Digital dual-cycle task management system for enterprise operation

Abstract

The present invention relates to the technical field of enterprise management. Specifically, disclosed is a digital dual-cycle task management system for enterprise operation. The system comprises a process sorting module, a digital reshaping module and an operation enablement module. In the solution, a dual-cycle task management system is constructed, and resource investment and improvement are performed in a targeted manner by using a strategic objective of an enterprise as a starting point and in combination with the conditions of digital operation management of each enterprise, thereby achieving optimization of resource configuration; a business process is comprehensively sorted and optimized by using a process mining algorithm, and the strategic objective of the enterprise is implemented in different business fields by means of objective disassembly, thereby realizing the visualization of a business process of a sub-objective, improving the efficiency of enterprise operation, and reducing costs; and by means of automatic analysis, refined management is performed on data generated during process operation, and tasks are automatically triggered and then assigned to corresponding business departments, thereby realizing targeted training and helping staff improve efficiency.

Description

A digital dual-loop task management system for enterprise operations Technical Field

[0001] The present invention relates to the field of enterprise management technology, and in particular to a digital dual-loop task management system for enterprise operations. Background Art

[0002] With the popularization and widespread application of information technology, the level of digitalization in enterprises continues to improve. Deploying digital systems to help enterprises improve operational efficiency, effectively improve quality, and reduce costs has become a focus of enterprises. However, typical enterprise digital deployments are fragmented, failing to effectively realize digital empowerment. They also neglect the support of system processes for achieving enterprise goals and the full use of data, resulting in an inability to realize the benefits of digital deployment. Traditional business process streamlining requires a significant investment of manpower and time, consumes a large amount of enterprise resources, and fails to cover all links, easily missing key nodes, resulting in poor accuracy and comprehensiveness of process streamlining. Traditional analytical methods struggle to gain a global perspective, and data between different business departments often exists independently, lacking integration and sharing. This ignores hidden inefficiencies and resource waste, leading to low reliability of analytical results.

[0003] Summary of the Invention

[0004] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a digital dual-loop task management system for enterprise operations. In view of the fact that the digital deployment of general enterprises is relatively scattered, digital empowerment cannot be realized well, and the support of system processes for the realization of enterprise goals and the full use of data are ignored, resulting in the problem that the benefits of digital deployment cannot be brought into play, this solution constructs a dual-loop task management system, which takes the strategic goals of the enterprise as the starting point, combines the status of digital operation management of each enterprise, and makes targeted resource investment and improvement to achieve the optimization of resource allocation; the traditional business process combing requires a lot of manpower and time investment, consumes a lot of enterprise resources, and cannot cover all links, and is prone to missing key nodes, resulting in the accuracy and completeness of process combing. To solve the problem of poor surface quality, this solution uses process mining algorithms to comprehensively sort out and optimize business processes, and implements corporate strategic goals into different business areas through goal decomposition, realizes the visualization of sub-goal business processes, improves the efficiency of corporate operations, and reduces costs; traditional analysis methods are difficult to obtain a global perspective, and data between different business departments often exist independently, lacking integration and sharing, ignoring hidden inefficiencies and waste of resources, resulting in low reliability of analysis results. This solution uses automatic analysis to conduct refined management of the data generated by process operations, and uses data support to clarify the behavioral trajectories and waste, inefficiency, and problem points of different business departments, automatically triggering task allocation to the corresponding business department, and realizing targeted training to help personnel improve efficiency.

[0005] The present invention provides a digital dual-loop task management system for enterprise operations, comprising a process streamlining module, a digital reshaping module, and an operation empowerment module;

[0006] The process combing module extracts log data from the internal and external data of the enterprise, uses an inductive mining algorithm to generate a business process model, and sends the business process model to the digital reshaping module;

[0007] The digital reshaping module builds a CATAC cycle model based on the business process model and sends the CATAC cycle model to the operation enablement module;

[0008] The operation enablement module builds a matching digital operation management mechanism and team, continuously monitors the operation data generated by the CATAC cycle model, optimizes the process, and feeds the optimization results back to the CATAC cycle model.

[0009] Furthermore, the method for constructing the business process model includes the following steps:

[0010] Step Q1: Determine the enterprise's strategic goals and define the objective function;

[0011] Step Q2: Decompose the objective function into each business department and determine the sub-objective function of each business department. The formula used is as follows: F = w1f1 + w2f2 + ··· + w n f n ;

[0012] Where F represents the objective function, n represents the number of business departments, f1, f2, f n Represent the sub-objective functions of different business departments, w1, w2, w n Respectively represent the weights of the sub-objective functions of different business departments;

[0013] Step Q3: Process mining, extract log data, analyze the log data using inductive mining algorithms, extract events from the log data, and mine the activities contained in the events to generate a business process model that shows the sequential, parallel, and cyclic structures of the activities;

[0014] Step Q4: Analyze the execution time, waiting time and resource usage of each activity;

[0015] Step Q5: Check whether the actual process complies with the preset objective function and identify deviation activities.

[0016] Furthermore, in step Q3, process mining includes the following steps:

[0017] Step Q31: Determine the business process scope of each business department based on the sub-objective function, and extract events from the log data, including event ID, activity name, timestamp, resource, and other context information;

[0018] Step Q32: Convert the event into a sequence. Each sequence represents an event. A sequence contains multiple activities. Determine the order of activities in the sequence.

[0019] Step Q33: Frequent set mining, use the Apriori algorithm to identify frequently occurring activity sets and adjust the weights of each business department. The steps are as follows:

[0020] Step Q331: Initialize frequent item sets, calculate the frequency of each individual activity, and filter out infrequent activities;

[0021] Step Q332: Generate candidate item sets, generate combinations of two activities, calculate the frequency of each combination, and filter out infrequent combinations;

[0022] Step Q333: Iteratively generate larger itemsets. For the frequent itemsets of k activities, generate candidate itemsets of k+1 activities. Continue filtering infrequent combinations until no larger itemsets can be generated.

[0023] Step Q34: Generalize the frequent activity set into a pattern to capture the generalized business process;

[0024] Step Q35: Generate a business process model based on the mined frequent activity sets and patterns;

[0025] Step Q36: Use graphs to visualize sequential, parallel, and cyclic structures between activities.

[0026] Furthermore, the method for constructing the CATAC cycle model comprises the following steps:

[0027] Step S1: Data collection, collecting activity data from the business process model, including equipment operation data, production data, and environmental data;

[0028] Step S2: Automatically analyze and calculate the average execution time, standard deviation, and degree of variation of each activity, and identify abnormal activities and structures in the business process model;

[0029] Step S3: Task triggering: when abnormal activities and structures are detected, tasks are automatically triggered and assigned to corresponding business departments;

[0030] Step S4: Action instructions: After the business department receives the task, the relevant equipment, systems and personnel take corresponding actions;

[0031] Step S5: Effect confirmation, automatically confirming whether the actions taken have effectively solved the problem.

[0032] Furthermore, in step S2, the automatic analysis includes the following steps:

[0033] Step S21: Calculate the average execution time, standard deviation, and coefficient of variation of each activity. The higher the coefficient of variation, the more unstable the activity. The formula used is as follows:

[0034] Where, represents the average execution time, i represents the activity number, t i represents the execution time of the ith activity, N represents the total number of activities, σ represents the standard deviation, and θ represents the coefficient of variation;

[0035] Step S22: Abnormal activity detection. Abnormal activities are detected using the 3σ principle. When the execution time of an activity exceeds the range of 3 standard deviations of the average execution time, the activity is marked as abnormal. The formula used is as follows:

[0036] or

[0037] Where t' i Indicates unusual activity;

[0038] Step S23: Abnormal structure identification, detecting the number of repetitions of an activity to identify abnormal loops in a loop structure. When the number of repetitions of an activity exceeds the normal range, it is marked as an abnormal loop. Detecting the synchronization of parallel activities to identify abnormal parallelism in a parallel structure. When the time difference of parallel activities exceeds the normal range, it is marked as an abnormal parallel structure. The formula used is as follows: D ij =|t j -t j '|;

[0039] Where C i Indicates the number of repetitions of each activity, s indicates the path number, n is represents the number of occurrences of the i-th activity in the s-th path, P represents the total number of paths, t j and t j ' represents the execution time of parallel activities, D ij Indicates the time difference between parallel activities.

[0040] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0041] (1) In view of the fact that the digital deployment of general enterprises is relatively scattered and cannot realize digital empowerment well, and ignores the support of system processes for the realization of enterprise goals and the full use of data, resulting in the inability to realize the benefits of digital deployment, this solution constructs a dual-circulation task management system. Taking the strategic goals of the enterprise as the starting point, combined with the status of digital operation management of each enterprise, it makes targeted resource investment and improvement to achieve the optimization of resource allocation.

[0042] (2) Traditional business process streamlining requires a lot of manpower and time investment, consumes a lot of corporate resources, and cannot cover all links. It is easy to miss key nodes, resulting in poor accuracy and comprehensiveness of process streamlining. This solution uses process mining algorithms to comprehensively streamline and optimize business processes. By decomposing goals, the company's strategic goals are implemented in different business areas, and the visualization of sub-goal business processes is achieved, which improves the efficiency of corporate operations and reduces costs.

[0043] (3) Traditional analysis methods are difficult to obtain a global perspective. Data between different business departments often exist independently, lack integration and sharing, and ignore hidden inefficiencies and resource waste, resulting in low reliability of analysis results. This solution uses automatic analysis to fine-tune the data generated by process operations. Through data support, it clarifies the behavior trajectories and waste, inefficiency, and problem points of different business departments, automatically triggers task allocation to the corresponding business department, and implements targeted training to help personnel improve their efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] FIG1 is a schematic diagram of a digital dual-loop task management system for enterprise operations proposed by the present invention;

[0045] FIG2 is a schematic diagram of the application of the CATAC cycle model in Example 4.

[0046] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0048] In the first embodiment, referring to FIG1 , the present invention provides a digital dual-loop task management system for enterprise operations, comprising a process streamlining module, a digital reshaping module, and an operation enabling module;

[0049] The process combing module extracts log data from the internal and external data of the enterprise, uses an inductive mining algorithm to generate a business process model, and sends the business process model to the digital reshaping module;

[0050] The digital reshaping module builds a CATAC cycle model based on the business process model and sends the CATAC cycle model to the operation enablement module;

[0051] The operation enablement module builds a matching digital operation management mechanism and team, continuously monitors the operation data generated by the CATAC cycle model, optimizes the process, and feeds the optimization results back to the CATAC cycle model.

[0052] By performing the above operations, we can solve the problem that the digital deployment of general enterprises is relatively scattered, digital empowerment cannot be achieved well, and the support of system processes for the realization of enterprise goals and the full use of data are ignored, resulting in the inability to realize the benefits of digital deployment. This solution builds a dual-loop task management system, taking the strategic goals of the enterprise as the starting point, combining the status of digital operation management of each enterprise, and making targeted resource investment and improvements to optimize resource allocation.

[0053] Example 2, referring to FIG1 , is based on the above example. The method for constructing the business process model includes the following steps:

[0054] Step Q1: Determine the enterprise's strategic goals and define the objective function;

[0055] Step Q2: Decompose the objective function into each business department and determine the sub-objective function of each business department. The formula used is as follows: F = w1f1 + w2f2 + ··· + w n f n ;

[0056] Where F represents the objective function, n represents the number of business departments, f1, f2, f n They represent the sub-objective functions of different business departments, w1, w2, and wn represent the weights of the sub-objective functions of different business departments;

[0057] Step Q3: Process mining, extract log data, analyze the log data using inductive mining algorithms, extract events from the log data, and mine the activities contained in the events to generate a business process model that shows the sequential, parallel, and cyclic structures of the activities;

[0058] Step Q4: Analyze the execution time, waiting time and resource usage of each activity;

[0059] Step Q5: Check whether the actual process complies with the preset objective function and identify deviation activities.

[0060] By performing the above operations, traditional business process streamlining requires a lot of manpower and time investment, consumes a lot of corporate resources, and cannot cover all links. It is easy to miss key nodes, resulting in poor accuracy and comprehensiveness of process streamlining. This solution uses process mining algorithms to comprehensively sort out and optimize business processes. Through target decomposition, the company's strategic goals are implemented in different business areas, and the visualization of sub-target business processes is realized, thereby improving the efficiency of enterprise operations and reducing costs.

[0061] Example 3, referring to FIG1 , is based on the above example. In step Q3, process mining includes the following steps:

[0062] Step Q31: Determine the business process scope of each business department based on the sub-objective function, and extract events from the log data, including event ID, activity name, timestamp, resource, and other context information;

[0063] Step Q32: Convert the event into a sequence. Each sequence represents an event. A sequence contains multiple activities. Determine the order of activities in the sequence.

[0064] Step Q33: Frequent set mining, use the Apriori algorithm to identify frequently occurring activity sets and adjust the weights of each business department. The steps are as follows:

[0065] Step Q331: Initialize frequent item sets, calculate the frequency of each individual activity, and filter out infrequent activities;

[0066] Step Q332: Generate candidate item sets, generate combinations of two activities, calculate the frequency of each combination, and filter out infrequent combinations;

[0067] Step Q333: Iteratively generate larger itemsets. For the frequent itemsets of k activities, generate candidate itemsets of k+1 activities. Continue filtering infrequent combinations until no larger itemsets can be generated.

[0068] Step Q34: Generalize the frequent activity set into a pattern to capture the generalized business process;

[0069] Step Q35: Generate a business process model based on the mined frequent activity sets and patterns;

[0070] Step Q36: Use graphs to visualize sequential, parallel, and cyclic structures between activities.

[0071] Example 4, referring to FIG1 and FIG2, this example is based on the above example, and the method for constructing the CATAC cycle model includes the following steps:

[0072] Step S1: Data collection, collecting activity data from the business process model, including equipment operation data, production data, and environmental data;

[0073] Step S2: Automatically analyze and calculate the average execution time, standard deviation, and degree of variation of each activity, and identify abnormal activities and structures in the business process model;

[0074] Step S3: Task triggering: When abnormal activities and structures are detected, tasks are automatically triggered and assigned to corresponding business departments. Projects are generated for key issues, focusing on project progress and project results after problem resolution.

[0075] Step S4: Action Instructions: After the business department receives the task, the relevant equipment, systems, and personnel take corresponding actions. After the task is completed, the causes of abnormal activities and structures are analyzed, and short-term and long-term countermeasures are generated for the task. Execution standards are optimized based on project results, and the project is continuously monitored. The monitored data is fed back into the CATAC cycle model.

[0076] Step S5: Effect confirmation, automatically confirming whether the actions taken have effectively solved the problem.

[0077] By performing the above operations, traditional analysis methods are difficult to obtain a global perspective. Data between different business departments often exist independently, lack integration and sharing, and ignore hidden inefficiencies and resource waste, resulting in low reliability of analysis results. This solution uses automatic analysis to fine-tune the management of data generated by process operations. Through data support, the behavior trajectories and waste, inefficiency and problem points of different business departments are clarified, and tasks are automatically triggered and assigned to the corresponding business departments, achieving targeted training and helping personnel improve their efficiency.

[0078] Example 5, referring to FIG1 , this example is based on the above example. In step S2, automatic analysis includes the following steps:

[0079] Step S21: Calculate the average execution time, standard deviation, and coefficient of variation of each activity. The higher the coefficient of variation, the more unstable the activity. The formula used is as follows:

[0080] Where, represents the average execution time, i represents the activity number, t i represents the execution time of the ith activity, N represents the total number of activities, σ represents the standard deviation, and θ represents the coefficient of variation;

[0081] Step S22: Abnormal activity detection. Abnormal activities are detected using the 3σ principle. When the execution time of an activity exceeds the range of 3 standard deviations of the average execution time, the activity is marked as abnormal. The formula used is as follows:

[0082] or

[0083] Where t' i Indicates unusual activity;

[0084] Step S23: Abnormal structure identification, detecting the number of repetitions of an activity to identify abnormal loops in a loop structure. When the number of repetitions of an activity exceeds the normal range, it is marked as an abnormal loop. Detecting the synchronization of parallel activities to identify abnormal parallelism in a parallel structure. When the time difference of parallel activities exceeds the normal range, it is marked as an abnormal parallel structure. The formula used is as follows: D ij =|t j -t j '|;

[0085] Where C i Indicates the number of repetitions of each activity, s indicates the path number, n is represents the number of occurrences of the i-th activity in the s-th path, P represents the total number of paths, t j and t j ' represents the execution time of parallel activities, D ij Represents the time difference between parallel activities.

[0086] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0087] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0088] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A digital dual-cycle task management system for enterprise operations, characterized by: Including process streamlining module, digital reshaping module and operation empowerment module; The process combing module extracts log data from the internal and external data of the enterprise, generates a business process model using an inductive mining algorithm, and sends the business process model to the digital reshaping module; The digital reshaping module builds a CATAC cycle model based on the business process model and sends the CATAC cycle model to the operation enablement module; The operation enablement module builds a matching digital operation management mechanism and team, continuously monitors the operation data generated by the CATAC cycle model, optimizes the process, and feeds the optimization results back to the CATAC cycle model; The method for constructing the business process model comprises the following steps: Step Q1: Determine the enterprise's strategic goals and define the objective function; Step Q2: Decompose the objective function into each business department and determine the sub-objective function of each business department. The formula used is as follows: F=w1f1+w2f2+···+w n f n ; In the formula, F represents the objective function, n represents the number of business departments, f1, f2, f n Represent the sub-objective functions of different business departments, w1, w2, w n Respectively represent the weights of the sub-objective functions of different business departments; Step Q3: Process mining, extract log data, analyze log data using inductive mining algorithms, extract events from log data, and mine the activities contained in the events to generate business process models that show the sequential, parallel, and cyclic structures of activities; Step Q4: Analyze the execution time, waiting time and resource usage of each activity; Step Q5: Check whether the actual process meets the preset objective function and identify deviation activities; The method for constructing the CATAC cycle model comprises the following steps: Step S1: Data collection, collecting activity data from the business process model, including equipment operation data, production data, and environmental data; Step S2: Automatically analyze and calculate the average execution time, standard deviation and degree of variation of each activity, and identify abnormal activities and structures in the business process model; Step S3: Task triggering, when abnormal activities and structures are detected, tasks are automatically triggered and assigned to corresponding business departments; Step S4: Action instructions: After the business department receives the task, the relevant equipment, systems and personnel take corresponding actions; Step S5: Effect confirmation, automatically confirming whether the actions taken have effectively solved the problem.

2. A digital dual-cycle task management system for enterprise operations according to claim 1, characterized in that: In step Q3, process mining, includes the following steps: Step Q31: Determine the business process scope of each business department according to the sub-objective function, and extract events from the log data, including event ID, activity name, timestamp, resource, and other context information; Step Q32: Convert the event into a sequence, each sequence represents an event, a sequence contains multiple activities, and determine the order of activities in the sequence; Step Q33: Frequent set mining, use the Apriori algorithm to identify frequently occurring activity sets and adjust the weights of each business department. The steps are as follows: Step Q331: Initialize frequent item sets, calculate the frequency of each individual activity, and filter out infrequent activities; Step Q332: Generate a candidate item set, generate combinations of two activities, calculate the frequency of each combination, and filter out infrequent combinations; Step Q333: Iteratively generate larger item sets. For the frequent item sets of k activities, generate candidate item sets of k+1 activities, and continue to filter infrequent combinations until no larger item sets can be generated. Step Q34: Generalize the frequent activity set into a pattern to capture the generalized business process; Step Q35: Generate a business process model based on the mined frequent activity sets and patterns; Step Q36: Use graphs to visualize sequential, parallel, and cyclic structures between activities.

3. A digital dual-cycle task management system for enterprise operations according to claim 2, characterized in that: In step S2, automatic analysis includes the following steps: Step S21: Calculate the average execution time, standard deviation and coefficient of variation of each activity. The higher the coefficient of variation, the more unstable the activity. The formula used is as follows: In the formula, represents the average execution time, i represents the activity number, t i represents the execution time of the ith activity, N represents the total number of activities, σ represents the standard deviation, and θ represents the coefficient of variation; Step S22: abnormal activity detection, using the 3σ principle to detect abnormal activities. When the execution time of an activity exceeds the range of 3 times the standard deviation of the average execution time, the activity is marked as an abnormal activity. The formula used is as follows: or Where t' i Indicates unusual activity; Step S23: abnormal structure identification, detecting the number of repetitions of activities to identify abnormal loops of loop structures, marking them as abnormal loops when the number of repetitions of activities exceeds the normal range, detecting the synchronization of parallel activities to identify abnormal parallelism of parallel structures, marking them as abnormal parallel structures when the time difference of parallel activities exceeds the normal range, and using the following formula: D ij =|t j -t j '|; In the formula, C i represents the number of repetitions of each activity, s represents the path number, and n is represents the number of occurrences of the ith activity in the sth path, P represents the total number of paths, and t j and t j ' represents the execution time of parallel activities, D ij Represents the time difference between parallel activities.

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