Lean optimization method and device capable of arranging process, equipment, medium and product

Abstract

The invention provides a lean optimization method, device and equipment for an orchestrable process, a medium and a product, and the method comprises the steps: receiving a design scheme for the orchestrable process, and disassembling the design scheme into a plurality of sub-processes; executing the plurality of sub-processes, and acquiring execution state data of each sub-process by utilizing a sensing mechanism; analyzing the plurality of sub-processes or the execution state data by using a pre-constructed lean analysis model to obtain a lean analysis result; and performing optimization reconstruction on the design scheme according to the lean analysis result to generate an optimization scheme. According to the method, automatic optimization of the orchestrable process is realized by executing perception, lean analysis and optimization reconstruction, the problems of complex analysis and difficult modification of the traditional manual static orchestration process are solved, and the optimization efficiency can be improved. Furthermore, the bottleneck and waste problems in traditional static arrangement are greatly eliminated through lean analysis and scheme reconstruction, the execution efficiency of the whole process is improved, the waiting time is saved, and the user experience is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of process scheduling, and in particular to a lean optimization method, device, equipment, medium and product capable of scheduling processes. Background Art

[0002] Process orchestration is a system construction method that standardizes the process nodes of the control flow, standardizes the input parameters, output parameters, context, and the data flow format and trigger logic between process nodes, so as to achieve configurability and even visualization of the process, thereby enhancing the flexibility and maintainability of the system, reducing the workload of redeveloping the original process due to business changes or new businesses, and greatly improving the efficiency of business launch. Therefore, process orchestration is widely used in cloud native services, function computing, low-code platforms and other scenarios.

[0003] Process orchestration is generally divided into design state and running state, that is, the orchestration design stage and the process execution stage. In the design state, the orchestration design of the process plan is carried out, and in the running state, it is executed according to the design plan. The execution efficiency of the process depends on the orchestration design. In the running state, it is impossible to automatically optimize according to the execution status of the process. At present, it is still necessary to manually modify the original design plan. When the process is complex and the number is large, relying on manual modification will lead to problems of inaccurate optimization and low efficiency. Summary of the invention

[0004] The present invention provides a lean optimization method, device, equipment, medium and product for a programmable process, which is used to solve the defects of the prior art that the original design scheme is manually modified by hand, such as complex process and inaccurate optimization results, realize automatic optimization of the design scheme of the programmable process, and improve the optimization efficiency.

[0005] The present invention provides a lean optimization method capable of arranging a process, comprising the following steps: receiving a design plan for a programmable process, and breaking the design plan into a plurality of sub-processes; Execute the multiple sub-processes, and obtain the execution status data of each sub-process by using the perception mechanism; Analyzing the multiple sub-processes or the execution status data using a pre-built lean analysis model to obtain a lean analysis result; wherein the lean analysis result is an analysis result aimed at improving the process execution efficiency of the design solution; The design scheme is optimized and reconstructed according to the lean analysis results to generate an optimized scheme.

[0006] According to a lean optimization method for programmable processes provided by the present invention, the lean analysis result includes a process node identification result; the lean analysis result is obtained by analyzing the multiple sub-processes or the execution status data using a pre-built lean analysis model, including: The multiple sub-processes are input into the pre-built lean analysis model so that the pre-built lean analysis model performs static analysis on the multiple sub-processes to obtain the process node identification results; the process node identification results include at least one of the non-essential process nodes, mergeable process nodes, replaceable process nodes and invalid dependencies.

[0007] According to a lean optimization method for programmable processes provided by the present invention, the lean analysis result includes an execution mode; the lean analysis result is obtained by analyzing the multiple sub-processes or the execution status data using a pre-built lean analysis model, including: The execution status data is input into the pre-built lean analysis model, so that the pre-built lean analysis model dynamically analyzes the execution status data to obtain the execution mode for each sub-process.

[0008] According to a lean optimization method for programmable processes provided by the present invention, the lean analysis result includes a portrait of an operation object; the lean analysis result is obtained by analyzing the multiple sub-processes or the execution status data using a pre-built lean analysis model, including: The response data obtained after the historical sub-processes corresponding to each sub-process are executed are input into the pre-built lean analysis model to obtain the operation object portrait output by the pre-built lean analysis model; wherein the operation object portrait is the operation object involved in each sub-process.

[0009] According to a lean optimization method for programmable processes provided by the present invention, executing the multiple sub-processes and acquiring the execution status data of each sub-process by using a perception mechanism includes: executing the plurality of sub-processes; Create collection and monitoring programs corresponding to each sub-process; The acquisition monitoring program is executed to acquire the execution status data of each sub-process.

[0010] According to a lean optimization method for programmable processes provided by the present invention, the execution status data includes at least one of waiting time, execution start time, execution end time, execution time, number of repetitions, success rate, error rate, data volume, input data and output data.

[0011] The present invention also provides a lean optimization device capable of arranging a process, comprising the following modules: A sub-process decomposition module is used to receive a design scheme for a programmable process and decompose the design scheme into multiple sub-processes; An execution status data acquisition module, used to execute the multiple sub-processes and acquire the execution status data of each sub-process by using a perception mechanism; A lean analysis result output module, used to analyze the multiple sub-processes or the execution status data using a pre-built lean analysis model to obtain a lean analysis result; wherein the lean analysis result is an analysis result aimed at improving the process execution efficiency of the design solution; The optimization scheme reconstruction module is used to optimize and reconstruct the design scheme according to the lean analysis results to generate an optimization scheme.

[0012] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the lean optimization method for the programmable process as described in any one of the above is implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the lean optimization method for the programmable process as described in any one of the above is implemented.

[0014] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned lean optimization methods for programmable processes.

[0015] The lean optimization method, device, equipment, medium and product of the programmable process provided by the present invention receive the design scheme for the programmable process, disassemble the design scheme into multiple sub-processes; execute multiple sub-processes, and use the perception mechanism to obtain the execution status data of each sub-process; use the pre-built lean analysis model to analyze multiple sub-processes or execution status data to obtain lean analysis results; wherein the lean analysis results are analysis results with the goal of improving the process execution efficiency of the design scheme; optimize and reconstruct the design scheme according to the lean analysis results to generate an optimization scheme. This application realizes the automatic optimization of programmable processes by executing perception, lean analysis, and optimization reconstruction, solves the problem that the traditional manual static program analysis is complex and difficult to modify, and makes the process orchestration more automated when applied to cloud native services, function computing, low-code platforms and other scenarios, improves optimization efficiency, and saves human resources. Furthermore, through lean analysis and program reconstruction, the bottleneck and waste problems in traditional static orchestration are greatly eliminated, the execution efficiency of the overall process is improved, waiting time is saved, and the user experience is greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flow chart of the lean optimization method for programmable processes provided by the present invention.

[0018] Figure 2 It is a schematic diagram of the design scheme of the programmable process provided by the present invention.

[0019] Figure 3 It is a schematic diagram of a reconstruction solution of a programmable process provided by the present invention.

[0020] Figure 4 It is a structural schematic diagram of the lean optimization system capable of arranging processes provided by the present invention.

[0021] Figure 5 It is a module schematic diagram of the lean optimization device capable of arranging processes provided by the present invention.

[0022] Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] Combine the following Figure 1-Figure 6 Specific embodiments of the present invention are described.

[0025] Figure 1 It is a flow chart of the lean optimization method for programmable processes provided by the present invention, such as Figure 1 As shown, the method includes the following: Step 101: Receive a design solution for a programmable process, and decompose the design solution into multiple sub-processes.

[0026] Among them, the design scheme of the choreographed process refers to the process designed by the user in a customized way based on the atomic capabilities provided by the system and the actual business needs in the design state (i.e. the choreography design stage), which is used to specify the task execution order and logic in the process execution stage; atomic capability is a relatively abstract concept, which describes the smallest and indispensable capability unit in a specific field or business process. The main contents of the design scheme include but are not limited to the dependency relationship, execution logic, trigger conditions, fault tolerance mechanism, and recovery mechanism of the process nodes.

[0027] Specifically, a design solution S of a programmable process is received, such as Figure 2 As shown, Figure 2 The schematic diagram of a design scheme for a choreographed process is shown. It is a directed graph that specifies the start and end nodes of the process, as well as several executable nodes with dependencies. The executable nodes can be automatically called by the scheduled tasks to call various types of interfaces, such as service calls, parameter configuration, function calls, information notifications, manual review, etc.

[0028] Optionally, the executable node includes more fine-grained sub-nodes, such as the cloud host application node includes sub-nodes such as cloud host activation, cloud hard disk activation, network activation, and security policy activation.

[0029] Receive design proposal After that, the plan Decompose into multiple sub-processes that can be executed atomically. Atomic execution means that an operation cannot be divided during execution, cannot be interrupted or divided into multiple independent parts, and is either fully executed or not executed at all, and will not be interrupted by other operations or threads. In a multi-threaded environment or concurrent programming, atomic operations can ensure that operations on shared resources will not be interfered with by other threads, thereby ensuring data consistency and correctness.

[0030] Step 102: execute the multiple sub-processes and obtain the execution status data of each sub-process by using the perception mechanism.

[0031] Among them, the perception mechanism refers to the creation of a collection and monitoring program for the execution of sub-processes to realize the collection of sub-process execution status data.

[0032] The execution status data includes at least one of waiting time, execution start time, execution end time, execution time, number of repetitions, success rate, error rate, data volume, input data and output data.

[0033] The above step 102 specifically includes: executing the multiple sub-processes; creating a collection monitoring program corresponding to each sub-process; and executing the collection monitoring program to collect execution status data of each sub-process.

[0034] Specifically, when executing multiple sub-processes, each sub-process can be executed in sequence according to the dependencies between them, and a perception mechanism can be added during the execution process to track and collect the execution status data of each sub-process for subsequent evaluation and analysis; the execution status data includes but is not limited to waiting time, start time, completion time, execution time, number of repetitions, success rate, error rate, input data, output data, and data volume.

[0035] Step 103, using a pre-built lean analysis model to analyze the multiple sub-processes or the execution status data to obtain a lean analysis result; wherein the lean analysis result is an analysis result aimed at improving the process execution efficiency of the design solution; Among them, the lean analysis model is a data analysis model that guides the decision-making process in the production, manufacturing and sales of enterprises through data analysis. Generally, a ternary analysis model is established from the perspectives of design plan, execution process and operation object, and lean optimization goals are set, such as minimizing the total execution time of the whole process and reducing the length of manual interruption.

[0036] Specifically, the above-mentioned multiple sub-processes or the execution status data collected during the execution of the sub-processes are input into a pre-built lean analysis model to obtain a lean analysis result. The lean analysis result is an analysis result aimed at improving the process execution efficiency of the above-mentioned design scheme. For example, the lean analysis model finds bottlenecks and waste links and provides optimization directions.

[0037] Step 104: Optimize and reconstruct the design solution according to the above lean analysis results to generate an optimized solution.

[0038] Specifically, the design scheme is optimized and reconstructed according to the above lean analysis results to generate an optimized scheme. ,like Figure 3 As shown, Figure 3 Demonstrated optimization solution Schematic diagram of the optimization scheme In the example, the invalid dependencies between node 3 and node 5, and between node 6 and node 3 are eliminated; node 3 and node 4 are merged to improve the efficiency of task dispatching and execution; node 7 is added to optimize and compress the data of node 2 to reduce the data transmission delay; and the data access efficiency is improved by increasing the cache when node 6 is executed.

[0039] Optionally, after the solution reconstruction is completed, the optimization solution can first be evaluated through simulation verification Feasibility and degree of process improvement, such as optimization plan The correctness of the results, the comparison of the execution time of the new and old solutions, etc., and then use it as the new version solution Synchronize to the solution library, and the optimization solution can be applied the next time the process is executed implement.

[0040] The above-mentioned embodiment, by receiving a design scheme for a programmable process, decomposes the design scheme into multiple sub-processes; executes multiple sub-processes, and uses a perception mechanism to obtain the execution status data of each sub-process; uses a pre-built lean analysis model to analyze multiple sub-processes or execution status data to obtain lean analysis results; wherein the lean analysis results are analysis results with the goal of improving the process execution efficiency of the design scheme; optimizes and reconstructs the design scheme according to the lean analysis results to generate an optimized scheme. This application realizes the automatic optimization of programmable processes by performing perception, lean analysis, and optimization reconstruction, and solves the problem that the traditional manual static orchestration process is complex to analyze and difficult to modify, so that process orchestration is more automated when applied to cloud native services, function computing, low-code platforms and other scenarios, improves optimization efficiency, and saves human resources. Furthermore, through lean analysis and scheme reconstruction, the bottlenecks and waste problems in traditional static orchestration are greatly eliminated, the execution efficiency of the overall process is improved, waiting time is saved, and the user experience is greatly improved.

[0041] In one embodiment, the lean analysis result includes a process node identification result; the above step 103 includes: inputting the multiple sub-processes into the pre-built lean analysis model, so that the pre-built lean analysis model performs static analysis on the multiple sub-processes to obtain the process node identification result; the process node identification result includes at least one of the non-essential process nodes, mergeable process nodes, replaceable process nodes and invalid dependencies.

[0042] Among them, static analysis refers to a theory and technology that analyzes program behavior without executing the program to identify possible errors and weaknesses.

[0043] Specifically, static analysis mainly uses static analysis to discover non-essential nodes, mergeable nodes, replaceable nodes, and invalid dependencies in the solution. Among them, non-essential nodes refer to nodes that have no substantial effect; mergeable nodes refer to multiple nodes that can be merged and packaged into a node for one execution; replaceable nodes refer to one or more nodes that can be replaced by a better node that achieves the same capabilities; invalid dependencies refer to dependency paths that have no practical significance introduced in the solution. Figure 2 In the example, through the analysis of the input and output parameters of the nodes, it is found that the dependency paths from node 3 to node 5 and from node 6 to node 3 are invalid dependencies, and removing them has no effect on the process; both node 3 and node 4 are executed by the same operation object, so they can be sent to the operation object for execution at one time through combined packaging, reducing the number of call waiting times and reducing network communication pressure.

[0044] The above embodiment identifies optimizable nodes through static analysis, thus providing a data basis for subsequent optimization and reconstruction.

[0045] In one embodiment, the above-mentioned lean analysis results include an execution method, and the above-mentioned step 103 includes: inputting the execution status data into the pre-built lean analysis model, so that the pre-built lean analysis model dynamically analyzes the execution status data to obtain the execution method for each sub-process.

[0046] Among them, dynamic analysis is the optimization of the execution process of each sub-process. The execution methods include task combination, parallel execution, batch execution, competitive execution, data optimization, etc. Execution status data refers to the status data generated during the execution of the program. The main contents include but are not limited to waiting time, start time, completion time, execution time, number of repetitions, success rate, error rate, input data and output data, data volume, etc. Specifically, through dynamic analysis of execution status data, and with the goal of reducing waiting time and improving process execution efficiency as the constraint conditions, the task start time, end time, execution time, waiting time, etc. of each sub-process node are calculated, and feasible optimization directions are calculated from the aspects of task combination, parallel execution, batch execution, competitive execution, data optimization, etc. Among them, task combination refers to splitting or recombining node tasks according to parameters such as the target object, precondition, execution mode, and execution cycle of the task to reduce unnecessary calls. Parallel execution is to execute nodes without dependencies in parallel, or to eliminate dependencies of nodes with weak dependencies through predefined parameters to create parallel conditions to reduce process waiting time. Batch execution refers to sending tasks of the same target operation object to execute at one time to reduce the number of network communications. Competitive execution refers to sending tasks to multiple operation objects at the same time and using the earliest returned data results. Data optimization refers to optimizing the data in process execution by multi-level caching, data compression, data clipping, etc., to reduce network congestion and waiting delay caused by remote data pulling or data storms.

[0047] The above-mentioned embodiment can realize optimization analysis of the execution process of the design solution through dynamic analysis.

[0048] In one embodiment, the lean analysis result includes a portrait of an operation object; the above-mentioned step 103 includes: inputting the response data obtained after the historical sub-process corresponding to each sub-process is executed into the pre-built lean analysis model, and obtaining the operation object portrait output by the pre-built lean analysis model; wherein the operation object portrait is the operation object involved in each sub-process.

[0049] The operation object refers to the object instance operated by the process node, including but not limited to third-party platforms, servers, devices, databases, middleware, virtualization software, and function objects.

[0050] Specifically, the response data obtained after the historical sub-processes corresponding to each sub-process are executed are input into the pre-built lean analysis model, and the operation object portrait is obtained through data analysis.

[0051] In the above-mentioned embodiment, a portrait of the operation object is established through the response data of the historical process execution, so as to provide data reference for the lean analysis of the execution process.

[0052] In one embodiment, if Figure 4 As shown, a lean optimization system for an orchestrated process is also provided, including an orchestration design subsystem, a lean analysis subsystem and a process execution subsystem, wherein the lean analysis subsystem is located between the orchestration design subsystem and the process execution subsystem, and is used to optimize the orchestrated process solution through real-time perception of the solution execution process.

[0053] The lean analysis subsystem mainly includes the following modules: solution decomposition, process perception, static analysis, dynamic analysis, optimization integration, lean model, solution reconstruction, and verification and evaluation.

[0054] Among them, the solution decomposition is mainly achieved by decomposing the design solution provided by the orchestration design module into atomically executable sub-processes, and passing the sub-process information to task execution.

[0055] Process perception mainly adds perception mechanisms to the sub-processes after the plan is decomposed, so as to complete the real-time tracking and collection of execution process data.

[0056] Static analysis mainly uses static analysis to discover unnecessary nodes, mergeable nodes, replaceable nodes and invalid dependencies in the solution.

[0057] Dynamic analysis mainly refers to the dynamic analysis of execution status data. The start time, end time, execution time, and waiting time of each process node task are calculated with the goal of reducing waiting time and improving process execution efficiency. The feasible optimization direction is calculated from the aspects of task combination, parallel execution, batch execution, competitive execution, data optimization, etc.

[0058] Optimization integration is to combine the results of static analysis and dynamic analysis to generate the final solution optimization suggestions. The lean model is to establish a ternary analysis model from the perspectives of design plan, execution process and operation object, and set lean optimization goals, such as minimizing the total execution time of the whole process and reducing the length of manual interruption.

[0059] Solution reconstruction is to reconstruct and adjust the original solution based on the solution optimization suggestions from the lean analysis results to build a new optimized solution.

[0060] Verification and evaluation evaluates the feasibility and degree of process improvement of the optimized solution through simulation verification, and synchronizes the improved solution to the orchestration design subsystem.

[0061] The above embodiments mainly solve the problem that in business orchestration, service orchestration, resource orchestration and other scenarios, the execution process of the program can be solidified due to its static design, which leads to difficulty in process optimization and poor execution efficiency in the subsequent execution process. By building a feedback loop of perception, analysis, and optimization, and through continuous optimization and improvement learning, a method and model are provided for lean process analysis.

[0062] The lean optimization device for a programmable process provided by the present invention is described below. The lean optimization device for a programmable process described below and the lean optimization method for a programmable process described above can be referred to each other.

[0063] like Figure 5 As shown, a lean optimization device capable of arranging processes is provided, comprising the following modules: The sub-process decomposition module 501 is used to receive a design scheme for a programmable process and decompose the design scheme into multiple sub-processes; An execution status data acquisition module 502, used to execute the multiple sub-processes and acquire the execution status data of each sub-process by using a perception mechanism; The lean analysis result output module 503 is used to analyze the multiple sub-processes or the execution status data using a pre-built lean analysis model to obtain a lean analysis result; wherein the lean analysis result is an analysis result aimed at improving the process execution efficiency of the design solution; The optimization solution reconstruction module 504 is used to optimize and reconstruct the design solution according to the lean analysis result to generate an optimization solution.

[0064] In one embodiment, the lean analysis results include process node identification results; the above-mentioned lean analysis result output module 503 is further used to: input the multiple sub-processes into the pre-built lean analysis model, so that the pre-built lean analysis model performs static analysis on the multiple sub-processes to obtain the process node identification results; the process node identification results include at least one of the non-essential process nodes, mergeable process nodes, replaceable process nodes and invalid dependencies.

[0065] In one embodiment, the lean analysis result includes an execution method; the above-mentioned lean analysis result output module 503 is further used to: input the execution status data into the pre-built lean analysis model, so that the pre-built lean analysis model dynamically analyzes the execution status data to obtain the execution method for each sub-process.

[0066] In one embodiment, the lean analysis results include an operation object portrait; the above-mentioned lean analysis result output module 503 is further used to: input the response data obtained after the historical sub-process corresponding to each sub-process is executed into the pre-built lean analysis model, and obtain the operation object portrait output by the pre-built lean analysis model; wherein the operation object portrait is the operation object involved in each sub-process.

[0067] In one embodiment, the execution status data acquisition module 502 is further used to: execute the multiple sub-processes; create a collection monitoring program corresponding to each sub-process; and execute the collection monitoring program to collect the execution status data of each sub-process.

[0068] In one embodiment, the execution status data includes at least one of waiting time, execution start time, execution end time, execution time, number of repetitions, success rate, error rate, data volume, input data and output data.

[0069] Figure 6 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 6 As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630 and a communication bus 640, wherein the processor 610, the communication interface 620 and the memory 630 communicate with each other through the communication bus 640. The processor 610 may call the logic instructions in the memory 630 to execute the lean optimization method of the programmable process, the method comprising: receiving a design scheme for the programmable process, disassembling the design scheme into multiple sub-processes; executing multiple sub-processes, and obtaining the execution status data of each sub-process by using the perception mechanism; analyzing multiple sub-processes or execution status data by using the pre-built lean analysis model to obtain the lean analysis result; wherein the lean analysis result is the analysis result with the goal of improving the process execution efficiency of the design scheme; optimizing and reconstructing the design scheme according to the lean analysis result to generate an optimization scheme.

[0070] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0071] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the lean optimization method of the programmable process provided by the above methods, and the method includes: receiving a design plan for the programmable process, and breaking the design plan into multiple sub-processes; executing multiple sub-processes, and using a perception mechanism to obtain execution status data of each sub-process; using a pre-built lean analysis model to analyze multiple sub-processes or execution status data to obtain lean analysis results; wherein the lean analysis results are analysis results aimed at improving the process execution efficiency of the design plan; optimizing and reconstructing the design plan according to the lean analysis results to generate an optimization plan.

[0072] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a lean optimization method for a programmable process provided by the above-mentioned methods, the method comprising: receiving a design scheme for a programmable process, and breaking the design scheme into multiple sub-processes; executing multiple sub-processes, and obtaining execution status data of each sub-process using a perception mechanism; analyzing multiple sub-processes or execution status data using a pre-built lean analysis model to obtain a lean analysis result; wherein the lean analysis result is an analysis result aimed at improving the process execution efficiency of the design scheme; and optimizing and reconstructing the design scheme according to the lean analysis result to generate an optimization scheme.

[0073] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0074] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A lean optimization method for programmable processes, characterized in that: include: receiving a design plan for a programmable process, and breaking the design plan into a plurality of sub-processes; Execute the multiple sub-processes, and obtain the execution status data of each sub-process by using the perception mechanism; Analyzing the multiple sub-processes or the execution status data using a pre-built lean analysis model to obtain a lean analysis result; wherein the lean analysis result is an analysis result aimed at improving the process execution efficiency of the design solution; The design scheme is optimized and reconstructed according to the lean analysis results to generate an optimized scheme.

2. The lean optimization method for programmable processes according to claim 1, characterized in that: The lean analysis results include process node identification results; The pre-built lean analysis model is used to analyze the multiple sub-processes or the execution status data to obtain lean analysis results, including: Inputting the multiple sub-processes into the pre-built lean analysis model, so that the pre-built lean analysis model performs static analysis on the multiple sub-processes to obtain the process node identification result; The process node identification result includes at least one of the non-essential process nodes, mergeable process nodes, replaceable process nodes and invalid dependency relationships.

3. The lean optimization method for programmable processes according to claim 1, characterized in that: The lean analysis result includes an execution method; the lean analysis result is obtained by analyzing the multiple sub-processes or the execution status data using the pre-built lean analysis model, including: The execution status data is input into the pre-built lean analysis model, so that the pre-built lean analysis model dynamically analyzes the execution status data to obtain the execution mode for each sub-process.

4. The lean optimization method for programmable processes according to claim 1, characterized in that: The lean analysis result includes a portrait of the operation object; the lean analysis result is obtained by analyzing the multiple sub-processes or the execution status data using the pre-built lean analysis model, including: The response data obtained after the historical sub-processes corresponding to each sub-process are executed are input into the pre-built lean analysis model to obtain the operation object portrait output by the pre-built lean analysis model; wherein the operation object portrait is the operation object involved in each sub-process.

5. The lean optimization method for programmable processes according to claim 1, characterized in that: The executing the multiple sub-processes and obtaining the execution status data of each sub-process by using the perception mechanism includes: executing the plurality of sub-processes; Create collection and monitoring programs corresponding to each sub-process; The acquisition monitoring program is executed to acquire the execution status data of each sub-process.

6. The lean optimization method for programmable processes according to any one of claims 1 to 5, characterized in that: The execution status data includes at least one of waiting time, execution start time, execution end time, execution time, number of repetitions, success rate, error rate, data volume, input data and output data.

7. A lean optimization device capable of arranging a process, characterized in that: include: A sub-process decomposition module is used to receive a design scheme for a programmable process and decompose the design scheme into multiple sub-processes; An execution status data acquisition module, used to execute the multiple sub-processes and acquire the execution status data of each sub-process by using a perception mechanism; A lean analysis result output module, used to analyze the multiple sub-processes or the execution status data using a pre-built lean analysis model to obtain a lean analysis result; wherein the lean analysis result is an analysis result aimed at improving the process execution efficiency of the design solution; The optimization scheme reconstruction module is used to optimize and reconstruct the design scheme according to the lean analysis results to generate an optimization scheme.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the lean optimization method for the programmable process as claimed in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the lean optimization method for a programmable process as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the lean optimization method for a programmable process as claimed in any one of claims 1 to 6 is implemented.

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