Production progress management and monitoring method based on cloud computing

Abstract

The invention discloses a production progress management and monitoring method based on cloud computing, relates to the technical field of production progress monitoring, and solves the technical problems that in the prior art, the monitoring efficiency of a sheet metal production processing flow is low, and visual monitoring management cannot be carried out according to a dependency relationship between a working procedure and a real-time processing process. Specifically, step-by-step evaluation is carried out on the procedures, and evaluation is carried out on all the procedures according to the sheet metal production process; procedure dependency relationship determination: after each procedure of the production process is determined to be stable, performing procedure dependency relationship determination according to the processing characteristics among the procedures, and performing procedure cooperation determination according to the procedure dependency relationship; path modeling determination: performing production path determination according to the dependency relationship of each production process, and performing evaluation according to reality; and construction of a process monitoring board: performing modeling according to the production path of each production process, constructing the monitoring board, and performing progress monitoring on the production process through the monitoring board.

Description

Technical Field

[0001] The present invention relates to the technical field of production progress monitoring, and in particular to a production progress management and monitoring method based on cloud computing. Background Art

[0002] Sheet metal processing is a cold working process for thin metal plates (usually with a thickness of 0.5-6mm). Through shearing, bending, stamping, welding, surface treatment and other processes, metal plates are processed into parts or products of various complex shapes. Sheet metal production is a relatively common type of production enterprise in the metal manufacturing industry.

[0003] However, in existing technologies, the monitoring efficiency of sheet metal production and processing processes is low. It is impossible to perform visual monitoring and management based on the dependencies between processes and real-time processing processes, so that when the processing process is abnormal, it is impossible to locate and trace the source in time. In addition, it is impossible to coordinate and communicate between the various process departments based on the dependencies between the various processes, and the efficient execution of the processing process cannot be guaranteed.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0005] The purpose of the present invention is to solve the above-mentioned problems and to propose a production progress management and monitoring method based on cloud computing.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A cloud computing-based production progress management and monitoring method, the progress management and monitoring method steps are as follows:

[0008] Step-by-step process evaluation: conduct execution evaluation of each process according to the sheet metal production process, and conduct process operation evaluation after each process execution evaluation is qualified;

[0009] Determine the process dependency. After ensuring that each process in the production process is stable, determine the process dependency based on the processing characteristics of each process, and determine the process coordination based on the process dependency.

[0010] Path modeling: Determine the production path based on the dependencies between various production processes. Based on actual execution evaluation, screen the production processes corresponding to the dependencies to determine the production path and model the selected path. Path modeling can facilitate production progress monitoring.

[0011] The process monitoring dashboard is constructed by modeling and building a monitoring dashboard based on the production path of each production process, and the progress of the production process is monitored through the monitoring dashboard.

[0012] As a preferred embodiment of the present invention, the step-by-step evaluation process is as follows:

[0013] The sheet metal production process is divided into several production processes, and each production process is evaluated. The continuous rising speed of the parameter floating span peak after the equipment operating parameters fluctuate when a single production process is executed is obtained. At the same time, the execution deviation of the production process task volume before and after the equipment operating parameter fluctuates is collected, and the collected data are marked as process floating characteristic parameters and process floating influence parameters respectively.

[0014] As a preferred embodiment of the present invention, if any of the process fluctuation characteristic parameter and the process fluctuation influence parameter exceeds the corresponding set threshold, it is inferred that the single execution evaluation of the production process is unqualified, and the process adjustment of the current production process is performed;

[0015] If any of the process float characteristic parameters and process float impact parameters does not exceed the corresponding set threshold, it is inferred that the single execution evaluation of the production process is qualified.

[0016] As a preferred embodiment of the present invention, a process operation evaluation is performed on the production processes that have passed the execution evaluation, and an operation evaluation is performed on the production processes that cooperate with the execution. The production equipment corresponding to the production processes that cooperate with the execution is obtained, and the production equipment is sorted according to the execution process. The task execution speed of the corresponding adjacent production equipment corresponding to the decrease in the current task execution amount of any production equipment is adapted to the adjustment buffer time. If the adaptation adjustment buffer time is within the set buffer time range, it is inferred that the production process operation evaluation is qualified; conversely, if the adaptation adjustment buffer time is not within the set buffer time range, it is inferred that the production process operation evaluation is unqualified, and the operation coordination debugging of each running equipment is performed.

[0017] As a preferred embodiment of the present invention, the process of determining the process dependency is as follows:

[0018] Analyze the dependency relationships of each production process, identify the processing entities corresponding to the production process, and monitor the production process operations received by the processing entities;

[0019] Obtain the execution operation position of the production process on the processing body, and set the production process with the overlapping execution operation position of the processing body as the dependent execution process;

[0020] Analyze the dependent execution processes, collect the production processes of each execution operation position in the processing subject, and infer whether the production processes have superposition characteristics based on the process characteristics of the production process, that is, different production processes at the same execution operation position are executed in different sequences. If the current production process has no effect on the processing quality of the corresponding processing subject, the current production process is marked as an alternating random process group; if the current production process has an effect on the processing quality of the corresponding processing subject, the current production process is marked as a fixed sequence process group.

[0021] As a preferred embodiment of the present invention, within a fixed-sequence process group, the completion of the execution operation of the previous production process is set as a trigger condition for the execution of the next production process; the dependency relationship of the alternating random process group in the entire production process is set as follows: the production process with a trigger condition uses the trigger relationship as the execution condition and is set as a trigger dependency relationship; the production process without a trigger condition is adaptively executed based on the operating status or operation execution progress of the current production process and is set as a criterion dependency relationship.

[0022] As a preferred embodiment of the present invention, the path modeling determination process is as follows:

[0023] The production path is set according to the dependency relationship between each production process, and each production process is sorted according to the production process. The sequence is adjusted according to the dependency relationship between the sorted production processes to build a preset production path;

[0024] The preset production paths are screened, and the trigger conditions of the production processes in the current preset production paths are identified. The same trigger conditions corresponding to different adjacent production processes are marked, and the current trigger conditions are marked as branch conditions. Priority is set for the production processes corresponding to the branch conditions. The number of movements of the processing subject corresponding to the actual production process is used as the priority judgment standard. When the production processes corresponding to the branch conditions are prioritized, the order of processes with the least number of movements of the processing subject is used as the execution order.

[0025] As a preferred embodiment of the present invention, after determining the preset production path, the processing subject is used as the process connection point, and each production process in the preset production path is used as the processing point; the preset production paths are connected in series, and a dynamic path is formed according to the continuous supply of the processing subject, and a monitoring board is constructed; in the monitoring stage of the monitoring board, progress monitoring is performed based on each production process type, the execution status of the production process is set as a precondition, and the precondition of the corresponding production process and the trigger condition of the adjacent production process are set as the monitoring point of the monitoring board. The production progress can be monitored at all times through the monitoring point, and production management is performed according to the cycle progress of the generation, execution and completion of the preconditions and trigger conditions of the monitoring point. In the event of an abnormality, the specific preconditions or trigger conditions are located to the production process, and traced back in combination with the execution status of the adjacent production processes.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. In the present invention, an execution evaluation is performed on each process according to the sheet metal production process. After each process execution evaluation is qualified, a process operation evaluation is performed to ensure the execution stability and efficiency of the sheet metal processing process. On the premise of ensuring the qualification of the sheet metal production process, production progress management and monitoring are performed to improve the feasibility of progress monitoring and avoid monitoring deviations caused by abnormal process execution during monitoring; the process dependency relationship is determined according to the processing characteristics of each process, and the process coordination is determined based on the process dependency relationship. Through this production path of a system with high process dependency, a fixed production process monitoring dashboard is constructed for production progress monitoring.

[0028] 2. In the present invention, the production processes corresponding to the dependency relationships are screened to determine the production path and modeling is performed based on the selected path. Modeling based on the path can promote production progress monitoring, improve the response speed of cross-departmental processes, and provide early warning of process delays. It can also improve equipment utilization through process path optimization. Modeling is performed based on the production path of each production process and a monitoring dashboard is constructed. The progress of the production process is monitored through the monitoring dashboard, and workshop operators can report processes or submit exceptions based on the monitoring dashboard. Planners can configure production path templates, adjust order scheduling, and handle progress exceptions. Administrators can interpret dashboard data (such as process delay rate and equipment utilization analysis), thereby improving the monitoring efficiency of the entire production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0030] Figure 1 A flowchart of the overall method of the present invention;

[0031] Figure 2This is a flowchart of the method for step-by-step evaluation of the process of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0033] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0034] See also Figure 1 As shown, a production progress management and monitoring method based on cloud computing, the specific method steps are as follows:

[0035] The process is evaluated step by step. Each process is evaluated according to the sheet metal production process. After each process is evaluated as qualified, the process operation evaluation is carried out to ensure the execution stability and efficiency of the sheet metal processing process. On the premise of ensuring the qualified sheet metal production process, the production progress management and monitoring are carried out to improve the feasibility of progress monitoring and avoid monitoring deviations caused by abnormal process execution during monitoring.

[0036] Determine process dependencies. After ensuring that each process in the production process is stable, determine process dependencies based on the processing characteristics of each process. Then, determine process coordination based on process dependencies. This highly dependent system uses production paths to build a fixed production process monitoring dashboard for production progress monitoring.

[0037] Path modeling determines the production path based on the dependencies between various production processes. Based on actual execution evaluation, the production processes corresponding to the dependencies are screened to determine the production path and model the selected path. Path modeling can facilitate production progress monitoring, improve cross-departmental process response speed, provide early warning of process delays, and improve equipment utilization through process path optimization.

[0038] The process monitoring dashboard is constructed by modeling and building a monitoring dashboard based on the production path of each production process. The monitoring dashboard monitors the progress of the production process, and workshop operators can use the monitoring dashboard to report process work or exceptions. Planners can configure production path templates, adjust order schedules, and handle progress exceptions. Administrators can interpret dashboard data (such as process delay rate and equipment utilization analysis), improving the monitoring efficiency of the entire production process.

[0039] See also Figure 2 As shown, the step-by-step evaluation process is as follows:

[0040] The sheet metal production process is divided into several production processes, and each production process is evaluated. The continuous rising speed of the peak value of the parameter fluctuation span after the equipment operating parameters fluctuate during the execution of a single production process is obtained. At the same time, the execution deviation of the production process task volume before and after the equipment operating parameter fluctuation is collected, and the collected data is marked as the process fluctuation characteristic parameter and the process fluctuation influence parameter respectively.

[0041] If any of the process floating characteristic parameters and process floating influence parameters exceeds the corresponding set threshold, it is inferred that the single execution evaluation of the production process has failed, and the current production process is adjusted to control the operating parameters of the equipment, reduce the floating span, and improve the anti-floating characteristics of the corresponding equipment of the process. When operating parameter fluctuations occur, timely operation compensation is carried out;

[0042] If any of the process float characteristic parameters and process float impact parameters do not exceed the corresponding set thresholds, it is inferred that the single execution evaluation of the production process is qualified;

[0043] Conduct process operation evaluation on production processes that have passed the execution evaluation, conduct operation evaluation on production processes that cooperate with the execution, obtain the production equipment corresponding to the production processes that cooperate with the execution, and sort the production equipment according to the execution process, obtain the task execution speed adaptation buffer time of the adjacent production equipment corresponding to the decrease in the current task execution volume of any production equipment, and if the adaptation adjustment buffer time is within the set buffer time range, it is inferred that the production process operation evaluation is qualified; conversely, if the adaptation adjustment buffer time is not within the set buffer time range, it is inferred that the production process operation evaluation is unqualified, and the operation coordination debugging of each running equipment is carried out;

[0044] It should be explained that the adaptive adjustment buffer time refers to the buffer execution time for the production equipment to reduce its operating speed. For example, during material processing, if the real-time unloading volume of the unloading equipment suddenly drops, the processing speed of the unloading processing equipment will be adjusted to avoid idling of the equipment.

[0045] The process of determining process dependencies is as follows:

[0046] Analyze the dependency relationships of various production processes based on the processing entities corresponding to the production process, such as substrates or steel plates in industrial processing; and monitor the production process operations received by the processing entities;

[0047] Obtain the execution operation position of the production process on the processing body, and set the production process with the overlapping execution operation position of the processing body as the dependent execution process;

[0048] Analyze the dependent execution processes, collect the production processes of each execution operation position in the processing body, and infer whether the production processes have superposition characteristics based on the process characteristics of the production process, that is, different production processes at the same execution operation position are executed in different sequences. If the current production process has no effect on the processing quality of the corresponding processing body, the current production process is marked as an alternating random process group; if the current production process has an effect on the processing quality of the corresponding processing body, the current production process is marked as a fixed sequence process group; it should be explained that the production processes in the sheet metal processing process have a sequence, such as the "welding" process is only performed after the "cutting" process is completed;

[0049] In a fixed sequence process group, the completion of the execution operation of the previous production process is set as the trigger condition for the execution of the next production process;

[0050] The dependency relationship of the alternating random process group in the entire production process is set as follows: if there is a trigger condition, the production process uses the trigger relationship as the execution condition and is set as a trigger dependency relationship;

[0051] Production processes without trigger conditions are adaptively executed based on the current operating status or execution progress of the production process and are set as accusatory dependencies.

[0052] That is, if the operating status is normal or there is no pressure to perform the task, it will be executed;

[0053] The path modeling determination process is as follows:

[0054] The production path is set according to the dependency relationship between each production process, and each production process is sorted according to the production process. The sequence is adjusted according to the dependency relationship between the sorted production processes to build a preset production path;

[0055] Screen the preset production paths, identify the production processes within the current preset production paths based on their triggering conditions, mark the adjacent production processes corresponding to the same triggering conditions, mark the current triggering conditions as branching conditions, set priorities for the production processes corresponding to the branching conditions, use the number of moves of the processing subject corresponding to the actual production process as the priority determination standard, and when prioritizing the production processes corresponding to the branching conditions, use the process sequence with the least number of moves of the processing subject as the execution order;

[0056] The process of building a process monitoring dashboard is as follows:

[0057] After determining the preset production path, the processing entity is used as the process connection point, and each production process within the preset production path is used as the processing point. The preset production paths are connected in series, and a dynamic path is formed based on the continuous supply of the processing entity to build a monitoring dashboard.

[0058] During the monitoring stage of the monitoring board, progress monitoring is carried out based on each production process type, and the execution status of the production process is set as a precondition, such as the execution status is the execution volume ratio of the task at the current moment; and the precondition of the corresponding production process and the trigger conditions of the adjacent production process are set as monitoring points of the monitoring board. The production progress can be monitored at all times through the monitoring points, and production management is carried out according to the cycle progress of the generation, execution and completion of the preconditions and trigger conditions of the monitoring points. In the event of an abnormality, the specific preconditions or trigger conditions are located to the production process, and traced back in combination with the execution status of the adjacent production processes.

[0059] Example 2

[0060] After the monitoring dashboard is built and continuously monitored, the production process can be continuously monitored and controlled in a timely manner. The monitoring dashboard also contains other data of actual sheet metal processing, as follows:

[0061] Production work order

[0062] After the production order is submitted and approved by the production plan, the system automatically generates a document with the material type mark according to the type of planned material;

[0063] A production work order is a document that carries the production records of all modules in the same order, batch, and region.

[0064] The production work order includes the following main fields:

[0065] Text fields: Project, Order, Batch, Region, Process Name, Production Status, Material Type

[0066] Date fields: planned shipping date, planned start date, actual start date, planned completion date, actual completion date, planned warning date;

[0067] Sub-table fields: module name, template number and associated fields, drawings;

[0068] Work Order Reminder

[0069] Work order reminders are process documents automatically generated by the system;

[0070] Generation method:

[0071] When the production status of a production work order is in the "Waiting for Production" state, the system will generate a work order reminder process at 15:00 on the day before the "Planned Start Date" to remind the "Process Manager" whether the production can be started normally. After confirmation, the order generation status will be changed to "In Production";

[0072] When the production status of a production work order is in "In Production", the system will generate a work order reminder process at 9:00 on the "Planned Warning Date" to remind the "Process Manager" whether the work order can be completed normally according to the "Planned Completion Date".

[0073] When the production status of a production work order is in the "Outsourcing" state, the system will generate a work order reminder process at 9:00 on the "Planned Warning Date" to remind the "Production Supervisor" to confirm whether the outsourcing can be completed normally according to the "Planned Completion Date";

[0074] Production Report

[0075] Production report can be generated by process document automatically generated by the system or manually;

[0076] The process manager receives a work order in the "Waiting for Production" status and is prompted to confirm whether to proceed with the production operation. After confirmation, a production report / outsourcing work order is generated;

[0077] When a process manager receives a work order in the "In Production" or "Outsourcing" status, he or she is prompted to confirm whether the work has been completed on schedule. If confirmed, a production work order for the next process is generated. If confirmed, a reason for the delay is stated and the extended delivery date is entered.

[0078] The process manager can initiate a production report / outsourcing work order on his own when no work order reminder is generated, and manually enter the next process;

[0079] Material handover (including quality inspection)

[0080] Material transfer can be initiated manually by the personnel of this process after completing material processing;

[0081] When initiating a material transfer, you need to fill in the module information and self-check results; after submission, the document will be transferred to the person in charge of the next process to receive the materials;

[0082] When the person in charge of the next process receives the material, he needs to confirm whether the module information is normal and complete the quality inspection of the previous process;

[0083] The sheet metal cutting team needs to submit the module labels along with the materials to the sheet metal unfolding team;

[0084] Packaging and warehousing

[0085] After all the processing steps of packaging and warehousing are completed, packaging and warehousing will be carried out; the counting operation of packaging and warehousing will be initiated by the packaging team leader;

[0086] Performance Management

[0087] After breaking down the above-mentioned processes and their management actions, they are integrated into the digital system. The system collects the response efficiency of each management action and analyzes and confirms the entered data. This includes but is not limited to the following:

[0088] Production plan achievement rate;

[0089] First inspection pass rate;

[0090] Order delivery rate;

[0091] Product qualification rate;

[0092] Timely response rate;

[0093] The data collected ultimately forms a performance summary table for team leaders in each process;

[0094] The monitoring dashboard can reflect and analyze the above data, so that administrators can further monitor and manage the production process.

[0095] When the present invention is in use, the process is evaluated step by step, and the execution evaluation of each process is carried out according to the sheet metal production process. After each process execution evaluation is qualified, the process operation evaluation is carried out; the process dependency is determined, and after it is determined that each process in the production process is stable, the process dependency is determined according to the processing characteristics between each process, and the process coordination is determined according to the process dependency; the path modeling is determined, and the production path is determined according to the dependency of each production process, and according to the actual execution evaluation, the production process corresponding to the dependency is screened to determine the production path and modeling is carried out according to the selected path. The path modeling can promote production progress monitoring; the process monitoring dashboard is constructed, and the production path of each production process is modeled and a monitoring dashboard is constructed, and the progress of the production process is monitored through the monitoring dashboard.

[0096] Thresholds, preset values, and preset ranges are set for comparative analysis of results to determine whether they are good or bad. The values ​​are set based on a combination of large-scale model analysis of sample data and manual experience, and can also be adjusted appropriately based on seasonal or common-sense factors.

[0097] The settings of weight ratio coefficients, influencing factors, etc. are assigned specific values ​​according to the influence of each parameter on the result, which ultimately reflects the impact on the result. They are also set and entered into storage through a combination of large-scale model analysis of sample data and manual experience. Appropriate adjustments can also be made based on seasonal or common-sense influencing conditions.

[0098] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A production progress management and monitoring method based on cloud computing, characterized in that: The steps of progress management and monitoring are as follows: Step-by-step process evaluation: conduct execution evaluation of each process according to the sheet metal production process, and conduct process operation evaluation after each process execution evaluation is qualified; Determine the process dependency. After ensuring that each process in the production process is stable, determine the process dependency based on the processing characteristics of each process, and determine the process coordination based on the process dependency. Path modeling: Determine the production path based on the dependencies between various production processes. Based on actual execution evaluation, screen the production processes corresponding to the dependencies to determine the production path and model the selected path. Path modeling can facilitate production progress monitoring. The process monitoring dashboard is constructed by modeling and building a monitoring dashboard based on the production path of each production process, and the progress of the production process is monitored through the monitoring dashboard.

2. The cloud computing-based production progress management and monitoring method according to claim 1, characterized in that: The step-by-step evaluation process is as follows: The sheet metal production process is divided into several production processes, and each production process is evaluated. The continuous rising speed of the parameter floating span peak after the equipment operating parameters fluctuate when a single production process is executed is obtained. At the same time, the execution deviation of the production process task volume before and after the equipment operating parameter fluctuates is collected, and the collected data are marked as process floating characteristic parameters and process floating influence parameters respectively.

3. The cloud computing-based production progress management and monitoring method according to claim 2, characterized in that: If any of the process float characteristic parameters and process float impact parameters exceeds the corresponding set threshold, it is inferred that the single execution evaluation of the production process is unqualified, and the current production process is adjusted; If any of the process float characteristic parameters and process float impact parameters does not exceed the corresponding set threshold, it is inferred that the single execution evaluation of the production process is qualified.

4. The cloud computing-based production progress management and monitoring method according to claim 3, characterized in that: Conduct process operation evaluation on the production processes that have passed the execution evaluation, conduct operation evaluation on the production processes that cooperate with the execution, obtain the corresponding production equipment of the production processes that cooperate with the execution, and sort the production equipment according to the execution process, obtain the task execution speed adaptation adjustment buffer time of the adjacent production equipment corresponding to the decrease in the current task execution amount of any production equipment, if the adaptation adjustment buffer time is within the set buffer time range, it is inferred that the production process operation evaluation is qualified; conversely, if the adaptation adjustment buffer time is not within the set buffer time range, it is inferred that the production process operation evaluation is unqualified, and the operation coordination debugging of each running equipment is carried out.

5. The cloud computing-based production progress management and monitoring method according to claim 4, characterized in that: The process of determining process dependencies is as follows: Analyze the dependency relationships of each production process, identify the processing entities corresponding to the production process, and monitor the production process operations received by the processing entities; Obtain the execution operation position of the production process on the processing body, and set the production process with the overlapping execution operation position of the processing body as the dependent execution process; Analyze the dependent execution processes, collect the production processes of each execution operation position in the processing subject, and infer whether the production processes have superposition characteristics based on the process characteristics of the production process, that is, different production processes at the same execution operation position are executed in different sequences. If the current production process has no effect on the processing quality of the corresponding processing subject, the current production process is marked as an alternating random process group; if the current production process has an effect on the processing quality of the corresponding processing subject, the current production process is marked as a fixed sequence process group.

6. The cloud computing-based production progress management and monitoring method according to claim 5, characterized in that: Within a fixed-sequence process group, the completion of the previous production process is set as the trigger condition for the execution of the next production process. The dependency relationship of the alternating random process group in the entire production process is set as follows: the production process with a trigger condition uses the trigger relationship as the execution condition and is set as a trigger dependency relationship. Production processes without trigger conditions are adaptively executed based on the operating status or execution progress of the current production process and are set as accusatory dependencies.

7. The cloud computing-based production progress management and monitoring method according to claim 1, characterized in that: The path modeling determination process is as follows: The production path is set according to the dependency relationship between each production process, and each production process is sorted according to the production process. The sequence is adjusted according to the dependency relationship between the sorted production processes to build a preset production path; The preset production paths are screened, and the trigger conditions of the production processes in the current preset production paths are identified. The same trigger conditions corresponding to different adjacent production processes are marked, and the current trigger conditions are marked as branch conditions. Priority is set for the production processes corresponding to the branch conditions. The number of movements of the processing subject corresponding to the actual production process is used as the priority judgment standard. When the production processes corresponding to the branch conditions are prioritized, the order of processes with the least number of movements of the processing subject is used as the execution order.

8. The cloud computing-based production progress management and monitoring method according to claim 7, characterized in that: The process of building a process monitoring dashboard is as follows: After determining the preset production path, the processing entity is used as the process connection point, and each production process within the preset production path is used as the processing point. The preset production paths are connected in series, and a dynamic path is formed based on the continuous supply of the processing entity to build a monitoring dashboard. During the monitoring stage of the monitoring board, progress monitoring is carried out based on each production process type, the execution status of the production process is set as the precondition, and the precondition of the corresponding production process and the trigger conditions of the adjacent production process are set as the monitoring points of the monitoring board. The production progress can be monitored at all times through the monitoring points, and production management is carried out according to the cycle progress of the generation, execution and completion of the preconditions and trigger conditions of the monitoring points. In the event of an abnormality, the specific preconditions or trigger conditions are located at the production process, and traced back in combination with the execution status of the adjacent production processes.

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