statistical weekly data, continuously count 32 weeks of data, first monitor the distribution statistics to find the reference value that can cover more than 80% of the weeks below the value point of the standard value, the value-the monitored node standard time-1 adjustment unit, if greater than 0, further identify that the actual average time is less than the time standard by 1 adjustment parameter data ratio, if the data ratio is greater than or equal to 24x0.8, which is 20, and has the latest continuous 6 weeks of data meeting the actual time less than the time standard by 1 adjustment parameter; it is considered that the time standard has tunability.

[0121] Multi-dimensional analysis of indicators, by tightening the standard by 1 time adjustment parameter (default 1 day), simultaneously monitor whether more than 80% of the data points are less than the time standard, and compare the actual data of the first 24 weeks with the new standard, if still more than 20 data are within the new standard time, and the latest continuous 6 weeks of data meet the time less than the new time standard; at the same time, use the average value of the first 2 weeks x 50% + the average value of the first 6 weeks x 30% + the average value of the first 12 weeks x 20% to calculate the average value of the next 2 weeks, if the predicted data is also less than the new time standard, it is considered that the time standard is tunable, and at least 1 time adjustment parameter can be adjusted.

[0122] The above time standard is further adjusted by one time parameter on the basis of adjusting one time parameter, that is, a new time standard is derived from the original time standard minus two time parameters. Similarly, it is monitored whether more than 80% of the data points are less than the time standard, and the actual data of 24 consecutive weeks is compared with the new standard. If more than 20 data points are still obtained within the new standard time, and the latest 6 consecutive weeks of data meet the time less than the new time standard, the average value of the last 2 weeks of data is calculated using the average value of the previous 2 weeks of data × 50% + the average value of the previous 6 weeks of data × 30% + the average value of the previous 12 weeks of data × 20%. If it is found that the predicted data is also less than the new time standard, it is considered that the time standard can be optimized, and at least two time adjustment parameters can be adjusted. If one of the above four data characteristics does not meet the requirement, it is considered that the adjustment of two time adjustment parameters is not acceptable. The reasonable recommended value is the original standard plus one time adjustment parameter. After adjustment, the time standard recommendation table is as shown in Figure 9 .

[0123] Step 105: Based on the adjustment result, the effectiveness is evaluated, the evaluation result is determined, and the control parameter is adjusted based on the evaluation result.

[0124] In one possible implementation, based on the adjustment result, the effectiveness is evaluated, the evaluation result is determined, and the control parameter is adjusted based on the evaluation result, including: tracking the node processing result based on the adjustment result; determining the early warning relevance based on the node processing result; based on the early warning relevance, the effectiveness is evaluated, the evaluation result is determined, and the control parameter is adjusted based on the evaluation result.

[0125] In actual application, referring to Figure 10 , the effectiveness of the automatic monitoring after parameter adjustment is monitored, whether the effect meets the standard is monitored, and big data analysis is carried out to correct the upper limit value of parameter adjustment according to the above.

[0126] Specifically, the system automatically identifies Class A and Class B parameters. Class A parameters are time standards, while Class B parameters are auxiliary parameters for parameter tuning, including but not limited to user profile-based pass rates, rework rates, processing habit statistics, historical delay rates, average processing time, first warning point rules for processes, warning escalation rules, average processing time of sub-control points and corresponding minimum time tuning units, adjusted time parameters, business processing timeliness thresholds, parameter adjustment validity verification weights, experience-based parameter tuning threshold limits, and node warning trigger points. Each Class B parameter can be set up with an automatic monitoring model to monitor its effectiveness and automatically update recommended values ​​or roll back recommended values. The validity of Class A parameters is calculated using the same logic as the intelligent parameter tuning in point four. Taking node warning rules as an example, the system tracks whether the decrease in the proportion of node delays triggered by the current rule shows a positive correlation. Specifically, it checks whether the proportion exceeds 80% of the statistical period and decreases compared to the baseline value. If so, no adjustment is needed; otherwise, the rule value is further reduced.

[0127] This specification provides a method and apparatus for adjusting process execution efficiency based on data analysis. The method includes: determining sub-control nodes based on 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 results, and generating a central control dashboard based on the sub-control nodes and the data analysis results; adjusting parameters based on the data analysis results to determine the adjustment results; conducting an effectiveness evaluation based on the adjustment results to determine the evaluation results, and adjusting the control parameters based on the evaluation results. Through big data analysis, intelligent recommendation of management parameters, pre-control early warning models, intelligent central control dashboards, and intelligent time standard parameter adjustment, this method achieves automated management and enhancement of the entire lifecycle of the process from end to end, and systematically addresses the needs of users for precise and efficient management of user processes and cross-processes.

[0128] Corresponding to the above method embodiments, this specification also provides embodiments of a process execution efficiency adjustment device based on data analysis. Figure 11 A schematic diagram of a data analysis-based process execution efficiency adjustment device according to one embodiment of this specification is shown. Figure 11 As shown, the device includes:

[0129] The node determination module 2001 is configured to determine the sub-control nodes based on the current role.

[0130] The data determination module 2002 is configured to determine control parameters and node data based on historical business execution data;

[0131] The data analysis module 2003 is configured to perform data analysis based on control parameters and node data, determine the data analysis results, and generate a central control dashboard based on the sub-control nodes and the data analysis results.

[0132] The parameter adjustment module 2004 is configured to perform parameter adjustment based on the data analysis result, determine an adjustment result, and

[0133] The effectiveness evaluation module 2005 is configured to perform effectiveness evaluation based on the adjustment result, determine an evaluation result, and adjust the management and control parameter based on the evaluation result.

[0134] In a possible implementation, the meta-sub-management and control node is determined according to the current role, including:

[0135] An initial flow is determined, and a relevant task step is determined in the initial flow based on the current role;

[0136] A relevant role step is determined based on the relevant task step;

[0137] The meta-sub-management and control node is determined based on the relevant task step and the relevant role step.

[0138] In a possible implementation, the management and control parameter and the node data are determined based on historical business execution data, including:

[0139] The pass rate, the rework rate, the historical processing condition, the historical delay rate, and the average processing time length are determined based on the historical business execution data;

[0140] The management and control parameter is determined based on the pass rate, the rework rate, the historical processing condition, the historical delay rate, and the average processing time length;

[0141] The node data is determined from the historical business execution data based on the management and control parameter.

[0142] In a possible implementation, data analysis is performed based on the management and control parameter and the node data, a data analysis result is determined, and a central control dashboard is generated based on the meta-sub-management and control node and the data analysis result, including:

[0143] The starting inspection point, the number of inspection points, and the inspection calculation deviation threshold value corresponding to the flow are determined based on the management and control parameter;

[0144] Data analysis is performed based on the starting inspection point, the number of inspection points, and the inspection calculation deviation threshold value, and a flow early warning result is determined;

[0145] The handler portrait and the node portrait are determined based on the flow early warning result;

[0146] The portrait result is determined based on the handler portrait and the node portrait;

[0147] The data analysis result is generated based on the flow early warning result and the portrait result, and the central control dashboard is generated based on the data analysis result.

[0148] In a possible implementation, the parameter adjustment is performed based on the data analysis result, the adjustment result is determined, and the adjustment result includes:

[0149] The historical time period data is determined based on the data analysis result.

[0150] The reference value is determined based on the historical time period data and the coverage ratio.

[0151] The parameter adjustment is performed based on the reference value, and the adjustment result is determined.

[0152] In a possible implementation, the parameter adjustment is performed based on the reference value, and the adjustment result is determined, and the adjustment result includes:

[0153] The standard time adjustment is performed based on the reference value, and the tunability is determined.

[0154] The parameter adjustment is performed based on the tunability, and the adjustment result is determined.

[0155] In a possible implementation, the effectiveness evaluation is performed based on the adjustment result, the evaluation result is determined, and the management and control parameter is adjusted based on the evaluation result, and the adjustment includes:

[0156] The node processing result is tracked based on the adjustment result.

[0157] The early warning relevance is determined based on the node processing result.

[0158] The effectiveness evaluation is performed based on the early warning relevance, the evaluation result is determined, and the management and control parameter is adjusted based on the evaluation result.

[0159] The above is a schematic scheme of the flow execution efficiency adjustment device based on data analysis of the embodiment. It should be noted that the technical scheme of the flow execution efficiency adjustment device based on data analysis belongs to the same concept as the technical scheme of the flow execution efficiency adjustment method based on data analysis described above. The technical scheme of the flow execution efficiency adjustment device based on data analysis is not described in detail, and the description of the technical scheme of the flow execution efficiency adjustment method based on data analysis is referred to.

[0160] Figure 12 A structural block diagram of a computing device 2100 is shown according to an embodiment of the present specification. 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 save data.

[0161] The computing device 2100 also includes an access device 2140 that enables the computing device 2100 to communicate via one or more networks 2160. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of networks such as the Internet. The access device 2140 can include one or more of any type of network interface (for example, a network interface card (NIC)) such as a wired or wireless network interface, 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).

[0162] In one embodiment of the present specification, the above-described components of the computing device 2100 and other components not shown in the Figure 12 may be connected to each other, such as through a bus. It should be understood that Figure 12 The computing device structure diagram shown is for the purpose of example only and is not a limitation on the scope of the present specification. Other components can be added or replaced as needed by those skilled in the art.

[0163] The computing device 2100 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (for example, a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, and the like), a mobile phone (for example, a smartphone), a wearable computing device (for example, a smartwatch, smart glasses, and the like), 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.

[0164] The processor 2120 is configured to execute the following computer-executable instructions, which implement the steps of the above-described process execution efficiency adjustment method based on data analysis when executed by the processor. The above is a schematic solution of the computing device of the embodiment. It should be noted that the technical solution of the computing device and the technical solution of the above-described process execution efficiency adjustment method based on data analysis belong to the same concept, and the details of the technical solution of the computing device that are not described in detail can be referred to the description of the technical solution of the above-described process execution efficiency adjustment method based on data analysis.

[0165] An embodiment of the present specification also provides a computer-readable storage medium storing computer-executable instructions, which implement the steps of the above-described process execution efficiency adjustment method based on data analysis when executed by the processor.

[0166] The above is a schematic solution of the computer-readable storage medium of the embodiment. It should be noted that the technical solution of the storage medium and the technical solution of the above-described process execution efficiency adjustment method based on data analysis belong to the same concept, and the details of the technical solution of the storage medium that are not described in detail can be referred to the description of the technical solution of the above-described process execution efficiency adjustment method based on data analysis.

[0167] An embodiment of the present specification also provides a computer program, which causes a computer to execute the steps of the above-described process execution efficiency adjustment method based on data analysis when the computer program is executed in the computer.

[0168] The above is a schematic solution of the computer program of the embodiment. It should be noted that the technical solution of the computer program and the technical solution of the above-described process execution efficiency adjustment method based on data analysis belong to the same concept, and the details of the technical solution of the computer program that are not described in detail can be referred to the description of the technical solution of the above-described process execution efficiency adjustment method based on data analysis.

[0169] The above describes specific embodiments of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the acts or steps recited in the claims can be performed in a different order than the order in which they are recited and still achieve desirable results. In addition, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0170] The computer readable medium can include any entity or apparatus capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, software distribution medium, etc. It should be noted that the computer readable medium can include appropriate additions or subtractions according to the requirements of legislation and patent practice in 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.

[0171] It should be noted that for the foregoing method embodiments, the description is made for the sake of brevity and clarity, and it can be apparent to those with ordinary skill in the art that the method embodiments can be practiced with the steps in other orders, and / or with elements added, omitted, and / or modified, than those employed in the above embodiments. Furthermore, those with ordinary skill in the art will appreciate that the various hardware and / or software elements, functions, etc. described herein can be implemented as discrete elements or in combination in various ways. In addition, it should be noted that while the embodiments will be described in the singular tense, the plural is meant to be implied unless specifically disclaimed.

[0172] In the above embodiments, the description of each embodiment is focused on, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0173] The above disclosed preferred embodiments of the present specification are only used to help explain the present specification. The alternative embodiments do not describe all the details and do not limit the invention to the specific embodiments described. Obviously, according to the content of the embodiments of the present specification, many modifications and changes can be made. The present specification selects and describes these embodiments in order to better explain the principles and practical applications of the embodiments of the present specification, so that those skilled in the art can well understand and use the present specification. The present specification is limited by the claims and their full scope and equivalents.

Claims

1. A data analysis-based method for adjusting process execution efficiency in an office system, characterized in that: include: Determine the element control node based on the current role; Determine control parameters and node data based on historical business execution data; Data analysis is performed based on the control parameters and the node data to determine the data analysis results, and a central control dashboard is generated based on the sub-control nodes and the data analysis results. Based on the data analysis results, parameters are adjusted, and the adjustment result is determined. An effectiveness assessment is conducted based on the adjustment results, the assessment results are determined, and the control parameters are adjusted based on the assessment results. The process of performing 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 sub-control nodes and the data analysis results includes: Based on the control parameters, determine the starting checkpoint, the number of checkpoints, and the deviation threshold for checkpoint calculation corresponding to the process; Based on the starting checkpoint, the number of checkpoints, and the check deviation threshold, data analysis is performed to determine the process warning result; Based on the process warning results, determine the profile of the person handling the matter and the profile of the node; The profile result is determined based on the profile of the applicant and the profile of the node. Data analysis results are generated based on the process warning results and the profile results, and a central control dashboard is generated based on the data analysis results; The early warning calculation formula for the process early warning includes: [(current time - process start time) - sum of the times of all nodes that have been completed] / sum of the times of all nodes that have been completed - deviation threshold; The central control instrument panel includes early warning documents, average rate, execution rate, self-driving rate, and anomaly points; Among them, the warning document: the link displays the details of the process's impending warning; The self-driving rate is: self-driving process duration / average duration × weight A + self-driving process duration / standard process duration × (1 - weight A). The execution rate is: the average execution time for the current user / the average execution time. The abnormal points are displayed using red, yellow, and green to indicate the health level, where 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 current number of abnormal points / the current total number of statistical control points and the health level threshold range. Based on the data analysis results, parameter adjustments are made, and the adjustment results are determined, including: Based on the data analysis results, historical time period data is determined; A baseline value is determined based on the historical time period data and coverage ratio; wherein, the baseline value is 80%. Based on the benchmark value, adjust the parameters and determine the adjustment result; The step of adjusting parameters based on the benchmark value and determining the adjustment result includes: Based on the aforementioned benchmark value, standard time adjustments are made to determine tunability; Based on the aforementioned tunability, parameter adjustments are determined, and the adjustment results are confirmed.

2. The method according to claim 1, characterized in that, The process of determining the primary control node based on the current role includes: Determine the initial process, and based on the current role, determine the relevant task steps in the initial process; Determine the relevant role steps based on the aforementioned relevant task steps; The element 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 determination of control parameters and node data based on historical business execution data includes: The pass rate, rework rate, historical processing status, historical delay rate, and average processing time are determined based on historical business execution data. Control parameters are determined based on the pass rate, the rework rate, the historical processing status, the historical delay rate, and the average processing time. Node data is determined from the historical business execution data based on the control parameters.

4. The method according to claim 1, characterized in that, The step of conducting an effectiveness assessment based on the adjustment results, determining the assessment results, and adjusting the control parameters based on the assessment results includes: Based on the adjustment results, track the node processing results; The relevance of the early warning is determined based on the node processing results; An effectiveness assessment is conducted based on the aforementioned early warning correlation, the assessment results are determined, and the control parameters are adjusted based on the assessment results.

5. A process execution efficiency adjustment device based on data analysis, characterized in that, The steps for implementing the data analysis-based process execution efficiency adjustment method according to any one of claims 1 to 4 include: The node determination module is configured to determine the sub-control nodes based on the current role; The data determination module is configured to determine control parameters and node data based on historical business execution data; The data analysis module is configured to perform data analysis based on the control parameters and the node data, determine the data analysis results, and generate a central control dashboard based on the sub-control nodes and the data analysis results. The parameter adjustment module is configured to adjust parameters based on the data analysis results and determine the adjustment result. The effectiveness assessment module is configured to perform an effectiveness assessment based on the adjustment results, determine the assessment results, and adjust the control parameters based on the assessment results.

6. 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, they implement the steps of the data analysis-based process execution efficiency adjustment method according to any one of claims 1 to 4.

7. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the data analysis-based process execution efficiency adjustment method according to any one of claims 1 to 4.

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