A method and system for industrial operations data processing

Abstract

The present disclosure provides a method and system for industrial operations data processing, the industrial operations data comprising a set of constraints, characterized in that the system comprises: an adjustment module configured to contract the set of constraints of the industrial operations data into a first set of constraints according to an adjustment function; a solving module configured to solve a first solution of the industrial operations data under the first set of constraints; and a finding module configured to substitute the first solution into the set of constraints to find a second set of constraints.

Description

Technical Field

[0001] This invention relates to the industrial field, specifically to the field of industrial operations research and optimization. This invention discloses a method and system for processing industrial operations research data, which is used to discover the reasons why industrial operations research and optimization problems are unsolvable. Background Technology

[0002] Operations research optimization techniques are widely used in industrial production scheduling, process optimization, process orchestration, warehouse layout, production line planning, and path planning. For example, in automobile production, an engine may consist of hundreds of parts, and the combinations of different parts constitute the constraints in an operations research optimization problem. Operations research optimization techniques are used to calculate whether there are feasible solutions for engine manufacturing under these constraints (i.e., solutions to the operations research optimization problem). Generally, to find the optimal or feasible solution to an operations research optimization problem, it may be necessary to establish models such as integer programming, linear programming, and nonlinear programming. With the continuous development of artificial intelligence technology and the increasingly comprehensive data reserves of industrial enterprises, more operations research optimization applications are being implemented at an accelerated pace. However, the number of unsolvable operations research optimization problems is also increasing.

[0003] In industrial applications, large-scale operations research optimization problems often become unsolvable due to data and constraint conflicts. Currently, operations research optimization problems are generally solved using commercial solvers. However, when an operations research optimization problem becomes unsolvable, commercial solvers can only determine whether a solution exists. If the problem is unsolvable, existing commercial solvers cannot find the cause of the unsolvability. Finding the cause of the unsolvability is a crucial issue in the practical application of operations research optimization techniques in industry. Furthermore, identifying the cause of an unsolvable operations research optimization system can hinder project implementation. Finding the cause of the unsolvability is key to the successful execution of an industrial project. For example, for an operations research optimization planning problem with 3 million constraints, if it is unsolvable, it may take a senior engineer 15 to 30 days to find the cause of the unsolvability.

[0004] Currently, the field of operations research mainly focuses on how to model and solve various operations research problems. There is relatively little research on methods for identifying the root causes of unsolvable problems in operations research. Neither academia nor industry has yet developed a mature method for effectively discovering the fundamental reasons for unsolvable problems. Summary of the Invention

[0005] This invention provides a method and system for processing industrial operations research data. This method and system can effectively identify the reasons why operations research optimization problems are unsolvable.

[0006] According to one aspect of the present invention, a system for processing industrial operations research data is provided, the industrial operations research data including a set of constraints, the system comprising: an adjustment module configured to shrink the set of constraints of the industrial operations research data into a first set of constraints according to an adjustment function; a solution module configured to solve the industrial operations research data for a first solution under the first set of constraints; and a search module configured to substitute the first solution into the set of constraints to find a second set of constraints.

[0007] Optionally, according to an embodiment of the present invention, the system further includes an iteration module, which is configured to iteratively call the solution module to solve the industrial operations research data under the second constraint set and iteratively call the search module to substitute the second solution into the constraint set to find a further second constraint set based on the second solution, until the second constraint set no longer changes.

[0008] Optionally, according to an embodiment of the present invention, the system further includes a comparison module configured to compare a second set of constraints based on a first solution with a second set of constraints based on a second solution; if the results are the same, the second set of constraints is the reason why the industrial operations research data has no solution.

[0009] Optionally, according to one embodiment of the present invention, the system further includes a preliminary inspection module configured to determine unsolvable data conflict problems in the industrial operations data.

[0010] Optionally, according to one embodiment of the present invention, the initial detection module is a data validator.

[0011] Optionally, according to one embodiment of the present invention, the adjustment module designs an adjustment function based on the data conflict problem.

[0012] Optionally, according to one embodiment of the present invention, the solution is the optimal solution of the industrial operations data.

[0013] Optionally, according to one embodiment of the present invention, the solving module is a commercial solver for the industrial operations data.

[0014] According to another aspect of the present invention, a method for processing industrial operations data is provided, the industrial operations data including a set of constraints, the method comprising: shrinking the set of constraints of the industrial operations data into a first set of constraints according to an adjustment function; solving the industrial operations data under the first set of constraints; and substituting the first solution into the set of constraints to find a second set of constraints.

[0015] Optionally, according to an embodiment of the present invention, the method further includes iteratively solving a second solution of the industrial operations data under a second set of constraints and iteratively substituting the second solution into the constraint set to find a further second set of constraints based on the second solution, until the second set of constraints no longer changes.

[0016] Optionally, according to an embodiment of the present invention, the method further includes comparing a second constraint set based on a first solution with a second constraint set based on a second solution; if the results are the same, then the second constraint set is the reason why the industrial operations data has no solution.

[0017] Optionally, according to one embodiment of the present invention, the method further includes determining unsolvable data conflict problems in the industrial operations data.

[0018] Alternatively, according to one embodiment of the present invention, the data conflict problem is determined by a data validator.

[0019] Optionally, according to one embodiment of the present invention, an adjustment function is designed based on the data conflict problem.

[0020] Optionally, according to one embodiment of the present invention, the solution is the optimal solution of the industrial operations data.

[0021] Alternatively, according to one embodiment of the invention, the solution is obtained using a commercial solver for the industrial operations data.

[0022] According to another aspect of the present invention, a computer-readable storage medium is also provided, wherein instructions are stored therein, which, when executed by a processor, cause the processor to perform the method as described above.

[0023] This method and system have been practically applied in automobile production and can effectively identify the causes of unsolvable problems. Using this method, users can find the reasons for unsolvable operations research optimization problems through several iterations, even without any prior business context. Attached Figure Description

[0024] The above and other objects and advantages of the present invention will become more fully clear from the following detailed description taken in conjunction with the accompanying drawings, wherein the same or similar elements are indicated by the same reference numerals.

[0025] Figure 1 A flowchart of a method 100 for industrial operations data processing according to an embodiment of the present invention is shown.

[0026] Figure 2 A flowchart of a method 200 for industrial operations data processing according to an embodiment of the present invention is shown.

[0027] Figure 3 A block diagram of a system 300 for industrial operations data processing according to an embodiment of the present invention is shown.

[0028] Figure 4 A block diagram of a system 400 for industrial operations data processing according to an embodiment of the present invention is shown.

[0029] Figure 5 A computer apparatus for an industrial operations data processing method is shown according to an embodiment of the present invention. Detailed Implementation

[0030] The following are some embodiments of the present invention, intended to provide a basic understanding of the invention. They are not intended to identify key or decisive elements of the invention or to limit the scope of protection sought.

[0031] For purposes of brevity and illustrativeness, the principles of the invention are described herein primarily with reference to exemplary embodiments thereof. However, those skilled in the art will readily recognize that the same principles are equivalently applicable to all types of application business process orchestration methods and systems, and that these same principles can be implemented therein, and that any such variations do not depart from the true spirit and scope of this patent application.

[0032] Furthermore, reference is made in the following description to the accompanying drawings, which illustrate specific exemplary embodiments. Electrical, mechanical, logical, and structural modifications may be made to these embodiments without departing from the spirit and scope of the invention. Moreover, while features of the invention are disclosed in conjunction with only one of several embodiments, this feature may be combined with one or more other features of other embodiments if desired and / or advantageous for any given or identifiable function. Therefore, the following description should not be considered limiting in any sense, and the scope of the invention is defined by the appended claims and their equivalents.

[0033] In the context of this invention, operations research data refers to data related to an operations research optimization problem, specifically including multiple constraints on the problem. Constraints can be represented as sets. For example, in engine manufacturing, the engine operations research optimization problem might involve 100 constraints. These constraints might include the processes required for engine manufacturing, industrial steps, required equipment, existing equipment, etc. A commercial solver can determine whether the engine operations research optimization problem has a solution based on the input constraints. The solution can include feasible solutions and optimal solutions (i.e., the feasible solution with the lowest cost). Commercial solvers can include solvers known in the art, such as the CPLEX solver, the GUROBI solver, etc.

[0034] Figure 1A flowchart of a method 100 for industrial operations data processing according to an embodiment of the present invention is shown. Figure 1 As shown, method 100 begins at step 101. In step 101, the system for industrial operations research data processing uses a business solver to determine if the operations research data is unsolvable. If the operations research data is unsolvable, optionally, in step 102, the system for industrial operations research data processing performs an initial check, i.e., shallow logic screening. The initial check (i.e., shallow logic screening) refers to directly identifying the underlying causes of unsolvability based on logical conflicts in the constraints of the operations research data. Causes may include master data mismatch, closed loops in the order of events, etc. This step can be completed by a data validator. The built-in algorithm of the data validator determines whether the data violates the model's data requirements and resource constraints, and whether there are conflicts between data, thereby locating the data problems that cause the operations research optimization model to be unsolvable. Specifically, the data validator determines whether the input operations research data matches and whether it satisfies the resource constant term constraints. If not, it is marked as a data problem that causes the model to be unsolvable in the shallow screening stage. Data validators can use knowledge from operations research, graph theory, topology, and computer science, combined with Hamiltonian cycle theory, to screen data for closed loops (i.e., deadlocks). The data validator can also employ depth-first search and topological sorting algorithms to trace the source and structure conflicting data, providing feedback on the conflict results to the user to help them decide on data modification directions. If operations research data is found to have closed loops (i.e., deadlocks) in the initial screening, the user can modify the data until the problem is no longer detected in the initial screening.

[0035] Optionally, after performing the initial screening (i.e., shallow logic screening), the system for industrial operations data processing can perform a deep logic screening in step 103. Deep logic screening will be described in detail below. Through deep logic screening, the system for industrial operations data processing can determine the causes of problems leading to unsolvable operations data.

[0036] Figure 2 A flowchart of a method 200 for industrial operations data processing according to an embodiment of the present invention is shown. Method 200 involves deep logic screening, specifically identifying the causes of unsolvable problems in operations data through iterative loops. The system for industrial operations data processing designs an adjustment function based on the initially identified problems. The adjustment function is a function that reduces the constraint set; for example, a linear adjustment function can reduce a constraint set containing 100 constraints to a constraint set containing 90 constraints. Therefore, in step 201, the system shrinks the constraint set of the industrial operations data into a first constraint set C0 that can be solved according to the adjustment function.

[0037] In step 202, the system's business solver can solve the industrial operations data for a first solution under the first set of constraints, that is, to obtain a feasible solution A0 for the first set of constraints C0.

[0038] In step 203, the system can derive a second constraint set based on the first solution. Specifically, the first solution A0 is substituted into the constraint set containing all constraints to obtain the constraint set C1 that is not satisfied by the first solution A0. By removing the constraint set C1 from the constraint set containing all constraints, the second constraint set C2 is obtained.

[0039] In step 204, the system compares the first constraint set C0 with the second constraint set C2. If the first constraint set C0 and the second constraint set C2 are the same, then the second constraint set is the reason why the industrial operations data has no solution.

[0040] In step 205, if the first constraint set C0 is different from the second constraint set C2, the system performs iteration on the second constraint set C2. The iteration method will be described in detail below.

[0041] The system uses the second constraint set C2 as the new first constraint set to obtain a new second solution A1. The system substitutes the second solution A1 into the constraint set containing all constraints, thus obtaining the constraint set C3 that the second solution A1 does not satisfy. By removing constraint set C3 from the constraint set containing all constraints, a new second constraint set C4 is obtained based on the second solution not satisfying the constraints.

[0042] The system uses the second constraint set C4, which is not satisfied by the second solution, as the new first constraint set to obtain a new third solution A2. The system substitutes the third solution A2 into the constraint set containing all constraints, thus obtaining the constraint set C5, which is not satisfied by the third solution A2. By removing constraint set C5 from the constraint set containing all constraints, a new second constraint set C6, which is not satisfied by the third solution, is obtained.

[0043] The system will iteratively execute the above process until the set of constraints that are not satisfied based on the new solution no longer changes relative to the set of constraints that are not satisfied based on the previous solution. The minimum set of constraints obtained is the reason for the lack of a solution.

[0044] Optionally, all of the above solutions can be either the optimal solution or a feasible solution A0.

[0045] Optionally, during the iteration process, a constraint set threshold can be set. The iteration can stop when the set of constraints for which the solution is not satisfied is smaller than this threshold set. Thus, a constraint set smaller than the threshold set can also be considered a reason why the operations research data has no solution.

[0046] Figure 3 A block diagram of a system 300 for industrial operations data processing according to an embodiment of the present invention is shown. Figure 3 As shown, system 300 includes a solution subsystem 301, a preliminary screening (shallow logic screening) subsystem 302, and a deep logic screening subsystem 303. In the solution subsystem 301, the system for processing industrial operations research data will use a commercial solver to determine if the operations research data is unsolvable. If the operations research data is unsolvable, optionally, in the preliminary screening subsystem 302, the system for processing industrial operations research data will perform a preliminary screening, i.e., shallow logic screening. Preliminary screening (i.e., shallow logic screening) refers to directly discovering the underlying causes of unsolvability based on logical conflicts in the constraints of the operations research data. Causes may include master data mismatch, closed loops in the order of events, etc. This step can be completed by a data validator. The built-in algorithm of the data validator determines whether the data violates the model's data requirements and resource constraints, and whether there are conflicts between data, thereby locating the data problems that cause the operations research optimization model to be unsolvable. Specifically, the data validator determines whether the input operations research data matches and whether it satisfies the resource constant term constraints. If not, it is marked as a data problem that causes the model to be unsolvable in the shallow screening stage. Data validators can leverage knowledge from operations research, graph theory, topology, and computer science, combined with Hamiltonian cycle theory, to screen for data containing closed loops (i.e., deadlocks). They can also employ depth-first search and topological sorting algorithms to trace the source and structure conflicting data, providing feedback to the user to help them decide on data modification directions. If operations research data is found to have closed loops (i.e., deadlocks) in the initial screening, the user can modify the data until the problem is no longer detected in the initial screening.

[0047] Optionally, after the initial screening (i.e., shallow logic screening), the system for industrial operations data processing can perform deep logic screening in the deep logic screening subsystem 303. Deep logic screening will be described in detail below. Through deep logic screening, the system for industrial operations data processing can determine the causes of problems that lead to unsolvable operations data.

[0048] Figure 4A block diagram of a deep logic screening system 400 for industrial operations data processing according to an embodiment of the present invention is shown. System 400 involves deep logic screening, specifically identifying the causes of unsolvable problems in operations data through iterative loops. The system's adjustment module 401 designs an adjustment function based on the initially identified problem. The adjustment function is a function that reduces the constraint set; for example, a linear adjustment function can reduce a constraint set containing 100 constraints to a constraint set containing 90 constraints. Therefore, in the adjustment module 401, the system shrinks the constraint set of the industrial operations data into a first constraint set C0 that can be solved, according to the adjustment function.

[0049] In the solution module 402, the system's commercial solver can solve the industrial operations data for the first solution under the first set of constraints, that is, to obtain a feasible solution A0 for the first set of constraints C0.

[0050] In the search module 403, the system can substitute the first solution A0 into the constraint set containing all constraints to obtain the constraint set C1 that the first solution A0 does not satisfy. By removing the constraint set C1 from the constraint set containing all constraints, the second constraint set C2 is obtained.

[0051] In comparison module 404, the system also compares the first constraint set C0 with the second constraint set C2. If the first constraint set C0 and the second constraint set C2 are the same, then the second constraint set is the reason why the industrial operations data has no solution.

[0052] In iteration module 405, the system iterates over the second constraint set C2 when the first constraint set C0 is different from the second constraint set C2. The iteration method will be described in detail below.

[0053] Iteration module 405 uses the second constraint set C2 as the new first constraint set to obtain a new second solution A1. The system substitutes the second solution A1 into the constraint set containing all constraints, thus obtaining the constraint set C3 that the second solution A1 does not satisfy. By removing the constraint set C3 from the constraint set containing all constraints, a new second constraint set C4 based on the second solution not satisfying is obtained.

[0054] Iteration module 404 uses the second constraint set C4, which is not satisfied by the second solution, as the new first constraint set to obtain a new third solution A2. The system substitutes the third solution A2 into the constraint set containing all constraints, thus obtaining the constraint set C5, which is not satisfied by the third solution A2. By removing the constraint set C5 from the constraint set containing all constraints, a new second constraint set C6, which is not satisfied by the third solution, is obtained.

[0055] The iteration module 404 will iteratively execute the above process until the set of constraints that are not satisfied based on the new solution no longer changes relative to the set of constraints that were not satisfied by the previous solution. The minimum set of constraints obtained is the reason for the lack of a solution.

[0056] Optionally, all of the above solutions can be either the optimal solution or a feasible solution A0.

[0057] Optionally, during the iteration process, a constraint set threshold can be set. The iteration can stop when the set of constraints for which the solution is not satisfied is smaller than this threshold set. Thus, a constraint set smaller than the threshold set can also be considered a reason why the operations research data has no solution.

[0058] Figure 5 A computer apparatus for an industrial operations data processing method according to an embodiment of the present invention is shown. Figure 5 As shown, computer device 500 includes a memory 501 and a processor 502. Although not shown, computer device 500 also includes a computer program stored on the memory 501 and executable on the processor 502. When the processor executes the program, it implements the steps of the method shown in the specification.

[0059] In addition, as described above, the present invention can also be implemented as a recording medium storing a program for causing a computer to execute the industrial operations data processing method described above.

[0060] Here, various recording media can be used as recording media, such as disks (e.g., magnetic disks, optical disks, etc.), cards (e.g., memory cards, optical cards, etc.), semiconductor memory (e.g., ROM, non-volatile memory, etc.), and tapes (e.g., magnetic tape, cassette tape, etc.).

[0061] By recording and distributing computer programs that enable computers to execute the industrial operations data processing methods described in the above embodiments or that enable computers to implement the functions of the industrial operations data processing methods described in the above embodiments in these recording media, it is possible to reduce costs and improve portability and versatility.

[0062] Furthermore, by loading the aforementioned recording medium onto a computer, the computer reads the computer program recorded on the recording medium and stores it in a memory. The computer's processor (CPU: Central Processing Unit, MPU: Micro Processing Unit) reads the computer program from the memory and executes it. Thus, the industrial operations data processing method described in the above embodiments can be executed, and the function of the apparatus for the industrial operations data processing method described in the above embodiments can be realized.

[0063] Although only some embodiments of the invention have been described, those skilled in the art will understand that the invention can be implemented in many other forms without departing from its spirit and scope. Therefore, the examples and embodiments shown are to be considered illustrative rather than restrictive, and the invention may encompass various modifications and substitutions without departing from the spirit and scope of the invention as defined by the appended claims.

Claims

1. A system for processing industrial operations data, wherein the industrial operations data includes a constraint set, characterized in that, The system comprises: an adjustment module configured to contract a constraint set of the industrial operations data into a first constraint set according to an adjustment function; a solving module configured to solve a first solution of the industrial operations data under the first constraint set; and a finding module configured to substitute the first solution into the constraint set to find a second constraint set; an iteration module configured to iteratively call the solving module to solve a second solution of the industrial operations data under the second constraint set and iteratively call the finding module to substitute the second solution into the constraint set to find a further second constraint set based on the second solution until the second constraint set no longer changes; a comparison module configured to compare the second constraint set based on the first solution with the second constraint set based on the second solution, and if the results are the same, the second constraint set is the reason that the industrial operations data has no solution.

2. The system for industrial operations data processing of claim 1, wherein, The system further comprises a preliminary detection module configured to determine a data conflict problem that the industrial operations data has no solution.

3. The system for industrial operations data processing of claim 2, wherein, The preliminary detection module is a data checker.

4. The system for industrial operations data processing of claim 2 or 3, wherein, The adjustment module designs the adjustment function according to the data conflict problem.

5. The system for industrial operations data processing of claim 4, wherein, The first solution and the second solution are optimal solutions of the industrial operations data.

6. The system for industrial operations data processing of claim 5, wherein, The solving module is a commercial solver for the industrial operations data.

7. A method for industrial operations data processing, the industrial operations data comprising a set of constraints, the method comprising: The method comprises: ​ contracting a constraint set of the industrial operations data into a first constraint set according to an adjustment function; solving a first solution of the industrial operations data under the first constraint set; and substituting the first solution into the constraint set to find a second constraint set; iteratively solving a second solution of the industrial operations data under the second constraint set and iteratively substituting the second solution into the constraint set to find a further second constraint set based on the second solution until the second constraint set no longer changes; comparing the second constraint set based on the first solution with the second constraint set based on the second solution, and if the results are the same, the second constraint set is the reason that the industrial operations data has no solution.

8. The method for industrial operations data processing of claim 7, wherein, The method further comprises determining a data conflict problem that the industrial operations data has no solution.

9. The method for industrial operations data processing of claim 8, wherein, The data conflict problem is determined by a data checker.

10. The method for industrial operations data processing according to claim 8 or 9, wherein, The adjustment function is designed according to the data conflict problem.

11. The method for industrial operations data processing of claim 10, wherein, The first solution and the second solution are optimal solutions of the industrial operations data.

12. The method for industrial operations data processing of claim 11, wherein, The solving is performed by a commercial solver for the industrial operations data.

13. A computer-readable storage medium having stored therein instructions, the computer-readable storage medium being characterized by, The instructions, when executed by a processor, cause the processor to perform the method of any one of claims 7-12. The instructions, when executed by a processor, cause the processor to perform the method of any one of claims 7-12.

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